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• 25 Apr 2023

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

• 18 Oct 2022
• Cold Call Podcast

Chewy.com’s Make-or-Break Logistics Dilemma

In late 2013, Ryan Cohen, cofounder and then-CEO of online pet products retailer Chewy.com, was facing a decision that could determine his company’s future. Should he stay with a third-party logistics provider (3PL) for all of Chewy.com’s e-commerce fulfillment or take that function in house? Cohen was convinced that achieving scale would be essential to making the business work and he worried that the company’s current 3PL may not be able to scale with Chewy.com’s projected growth or maintain the company’s performance standards for service quality and fulfillment. But neither he nor his cofounders had any experience managing logistics, and the company’s board members were pressuring him to leave order fulfillment to the 3PL. They worried that any changes could destabilize the existing 3PL relationship and endanger the viability of the fast-growing business. What should Cohen do? Senior Lecturer Jeffrey Rayport discusses the options in his case, “Chewy.com (A).”

• 12 Jul 2022

Can the Foodservice Distribution Industry Recover from the Pandemic?

At the height of the pandemic in 2020, US Foods struggled, as restaurant and school closures reduced demand for foodservice distribution. The situation improved after the return of indoor dining and in-person learning, but an industry-wide shortage of truck drivers and warehouse staff hampered the foodservice distributor’s post-pandemic recovery. That left CEO Pietro Satriano to determine the best strategy to attract and retain essential workers, even as he was tasked with expanding the wholesale grocery store chain (CHEF’STORE) that US Foods launched during the pandemic lockdown. Harvard Business School Professor David E. Bell explores how post-pandemic supply chain challenges continue to affect the foodservice distribution industry in his case, “US Foods: Driving Post-Pandemic Success?”

• 05 Jul 2022
• What Do You Think?

Have We Seen the Peak of Just-in-Time Inventory Management?

Toyota and other companies have harnessed just-in-time inventory management to cut logistics costs and boost service. That is, until COVID-19 roiled global supply chains. Will we ever get back to the days of tighter inventory control? asks James Heskett. Open for comment; 0 Comments.

• 19 Oct 2021
• Research & Ideas

Fed Up Workers and Supply Woes: What's Next for Dollar Stores?

Willy Shih discusses how higher costs, shipping delays, and worker shortages are putting the dollar store business model to the test ahead of the critical holiday shopping season. Open for comment; 0 Comments.

• 26 Mar 2014

How Electronic Patient Records Can Slow Doctor Productivity

Electronic health records are sweeping through the medical field, but some doctors report a disturbing side effect. Instead of becoming more efficient, some practices are becoming less so. Robert Huckman's research explains why. Open for comment; 0 Comments.

• 11 Nov 2013
• Working Paper Summaries

Increased Speed Equals Increased Wait: The Impact of a Reduction in Emergency Department Ultrasound Order Processing Time

This study of ultrasound test orders in hospital emergency departments (EDs) shows that, paradoxically, increasing capacity in a service setting may not alleviate congestion, and can actually increase it due to increased resource use. Specifically, the study finds that reducing the time it takes to order an ultrasound counter intuitively increases patient throughput time as a result of increased ultrasound use without a corresponding increase in quality of care. Furthermore, the authors show that in the complex, interconnected system or hospitals, changes in resource capacity affects not only the patients who receive the additional resources, but also other patients who share the resource, in this case, radiology. These results highlight how demand can be influenced by capacity due to behavioral responses to changes in resource availability, and that this change in demand has far reaching effects on multiple types of patients. Interestingly, the increased ultrasound ordering capacity was achieved by removing what appeared to be a "wasteful" step in the process. However, the results suggest that the step may not have been wasteful as it reduced inefficient ultrasound orders. In healthcare, these results are very important as they provide an explanation for some of the ever-increasing costs: reducing congestion through increased capacity results in even more congestion due to higher resource use. Overall, the study suggests an operations-based solution of increasing the cost/difficulty of ordering discretionary but sometimes low-efficacy treatments to address the rise in healthcare spending. Therefore, to improve hospital performance it could be optimal to put into place "inefficiencies" to become more efficient. Key concepts include: A process improvement can inadvertently cause an increase in demand for a service as well as associated shared resources, which results in congestion, counter intuitively decreasing overall system performance. While individual patients and physicians may benefit from the reduced processing time, there can be unintended consequences for overall system performance. Closed for comment; 0 Comments.

• 25 Jan 2013

Why a Harvard Finance Instructor Went to the Kumbh Mela

Every 12 years, millions of Hindu pilgrims travel to the Indian city of Allahabad for the Kumbh Mela, the largest public gathering in the world. In this first-person account, Senior Lecturer John Macomber shares his first impressions and explains what he's doing there. Closed for comment; 0 Comments.

• 07 Aug 2012

Off and Running: Professors Comment on Olympics

The most difficult challenge at The Olympics is the behind-the-scenes efforts to actually get them up and running. Is it worth it? HBS professors Stephen A. Greyser, John D. Macomber, and John T. Gourville offer insights into the business behind the games. Open for comment; 0 Comments.

• 19 Oct 2010

The Impact of Supply Learning on Customer Demand: Model and Estimation Methodology

"Supply learning" is the process by which customers predict a company's ability to fulfill product orders in the future using information about how well the company fulfilled orders in the past. A new paper investigates how and whether a customer's assumptions about future supplier performance will affect the likelihood that the customer will order from that supplier in the future. Research, based on data from apparel manufacturer Hugo Boss, was conducted by Nathan Craig and Ananth Raman of Harvard Business School, and Nicole DeHoratius of the University of Portland. Key concepts include: Two key measures of supplier performance include "consistency", which is the likelihood that a company will continue to keep items in stock and meet demand, and "recovery", which is the likelihood that a company will deliver on time in spite of past stock-outs. Improvements in consistency and recovery are associated with increases in orders from retail customers. Increasing the level of service may lead to an increase in orders, even when the service level is already nearly perfect. Closed for comment; 0 Comments.

• 19 Jul 2010

How Mercadona Fixes Retail’s ’Last 10 Yards’ Problem

Spanish supermarket chain Mercadona offers aggressive pricing, yet high-touch customer service and above-average employee wages. What's its secret? The operations between loading dock and the customer's hands, says HBS professor Zeynep Ton. Key concepts include: The last 10 yards of the supply chain lies between the store's loading dock and the customer's hands. Poor operational decisions create unnecessary complications that lead to quality problems and lower labor productivity and, in general, make life hard for retail employees. Adopting Mercadona's approach requires a long-term view and a leader with a strong backbone. Closed for comment; 0 Comments.

• 12 Jul 2010

Rocket Science Retailing: A Practical Guide

How can retailers make the most of cutting-edge developments and emerging technologies? Book excerpt plus Q&A with HBS professor Ananth Raman, coauthor with Wharton professor Marshall Fisher of The New Science of Retailing: How Analytics Are Transforming the Supply Chain and Improving Performance. Key concepts include: Retailers can better identify and exploit hidden opportunities in the data they generate. Integrating new analytics within retail organizations is not easy. Raman outlines the typical barriers and a path to overcome them. Incentives must be aligned within organizations and in the supply chain. The first step is to identify the behavior you want to induce. To attract and retain the best employees, successful retailers empower them in specific ways. Closed for comment; 0 Comments.

• 05 Jul 2006

The Motion Picture Industry: Critical Issues in Practice, Current Research & New Research Directions

This paper reviews research and trends in three key areas of movie making: production, distribution, and exhibition. In the production process, the authors recommend risk management and portfolio management for studios, and explore talent compensation issues. Distribution trends show that box-office performance will increasingly depend on a small number of blockbusters, advertising spending will rise (but will cross different types of media), and the timing of releases (and DVDs) will become a bigger issue. As for exhibiting movies, trends show that more sophisticated exhibitors will emerge, contractual changes between distributor and exhibitors will change, and strategies for tickets prices may be reevaluated. Key concepts include: Business tools such as quantitative and qualitative research and market research should be applied to the decision-making process at earlier stages of development. Technological developments will continue to have unknown effects on every stage of the movie-making value chain (production, distribution, exhibition, consumption). Closed for comment; 0 Comments.

• 20 Dec 2004

How an Order Views Your Company

HBS Professors Benson Shapiro and Kash Rangan bring us up to date on their pioneering research that helped ignite today’s intense focus on the customer. The key? Know your order cycle management. Closed for comment; 0 Comments.

• 15 Apr 2002

In the Virtual Dressing Room Returns Are A Real Problem

That little red number looked smashing onscreen, but the puce caftan the delivery guy brought is just one more casualty of the online shopping battle. HBS professor Jan Hammond researches what the textile and apparel industries can do to curtail returns. Closed for comment; 0 Comments.

• 26 Nov 2001

How Toyota Turns Workers Into Problem Solvers

Toyota's reputation for sustaining high product quality is legendary. But the company's methods are not secret. So why can't other carmakers match Toyota's track record? HBS professor Steven Spear says it's all about problem solving. Closed for comment; 0 Comments.

• 19 Nov 2001

Wrapping Your Alliances In a World Wide Web

HBS professor Andrew McAfee researches how the Internet affects manufacturing and productivity and how business can team up to get the most out of technology. Closed for comment; 0 Comments.

• 22 Jan 2001

Control Your Inventory in a World of Lean Retailing

"Manufacturers of consumer goods are in the hot seat these days," the authors of this Harvard Business Review article remind readers. But there is no need to surrender to escalating costs of inventories. In this excerpt, they describe one new way to help lower inventory costs. Closed for comment; 0 Comments.

• 12 Oct 1999

Decoding the DNA of the Toyota Production System

How can one production operation be both rigidly scripted and enormously flexible? In this summary of an article from the Harvard Business Review, HBS Professors H. Kent Bowen and Steven Spear disclose the secret to Toyota's production success. The company's operations can be seen as a continuous series of controlled experiments: whenever Toyota defines a specification, it is establishing a hypothesis that is then tested through action. The workers, who have internalized this scientific-method approach, are stimulated to respond to problems as they appear; using data from the strictly defined experiment, they are able to adapt fluidly to changing circumstances. Closed for comment; 0 Comments.

Rapid Response: Inside the Retailing Revolution

A simple bar code scan at your local department store today launches a whirlwind of action: data is transmitted about the color, the size, and the style of the item to forecasters and production planners; distributors and suppliers are informed of the demand and the possible need to restock. All in the blink of an electronic eye. It wasn’t always this way, though. HBS Professor Janice Hammond has focused her recent research on the transformation of the apparel and textile industries from the classic, limited model to the new lean inventories and flexible manufacturing capabilities. Closed for comment; 0 Comments.

• Original article
• Open access
• Published: 22 September 2021

Assessment of logistics service quality dimensions: a qualitative approach

• Gamze Arabelen 1 &
• Hasan Tolga Kaya   ORCID: orcid.org/0000-0003-0150-4182 2  

Journal of Shipping and Trade volume  6 , Article number:  14 ( 2021 ) Cite this article

7 Citations

Metrics details

Globalization and complex supply chain networks have been affecting Logistics Services Providers’ (LSPs) service delivery and service expectations. Logistics Service Quality (LSQ) is becoming a more important aspect for LSPs and logistics service customers. In recent years, there has been an increase in the studies on service quality in logistics. Researchers have been trying to identify aspects of LSQ and its dimensions in order to create a measurement model that could be used in overall logistics services. However, there is still neither a unified nor agreed LSQ measurement model in the literature and researchers have been debating continuously on the proposed models. This paper targets to investigate and suggest LSQ measurement dimensions obtained from previous studies by analyzing the findings within a systematic approach and improving the findings with semi-structured interviews. In this study, systematic literature analysis has been conducted to research papers published in selected academic databases with specific keyword and keyword cluster searches to identify the related articles published within a specified period. Papers have been selected in accordance with the predefined criteria. As a result, a total of 59 articles have been determined for the search criteria and the findings obtained were analyzed. Most frequently used research trends and methods on service quality in logistics have been identified. In addition, the most frequently used LSQ dimensions and factors have been reviewed. Moreover, the most frequently used service quality approaches and measurement models have been analyzed. The results received from systematic literature review have been composed and dimensions have been identified. Semi-structured interviews with LSPs and customers of LSPs in Germany-based companies have been conducted to strengthen the findings gained from systematic literature review. 5 LSQ dimensions and 24 factors have been formed with the help of semi-structured interviews. This paper represents the basis for further research for empirical studies and can be used as a guideline for quality management practices in logistics applications and transport.

Introduction

Globalization and growing supply chain networks have been pushing logistics service providers to focus on the provided logistics practices. Simultaneously, service types offered by logistics service providers have increased quickly. Importance of logistics services also has been increased universally; hence, service quality has become an important driver for LSPs. The importance of logistics services is known by practitioners and academics. Significance and interest in Logistics service quality (LSQ) have been also increasing. The concept of LSQ is equally important for customers and LSPs (Mentzer et al. 1999 ; Thai 2013 ). High level of LSQ increases logistics providers’ competitive advantage among compelling business environments (Wang and Hu 2016 ). Good service quality offered to customers generates customer satisfaction as well as customer loyalty for the service provider (Franceschini and Rafele 2000 ; Davis and Mentzer 2006 ; Baki et al. 2009 ).

There has not been any clear understanding of the LSQ concept despite the increasing number of research papers. Major focus of the researchers has been on the concept of the LSQ and its quality attributes, how to analyze and measure the quality of the services (Bienstock et al. 1997 ; Mentzer et al. 1999 ; Franceschini and Rafele 2000 ; Rafele 2004 ). Nonetheless, researchers have developed different ideas on logistics concept and service quality dimensions over time. There have been very few studies with the holistic approach on the LSQ to analyze overall developed dimensions and the attributes along with the general framework. Therefore, a comprehensive LSQ model that would incorporate different sectors is not available at present.

General approach of the researchers developing a study in LSQ has kept the literature review part very short and directed it to particular approaches without critically viewing the literature. This paper is aiming to address the previously mentioned issue by analyzing papers related to LSQ with a systematic approach. This will ensure that previous findings from scientific papers are systematically analyzed and presented and findings can be used in future studies to develop scientific or practical LSQ studies. Additionally, this study is anticipating LSQ attributes by analyzing research trends and general usage of LSQ dimensions, research methods, and fields of sectors. Furthermore, it is aiming to conduct a semi-structured interview with logistics professionals in order to confirm and enhance the outcome of the systematic literature review.

This paper has been developed through multiple sections. In the first section, research methodology has been explained. General approach in the systematic literature review, paper selection criteria, keywords, databases, and preliminary paper classification have been described in the second section. In the third section, descriptive analysis of the selected papers has been carried out. In the fourth section, LSQ dimensions and attributes have been analyzed and the LSQ measurement model has been created to discuss the findings in semi-structured interviews. In the fourth section, semi-structured interviews and findings from business professionals’ contributions have been explained. In the fifth section, a brief overview of this study has been presented and in addition notes on future works have been provided.

Research methodology

Systematic literature review methodology has been used in this study to have a holistic approach towards LSQ studies and interpret the findings obtained from previous papers. Systematic literature analysis method has been considered a technique of systematic, qualitative, objective, and quantitative description in the research area (Berelson 1952 ). A systematic content analysis methodology has been considered a very powerful and an explicit tool because of its ability to combine qualitative approaches retaining rich meaning with quantitative analyses (Duriau et al. 2007 ; Fink 2005 ). Additionally, the main difference between systematic literature review and traditional literature review has been considered the first comprehensive search section (Crossan and Apaydin 2010 ). In order the follow a structured method with valid results, a systematic literature review approach from the literature has been applied (Seuring and Gold 2012 ). In this regard, a systematic literature review has been planned in this study with several steps as: material collection, descriptive analysis, category selection, material evaluation. Material collection reflects gathering all necessary papers from previously created criteria. Collection of materials has been the most crucial step in systematic literature reviews. In the study, literature regarding the LSQ has been selected from peer-reviewed journals and literature databases, Web of Science, ScienceDirect, Emerald, Taylor and Francis, JSTOR, Business Source Premier, and the web. Second part of the systematic literature review has been descriptive analysis. Only studies in English language and published between 1995 and 2020 have been selected for the future classification. The formal characteristics of the selected papers have been set out in the descriptive analysis section to provide background for the content. Consequently, publication years, research methods and research fields of reviewed journals have been documented. Structural dimensions and related categories for future analytics have been selected in category selection. In the material evaluation section, all analyses have been presented according to determined categories and parameters.

Semi-structured interviews have been used in this study to consolidate the LSQ dimension findings from systematic literature analysis, as it is the most frequently used interview method (Taylor 2005 ; Dicocco-Bloom and Crabtree 2006 ). Flexibility and reciprocity of semi-structured interviews have benefited the LSQ discussion. Questions regarding service quality in logistics have been prepared prior to meetings, which were shaped around the systematic literature review findings and perceptions of the participants. In semi-structured interviews, following a strict structure is not advised (Kallio et al. 2016 ). Definite resolution on logistics quality and definition of quality dimensions have not been agreed upon for LSQ, therefore a semi-structured interview qualitative approach is considered more convenient in order to allow participants to express themselves. In order to create successful semi-structured interviews, a five-step model has been utilized (Kallio et al. 2016 ). Firstly, prerequisites of the interviews have been decided. Due to the coronavirus pandemic situation, related global restrictions and organizations, new working models such as online meeting method have been selected. Second step is gathering previous knowledge on data by using the systematic literature review. This has allowed the interviewer to gain knowledge and confidence in regular spontaneous follow-up questions. In the third step, guidelines of the interview have been developed. Questions have been prepared regarding participants’ understanding of LSQ, participants’ perception of the identified LSQ dimensions and follow-up questions regarding examples for the in-depth analysis of the topic. In the fourth step, a pilot has been conducted with one logistics business professional to test the clarity of the developed approach. In the final step, semi-structured interviews have been performed with five logistics professionals.

Systematic literature review

Systematic literature review is advised to be applied to a specified period of time. Therefore, materials have been selected from research papers that were published between 1995 and 2020. Specific keywords related to service quality in logistics have been used in literature databases such as Web of Science, ScienceDirect, Emerald, Taylor and Francis, JSTOR, Business Source Premier, and the web to identify the first step. Only papers that have been peer-reviewed in English language have been selected for further analysis for systematic review. Table 1 provides a summary of sample paper selection. In literature databases with keyword matches in their titles, 221 papers that are fit for the search criteria have been found. Furthermore, the suitability of the sample has been checked by applying a two-stage screening process. First screening has been applied to the abstracts of the selected papers. After analyzing the abstracts of 221 papers, sources that were irrelevant or with little relevance to the topic have been excluded from further analysis. However, studies with no abstract or with unclear information have been directly transferred to the second stage. In the second screening process, full paper review has been applied to enforce the relevance of the selected literature sample. Additionally, papers that have been cited multiple times and fit to the criteria of this research have been included in the samples. As a result, final sample has consisted of 59 papers.

After collecting the sample based on criteria, descriptive analysis has been followed, as it would create a framework for the systematic analysis. In this context, formal characteristics have been analyzed. Consequently, publication years and service fields have been analyzed to identify the preliminary framework of the selected literature sample. Publication years of the selected studies have shown that the trend towards the research topic of LSQ had been increasing. Findings of the study have shown that the LSQ is still a discussion subject among researchers. In order to show the academic interest in the LSQ topic, selected timeline of 25 years has been divided into five years of periods. The results have shown that 23 papers were published between 2015 and 2020, which clearly shows the increasing relevancy and interest in the research topic. Furthermore, search fields of selected papers have been analyzed and results reflected that 49% of the studies have been conducted in the logistics field and the second most popular research field groups have indicated the industrial management field with 20% of the total sample.

After analyzing the descriptive specifications of the selected research paper samples, analytic categories have been selected including research methods, data analysis methods, LSQ dimensions, service quality measurement models, approach of the studies. In the last part of the systematic literature analysis, selected categories have been analyzed and categorized to create some practical guidance on the LSQ research question. According to Avenier (2010), decontextualized evaluation of the literature analysis’ results brings out the possibility of proposing a certain degree of generalization for the findings. Therefore, systematic literature review findings have been used to identify the first design of the LSQ dimensions and later discussed in semi-structured interviews.

Analysis of the Categories

Previously founded categories have been analyzed to create further research design with transparency. Therefore, used data analysis and research method of selected literature sample have been analyzed. Table 2 provides an overview of the used researched methodology. According to the results, linear usage of qualitative, quantitative, and multiple data analysis known as triangulation has been used among 51% of the studies and 76% of the studies have had empirical approach.

There is an increase in empirical studies about the LSQ topic in addition to using existing created models and trying to validate the quality measurement models. Besides, many researchers have been searching the relationship between the LSQ and other attributes such as loyalty and satisfaction. Consequently, this increase in validation studies may refer to a reaction to unconformity on the search field and in search of study and generalized LSQ measurement model. Moreover, qualitative LSQ dimensions developing studies have been mainly observed in early periods and most researchers preferred to create an LSQ model and validate its reliability by quantitative methods throughout the time. Details of the research approach method with corresponding publications has been presented in Table 3 . From triangulation, Mentzer et al. ( 1999 ), created a nine-dimension service quality measurement model which is broadly used, Feng et al. ( 2007 ), Gil-Saura et al. ( 2008 ) and Thai ( 2013 ) also developed different models which were created for the need of the search in LSQ with different approaches.

Table 4 provides an overview of the ratio of used LSQ measurement models and, it is clear that most of the researchers preferred to create a unique service quality measurement model for logistics or preferred to add a modification to generally used methods instead of directly using developed and proved reliable methods. Logistics services have been always a chain of multiple services and findings may show differences among supply field, region or service expertise. For instance, Zailani et al. ( 2018 ) focused on LSQ considering halal logistics network and developed an individual service quality model. Thai ( 2008 ) has provided a service quality method for port operations and defined six brand new dimensions: resources, outcome, process, management, image or reputation and social responsibility. Despite having specialized service quality measurement models for logistics operations, most of the researchers have used the classical model of SERVQUAL in quantitative research. This approach also provides an insight into the inefficient LSQ measurement model for general usage.

In addition, the LSQ scale created by Mentzer et al. ( 1999 , 2001 ) has been used by researchers particularly. Rafiq and Jaafar ( 2007 ) had used the LSQ scale to measure customer perception on 3PL service providers, authors suggested generalizability of the LSQ scale on a similar sample model. Bouzaabia et al. ( 2013 ) has utilized the LSQ scale to compare the LSQ perception between Romania and Tunisia in retail logistics. Yumurtaci Huseyinoglu et al. ( 2018 ) has investigated the service quality scale model on Omni-channel capability. Table 5 provides an overview of LSQ dimensions and how often they are used in literature. The publication list has been submitted in chronological order to provide an overview of the development of LSQ dimensions that have been used throughout the period of the systematic research analysis. Due to different naming conventions on similar meanings, LSQ dimensions have been grouped by their relevance to each other. As a result, most frequently used LSQ measurement dimensions have been identified. Dimensions related to communication have been used 27 times in total, which have been mentioned under different names such as personal contact quality; responsiveness; customer focus etc. Second most frequently used LSQ dimensions are process-related and have been mentioned 20 times in the selected sample publications. Process related dimensions have been mentioned as order release quantities, order accuracy, order discrepancy handling, order quality and correctness, etc. Third but not least used dimensions are time-related and have been used 19 times in publications throughout the period. Time-related dimensions have been named in different forms such as timeliness, on-time delivery, lead time, etc. Over time, it has become clearly visible that while the focus of the operational quality has lost its importance and significance, communication-related dimensions and empathy dimension usage and their relation to quality have gained importance due to factors such as, responsiveness, empathy, personnel contact quality, etc.

The findings have indicated that the LSQ research area has remained incomplete in the literature. Thus, tailored service quality with hierarchical dimensions for logistics services are more applicable to analyze LSQ. Dimensions have been selected based on their relevance and frequency of use. As it has been noticed from the studies, focus on customer-related services in logistics operations is increasing, therefore, dimensions related to customer focus quality have been selected as the first dimension for this study to analyze further in the interviews. Additionally, by the image of the company and social responsibility acts investigated under a total of six LSQ dimensions and twenty sub-factors have been identified by their relevancy on logistics and the frequency of the use: Information quality, customer focus quality, order fulfillment quality, timeliness quality, corporate image and social responsibility were selected.

Semi-structured interviews

Semi-structured interviews allow participants and the interviewer to interchange knowledge within mutual benefit and, allow the interviewer to ask follow-up questions to participants based on the development of the answers (Rubin and Rubin 2005 ). In order to benefit from the professional view of the participants, semi-structured interview method has been selected. Semi-structured interview method is considered more fit for further investigation on LSQ dimensions because the topic is broadly discussed and has no consensus has been reached either on the definition or on the quality dimensions. Semi-structured interview has allowed participants to roam freely around the topic, and follow-up questions have provided preferable inputs and modifications on developed LSQ dimensions and sub-factors. As shown in Table 6 , interviews were carried out with five logistics business professionals. Two of the participants were logistics managers in retail business, one was the logistics service provider team lead and two of them were logistics specialists for logistics service providers. All interview participants and their companies were located in Germany and companies have the scope of working in global logistics and supply chain businesses. All interviews have been conducted through online calls, and meetings have been recorded. Five interviews lasted average of thirty minutes for each participant.

Semi-structured interview questions have been designed according to the outcome of the systematic literature review. Open-ended questions have invited participants to follow-up the topic. Open-ended questions have been designed for each participant and their companies. Next set of questions have been designed for each quality dimension that has been identified in the systematic literature analysis and the said questions asked participants their point of view to validate and modify the proposed model. In general, participants have been directed with general questions to understand their personal quality perceptions and followed-up with prompt questions.

As a result, construction of the preliminary proposed quality dimensions has changed. All participants have expressed the importance of their customer value and its relation with quality perception, also they have highlighted that quality dimension is in fact a customer obsession. Therefore, naming has been changed to ‘ customer obsession quality’ from ‘ customer focus quality’ . Additionally, all participants have highlighted and agreed on the social responsibility activities are related to companies’ image; therefore, LSQ dimensions have merged under one quality dimension: social responsibility and company image. Additionally, LSQ factors have also been discussed and modified as a consequence of the interviews. Sub-dimensional quality factors have raised to 24 from 20 in total. Final LSQ dimensions and factors have been defined as shown in Table 7 .

After the final evaluation of semi-structured reviews, shipment tracing capability, innovative solutions in logistics services; reliability, regularity, flexibility and availability of service, company’s reputation for reliability have been added to the LSQ factors and LSQ scale has been developed with five quality dimensions and 24 factors in total.

Research findings

Research findings have been developed with qualitative research techniques. Firstly, systematic literature analysis has been applied to the LSQ related papers with specified criteria between 1995 and 2020. Samples have been analyzed with systematically created filtering and descriptive analysis. Results have been analyzed and shown that researchers have not reached a consensus either on the LSQ perception or the measurement method. Additionally, a paradigm shift towards customer-oriented services from the natural physical movement of the cargoes has been observed in recent years. As a result, logistics service customers are giving more importance to business-to-business or business-to-customer communication and empathy. This change has been seen in the recent LSQ publications as well. As a consequence of the initial analysis, six dimensions and twenty logistics factors have been developed. Preliminary findings have been discussed in five semi-structured interviews. Logistics professionals’ contributions have been included in this study to ensure that literature key findings are in line with actual business and quality dimensions have improved by the outcome of the results.

As a result, systematic literature analysis has shown that SERVQUAL quality measurement method is still broadly used; however, there have been great contributions from many authors towards LSQ and the creation of logistics specific quality measurement model. Despite these improvements, there has been no consensus on the singular quality measurement model. This research proposes LSQ dimensions and factors created from systematic literature analysis and semi-structured interviews. Firstly, six-dimensional twenty factors have been developed and findings have been improved after the semi-structured interviews. Final model proposes five LSQ dimensions with twenty-four factors.

Conclusion and recommendations for further researches

Logistics services have been continuously growing around the world. These improvements and developments have increased the competition among service providers. There has been an increment in the number of research papers exploring this area. Service providers are trying to leverage operational excellence with high quality of services to maintain customer satisfaction, loyalty, and market competition. A regularly dynamic environment requires dynamic solutions, therefore, logistics services are constantly in development. Consequently, the perception of LSQ has been changing.

It has been found that LSQ understanding and applications have been evolving around the business focus of LSPs. Throughout the development of the quality dimensions in logistics, there have been different approaches from different authors. In the literature, the focus of the LSQ dimensions has been differing among different periods of the samples and it clearly shows the change in the focus on the quality. After observing a period of twenty years, early developed LSQ dimensions have shown that quality focus is mainly on the physical attributes of the operations, such as physical distribution and timeliness related dimensions. Over time, logistics services have accumulated more customer-oriented operations hence, in later periods customer-related LSQ dimensions have been observed, such as personal service/contact, empathy. The dimensional switch has also been accepted in semi-structured interviews and recorded as the most important dimension of the LSPs. Therefore, currently keeping positive relations with customers by providing emphatic continuous relationship has been more important for LSPs.

Despite having a high rate in empirical studies, findings suggest that researchers used repeatedly SERVQUAL model in LSQ measurement even though there have been measurement models created specifically for logistics services. This indicates that the search of the LSQ dimensions and measurement methods have not been completed; hence, it is open for improvement and eventually reaching the recognized LSQ measurement method. This study is providing a framework for service quality in logistics for researchers and logistics professionals by systematically analyzing the previously developed studies and measurement models. Primary quality dimensions have been developed from systematic literature analysis by systemizing and organizing the existing literature. Then, additional interviews have been conducted with service professionals. As a result, framework of LSQ has been developed with five dimensions with customer obsession quality, order fulfillment quality, timeliness quality, information quality, corporate image and social responsibility and twenty-four factors. The holistic approach of the research model has asserted LSQ dimensions for further measurement models.

Proposed model may be used as a framework for further studies and can be strengthened by empirically testing in multiple regions of the world. LSQ dimensions may be improved by conducting focus group meetings and additional interviews with logistics professionals from different regions of the world. Additionally, professionals may use these LSQ dimensions as an internal quality indicator and use factors and dimensions as quality key performance metrics. Managers may benefit from the findings to create quality-oriented logistics services or improve existing service models.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Logistics service quality

Logistics Services providers

• Service quality

Service performance

Baki B, Sahin Basfirinci C, Murat AR I, Cilingir Z (2009) An application of integrating SERVQUAL and Kano's model into QFD for logistics services. Asia Pac J Mark Logistics 21(1):106–126

Berelson B (1952) Content analysis in communications research. Glencoe, USA

Bienstock CC, Mentzer JT, Bird MM (1997) Measuring physical distribution service quality. J Acad Mark Sci 25(1):31–44

Article   Google Scholar  

Bienstock CC, Royne MB, Sherrell D, Stafford TF (2008) An expanded model of logistics service quality: incorporating logistics information technology. Int J Prod Econ 113:205–222

Birdogan B, Basfirinci CS, Ar IM, Cilingir Z (2009) An application of integrating SERVQUAL and Kano’s model into QFD for logistics services: a case study from Turkey. Asia Pac J Mark Logist 21(1):106–126

Bouzaabia R, Bouzaabia O, Capatina A (2013) Retail logistics service quality: a cross-cultural survey on customer perceptions. Int J Retail Distrib Manag 41(8):627–647

Caplice C, Sheffi Y (1995) A review and evaluation of logistics performance measurement systems. Int J Logist Manag 6(1):61–74

Chen MC, Hsu CL, Lee LH (2020) Investigating pharmaceutical logistics service quality with refined Kano’s model. J Retail Consum Serv 57:102231

Crossan MM, Apaydin M (2010) A multi-dimensional framework of organizational innovation: a systematic review of the literature. J Manage Stud 47(6):1154–1191

Davis BR, Mentzer JT (2006) Logistics Service driven loyalty: an exploratory study. J Bus Logist 27(2):53–73

DiCicco-Bloom B, Crabtree BF (2006) The qualitative research interview. Med Educ 40(4):314–321

Duriau VJ, Rhonda K, Pfarrer MD (2007) A content analysis of the content analysis literature in organization studies: research themes, data sources, and methodological refinements. Organ Res Methods 10(1):5–34

Durvasula S, Lysonski S, Mehta SC (1999) Testing the SERVQUAL scale in the business-to-business sector: the case of ocean freight shipping service. J Serv Mark 13(2):132–150

Emerson CJ, Grimm CM (1996) Logistics and marketing components of customer service: an empirical test of the Mentzer, Gomes and Krapfel model. Logist Mark Custom Serv 26(8):29–42

Google Scholar  

Feng YX, Zheng B, Tan JR (2007) Exploratory study of logistics service quality scale based on online shopping malls. J Zhejiang Univ Sci A 8:926–931

Fink A (2005) Conducting research literature reviews: from paper to the internet. Sage Publications, CA

Franceschini F, Rafele C (2000) Quality evaluation in logistic services. Int J Agil Manag Syst 2(1):49–54

Fugate BS, Mentzer JT, Stank TP (2010) Logistics performance: efficiency, effectiveness, and differentiation. J Bus Logist 31(1):43–62

Fung P, Wong A (1998) Case study: managing for total quality of logistics services in the supply chain. Logist Inf Manag 11(5):324–329

Gil-Saura I, Ruiz-Molina ME (2011) Logistics service quality and buyer-customer relationships: the moderating role of technology in B2B and B2C contexts. Serv Ind J 31(7):1109–1123

Gil-Saura I, Servera-Frances D, Contri GB, Fuentes-Blasco M (2008) Logistics service quality: a new way to loyalty. Ind Manag Data Syst 108(5):650–668

Gil-Saura I, Servera-Frances D, Fuentes-Blasco M (2010) Antecedents and consequences of logistics value: and empirical investigation in the Spanish market. Ind Mark Manage 39:493–506

Giovanis AN, Tomaras P, Zondiros D (2013) Suppliers logistics service quality performance and its effect on retailers’ behavioral intentions. Procedia Soc Behav Sci 73:302–309

Gotzamani K, Longinidis P, Vouzas F (2010) The logistics services outsourcing dilemma: quality management and financial performance perspectives. Supply Chain Manag Int J 15(6):438–453

Gulc A (2017) Models and methods of measuring the quality of logistic service. Procedia Eng 182:255–264

Hong PV, Nguyen TT (2020) Factors affecting marketing strategy of logistics business: case of Vietnam. Asian J Shipp Logist 36:212–222

Huma S, Ahmed W, Ikram M, Khawaja MI (2019) The effect of logistics service quality on customer loyalty: case of logistics service industry. South Asian J Bus Stud 9(1):43–61

Iqbal T (2020) Investigating logistics issues in service quality of SMEs in Saudi Arabia. Uncertain Supply Chain Manag 8:875–876

Jang HM, Marlow PB, Mitroussi K (2013) The effect of logistics service quality on customer loyalty through relationship quality in the container shipping context. Transp J 52(4):493–521

Jang P, Kwon OK, Kim M (2014) The role of logistics service quality in long-term partnership development with shippers. Supply Chain Forum: Int J 15(2):82–108

Juga J, Juntunen J, Grant DB (2010) Service quality and its relation to satisfaction and loyalty in logistics outsourcing relationships. Manag Serv Qual 20(6):496–510

Kallio H, Pietila AM, Johnson M, Kangasniemi M (2016) Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. J Adv Nurs 72(12):2949–3217

Kavaliauskiene IE, Aranskis A, Litvinenko M (2014) Consumer satisfaction with the quality of logistics services. Procedia Soc Behav Sci 110:330–340

Kersten W, Koch J (2010) The effect of quality management on the service quality and business success of logistics service providers. Int J Qual Reliab Manage 27(2):185–200

Kilibarda M, Nikolicic S, Andrejic M (2016) Measurement of logistics service quality in freight forwarding companies: a case study of the Serbian market. Int J Logist Manag 27(3):770–794

Kilibarda M, Zecevic S, Vidovic M (2012) Measuring the quality of logistic service as an element of the logistics provider offering. Total Qual Manag Bus Excell 23(11–12):1345–1361

Knop K (2019) Evaluation of quality of services provided by transport and logistics operator from pharmaceutical industry for improvement purposes. Transp Res Procedia 40:1080–1087

Lan S, Zhang H, Zhong RY, Huang GQ (2016) A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process. Ind Manag Data Syst 116(5):1024–1042

Le DC, Nguyen HT, Truong PH (2019) Port logistics service quality and customer satisfaction: empirical evidence from Vietnam. Asian J Shipp Logist 36(2):89–103

Limbourg S, Giang HTQ, Cools M (2016) Logistics service quality: the case of da Nang City. Procedia Eng 142:123–129

Mentzer JT, Flint DJ, Kent JL (1999) Developing a logistics service quality scale. J Bus Logist 20(1):9–32

Mentzer JT, Flint DJ, Hult TM (2001) Logistics service quality as segment-customized process. J Mark 65(4):82–104

Mentzer JT, Myers MB (2004) Global market segmentation for logistics services. Ind Mark Manage 33:15–20

Mentzer JT, Rutner SM, Matsuno K (1997) Application of the means-end value hierarchy model of understanding logistics service quality. Int J Phys Distrib Logist Manag 27(10):230–243

Murfield ML, Boone C, Rutner P, Thomas RW (2017) Investigating logistics service quality in omni-channel retailing. Int J Phys Distrib Logist Manag 47(4):263–296

Panayides PM, So M (2005) The impact of integrated logistics relationships on third-party logistics service quality and performance. Marit Econ Logist 7:36–55

Philipp B, Grant DB (2015) Does B2C online logistics service quality impact urban logistics? Logist Manag 23(2):45–54

Politis Y, Politis G, Binioris S (2014) Logistics service quality and its effects on customer satisfaction in the manufacturing companies’ supply chains empirical evidence from Greece. J Model Manag 9(2):215–237

Rabinovich E, Bailey JP (2004) Physical distribution service quality in internet retailing: service pricing, transaction attributes, and firm attributes. J Oper Manag 21(6):651–672

Rafele C (2004) Logistic service measurement: a reference framework. J Manuf Technol Manag 15(3):280–290

Rafiq M, Jaafar HS (2007) Measuring customers’ perceptions of logistics service quality of 3PL service providers. J Bus Logist 28(2):159–175

Rahmat AK, Faisol N (2016) Manufacturers satisfaction on logistics service quality: operational, relational and national culture. Procedia Soc Behav Sci 224:339–346

Rao S, Goldsby TJ, Griffis SE, Iyengar D (2011) Electronic logistics service quality (e-LSQ): its impact on the customer’s purchase satisfaction and retention. J Bus Logist 32(2):167–179

Roslan NAA, Wahab E, Abdullah NH (2015) Service quality: a case study of logistics sector in Iskandar Malaysia using SERVQUAL model. Procedia Soc Behav Sci 172:457–462

Rubin HJ, Rubin IS (2005) Qualitative interviewing: the art of hearing the data, 2nd edn. Sage, California

Book   Google Scholar  

Rutner SM, Langley CJ Jr (2000) Logistics value: definition, process and measurement. Int J Logist Manag 11(2):73–82

Seth N, Deshmukh SG, Vrat P (2006) A conceptual model for quality of service in the supply chain. Int J Phys Distrib Logist Manag 36(7):547–575

Seuring S, Gold S (2012) Conducting content analysis based literature reviews in supply chain management. Supply Chain Manag Int J 17(5):544–555

Sohn J, Woo SH, Kim TW (2017) Assessment of logistics service quality using the Kano model in a logistics triadic relationship. Int J Logist Manag 28(2):680–698

Stank TP, Goldsby TJ, Vickery SK (2003) Logistics service performance: estimating its influence on market share. J Bus Logist 24(1):27–55

Taylor MC (2005) Interviewing. In: Holloway ED (ed) Qualitative research in health care. Open University Press, New York

Thai VV (2008) Service quality in maritime transport: conceptual model and empirical evidence. Asia Pac J Mark Logist 20(4):493–518

Thai VV (2013) Logistics service quality: conceptual model and empirical evidence. Int J Logist Res Appl A Lead J Supply Chain Manag 16(2):114–131

Vazifehdan MN, Darestani SA (2019) Green logistics outsourcing employing multi criteria decision making and quality function deployment in the petrochemical industry. Asian J Shipp Logist 35(4):243–254

Vu TP, Grant DB, Menachof DA (2019) Exploring logistics service quality in Hai Phong. Vietnam Asian J Shipp Logist 36(3):54–64

Vural CA, Tuna O (2015) The prioritisation of service dimensions in logistics centres: a fuzzy quality. Int J Log Res Appl 19(3):159–180

Wang F, Hu Y (2016) Research on certification standards of LSQ. Am J Ind Bus Manag 6(8):895–899

Weli AN, Idris S, Yaakob AR (2020) Level of satisfaction among industrial customers in relation to logistics service provider in Sabah. Glob Bus Manag Res 12(4):466–476

Wojciechowska SW (2011) On logistics service quality evaluation—case study. Logist Transp 2(13):45–56

Xu M, Tang W, Zhou C (2019) Procurement strategies of E-retailers under different logistics distributions with quality- and service-dependent demand. Electron Commerce Res Appl 35:100853

Yu K, Cadeaux J, Song H (2016) Flexibility and quality in logistics and relationships. Ind Mark Manage 62:211–225

Yumurtaci Hüseyinoğlu IÖ, Sorkun MF, Börühan G (2018) Revealing the impact of operational logistics service quality on omni-channel capability. Asia Pac J Mark Logist 30(5):1200–1221

Zailani S, Jafarzahed S, Iranmanesh M, Nikbin D, Selim NII (2018) Halal Logistics Service Quality: Conceptual Model and Empirical Evidence. British Food J 120(11):2599–2614

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The author HTK analyzed and interpreted the historical research data regarding Logistics Service Quality and conducted descriptive analysis. HTK conducted interviews with business professionals. The author GA, analyzed historical service quality dimensions, developed inferences between historical findings and periodic trends among service quality dimensions, and is a major contributor in writing the manuscript. All authors read and approved the final manuscript.

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Arabelen, G., Kaya, H.T. Assessment of logistics service quality dimensions: a qualitative approach. J. shipp. trd. 6 , 14 (2021). https://doi.org/10.1186/s41072-021-00095-1

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Big data analytics in logistics and supply chain management

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 14 May 2018

Fosso Wamba, S. , Gunasekaran, A. , Papadopoulos, T. and Ngai, E. (2018), "Big data analytics in logistics and supply chain management", The International Journal of Logistics Management , Vol. 29 No. 2, pp. 478-484. https://doi.org/10.1108/IJLM-02-2018-0026

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Introduction

In recent years, big data analytics (BDA) capability has attracted significant attention from academia and management practitioners. We are living in an era where there has been an explosion of data ( Choi et al. , 2017 ). Kiron et al. (2014) argued that a majority of fortune 1,000 firms is pursuing BDA-related development projects. Chen and Zhang (2014) argued that big data (BD) has enough potential to revolutionize many fields including business, scientific research and public administration and so on. The use of BDA in the field of marketing and finance is on the rise. However, the operations and supply chain professionals are yet to exploit the true potential of the BDA capability in order to improve the supply chain operational decision-making skills ( Srinivasan and Swink, 2017 ). Operations and supply chain professionals have access not only to data, which is continuously generated by traditional devices such as POS, RFID, but also GPS to a vast amount of data generated from unstructured data sources such as digital clickstreams, camera and surveillance footage, imagery, social media postings, blog/wiki entries and forum discussions ( Sanders and Ganeshan, 2015 ). Today, supply chains are highly supported by advanced networking technologies – sensors, tags, tracks and other smart devices, which are gathering data on real-time basis ( Wang et al. , 2016 ; Gunasekaran et al. , 2017 ), which provides end to end demand and supply visibility ( Gunasekaran et al. , 2017 ; Srinivasan and Swink, 2017 ). Schoenherr and Speier-Pero (2015) argued that supply chain managers need to process a large amount of data to make decisions that may help reduce costs and increase the product availability to the customers.

The extant literature defines a BDA capability as a technologically enabled ability which can help process large volume, high velocity and several varieties of data to extract meaningful and useful insights; hereby enabling the organizations to gain competitive advantage ( Fosso Wamba et al. , 2015, 2017 ). Galbraith (2014) further noted that historically, supply chain managers used to analyze data gathered from traditional data warehouses to gain insights. Moreover, Hazen et al. (2014) argued that the effectiveness of decision making in supply chains often hinges upon the quality of the data processed via organizational infrastructure, which enables the supply chain managers to quickly acquire, process and analyze data. Papadopoulos et al. (2017) argued that insights gained via increased information processing capability can reduce uncertainty, especially when operational tasks such as disaster relief operations are highly complex. However, despite increasing efforts from the operations and supply chain community to understand the associations between different types of operational visibility and analytics capabilities, the theory-driven research is limited. Hazen et al. (2016) further outlined how the use of organizational theories can help explain the complexity associated with the use of BDA capability to explain supply chain sustainability. Waller and Fawcett (2013a) noted that the intersection of logistics and supply chain management field with data science, predictive analytics and BD can provide numerous opportunities for research. However, in the absence of adequate skills, the supply chain managers often face a myriad of challenges to extract information from BD to take effective supply chain operational decisions (Waller and Fawcett, 2013a; Dubey and Gunasekaran, 2015a ; Gupta and George, 2016 ). The role of contextual factors in developing BDA capability is well discussed in the information systems literature. What is less understood is how BDA under the effect of contextual factors affect logistics and supply chain processes. Waller and Fawcett (2013b) argued that recent experience with BD may help to explain some of the complex phenomena and unanswered questions in logistics and supply chain management.

The main objective of this special issue (SI) is to provide a significant opportunity to the logistics and supply chain management community to affect practice through fundamental research on how BDA capability can be exploited by the organizations to provide logistics and supply chain insights.

Review of articles included in the SI

Our SI attracted 44 submissions. Each manuscript was examined to ensure that it was in line with our stated objectives in the published call for papers. We desk rejected some of the papers which failed to meet our objectives or the objectives of the International Journal of Logistics Management (IJLM). Next, the manuscripts which were in line with our SI and IJLM objectives, as well as fit for the next round, were submitted for review to two or more experts per manuscript. Based on the reviewers’ and guest editors’ review, we rejected or invited the authors to undertake substantial revision based on the reviewers’ inputs. Finally, after multiple rounds of review, we finally accepted 13 papers for our SI. All accepted papers in this SI are in line with our and IJLM objectives. The papers that are included in this: Dubey et al. (2018) , Jeble et al. (2017) , Song et al. (2018) , Brinch et al. (2018) , Hopkins and Hawking (2018) , Gravili et al. (2018) , Lamba and Singh (2018) , Gupta et al. (2018) , Lai et al. (2018) , Hoehle et al. (2018) , Bhattacharjya et al. (2018) , Hofmann and Rutschmann (2018) and Queiroz and Telles (2018) .

The first paper in this SI is on the application of big data and predictive analytics (BDPA) on humanitarian supply chains by Dubey et al. (2018) . This paper examines what the antecedents of BDPA are. Second, how the BDPA can improve the visibility of humanitarian supply chains and coordination among the actors in humanitarian supply chains. Third, the authors examine the moderating role of swift trust on the path joining BDPA and visibility/coordination. To answer these research questions, the authors have grounded their model in contingent resource-based view (CRBV). In addition, the authors have tested their theoretical model using survey data gathered from informants at international NGOs that are engaged in disaster relief operations. The findings of the study offer some interesting contributions to BD, predictive analytics literature and swift-trust theory. Furthermore, it offers numerous directions to the managers who are engaged in disaster relief operations.

The second paper in this SI is on the application of BDPA on supply chain sustainability by Jeble et al. (2017) . This paper examines what the resources needed to build BDPA capability are. Second, the paper examines how BDPA affects the supply chain sustainability under the moderating effect of supply base complexity. To answer these research questions, the authors grounded their model in the CRBV. The authors also tested their model using data gathered via the single-informant instrument. The findings of the study contribute to the growing debate surrounding BD, predictive analytics and supply chain sustainability.

The third paper in this SI is on the use of large data sets to examine the impact of financial restrictions on green innovation capability in the context of the global supply chain by Song et al. (2018) . In this study, the authors have proposed a linear relationship between green innovation as a dependent variable; green supply chain integration and financial restriction as dependent variables. The study utilized customs, import and export data from 222,773 Chinese enterprises to test their proposed model. The findings suggest that greater supply chain integration and relaxation in financial restriction will boost the green innovation initiative of these firms. The study contributes to the prior research calls of scholars (see Waller and Fawcett, 2013a ; Wang et al. , 2016 ), and how BDPA can be used to advance existing debates surrounding SCM.

The fourth paper in this SI is an exploratory study which aims to understand how supply chain practitioners view BD and its application in supply chain management by Brinch et al. (2018). In this study, the authors have used mixed research methods to address their research questions. First, the authors used the Delphi technique to understand the extent to which the supply chain practitioners were familiar with the application of BD in SCM. They further ranked the applications of BD in the SCOR process framework. The authors also supported the Delphi study via cross-sectional data gathered using the survey-based instrument. The study provides an in-depth understanding of the various applications of BD in SCM. Second, the authors explore how BD applications in various stages in the supply chain can help the firm gain a competitive advantage. The study provides numerous directions for further research, which may help to expand logistics and supply chain management literature.

The fifth paper in this SI investigates the application of BDA and IoT in logistics by Hopkins and Hawking (2018) . In this study, the authors have tried to develop a theoretical framework using a case study approach to understanding how logistics firms use BDA and IoT to support strategies to improve driver safety, reduce operating costs and reduce the negative effects of automobiles on the environment. The study provides directions for the logistics companies on how effective deployment of BDA and IoT can address some of the perennial problems of the logistics industry.

The sixth paper in this SI is on the influence of digital divide (DD) and digital alphabetization (DA) on the BD generation in supply chain management by Gravili et al. (2018) . In this study, the authors have investigated the influence of the DD and DA on the BD generation process in order to gain insight into how BD could become a useful tool in the decision-making process of SCM. In addition, the authors have used a systematic literature review to understand the relationship between the literature on BDA, DD and SCM. The authors also explored the vector autoregressive, which is a stochastic technique to capture the linear interdependence between DD (as a part of internet usage) and trade in the context of the European Union. By examining the association between DD and internet acquisitions, a positive and long-lasting impulse response function was revealed, followed by an ascending trend. The findings suggest that a self-multiplying effect is being generated, and it is, in effect, reasonable to assume that the more individuals use the internet, the more electronic acquisitions occur. Thus, the improvement of the BD and SCM process is strongly dependent on the quality of the human factor.

The seventh paper in this SI attempts to develop a theoretical model, which tries to explain how the enablers of BD in operations and supply chain management are associated with each other by Lamba and Singh (2018) . In this study, the authors have used fuzzy TISM to develop a theoretical model and have further examined the causality of the linkages using the DEMATEL technique. These techniques are grounded in graph theory. The current contribution of the authors makes significant strides toward the theoretical advancement of BDA and its application in the operations and supply chain management context. In the future, the proposed model may be tested using longitudinal data.

The eighth paper in this SI examines the role of cloud ERP on organizational performance by Gupta et al. (2018) . Cloud-based ERP enables an organization to pay for the services they need and removes the need to maintain information technology infrastructure. In this paper, the authors have grounded their model in a CRBV and have further tested the role of cloud-based ERP services on supply chain performance and organizational performance, with cross-sectional data collected via a single-informant questionnaire. The findings of the study indicate that cloud ERP has a positive influence on supply chain performance and organizational performance measured in terms of market and financial performance. Furthermore, the study indicates that the supply base complexity has a significant moderating influence on the path joining cloud ERP and market/financial performance. The study contributes to the extant literature and further provides direction to the management practitioners.

The ninth paper in this SI examines the determinants of BDA in logistics and supply chain management by Lai et al. (2018) . The authors have undertaken an extensive literature review of extant literature on BDA and SCM and have further classified the factors into four constructs: technological factors, organizational factors, environmental factors and supply chain characteristics. Furthermore, drawing from the innovation diffusion theory, the authors have proposed their theoretical model using the four constructs, and have further tested the process using single-informant survey data from 210 organizations. The findings of the study suggest that perceived benefits and top management support have a significant influence on the adoption intention. Subsequently, environmental factors such as competitors’ adoption, government policy and supply chain connectivity have a significant moderating effect on the direct relationship between driving factors and the adoption intention. The results offer some interesting contributions to the BDA and SCM literature.

The tenth paper in this SI examines the customer’s tolerance in the context of omnichannel retail stores via logistics and supply chain analytics by Hoehle et al. (2018) . In this study, the authors argued that mobile technologies are increasingly being used as a data source to enable BDA. These BDA enable inventory control and logistics planning for omnichannel businesses. First, the authors in this study introduced three emerging mobile shopping checkout processes in the retail store. Second, they suggested that new validation procedures (i.e. exit inspections) necessary for implementation of mobile technology-enabled checkout processes may disrupt traditional retail service processes. Third, the authors have proposed a construct labeled “tolerance for validation” defined as customer reactions to checkout procedures. The authors have also developed a measurement scale for the proposed construct and gathered data using a structured questionnaire from 239 customers. The statistical analyses suggest that customers have a higher tolerance for validation under scenarios in which mobile technologies are used in the checkout processes, as compared to the traditional self-service scenario in which no mobile technology is used. The customers do not particularly show a clear preference for specific mobile shopping scenarios. Hence, these findings contribute to our understanding of the challenges that omnichannel businesses may face as they leverage data from digital technologies to enhance collaborative planning, forecasting and replenishment processes. The proposed construct and measurement scales can be used in future work on omnichannel retailing.

The 11th paper of this SI examines how unstructured data in the form of tweets can be exploited to improve customer service by Bhattacharjya et al. (2018) . In this study, the authors argued that in recent days, the interaction between firms and their customers in the form of tweets have increased. However, these tweets often constitute a large volume and the extraction of valuable information from these unstructured data may offer unique opportunities to understand their customers’ need. The authors have demonstrated the need for tweet analytics via parcel shipping companies and their interactions with customers in Australia, the UK and the USA. The findings from the study contribute to the customer engagement theory. The research provides a unique opportunity for the practitioners, confirming that tweet analytics can be exploited to address other logistics and supply chain activities.

The 12th paper of this SI examines how BDA can be used for forecasting in supply chains by Hofmann and Rutschmann (2018) . In this study, the authors argued that BD can minimize the forecast errors, thereby improving the forecast accuracy. The authors have proposed a conceptual structure based on the design-science paradigm via three steps: description of conceptual elements of the framework utilizing justifiable knowledge; specification of the principles of the theoretical framework to explain the interplay between elements; and creation of a matching framework by conducting investigations within the retail industry. The developed framework could serve as the first guide for meaningful BDA initiatives in the supply chain. This study attempts to offer unique contributions to the forecasting technique via BDA.

The 13th paper of this SI examines the role of BDA in logistics and supply chain by Queiroz and Telles (2018) . In this study, the authors have investigated the role of supply chain partnerships, human knowledge and innovation culture on supply chains in BD environments. The authors have further tested their proposed BDA-SCM triangle using data gathered via single-informant instrument from Brazilian corporations. The study provides an understanding of the barriers related to BDA adoption and the relationship between supply chain levels and BDA knowledge. The authors have further noted their limitations, which offer unique opportunities to the BDA and SCM scholars to build upon current findings.

Limitations and future research directions

When should we use BDPA in SCM?

Under what context can BDPA in SCM be used?

How can predictive analytics be used to advance theory in SCM?

How does BDPA in SCM affect organizational performance and under what circumstances?

How can BDPA be used in inventory planning?

How can BDPA improve information sharing?

How can BDPA be used for facility layout design?

How can BDPA be used in vehicle routing problems?

How can BDPA help to minimize environmental uncertainties?

Hence, we can argue that we need strong predictive analytics capability because consumer behavior has become an integral part of the supply chain ( Waller and Fawcett, 2013b ). Thus, the ability to predict the consumer behavior has implications for product innovation, product manufacturing, distribution, design and demand.

Concluding remarks

The BDA is one of the most promising topics which can provide numerous opportunities for academic and management practitioners. It can be used for building theories which is one of the untapped potentials of the BDPA; even though many scholars often term BDA as one of the management fads. Despite criticisms, we believe that BDA have immense potential to revolutionize existing supply chain theories.

Bhattacharjya , J. , Ellison , A.B. , Pang , V. and Gezdur , A. ( 2018 ), “ Creation of unstructured big data from customer service: the case of parcel shipping companies on Twitter ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-06-2017-0157

Brinch , M. , Stentoft , J. , Jensen , J.K. and Rajkumar , C. ( 2018 ), “ Practitioners understanding of big data and its applications in supply chain management ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-05-2017-0115

Chen , C.P. and Zhang , C.Y. ( 2014 ), “ Data-intensive applications, challenges, techniques and technologies: a survey on big data ”, Information Sciences , Vol. 275 , pp. 314 - 347 .

Choi , T.M. , Wallace , S.W. and Wang , Y. ( 2017 ), “ Big data analytics in operations management ”, Production and Operations Management , doi: 10.1111/poms.12838 .

Dubey , R. and Gunasekaran , A. ( 2015a ), “ Education and training for successful career in big data and business analytics ”, Industrial and Commercial Training , Vol. 47 No. 4 , pp. 174 - 181 .

Dubey , R. , Luo , Z. , Gunasekaran , A. , Akter , S. , Hazen , B.T. and Douglas , M.A. ( 2018 ), “ Big data and predictive analytics in humanitarian supply chains: enabling visibility and coordination in the presence of swift trust ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-02-2017-0039

Fosso Wamba , S. , Akter , S. , Edwards , A. , Chopin , G. and Gnanzou , D. ( 2015 ), “ How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study ”, International Journal of Production Economics , Vol. 165 , pp. 234 - 246 .

Fosso Wamba , S. , Gunasekaran , A. , Akter , S. , Ren , S.J.F. , Dubey , R. and Childe , S.J. ( 2017 ), “ Big data analytics and firm performance: effects of dynamic capabilities ”, Journal of Business Research , Vol. 70 , pp. 356 - 365 .

Galbraith , J.R. ( 2014 ), “ Organization design challenges resulting from big data ”, Journal of Organizational Design , Vol. 3 No. 1 , pp. 2 - 13 .

Gravili , G. , Benvenuto , M. , Avram , A. and Viola , C. ( 2018 ), “ The influence of the Digital Divide on Big data generation within supply chain management ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-06-2017-0175

Gunasekaran , A. , Papadopoulos , T. , Dubey , R. , Wamba , S.F. , Childe , S.J. , Hazen , B. and Akter , S. ( 2017 ), “ Big data and predictive analytics for supply chain and organizational performance ”, Journal of Business Research , Vol. 70 , pp. 308 - 317 .

Gupta , M. and George , J.F. ( 2016 ), “ Toward the development of a big data analytics capability ”, Information & Management , Vol. 53 No. 8 , pp. 1049 - 1064 .

Gupta , S. , Kumar , S. , Singh , S.K. , Foropon , C. and Chandra , C. ( 2018 ), “ Role of cloud ERP on the performance of an organization: contingent resource based view perspective ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-07-2017-0192

Hazen , B.T. , Boone , C.A. , Ezell , J.D. and Jones-Farmer , L.A. ( 2014 ), “ Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications ”, International Journal of Production Economics , Vol. 154 , pp. 72 - 80 .

Hazen , B.T. , Skipper , J.B. , Ezell , J.D. and Boone , C.A. ( 2016 ), “ Big data and predictive analytics for supply chain sustainability: a theory-driven research agenda ”, Computers & Industrial Engineering , Vol. 101 , pp. 592 - 598 .

Hoehle , H. , Aloysius , J.A. , Chan , F. and Venkatesh , V. ( 2018 ), “ Customers’ tolerance for validation in omnichannel retail stores: enabling logistics and supply chain analytics ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-08-2017-0219

Hofmann , E. and Rutschmann , E. ( 2018 ), “ Big data analytics and demand forecasting in supply chains: a conceptual analysis ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-04-2017-0088

Hopkins , J. and Hawking , P. ( 2018 ), “ Big data analytics and IoT in logistics: a case study ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-05-2017-0109

Jeble , S. , Dubey , R. , Childe , S.J. , Papadopoulos , T. , Roubaud , D. and Prakash , A. ( 2017 ), “ Impact of big data & predictive analytics capability on supply chain sustainability ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-05-2017-0134

Kiron , D. , Prentice , P.K. and Ferguson , R.B. ( 2014 ), “ The analytics mandate ”, MIT Sloan Management Review , Vol. 55 No. 4 , pp. 1 - 25 .

Lai , Y. , Sun , H. and Ren , J. ( 2018 ), “ Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: an empirical investigation ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-06-2017-0153

Lamba , K. and Singh , S.P. ( 2018 ), “ Modeling big data enablers for operations and supply chain management ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-07-2017-0183

Papadopoulos , T. , Gunasekaran , A. , Dubey , R. , Altay , N. , Childe , S.J. and Fosso-Wamba , S. ( 2017 ), “ The role of big data in explaining disaster resilience in supply chains for sustainability ”, Journal of Cleaner Production , Vol. 142 , pp. 1108 - 1118 .

Queiroz , M.M. and Telles , R. ( 2018 ), “ Big data analytics in supply chain and logistics: an empirical approach ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-05-2017-0116

Sanders , N.R. and Ganeshan , R. ( 2015 ), “ Special issue of production and operations management on ‘big data in supply chain management’ ”, Production and Operations Management , Vol. 24 No. 7 , pp. 1193 - 1194 .

Schoenherr , T. and Speier‐Pero , C. ( 2015 ), “ Data science, predictive analytics, and big data in supply chain management: current state and future potential ”, Journal of Business Logistics , Vol. 36 No. 1 , pp. 120 - 132 .

Srinivasan , R. and Swink , M. ( 2017 ), “ An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective ”, Production and Operations Management , doi: 10.1111/poms.12746 .

Song , M. , Chen , M. and Wang , S. ( 2018 ), “ Global supply chain integration, financing restrictions, and green innovation: analysis based on 222,773 samples ”, The International Journal of Logistics Management , available at: https://doi.org/10.1108/IJLM-03-2017-0072

Waller , M.A. and Fawcett , S.E. ( 2013a ), “ Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management ”, Journal of Business Logistics , Vol. 34 No. 2 , pp. 77 - 84 .

Waller , M.A. and Fawcett , S.E. ( 2013b ), “ Click here for a data scientist: big data, predictive analytics, and theory development in the era of a maker movement supply chain ”, Journal of Business Logistics , Vol. 34 No. 4 , pp. 249 - 252 .

Wang , G. , Gunasekaran , A. , Ngai , E.W. and Papadopoulos , T. ( 2016 ), “ Big data analytics in logistics and supply chain management: certain investigations for research and applications ”, International Journal of Production Economics , Vol. 176 , pp. 98 - 110 .

Further reading

Chae , B.K. ( 2015 ), “ Insights from hashtag♯ supplychain and Twitter analytics: considering Twitter and Twitter data for supply chain practice and research ”, International Journal of Production Economics , Vol. 165 , pp. 247 - 259 .

Dubey , R. and Gunasekaran , A. ( 2015b ), “ The role of truck driver on sustainable transportation and logistics ”, Industrial and Commercial Training , Vol. 47 No. 3 , pp. 127 - 134 .

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Forecasting e-commerce consumer returns: a systematic literature review

• Open access
• Published: 21 May 2024

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• David Karl   ORCID: orcid.org/0000-0002-0326-5982 1  

The substantial growth of e-commerce during the last years has led to a surge in consumer returns. Recently, research interest in consumer returns has grown steadily. The availability of vast customer data and advancements in machine learning opened up new avenues for returns forecasting. However, existing reviews predominantly took a broader perspective, focussing on reverse logistics and closed-loop supply chain management aspects. This paper addresses this gap by reviewing the state of research on returns forecasting in the realms of e-commerce. Methodologically, a systematic literature review was conducted, analyzing 25 relevant publications regarding methodology, required or employed data, significant predictors, and forecasting techniques, classifying them into several publication streams according to the papers’ main scope. Besides extending a taxonomy for machine learning in e-commerce, this review outlines avenues for future research. This comprehensive literature review contributes to several disciplines, from information systems to operations management and marketing research, and is the first to explore returns forecasting issues specifically from the e-commerce perspective.

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1 Introduction

E-commerce has witnessed substantial growth rates in recent years and continues growing by double-digit margins (National Retail Federation/Appriss Retail 2023 ). However, lenient consumer return policies have resulted in $212 Billion worth of merchandise being returned to online retailers in the U.S. in 2022, accounting for 16.5% of online sales (National Retail Federation/Appriss Retail 2023 ). While high rates of consumer returns mainly concern specific sectors and product categories, online fashion retailing is particularly affected (Diggins et al. 2016 ). Recent studies report average shipment-related return rates for fashion retailers in the 40–50% range (Difrancesco et al. 2018 ; Karl and Asdecker 2021 ). In addition to missed sales and reduced profits (Zhao et al. 2020 ), consumer returns pose operational challenges (Stock and Mulki 2009 ), including unavoidable processing costs (Asdecker 2015 ) and uncertainties regarding logistics capacities, inventory management, procurement decisions, and marketing activities. Hence, effectively managing consumer returns is an essential part of the e-commerce business model (Urbanke et al. 2015 ).

Similar to the research conducted by Abdulla et al. ( 2019 ), this work focuses on consumer returns in online retailing (e-commerce), excluding the larger body of closed-loop supply chain (CLSC) management, which encompasses product returns related to end-of-life and end-of-use scenarios involving raw material recycling or remanufacturing. In contrast to CLSC returns, retail consumer returns are typically sent or given back unused or undamaged shortly after purchase, without any quality-related defects. These returns should be reimbursed to the consumer and are intended to be resold “as new” (de Brito et al. 2005 ; Melacini et al. 2018 ; Shang et al. 2020 ).

Regarding forecasting aspects, demand forecasting is a crucial activity for successful retail management (Ge et al. 2019 ). In contrast to demand and sales, returns constitute the “supply” side of the return process (Frei et al. 2022 ). Consequently, forecasting becomes a complex task and a significant challenge in managing returns due to the inherently uncertain nature of customer decisions regarding product retention (Frei et al. 2022 ). Moreover, return forecasts are interconnected with sales forecasts and promotional activities (Govindan and Bouzon 2018 ; Tibben-Lembke and Rogers 2002 ). Hence, forecasting objectives may vary, encompassing return quantities, timing (Hachimi et al. 2018 ), and even individual return probabilities. Minimizing return forecast errors is critical to reduce and minimize reactive planning (Hess and Mayhew 1997 ). Accurate forecasts rely on (1) comprehensive data collection, e.g., regarding consumer behavior, and (2) information and communications technology (ICT) for data processing, such as big data analytics. Despite extensive research in supply chain management (SCM), Barbosa et al. ( 2018 ) noted a lack of relevant publications exploring the "returns management" process of SCM in conjunction with big data analytics. Specifically, “the topic of forecasting consumer returns has received little attention in the academic literature” (Shang et al. 2020 ). Nonetheless, precise return forecasts positively impact reverse logistics activities’ economic, environmental, and social performance, primarily concerning quantity, quality, and timing predictions (Agrawal and Singh 2020 ). Hence, forecasting returns holds significant relevance across various supply chain stages.

1.1 Previous meta-research

Hess and Mayhew ( 1997 ) emphasized the need for extensive data analysis concerning reverse flows, which forms the basis for returns forecasting. Subsequently, research on consumer returns and reverse logistics has proliferated. Thus, before collecting data and reviewing the topic of consumer returns forecasting, we first examined existing reviews and meta-studies relevant to the subject matter. To accomplish this, we referred to Web of Science, Business Source Ultimate via EBSCOhost, JSTOR and the AIS Electronic Library as primary sources of knowledge (search term: "literature review" AND "return*" AND "forecast*”). As a secondary source, we appended the results of Google Scholar, Footnote 1 for which a different search term was used (intitle:"literature review" ("product return" OR "consumer return" OR "retail return" OR "e-commerce return") forecast) due to unavailable truncations and to reduce the vast amount of literature with financial focus the search term “return” would lead to. Table 1 presents the most pertinent literature reviews related to the scope of this paper.

Agrawal et al. ( 2015 ) identified research gaps within the realm of reverse logistics, finding “forecasting product returns” as a crucial future research path. However, among 21 papers focusing on “forecasting models for product returns”, the emphasis was predominantly on CLSC, reuse, remanufacturing, and recycling, which do not align with the aim of this review. Agrawal et al. also noted a lack of comprehensive analysis of underlying factors in returns forecasting, such as demographics or consumer behavior.

Similarly, Hachimi et al. ( 2018 ) addressed forecasting challenges within the broader context of reverse logistics. They classified their literature using various forecasting approaches: time series and machine learning, operations research methods, and simulation programs. The research gaps they identified included a limited number of influencing factors taken into account, the absence of established performance indicators, and methodological issues related to dynamic lot-sizing with returns. Although this review focused on reverse logistics, the call for research into predictors of future returns is equally applicable to consumer returns in e-commerce.

The review of Abdulla et al. ( 2019 ) centers on consumer returns within the retail context, particularly in relation to return policies. While they discuss consumer behavior and planning and execution of returns, they do not present any sources explicitly focused on forecasting issues.

Micol Policarpo et al. ( 2021 ) reviewed the literature on the use of machine learning (ML) in e-commerce, encompassing common goals of e-commerce studies (e.g., purchase prediction, repurchase prediction, and product return prediction) and the ML techniques suitable for supporting these goals. Their primary contribution is a novel taxonomy of machine learning in e-commerce, covering most of the identified goals. However, within the taxonomy developed, the aspect of return predictions is disregarded.

The most exhaustive literature review to date regarding product returns, conducted by Ambilkar et al. ( 2021 ), analyzed 518 papers and adopted a holistic reverse logistics approach encompassing all supply chain stages. The authors categorized the papers into six categories, including “forecasting product returns”, for which they found and concisely described 13 papers. Due to the broader research scope, none of the analyzed papers focused on consumer returns within the retail context.

The review by Duong et al. ( 2022 ) employed a hybrid approach combining machine learning and bibliometric analysis. Regarding forecasts of product returns, they identified three relevant papers (Clottey and Benton 2014 ; Cui et al. 2020 ; Shang et al. 2020 ) within the “operations management” category. They explicitly call for further research on predicting customer returns behavior in the pre-purchase stage, highlighting the importance of a better understanding of online product reviews and customers’ online interactions.

1.2 Research gaps and research questions

Why is a systematic literature review necessary for investigating consumer returns and forecasting? On the one hand, there are empirical and conceptual papers that touch upon this topic, including brief literature reviews that align with the subject’s focus (e.g., Hofmann et al. 2020 ). However, narrative reviews lack transparency and replicability (Tranfield et al. 2003 ) and often induce selection bias (Srivastava and Srivastava 2006 ) as they tend to approach a field from a specific perspective. In contrast, systematic reviews strive to present a holistic, differentiated, and more detailed picture, incorporating the complete available literature (Uman 2011 ). On the other hand, existing systematic reviews provide structured yet relatively superficial overviews of literature on end-of-use and end-of-life forecasting (Shang et al. 2020 ), but they do not specifically address consumer returns. Furthermore, we contend that a review dedicated to general reverse logistics forecasting would not adequately capture the distinctive context and requirements inherent in the consumer-retailer relationship within the realm of e-commerce (Abdulla et al. 2019 ).

Consequently, based on existing reviews and papers, we have identified research gaps worth examining more in detail: (1) Returns forecasting techniques and relevant predictors for the respective underlying purposes, especially in the context of e-commerce (RQ1 and RQ2); (2) the integration of return forecasts into an existing but incomplete taxonomy of machine learning in e-commerce (Micol Policarpo et al. 2021 ; RQ3); and (3) future research directions pertaining to e-commerce returns forecasting (RQ4). Therefore, this review aims to shed more light on consumer returns forecasting in the retail context. The following research questions outline the primary objectives:

RQ1: What key research problems (e.g., forecasting purposes, technological approaches) have been addressed in the literature on forecasting consumer returns over time?

RQ2: What are the …

Publication outlets and research disciplines,

Research types and methodologies,

Product categories and industries,

Data sources and characteristics,

Relevant forecasting predictors,

Techniques and algorithms

… used to address these key problems?

RQ3: How can returns forecasting be integrated into a taxonomy of machine learning in e-commerce?

RQ4: What are promising or emerging future research directions regarding forecasting consumer returns?

The paper is organized as follows: Sect.  2 describes selected fundamental concepts and the delimitation of the research field on consumer returns forecasting. Section  3 contains the methodology for the review, drawing on the PRISMA guideline (Page et al. 2021 ) while integrating the approaches of Denyer and Tranfield ( 2009 ) and Webster and Watson ( 2002 ). Section  4 presents the review’s main results, answering RQs 1 (Sect.  4.1 ), RQ2 (Sects.  4.2 – 4.5 ), and RQ 3 (Sect.  4.6 ). A research framework developed in Sect.  5 structures the discussion regarding future research directions (RQ4). Section  6 subsumes the overall contribution of this review.

2 Consumer returns and forecasting

2.1 consumer returns and return reasons.

Reverse product flows, commonly referred to as product returns, can be classified into three categories: manufacturing returns, distribution returns, and consumer returns (Shaharudin et al. 2015 ; Tibben-Lembke and Rogers 2002 ). Among these, consumer returns are further differentiated between returns in brick-and-mortar retail or mail-order/e-commerce returns (Tibben-Lembke and Rogers 2002 ) and are also known as commercial returns (de Brito et al. 2005 ) or retail (product) returns (Bernon et al. 2016 ). With sky-rocketing e-commerce sales, online consumer returns have emerged as the dominant segment, making them a highly relevant field of research (Abdulla et al. 2019 ; Frei et al. 2020 ). Additionally, the digitization of retail provides numerous opportunities for data collection, as digital customer accounts facilitate more efficient analytical monitoring of customer behavior (Akter and Wamba 2016 ). Simultaneously, as competitive pressures intensify in e-commerce due to increased price transparency and substitution possibilites, retailers aiming to stimulate impulse purchases face hightened return rates (Cook and Yurchisin 2017 ; Karl et al. 2022 ).

The spatial decoupling of supply and demand introduces a higher level of uncertainty for e-commerce customers regarding various product attributes compared to bricks-and-mortar retailing (Hong and Pavlou 2014 ). As consumers are unable to physically assess the products they order, this translates into returns being essential part of the e-commerce business model. Besides fit uncertainty, other reasons for returns exist. Stöcker et al. ( 2021 ) classify the drivers triggering consumer returns into consumer behavior related reasons (e.g., impulsive purchases, showrooming), fulfillment/service related reasons (e.g., wrong/delayed delivery) and information gap related reasons (product fit, insufficient visualization). By mitigating customers’ return reasons, retailers try to reduce the return likelihood (“return avoidance”) (Rogers et al. 2002 ). Another, but less promising way of reducing returns, is preventing customers who intend to return from actually doing so (e.g., by incurring additional effort or by rejecting returns) (Rogers et al. 2002 ).

Adapted from Abdulla et al. ( 2019 ) and Vakulenko et al. ( 2019 ), a simplified parallel process of a return transaction from the consumer’s and retailer’s perspective is visualized in Fig.  1 . Retailers can use forecasting in all transaction phases (Hess and Mayhew 1997 ). Targeting customer interventions pre-purchase (real-time forecasting) could be implemented by using dynamically generated (Dalecke and Karlsen 2020 ) digital nudging elements (Kaiser 2018 ; Thaler and Sunstein 2009 ; Zahn et al. 2022 ) in case of a predicted high return propensity. In the post-purchase phase, forecasting could stimulate different interventions (e.g., customer support) or can be helpful for logistics and inventory planning activities (Hess and Mayhew 1997 ). In the phase after the return decision, data analysis, including segmentation on different levels, e.g., for customers, products, or brands (Shang et al. 2020 ), can support managerial decision-making regarding assortment or (individualized) return policies for future orders (Abdulla et al. 2019 ). In other words, forecasting (or modeling) of returns in later phases of the process can substantiate interventions in earlier phases of the process (e.g., a temporary return policy change, or the suspension of product promotions due to particular forecasts). However, such data-driven interventions itself also represent an influencing factor to be taken into account in future forecasts; thus, different forecasting purposes can be linked, at least when it comes to the data required. All these interdependencies hint at the circularity of the returns process, with an adequate management of returns representing an opportunity for generating customer satisfaction and retention (Ahsan and Rahman 2016 ; Röllecke et al. 2018 ).

Purchase and return process concerning forecasting issues (adapted from Abdulla et al. 2019 ; Vakulenko et al. 2019 )

Although primarily focussing on the online retailers’ process, it is worth noting that the issue at hand is equally applicable to brick-and-mortar retail (Santoro et al. 2019 ), which can benefit from the application of advanced data analysis techniques for forecasting purposes (Hess and Mayhew 1997 ).

2.2 Forecasting purposes and corresponding techniques

Accurate forecasting holds significant importance in the realm of e-commerce. Precise demand forecasts (“predictions”) play a pivotal role in inventory planning, pricing, and promotions and ultimately impact the commercial success of retailers (Ren et al. 2020 ). Forecasting consumer returns affects similar business aspects and resorts to comparable existing technical procedures. The data science and statistics literature offers diverse methods and algorithms for forecasting consumer returns. The choice of approach depends on the specific objective, with the outcome variable being scaled accordingly. For instance, when forecasting whether a single product will be returned, the dependent variable is either binary or expressed as a propensity value ranging form 0 to 1. On the other hand, forecasting the quantitay or timing of returns entails continuous outcome variables. As a result, various techniques, from time-series forecasting to machine learning approaches can be applied, which will be briefly outlined in the subsequent sections.

2.2.1 Return classifications and propensities

A naïve method for determining the propensity or return decision forecast is using lagged (historical) return information (return rates), either for a given product, a given customer, or any other reference, to calculate a historical return probability (Hess and Mayhew 1997 ). Return rate forecasts are a reference-specific variant of forecasting return propensities.

Simple causal models based on statistical regression methods utilize one or more independent exogenous variables. The logistic regression (logit model) is employed when the dependent variable is binary or contains more nominal outcomes (multinomial logistic regression). For each observation, the binary logistic regression assesses the probability that the dependent variable takes the value “1” (Hastie et al. 2017 ). Consequently, this approach finds application for return decisions and return propensities. Comparatively, linear discriminant analysis (Fisher 1936 ) bears a resemblance to logistic regression by generating a linear combination of independent variables to best classify available data. This classification process involves determining a score for each observation, subsequently compared to a critical discriminant score threshold, and distinguishing between return and keep.

More sophisticated machine learning (ML) techniques such as neural networks, decision tree-based methods, ensemble learning, and boosting methods are highly suitable for this forecasting purpose. For a general exposition of ML techniques in the domain of e-commerce, we refer to Micol Policarpo et al. ( 2021 ). Additionally, for a comparative study of several state-of-the-art ML classification techniques, see Fernández-Delgado et al. ( 2014 ). Artificial Neural Networks (NN) consist of interconnected nodes (“neurons”) organized in layers, exchanging signals to ascertain a function that accurately assigns input data to corresponding outputs. Typically, supervised learning techniques such as backpropagation compare the network outputs with known actual values (Hastie et al. 2017 ). Notably, neural networks are the most popular machine learning algorithm in last years’ e-commerce research (Micol Policarpo et al. 2021 ), and deep learning extensions like Long Short-Term Memory (Bandara et al. 2019 ) are gaining attention. Decision Trees (DT) manifest as hierarchical structures of branches representing conjunctions of specific characteristics and leaf nodes denoting class labels. This approach endeavors to construct an optimal decision tree for classifying available observations. Many decision tree algorithms have been introduced to serve this purpose (e.g., Breiman et al. 1984 ; Pandya and Pandya 2015 ). Ensemble learning methods adopt a voting mechanism involving multiple algorithms to enhance predictive performance (Polikar 2006 ). Analogously, boosting and bagging techniques are incorporated in algorithms like AdaBoost or the tree-based Random Forest (RF) to augment the input data, aiming at more generalizable forecasting models less prone to overfitting issues (Hastie et al. 2017 ). Support Vector Machines (SVM) stand as another example of a supervised ML algorithm, having demonstrated efficacy in tackling classification problems within e-commerce (Micol Policarpo et al. 2021 ).

2.2.2 Return timing and volume forecasts

For product returns, timing is crucial in forecasting end-of-life, end-of-use, or remanufacturing returns that can occur years after the initial purchase (Petropoulos et al. 2022 ). In contrast, for consumer returns, the possible time window in which products are regularly returned in new condition with the aim of a refund is much shorter (usually less than 100 days and mostly less than 30 days), and priorities are more on forecasting return volumes. Forecasting return volumes can be multi-faceted, ranging from forecasting the total return volume a retailer has to process within its logistics department through forecasting product-specific return numbers up to forecasting costly return shares, e.g., return fraud volume. Because returns depend on fluctuating sales, time-series forecasting of return volumes performs only well with constant sales volumes or under risk-pooling (Petropoulos et al. 2022 ). Thus, for a naïve return volume forecast, sales forecasts for a given timeframe are multiplied by the lagged return rate (historical data of products/consumers or any other reference). Possible algorithms for estimating historical return rates include time series forecasting to causal predictions comprising ML approaches (Hachimi et al. 2018 ).

Time-series techniques, e.g., single exponential smoothing (SES) or Holt-Winters-approaches (HW), are based on the assumption that the future development of an outcome variable (e.g., return volume) is dependent on its past numbers, while time acts as the only predictor. Most of these models can be generalized as autoregressive moving averages (ARIMA) models, for which numerous extensions are available. These models can approximate more complex temporal relationships. Similarly, time-series regression models use univariate linear regression with time as a single exogenous variable.

The mentioned multivariate regression models are essential statistical tools and can predict metric variables such as return volume or time. The logic is to fit a linear function of a given set of input variables (“features”) to the outcome variable with the criteria of minimizing the residual sum of squares (Hastie et al. 2017 ). Many variants of regression models are derived from this logic (e.g., generalized linear models), and various extensions are built upon this base (e.g., LASSO for variable selection, Tibshirani 1996 ).

Emerging from more complex statistical methods and using the possibilities of continuously increasing computing power, IT-based machine learning (ML) approaches were developed. Some of these approaches have already been presented in Sect. 2.2.1, being suitable for predicting metric variables in addition to classification tasks, e.g., neural networks, decision tree algorithms, and especially ensemble techniques like random forests.

3 Methodology

Methodologically, the research process of this review follows the PRISMA guideline (Page et al. 2021 ) where applicable and is structured in five steps (Denyer and Tranfield 2009 ; Webster and Watson 2002 ): (1) question formulation; (2) locating studies; (3) study selection and evaluation; (4) (concept-centric) analysis and synthesis; and (5) reporting and using the results for defining an agenda for future research.

The first step refers to the research questions already formulated in the introduction. The second step involves selecting the databases and defining the search terms. In that respect, five scientific databases were selected, aiming at journal as well as conference publications: AIS Electronic Library (AISeL), Business Source Ultimate (BS) via EbscoHost, JSTOR (JS), Science Direct (SD), and Web of Science (WoS). To ensure inclusivity and to account for potential variations in spelling or phrasing, the final search strings incorporate truncations where applicable. The search query utilized in this review comprises two key components. Firstly, it pertains to consumer returns, encompassing products returned by consumers, primarily in the context of e-commerce, to the retailer. While it is recommended to use reasonably general search terms, the term “return” alone would yield results for various stages of reverse logistics and a vast amount of financial literature. Therefore, we conducted a more specific search using the phrase “consumer return*” and the related terms “e-commerce return*”, “product return*”, “return* product”, “customer return*”, and “retail return*”. Secondly, this paper specifically focuses on forecasting (“forecast*”), which can be alternately referred to as “predict*” or “prognos*”. The combination of these terms was searched for in the Title, Abstract and Keywords fields.

The search includes results up to the middle of 2022 and resulted in 725 initial search hits (see Fig.  2 ). As this review aims to identify papers dealing with consumer returns and forecasting, the inclusion criteria for eligibility were:

The title or keywords referred to consumer returns or forecasting (in a broader sense, including data preparation). A connection to the respective subject area and applicability to the retail domain should at least be plausible.

Manuscript in English: No important study would be written and published in a language different than English.

The paper has undergone a single- or double-blind peer-review process, either as a journal publication or as a publication in peer-reviewed conference proceedings.

Research process flow diagram

In the third step, duplicates were removed, resulting in a set of 650 unique records. Subsequently, the papers underwent screening based on title, keywords, and language to determine whether they warranted further examination. This preliminary screening phase reduced the number of papers to 85. These papers’ abstracts and full texts were thoroughly reviewed to assess their relevance. This step encompasses all papers pertaining to returns forecasting for retailers or direct-selling manufacturers while excluding those focused on closed-loop supply chain management or remanufacturing, recycling, and end-of-life returns. Ultimately, a final sample of 20 publications was identified, serving as a foundation for identifying additional relevant papers (vom Brocke et al. 2009 ; Webster and Watson 2002 ) through a forward search using Google Scholar and snowballing via backward search. This process yielded an additional five papers, resulting in a total of 25 papers included for review (Table  2 ).

The fourth step comprises the analysis and synthesis of the relevant papers. Data, including bibliographic statistics, were collected in accordance with the research questions. A two-way concept-centric analysis, as described by Webster and Watson ( 2002 ), was conducted, encompassing confirmatory aspects based on the fundamentals outlined in Sect.  2 of this paper, as well as exploratory elements aimed at enriching existing categories and concepts. The objective was to comprehensively describe the relevant concepts, approaches, and dimensions discussed in the literature.

Moving on to the fifth and final step (Denyer and Tranfield 2009 ), the results are presented. Initially, the main scope of the papers included in the analysis is presented. Next, bibliographic data pertaining to the included papers are provided to offer a concise overview of the research area and its recent developments, followed by a content analysis and synthesis of the relevant literature to delve into the current state of research and highlight key findings. Finally, Sect.  5 outlines a research agenda for the domain (vom Brocke et al. 2009 ).

4 Results of the systematic review

After outlining the main scope of the relevant publications (4.1), a short bibliographic characterization (4.2) is given. Next, this section presents the results of the systematic review, focussing on the methodology and datasets used (4.3), predictors used for returns forecasting (4.4), and forecasting techniques employed (4.5). The integration of consumer returns forecasting into an existing taxonomy for e-commerce and machine learning (Micol Policarpo et al. 2021 ) summarizes and concludes the presentation of the results.

4.1 Overview and main scope of the relevant publications

Table 3 provides an overview of the forecasting purpose of the papers, the data source for the forecasting, the algorithms employed, and the predictors used in the forecasting models. The contributions of the respective papers regarding forecasting issues are summarized in the Appendix.

For identifying research streams, the publications are analyzed regarding the intention and main scope, as described in the abstract, the respective research questions, and the remainder of the papers. Most papers were assigned to an unequivocal research scope, while some contributed to two key topics (Fig.  3 ).

Classification of main scopes (n = 25; not mutually exclusive)

At first, we identified a stream of literature regarding the comparison of different forecasting models and algorithms (Asdecker and Karl 2018 ; Cui et al. 2020 ; Drechsler and Lasch 2015 ; Heilig et al. 2016 ; Hess and Mayhew 1997 ; Hofmann et al. 2020 ; Imran and Amin 2020 ). These papers use existing approaches, adapt them for individual forecasting purposes, apply models to one or more datasets, and compare and evaluate the resulting forecasting performance. One paper claims that the difference in forecasting accuracy of easily interpretable algorithms is relatively small compared to more sophisticated ML algorithms (Asdecker and Karl 2018 ). This statement is partially confirmed (Cui et al. 2020 ), as the ML algorithms show advantages over simpler models in the training data set but have lower prediction quality due to overfitting issues in the test data. Nevertheless, fine-tuned ML approaches (e.g., deep learning with TabNet) outperform simpler models and gain accuracy when correcting class imbalances during the data preparation phase (Imran and Amin 2020 ). When confronted with large class imbalances (e.g., low return rates), boosting algorithms like Gradient Boosting work well without oversampling (Hofmann et al. 2020 ). Fundamentally, ensemble models incorporating different techniques show the maximum possible accuracy (Asdecker and Karl 2018 ; Heilig et al. 2016 ). Forecasting of return timing is more erroneous than return decisions, and split-hazard-models outperform simple OLS approaches (Hess and Mayhew 1997 ). Time series prediction only works reliably when return rates do not fluctuate heavily (Drechsler and Lasch 2015 ).

The second stream we identified focuses on feature generation or selection and dataset preparation (Ahmed et al. 2016 ; Ding et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Urbanke et al. 2015 , 2017 ). Besides this central topic, some papers also compare different forecasting algorithms (Ahmed et al. 2016 ; Hofmann et al. 2020 ; Rezaei et al. 2021 ; Urbanke et al. 2015 , 2017 ). For example, random oversampling of data with large class imbalances can improve the performance of different forecasting algorithms, while models based only on sales/return history perform worse than models with more features (Hofmann et al. 2020 ). Two similar approaches are based on product, basket, and clickstream data, using different algorithms for feature extraction (Urbanke et al. 2015 , 2017 ). The first developed a Mahalanobis Feature Extraction algorithm, proving superior to other algorithms like principal component analysis or non-negative matrix factorization (Urbanke et al. 2015 ). The second develops a NeuralNet algorithm to extract interpretable features from a high-dimensional dataset, showing superior performance and giving reasonable interpretability of the most important factors (Urbanke et al. 2017 ). For the automated integration of different data sources into single flat tables and the generation of discriminating features, a rolling-path algorithm is developed, improving performance when data is imbalanced (Ahmed et al. 2016 ). Similarly, the software “Dataconda” can automatically generate and integrate relational attributes from different sources into a flat table, which is often the required prerequisite for forecasting algorithms (Samorani et al. 2016 ). A different selection approach clusters the features into groups and applies selection algorithms to the groups, aiming to select a smaller set of attributes (Rezaei et al. 2021 ). As quite an offshoot, one paper predicts a seller’s overall daily return volume dependent on his current “reputation” measured by tweets (Ding et al. 2016 ), which needs sentiment analysis to be integrated into the forecast.

A quite heterogenous research stream belongs to the development of algorithms, heuristics, and models that go beyond a straightforward adaption of existing approaches (Fu et al. 2016 ; Joshi et al. 2018 ; Li et al. 2018 ; Potdar and Rogers 2012 ; Rajasekaran and Priyadarshini 2021 ; Shang et al. 2020 ; Sweidan et al. 2020 ; Zhu et al. 2018 ). Potdar and Rogers ( 2012 ) developed a methodology for forecasting product returns based on reason codes and consumer behavior data. Fu et al. ( 2016 ) developed a conditional probability-based statistical model for predicting return propensities while revealing return reasons and outperforming some baseline benchmark models. Li et al. ( 2018 ) describe their “HyperGo” approach as a ‘framework’ and develop an algorithm for forecasting return intention after basket composition. Zhu et al. ( 2018 ) describe a “LoGraph” random walk algorithm for predicting returned customer/product combinations within their framework. Although Joshi et al. ( 2018 ) label their approach as a “framework”, they describe a specific two-stage algorithm for forecasting return decisions based on network science and ML. Rajasekaran and Priyadarshini ( 2021 ) developed a hybrid metaheuristic-based regression approach to predict return propensities.

Seven papers deal with concepts, meta-models, or substantial frameworks for returns forecasting (Fu et al. 2016 ; Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ; Li et al. 2018 ; Shang et al. 2020 ; Zhu et al. 2018 ). A generic framework for a scalable cloud-based platform, which enables a vertical and horizontal adjustment of resources, could enable the practical real-time use of computationally intensive ML algorithms for forecasting returns in an e-commerce platform (Heilig et al. 2016 ). Two papers (Fuchs and Lutz 2021 ; Hofmann et al. 2020 ) are based on design science research (DSR, Hevner et al. 2004 ) for developing artifacts like meta models and frameworks. The first also refers to CRISP-DM, the “Cross Industry Standard Process for Data Mining” (Wirth and Hipp 2000 ), and develops a shopping-basket-based general forecasting approach suitable across different industries without domain knowledge and attributes needed (Hofmann et al. 2020 ). In a similar approach, based on the basket composition and user interactions, a generic model for real-time return prediction and intervention is developed (Fuchs and Lutz 2021 ) and prepared for integration into an ERP system. Fu et al. ( 2016 ) present a generalized return propensity latent model framework by decomposing returns into different inconsistencies (unmet product expectations, shipping issues, and both factors combined) and enriching the derived propensities with product features and customer profiles. Li et al. ( 2018 ) developed a “HyperGo” framework for forecasting the return intention in real-time after basket composition, including a hypergraph representation of historical purchase and return information. Similarly, Zhu et al. ( 2018 ) developed a “HyGraph” representation of historical customer behavior and customer/product similarity, combined with a “LoGraph” random-walk-based algorithm for predicting customer/product combinations that will be returned. Shang et al. ( 2020 ) discuss two opposing forecasting concepts, demonstrating that their predict-aggregate framework is superior to common and more naïve aggregate-predict approaches.

The last stream covers the detection and forecasting of return fraud and abuse (Drechsler and Lasch 2015 ; John et al. 2020 ; Ketzenberg et al. 2020 ; Li et al. 2019 ). On the employees’ side, one paper tries to automatically predict fraudulent return behavior of agents (employees), e.g., regarding unjustified refunds, by a penalized logit model, enabling a lift in detection (John et al. 2020 ). On the customers’ side, misused returns as a cost-incurring problem are the forecasting purpose of different time series prediction models (Drechsler and Lasch 2015 ). Instead of focussing on fraudulent transactions, a trust-aware random walk model identifies consumer anomalies, enabling retailers to apply targeted measures to specific customer groups (selfish, honest, fraud, and irrelevant customers) (Li et al. 2019 ). Similarly, returning customers can be categorized into abusive, legitimate, and nonreturners (Ketzenberg et al. 2020 ). Based on the characterization of abusive return behavior, a neural network classifier recaptures almost 50% of lost profits due to return abuse (Ketzenberg et al. 2020 ).

One paper (Sweidan et al. 2020 ) could not be assigned to the other scopes. It applies a single algorithm (RF) to a given dataset, and it contributes to the idea that only forecasted return decisions with high confidence should be used for targeted interventions due to their overproportional reliability.

4.2 Bibliographic literature analysis

Forecasting consumer returns has gained more research attention since 2016 (Fig.  4 ). The majority of the sample are conference publications, a couple of years ahead of the rise in journal publications. Compared to the publications on returns forecasting in the broader context of reverse logistics, which emerged in 2006 (Agrawal et al. 2015 ), the research on consumer returns moved into the spotlight about ten years later. This development is linked to a massive increase in e-commerce sales pre- and in-pandemic (Alfonso et al. 2021 ).

Publication trend by publication outlet

Out of 9 journal publications in the final sample, only two are published in the same journal (Journal of Operations Management). Out of 16 conference papers, 6 are published at conferences of the Association for Information Systems. In total, 16 of the 25 papers found are published in Information Systems (IS) and related outlets. Others can be assigned to the Management Science / Operations Research discipline (3), Strategy & Management in a broader sense (4), Marketing (1), and Research Methods (1) (Fig.  5 ).

Distribution of publication disciplines

Regarding the researchers’ geographical perspective, one paper was jointly published by authors from the US and China, 10 of 25 papers were authored from North America, followed by authors from Germany (7), India (3), China (1), and one paper each from Bangladesh, Singapore, and Sweden.

The most cited paper (200 external citations Footnote 2 ) from Hess and Mayhew ( 1997 ) could be thought of as the root of this research field (Table  4 ). However, only 10 out of 24 papers reference this work. Although Urbanke et al. ( 2015 ) received only 15 citations in total, within the sample, it is the second most cited paper (8 citations) and could eventually be classified as a research strand and origin of returns forecasting in the IS domain. Concerning the remaining papers, no unique strands of literature are recognizable based on citation analysis.

4.3 Methodology and data characterization

Regarding methodology, most of the papers start with a short narrative literature review regarding their respective focus. Not a single paper was based on interviews, surveys, questionnaires, or field experiments. 3 out of 25 papers formulated and tested conventional hypotheses. All of the publications use quantitative data for analysis and forecasting in a “case study” style, including numerical experiments based on real or simulated data.

Table 5 lists further details about the data used in the publications. 4 out of 25 papers rely on simulated data, and 23 out of 25 integrate actual data gained from a retailer. Two papers use both data types. 5 papers use more than one dataset (Ahmed et al. 2016 ; Cui et al. 2020 ; Rezaei et al. 2021 ; Samorani et al. 2016 ; Shang et al. 2020 ). The most frequently studied industry is fashion/apparel (10 papers), followed by five consumer electronics datasets. Two publications are based on data from a Taobao cosmetics retailer, and two datasets originate from general and wide assortment retailers. Two datasets incorporate building material and hardware store articles, and the detailed products are not named for three publications. Based on the previous studies, it is evident that consumer returns forecasting is most relevant for e-commerce, as 19 of the 25 publications refer to e-tailers. Nevertheless, 7 publications refer to brick-and-mortar retailing. Direct selling/marketing is represented in 2 data sets.

4.4 Predictors for consumer returns

There is an individual stream of research into factors that influence or help avoid consumer returns (e.g., Asdecker et al. 2017 ; De et al. 2013 ; Walsh and Möhring 2017 ), which is not part of this review. Nevertheless, the forecasting literature gives insights into return drivers, as the input variables (features, predictors, exogenous variables) for forecasting models represent some of these factors. Table 6 presents the most used predictors and tries to map these to the return driver categorization from Sect.  2.2 (Stöcker et al. 2021 ).

Although only a part of the publications interprets the predictors, some insights can be extracted. For total return volume , sales volume is the most critical predictor (Cui et al. 2020 ; Shang et al. 2020 ). Historical return volume trends can include behavioral aspects (e.g., impulse purchases) in a given timeframe (Cui et al. 2020 ; Shang et al. 2020 ). The product type significantly impacts the volume of returns (Cui et al. 2020 ), confirmed by widely varying return rates between different industries/sectors. Adding transaction-, customer-, or product-level predictors led to a surprisingly small forecasting accuracy gain (4% reduction of RMSE, Shang et al. 2020 ). The latter input variables may be more critical in forecasting return decisions and propensities.

Regarding product attributes , product or order price is one of the most common predictors, while some papers also include price discounts. In most models, price is hypothesized to increase returns (e.g., Asdecker and Karl 2018 ; Hess and Mayhew 1997 ). Promotional (discounted) orders also seem to result in more returns (Imran and Amin 2020 ), which could be explained by the stimulation of impulse purchases. Footnote 3 Brand perception influences return decisions (positive brands, lower returns) (Samorani et al. 2016 ). The order and return history of products are also relevant for predicting future orders and returns (Hofmann et al. 2020 ). Fit importance as a product attribute does not significantly change return propensities (Hess and Mayhew 1997 ).

Concerning customer attributes , gender seems essential, as female customers return significantly more items than men (Asdecker and Karl 2018 ; Fu et al. 2016 ). Younger customers show a slightly lower propensity to return (Asdecker and Karl 2018 ), but age played a more prominent role in predicting return fraud among employees than in customers (John et al. 2020 observed more fraud among younger employees). Customers with low credit scores returned more (Fu et al. 2016 ). The return history of a customer is possibly the most important predictor of future return behavior (Samorani et al. 2016 ). Some papers argue that consumer attributes, including purchase and return history (e.g., number and value of orders), are more relevant predictors than product or transaction profiles, reflecting more or less stable consumer preferences (Li et al. 2019 ).

Basket interactions are significant (Urbanke et al. 2017 ) in returns prediction. E.g., the larger the basket, the higher the return propensity will be (Asdecker and Karl 2018 ). Selection orders (same product in different sizes or colors) increase the return propensity (Li et al. 2018 ). Logistics attributes like delivery times only show minor effects (Asdecker and Karl 2018 ). Regarding the payment method, prepaid products are sent back less frequently than those with post-delivery payment options (Imran and Amin 2020 ), confirming other research results (Asdecker et al. 2017 ).

One literature stream focuses on the automated generation of features , as different and large-scale data sources need to be integrated and prepared for forecasting algorithms. Thus, possible interrelationships are complex to find manually, and ML approaches might outperform human analysts (Rezaei et al. 2021 ). While some approaches generate a large number of features that are hard to make sense of (Ahmed et al. 2016 ), the approach of Urbanke et al. ( 2017 ) aims to maintain the interpretability of automatically generated input variables. Some unexpected but meaningful interrelations might be found by automatic feature generation, e.g., the price of the last returned orders (Samorani et al. 2016 ). Nevertheless, automatic feature generation might be computation-intensive; thus, a parallel integration of feature selection could be advantageous for large data sets (Rezaei et al. 2021 ).

A remarkable research path based on artificial intelligence is integrating qualitative information like product reviews as predictors, going beyond numerical feedback (Rajasekaran and Priyadarshini 2021 ) or tweets. These data can be processed and made accessible for forecasting with ML-based sentiment analysis techniques (Ding et al. 2016 ).

4.5 Forecasting techniques and algorithms

To describe the techniques and algorithms employed, we sorted the papers by forecasting purpose as described in Sect.  2 , then assigned them to different algorithms, either from time series forecasting, statistical techniques, or ML algorithms. Table 7 lists all papers for which an assignment was possible, and the respective techniques used. If a comparison was possible, the best-performing algorithm is marked in this table.

The approaches listed in Table  7 are overlap-free, but some papers use more than one version of an approach, i.e., more than one algorithm from a category. E.g., TabNet is a DeepLearning version of neural networks (NN), and different variants of GradientBoosting are compared in one paper (CatBoost/LightGBM, not differentiated in the table below) (Imran and Amin 2020 ).

The algorithm used most frequently (Fig.  6 ) is the Random Forest algorithm (RF, 10 papers), followed by Support Vector Machines (SVM, 8 papers), Neural Networks (NN, 6 papers), logistic regression (Logit, 6 papers), GradientBoosting (5 papers), Ordinary Least Squares regression (OLS, 4 papers), Adaptive Boosting (AdaBoost), Linear Discriminant Analysis (LDA), and CART (Classification and Regression Trees, 3 papers each).

Most frequently used algorithms (used in at least three papers)

The papers focusing on return volume use time series forecasts like (AutoRegressive) Moving Averages (MA), Single Exponential Smoothing (SES), and Holt-Winters Smoothing (HWS) more frequently than ML algorithms. Nevertheless, when considering a predict-aggregate approach as proposed by Shang et al. ( 2020 ), these ML techniques could be helpful in forecasting return decisions first and cumulating the propensity results for the volume prediction in the second step.

In forecasting binary return decisions, Random Forests (RF) (Ahmed et al. 2016 ; Heilig et al. 2016 ; Ketzenberg et al. 2020 ), Neural Networks (NN) (Imran and Amin 2020 ; Ketzenberg et al. 2020 ), as well as Adaptive Boosting (AdaBoost) (Urbanke et al. 2015 , 2017 ) showed high prediction performance. The performance of different algorithms varies depending on the data set, the implementation, and the parameterization used. For this reason, it is hardly possible to make a generally valid statement regarding performance levels. Combining several algorithms in ensembles (Asdecker and Karl 2018 ; Heilig et al. 2016 ) seems advantageous, at least for retrospective analytical purposes, when the required computing resources are less relevant.

When evaluating different forecasting algorithms for return decisions, imbalanced classes (especially evident for low return shares in non-fashion datasets) seem to be handled differently depending on the algorithms. Class imbalances might distort comparison results in some publications. Random oversampling as a measure of data preparation can solve this problem (Hofmann et al. 2020 ).

High-performance algorithms are needed for real-time predictions, e.g., graph and random-walk-based (Li et al. 2018 ; Zhu et al. 2018 ). According to Li et al. ( 2018 ), the proposed algorithm “HyperGo” performs best for most performance metrics.

4.6 E-Commerce and machine learning taxonomy extension

In their literature review regarding the use of ML techniques in e-commerce, Micol Policarpo et al. ( 2021 ) propose a taxonomy to visualize specific ML algorithms in the context of e-commerce platforms. This novel kind of taxonomy is based on direct acyclic graphs, i.e., all input variables need to be fulfilled to reach the target. The first level of the taxonomy represents different target goals for the use of ML in e-commerce. While returns forecasting (“product return prediction”) is identified as an essential goal among others (purchase prediction, repurchase prediction, customer relationship management, discovering relationships between data, fraud detection, and recommendation systems), it was excluded from the taxonomy they developed, possibly because the review comprised only two relevant papers on this topic (Micol Policarpo et al. 2021 ). The review at hand proposes an extension of Micol Policarpo’s taxonomy, renaming the goal to “consumer returns forecasting”. This extension reflects and synthesizes the consumer returns forecasting studies reviewed.

The middle level of the taxonomy represents properties and features that support this superordinate goal. On this level, our extension does not include return fraud detection, which we propose to be integrated into the existing category of “fraud detection”, separated into transaction analysis and consumer analysis (Micol Policarpo et al. 2021 ). Circles represent the necessary data to execute the analysis, referring to categories introduced in (Micol Policarpo et al. 2021 ), with an additional “return history” category. The bottom level presents the algorithms described frequently, while some streamlining is required regarding the tools and approaches that seem the most common or most appropriate.

The schematic above (Fig.  7 ) is to be read as follows: In the context of E-Commerce  +  Artificial Intelligence (Layer 1), Consumer Return Forecasting (Layer 2) is an essential goal among six other goals. Layer 3 presents different purposes of analysis, which are the base for return forecasting. Realtime Basket Analysis is based on clickstream data and basket composition (browsing activities) to target interventions. Basket analysis benefits from customer and product information (dotted line). Graph-based approaches (Li et al. 2018 ; Zhu et al. 2018 ) are promising for real-time analysis due to their lower computing requirements, although cloud-based implementation of more complex algorithms or ensemble models might be feasible (Fuchs and Lutz 2021 ; Heilig et al. 2016 ; Hofmann et al. 2020 ). Customer Analysis and Product Analysis (e.g., Potdar and Rogers 2012 ) require adequate Data Preparation in the sense of input variable generation, extraction, and selection (Urbanke et al. 2015 , 2017 ). For these purposes, data regarding return history (e.g., Hofmann et al. 2020 ; Ketzenberg et al. 2020 ), purchase history (e.g., Cui et al. 2020 ; Fu et al. 2016 ), customer personal information (e.g., Heilig et al. 2016 ; Ketzenberg et al. 2020 ), clickstream data, and browsing activities are required as input (shown by cross-hatched circles). For each purpose, one or more possible algorithms are shown.

Proposed consumer returns forecasting extension to the E-commerce and Machine Learning techniques taxonomy of Micol Policarpo et al. ( 2021 , p. 13)

Compared to predicting purchase intention, return predictions seem to require more levels of data. Nevertheless, even simple rule-based interventions can promise benefits, e.g., selection orders that inevitably lead to a return shipment can be easily recognized (Hofmann et al. 2020 ; Sweidan et al. 2020 ). Different ML techniques are helpful for data preparation and input variable (feature) extraction and generation when considering more complex interrelations. NeuralNet is one example of an automatic selection of relevant features (Urbanke et al. 2017 ). These approaches are not only able to enhance forecasting accuracy (Rezaei et al. 2021 ) but can also render the many possible variables interpretable about their content.

5 Discussion

The analysis of the papers above revealed that research in this discipline seems heterogeneous and partly fragmented, and clear-cut research strands are still hard to identify. Thus, the existing literature calls for further publications to render this research field more comprehensive. Below, research opportunities are derived and embedded in a conceptual research framework derived from the results of the existing literature, also integrating the extension of the E-Commerce and Machine Learning taxonomy (Fig.  7 ). A conceptual framework improves the understanding of a complex topic by naming and explaining key concepts and their relationships important to a specific field (Jabareen 2009 ; Miles et al. 2020 ). Thus, this framework aims to organize problems and solutions discussed in the consumer returns forecasting literature and to embed and classify potential future research topics in the existing knowledge base (Ravitch and Riggan 2017 ). The subsections following the framework outline some potential research avenues (P1–P6) that have been touched on in the past but still leave considerable opportunities for further insights. These proposals should not be seen as comprehensive due to numerous other research opportunities in this field but rather as prioritization based on the current literature.

The framework derived (Fig.  8 ) underlines the interdisciplinary nature of this research field, integrating different perspectives (information systems research, marketing and operations perspective, and strategy and management perspective). From a managerial point of view, the literature included in this review is biased towards the information systems perspective. Thus, in contrast to the framework developed by Cirqueira et al. ( 2020 ) for purchase prediction, we do not take a process perspective but instead emphasize the interdependencies and interactions between research topics and highlight the managerial need to take a strategical perspective similar to the framework developed by Winklhofer et al. ( 1996 ). Consequently, a meta-layer on forecasting frameworks and practices includes the mainly technical development frameworks in this review but also accentuates the need for further research regarding actual organizational forecasting practices (e.g., P2, P5, P6). Around this meta-layer, some related research strands are linked in order to embed the topic of returns forecasting in the research landscape. E.g., in general, forecasting purchases and returns could be linked (P6), also effecting inventory decisions.

Conceptual Consumer Return Forecasting Framework

The center of the framework consists of three dimensions, namely purposes and tasks, predictors, and techniques. Depending on the strategical purpose, tasks are derived that determine (1) the data (predictors) needed and (2) the usable techniques to execute the forecasting. Different forecasting techniques require an individual set of predictors, whereas the availability of specific data allows and determines the use of more or less sophisticated algorithms.

In the literature, some forecasting purposes were more pronounced (return decisions or propensities), while others have gained less attention (return timing, P1). Regarding the data necessary for accurate forecasting, the return predictors discussed often were hardly comparable, as they originated from different data sources, different industries, were related to different dimensions, or were aggregated in another way. Systematically linking forecasting predictors and research on return drivers and reasons could contribute significant insights (P4) that, from a marketing perspective, may support the development of effective preventive instruments. Furthermore, the literature mainly refers to the fashion or consumer electronics industry, leaving room to validate the findings in the context of other industries (P3).

When (automatically) selecting or creating predictors, the boundaries between predictors and prediction techniques are blurred as machine learning algorithms prepare the input data before executing a forecasting model. Regarding forecasting techniques, time series forecasting was seldom used in recent publications. Machine learning algorithms were the most popular subject of investigation, with random forests, support vector machines, and neural networks as the most popular implementations. Classical statistical models like logit models for return decisions or OLS regression gained less research attention. Literature on end-of-life return forecasting could complement the research on techniques and their accuracy. Most publications used technical indicators for assessing the accuracy of forecasting models, which is the information systems perspective. From a managerial position, evaluating (monetary) performance outcomes (e.g., Ketzenberg et al. 2020 ) of forecasting systems should be more relevant.

5.1 Research proposal P1: return timing for consumer returns

Toktay et al. ( 2004 ) encouraged the integrated forecasting of the return rate and the return time lag. In line with this, Shang et al. ( 2020 ) criticize the missing focus on the timing of return forecasts. The reviewed literature confirms that forecasting return propensities and decisions are more prominent than timing and volume forecasts. While the knowledge of when a return is expected is vital in managing end-of-life returns that occur over the years, for retail consumer returns, return periods are mostly 14–30 days. Thus, the variability of return timing seems limited compared to end-of-life returns in this context, which makes this forecasting purpose less critical. Nevertheless, some retailers offer up to 100 days of free returns (e.g., Zalando). Consequently, more studies about the importance of return timing forecasts in the e-commerce context from a business and planning perspective and their interdependence with return processing or warehousing issues could shed light on this topic and complement the current literature (Toktay et al. 2004 ; Shang et al. 2020 ).

5.2 Research proposal P2: realtime forecasting systems

Another research gap became apparent regarding the real-time use of forecasting systems and the associated activities and interventions, building on the initial research and the frameworks already published (e.g., Heilig et al. 2016 ; Urbanke et al. 2015 ). The generic framework developed by Fuchs and Lutz ( 2021 ) could serve as a launching pad for this stream of research.

The paper from Ketzenberg et al. ( 2020 ) could act as a stimulus and inspiration for a similar approach, not only focusing on return abuse as already examined but on return forecasting in general, the possible associated interventions for various consumer groups, and the resulting consequences for the retailer’s profit. Even the methodology of customer classification could be helpful for many retailers in targeting interventions.

Before real-time return forecasting is implemented, associated preventive return management instruments need to be designed and evaluated. Many of these measures are discussed (e.g., Urbanke et al. 2015 ; Walsh et al.  2014 ), but an overview of which preventive measures (for some examples, see Walsh and Möhring 2017 ) are effective in general (1) and how forecasting accuracy interdepends with their usefulness (2) is still missing, to substantially link the topics of forecasting and interventions. No answers could be found to the call by Urbanke et al. ( 2015 ) for field experiments to investigate such a link.

Thanks to cloud and parallelization technologies and the associated scalability of computing power (Bekkerman et al. 2011 ), algorithm runtimes are becoming less relevant. However, especially for real-time use, it should be evaluated which algorithms and underlying datasets exhibit an appropriate relationship between the targeted forecasting accuracy, the expected benefit, and the required computing power.

Recommendations concerning the algorithms and techniques can be derived (Urbanke et al. 2015 ), and a generic implementation framework was developed (Fuchs and Lutz 2021 ). However, from a business perspective, no contributions could be found regarding the actual implementation of real-time forecasting systems, the interventions involved, and their impact on consumer behavior or profit (also see proposal P5). In addition, the implementations of such systems need to be analyzed concerning the cost-effectiveness of the required investments.

5.3 Research proposal P3: cross-industry and multiple dataset studies

Many publications rely on a single data set from a specific industry or retailer. Only a few compare several retailers (e.g., Cui et al. 2020 ). Studies including and comparing different countries are missing, which is especially interesting since legal regulations for returns vary. For example, in contrast to the U.S., citizens within the EU are granted a 14-day right of withdrawal for distance selling purchases. Footnote 4 Although in most developed countries, liberal and broadly comparable returns policies are standard in practice due to competitive pressure, the generalizability of the results is frequently limited. One remedy for this problem is to use multiple data sets from different retailers (e.g., electronics vs. jewelry, Shang et al. 2020 ). Admittedly, it is challenging to simultaneously collaborate with several retailers and to combine different data sets, due to reasons of preserving corporate privacy and synchronizing various data sources. Nevertheless, research needs to draw conclusions from single data points, as well as logically replicate or falsify those results by integrating more data points to find patterns of similarities and differences, either within or cross-study (Hamermesh 2007 ). Therefore, we suggest that future studies acquire industry-related datasets from several retailers at once or replicate existing studies, which aligns with the aim and scope of Management Review Quarterly (Block and Kuckertz 2018 ). Cross-industry or cross-country manuscripts, which go beyond the mere assertion of an industry-agnostic approach (Hofmann et al. 2020 ) and jointly investigate data from several sectors, would promise an additional gain in knowledge and could be less challenging from a privacy perspective.

5.4 Research proposal P4: extended study of relevant predictors in forecasting applications

Although not the main focus of this review, predictors of consumer returns are especially interesting for marketing and e-commerce research, for example, regarding preventive measures for avoiding returns. In the past, many consumer return papers highlighted single aspects or a limited selection of return drivers or preventive measures employed but rarely attempted to model return behavior as comprehensively as possible. However, the latter is the very objective of returns forecasting, which is why the findings on influencing factors in articles with a forecasting focus tend to be more holistic, although not sufficiently complete (Hachimi et al. 2018 ). Some return reasons named in the literature (e.g., Stöcker et al. 2021 ) have not yet been included in forecasting approaches, and vice versa, only a part of the influencing factors investigated could be mapped to a return reason categorization. The reason categories assigned (Sect.  4.4 , Table  6 ) still contain some uncertainty. For example, a customer’s product return history may reflect the general returning behavior of a customer to some extent, while it can not be ruled out that repeated logistical problems caused the returns. Product attributes may reflect information gaps that consumers can only assess after physically inspecting the product, whereas product price–frequently cited and influential product attribute—is only related to information gaps when considering the price-performance ratio (Stöcker et al. 2021 ). Technical information about the web browser or device used by the customer is difficult to categorize, as it may reflect behavioral (impulse-driven mobile shopping) as well as informational (small display with few visible information) aspects. The payment method chosen by a customer, for example, could not be linked to one of the reason categories.

This reasoning should serve as a basis for linking forecasting predictors and return reasons more closely in the future. For example, the respective relative weighting of return drivers is more likely to be obtained considering as many factors involved as possible, minimizing the unexplained variation. From the reviewed literature, we extracted 18 different return predictor categories. For instance, seven papers (Cui et al. 2020 ; Fu et al. 2016 ; Ketzenberg et al. 2020 ; Li et al. 2018 , 2019 ; Urbanke et al. 2015 , 2017 ) integrated more than five predictor categories. But even though some papers integrate more than 5,000 features for automated feature selection (Ketzenberg et al. 2020 ), there are still combinations of input variable categories that have not been investigated and, more importantly, interpreted yet. Therefore, we call for more comprehensive research on return predictors and their interpretation, including associated preventive return measures, in the context of return forecasting.

5.5 Research proposal P5: descriptive case studies and business implementations surveys

This review identified a lack of publications regarding the actual benefit and the diffusion of consumer returns forecasting systems in different scopes and industries, building on the papers presenting return forecasting frameworks. In 2013, less than half of German retailers analyzed the likelihood of returns (Pur et al. 2013 ). Most of those who did were using naïve approaches that might be outperformed by the models presented in this review. Still, we do not know the status quo regarding the degree of adoption and implementation of forecasting systems for consumer returns in e-commerce firms (e.g., see Mentzer and Kahn 1995 for sales forecasting systems), country-specific and internationally.

Furthermore, the impact of return forecasting practices on company performance should be examined not only based on modeling, but on retrospective data (e.g., see Zotteri and Kalchschmidt 2007 for a similar study on demand forecasting practices in manufacturing). A possible hypothesis to examine might be that accuracy measures like RMSE or precision/recall and subsequently even the choice of the most accurate machine learning algorithm (e.g., see Asdecker and Karl 2018 ) are less relevant from a business perspective: (1) No algorithm clearly outperforms all other algorithms, and (2) the correlation between technical indicators and business value is unstable (Leitch and Tanner 1991 ). Methodologically, implementations of consumer returns forecasting in e-commerce should thus be surveyed and analyzed with multivariate statistical methods to examine critical factors and circumstances of return forecasting systems – similar to publications on reverse logistics performance (Agrawal and Singh 2020 ).

5.6 Research proposal P6: holistic forward and backward forecasting framework for e-tailers

Some publications present frameworks for forecasting returns (Fuchs and Lutz 2021 ). Nevertheless, in the past, forecasting in retail and especially e-commerce commonly focused more on demand (Micol Policarpo et al. 2021 ) than returns. Current approaches for demand forecasting try to predict individual purchase intentions based on click-stream data, online session attributes, and customer history (e.g., Esmeli et al. 2021 ). Our systematic approach could not identify any paper that connects and integrates both directions in e-commerce forecasting, neither conceptual (frameworks) nor with a quantitative or case-study-like approach. Nevertheless, first implementations of return predictions in inventory management are presented (e.g., Goedhart et al. 2023 ). Subsequently, similar to Goltsos et al. ( 2019 ), we call for research addressing both demand and return uncertainties by providing a holistic forecasting framework in the context of e-commerce.

6 Conclusion

To date, no systematic literature review has undertaken an in-depth exploration of the topic of forecasting consumer returns in the e-commerce context. Previous reviews have primarily focused on product returns forecasting within the broader context of reverse logistics or closed-loop supply chain management (Agrawal et al. 2015 ; Ambilkar et al. 2021 ; Hachimi et al. 2018 ). Regrettably, the interdisciplinary nature of this subject has often been overlooked, also neglecting the inclusion of results from information systems research.

The review first aims to provide an overview of the existing literature (Kraus et al. 2022 ) on forecasting consumer returns. The findings confirm that this once novel topic has significantly evolved in recent years. Consequently, this review is timely in examining current gaps and establishing a robust foundation for future research, which forms a second goal of systematic reviews (Kraus et al. 2022 ). The current body of work encompasses various aspects from different domains, including marketing, operations management/research, and information systems research, highlighting the interdisciplinary nature of e-commerce analytics and research. As a result, future studies can find suitable publication outlets in domain-specific as well as methodologically oriented journals and conferences.

Scientifically, the algorithms and predictors investigated in previous research serve as a foundational reference for subsequent publications and informed decisions regarding research design, ensuring that specific predictors and techniques are not overlooked. Researchers can utilize this review and the research framework developed as a structuring guide, e.g., regarding relevant publications on already examined algorithms or predictors.

Managerially, the extended taxonomy for machine learning in e-commerce (Micol Policarpo et al. 2021 ) can serve as a guideline for implementing forecasting systems for consumer returns. This review classifies possible prediction purposes, allowing businesses to apply them based on their respective challenges. Exploring the most frequently used predictors reveals the data that must be collected for the respective purposes. This review also offers valuable insights into data (pre-)processing and highlights popular algorithms. Furthermore, frameworks are outlined that support the design and implementation phase of such forecasting systems, supporting analytical purposes or enabling direct interventions during the online shopping process flow. As an exemplary and promising application, return policies could be personalized (Abbey et al. 2018 ) by identifying opportunistic or fraudulent basket compositions or high-returning customers, thereby reducing unwanted returns (Lantz and Hjort 2013 ).

Finally, a limitation of this review is the exclusion of forecasting algorithms for end-of-use returns, which could potentially be applicable to forecasting shorter-term retail consumer returns. However, the closed-loop supply chain and reverse logistics literature has been systematically excluded. Hence, future reviews could synthesize previous reviews on reverse logistics forecasting with the more detailed findings presented in this paper.

The use of Google Scholar for systematic scientific information search is controversely discussed (e.g., Halevi et al. 2017 ) due to the missing quality control and indexing guidelines, as well as limited advanced search options. But as an additional database for an initial search, the wide coverage of this search system can enrich the results.

External citations according to Google Scholar, which is preferable for citation tracking over controlled databases (Halevi et al. 2017 ).

Other literature also describes a counteracting effect of a reduced price due to lowered quality expectations or a higher perceived value of the “deal” itself (e.g., Sahoo et al. 2018 ).

It should be noted that the relevance of the forecasting topic depends on the maturity of the e-commerce sector. In most developing countries, B2C e-commerce is comparatively young and consumer returns are not yet a common phenomenon, which is why research on return forecasts is relatively insignificant for these countries.

References

Abbey JD, Ketzenberg ME, Metters R (2018) A more profitable approach to product returns. MIT Sloan Manag Rev 60(1):71–74

Google Scholar  

Abdulla H, Ketzenberg ME, Abbey JD (2019) Taking stock of consumer returns: a review and classification of the literature. J Oper Manag 65(6):560–605. https://doi.org/10.1002/joom.1047

Article   Google Scholar  

Agrawal S, Singh RK (2020) Forecasting product returns and reverse logistics performance: structural equation modelling. MEQ 31(5):1223–1237. https://doi.org/10.1108/MEQ-05-2019-0109

Agrawal S, Singh RK, Murtaza Q (2015) A literature review and perspectives in reverse logistics. Resour Conserv Recycl 97:76–92. https://doi.org/10.1016/j.resconrec.2015.02.009

Ahmed F, Samorani M, Bellinger C, Zaiane OR (2016) Advantage of integration in big data: feature generation in multi-relational databases for imbalanced learning. In: Proceedings of the 4th IEEE international conference on big data, pp 532–539. https://doi.org/10.1109/BigData.2016.7840644

Ahsan K, Rahman S (2016) An investigation into critical service determinants of customer to business (C2B) type product returns in retail firms. Int Jnl Phys Dist Log Manage 46(6/7):606–633. https://doi.org/10.1108/IJPDLM-09-2015-0235

Akter S, Wamba SF (2016) Big data analytics in e-commerce: a systematic review and agenda for future research. Electron Markets 26(2):173–194. https://doi.org/10.1007/s12525-016-0219-0

Alfonso V, Boar C, Frost J, Gambacorta L, Liu J (2021) E-commerce in the pandemic and beyond. BIS Bulletin 36

Ambilkar P, Dohale V, Gunasekaran A, Bilolikar V (2021) Product returns management: a comprehensive review and future research agenda. Int J Prod Res. https://doi.org/10.1080/00207543.2021.1933645

Asdecker B (2015) Returning mail-order goods: analyzing the relationship between the rate of returns and the associated costs. Logist Res 8(1):1–12. https://doi.org/10.1007/s12159-015-0124-5

Asdecker B, Karl D (2018) Big data analytics in returns management–are complex techniques necessary to forecast consumer returns properly? In: Proceedings of the 2nd international conference on advanced research methods and analytics, Valencia, pp 39–46. https://doi.org/10.4995/CARMA2018.2018.8303

Asdecker B, Karl D, Sucky E (2017) Examining drivers of consumer returns in e-tailing with real shop data. In: Proceedings of the 50th Hawaii international conference on system sciences (HICSS). https://doi.org/10.24251/HICSS.2017.507

Bandara K, Shi P, Bergmeir C, Hewamalage H, Tran Q, Seaman B (2019) Sales Demand forecast in e-commerce using a long short-term memory neural network methodology. In: Gedeon T, Wong KW, Lee M (eds) Neural information processing: proceedings of the 26th international conference on neural information processing, 1st edn., vol 11955, pp 462–474. https://doi.org/10.1007/978-3-030-36718-3_39

Barbosa MW, La Vicente AdC, Ladeira MB, de Oliveira MPV (2018) Managing supply chain resources with big data analytics: a systematic review. Int J Log Res Appl 21(3):177–200. https://doi.org/10.1080/13675567.2017.1369501

Bekkerman R, Bilenko M, Langford J (2011) Scaling up machine learning. In: Proceedings of the 17th ACM SIGKDD international conference tutorials, p 1. https://doi.org/10.1145/2107736.2107740

Bernon M, Cullen J, Gorst J (2016) Online retail returns management. Int J Phys Distrib Logist Manag 46(6/7):584–605. https://doi.org/10.1108/IJPDLM-01-2015-0010

Block J, Kuckertz A (2018) Seven principles of effective replication studies: strengthening the evidence base of management research. Manag Rev Q 68(4):355–359. https://doi.org/10.1007/s11301-018-0149-3

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA

Cirqueira D, Hofer M, Nedbal D, Helfert M, Bezbradica M (2020) Customer purchase behavior prediction in e-commerce: a conceptual framework and research Agenda. In: Ceci M, Loglisci C, Manco G, Masciari E, Raś Z (eds) New frontiers in mining complex patterns, vol 11948. Springer, Cham, pp 119–136. https://doi.org/10.1007/978-3-030-48861-1_8

Chapter   Google Scholar  

Clottey T, Benton WC (2014) Determining core acquisition quantities when products have long return lags. IIE Trans 46(9):880–893. https://doi.org/10.1080/0740817X.2014.882531

Cook SC, Yurchisin J (2017) Fast fashion environments: consumer’s heaven or retailer’s nightmare? Int J Retail Distrib Manag 45(2):143–157. https://doi.org/10.1108/IJRDM-03-2016-0027

Cui H, Rajagopalan S, Ward AR (2020) Predicting product return volume using machine learning methods. Eur J Oper Res 281(3):612–627. https://doi.org/10.1016/j.ejor.2019.05.046

Dalecke S, Karlsen R (2020) Designing dynamic and personalized nudges. In: Chbeir R, Manolopoulos Y, Akerkar R, Mizera-Pietraszko J (eds) Proceedings of the 10th international conference on web intelligence, mining and semantics. ACM, New York, pp 139–148. https://doi.org/10.1145/3405962.3405975

De P, Hu Y, Rahman MS (2013) Product-oriented web technologies and product returns: an exploratory study. Inf Syst Res 24(4):998–1010. https://doi.org/10.1287/isre.2013.0487

de Brito MP, Dekker R, Flapper SDP (2005) Reverse logistics: a review of case studies. In: Klose A, Fleischmann B (eds) Distribution logistics, vol 544. Springer. Berlin, Heidelberg, pp 243–281

Denyer D, Tranfield D (2009) Producing a systematic review. In: Buchanan DA, Bryman A (eds) The Sage handbook of organizational research methods. Sage, Thousand Oaks, CA, pp 671–689

Difrancesco RM, Huchzermeier A, Schröder D (2018) Optimizing the return window for online fashion retailers with closed-loop refurbishment. Omega 78:205–221. https://doi.org/10.1016/j.omega.2017.07.001

Diggins MA, Chen C, Chen J (2016) A review: customer returns in fashion retailing. In: Choi T-M (ed) Analytical modeling research in fashion business. Springer, Singapore, pp 31–48. https://doi.org/10.1007/978-981-10-1014-9_3

Ding Y, Xu H, Tan BCY (2016) Predicting product return rate with “tweets”. In: Proceedings of the 20th Pacific asia conference on information systems

Drechsler S, Lasch R (2015) Forecasting misused e-commerce consumer returns. In: Logistics management: proceedings of the 9th conference “Logistikmanagement”. Cham, pp 203–215.

Duong QH, Zhou L, Meng M, van Nguyen T, Ieromonachou P, Nguyen DT (2022) Understanding product returns: a systematic literature review using machine learning and bibliometric analysis. Int J Prod Econ 243:108340. https://doi.org/10.1016/j.ijpe.2021.108340

Esmeli R, Bader-El-Den M, Abdullahi H (2021) Towards early purchase intention prediction in online session based retailing systems. Electron Markets 31(3):697–715. https://doi.org/10.1007/s12525-020-00448-x

Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133–3181

Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

Frei R, Jack L, Brown S (2020) Product returns: a growing problem for business, society and environment. IJOPM 40(10):1613–1621. https://doi.org/10.1108/IJOPM-02-2020-0083

Frei R, Jack L, Krzyzaniak S-A (2022) Mapping product returns processes in multichannel retailing: challenges and opportunities. Sustainability 14(3):1382. https://doi.org/10.3390/su14031382

Fu Y, Liu G, Papadimitriou S, Xiong H, Li X, Chen G (2016) Fused latent models for assessing product return propensity in online commerce. Decis Support Syst 91:77–88. https://doi.org/10.1016/j.dss.2016.08.002

Fuchs K, Lutz O (2021) A stitch in time saves nine–a meta-model for real-time prediction of product returns in ERP systems. In: Proceedings of the 29th european conference on information systems

Ge D, Pan Y, Shen Z-J, Di Wu, Yuan R, Zhang C (2019) Retail supply chain management: a review of theories and practices. J Data Manag 1:45–64. https://doi.org/10.1007/s42488-019-00004-z

Goedhart J, Haijema R, Akkerman R (2023) Modelling the influence of returns for an omni-channel retailer. Eur J Oper Res 306(3):1248–1263. https://doi.org/10.1016/j.ejor.2022.08.021

Goltsos TE, Ponte B, Wang SX, Liu Y, Naim MM, Syntetos AA (2019) The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems. Int J Prod Res 57(23):7361–7394. https://doi.org/10.1080/00207543.2018.1510191

Govindan K, Bouzon M (2018) From a literature review to a multi-perspective framework for reverse logistics barriers and drivers. J Clean Prod 187:318–337. https://doi.org/10.1016/j.jclepro.2018.03.040

Hachimi HEL, Oubrich M, Souissi O (2018) The optimization of reverse logistics activities: a literature review and future directions. In: Proceedings of the 5th IEEE international conference on technology management, operations and decisions, Piscataway, NJ, pp 18–24. https://doi.org/10.1109/ITMC.2018.8691285

Halevi G, Moed H, Bar-Ilan J (2017) Suitability of Google Scholar as a source of scientific information and as a source of data for scientific evaluation—review of the Literature. J Informet 11(3):823–834. https://doi.org/10.1016/j.joi.2017.06.005

Hamermesh DS (2007) Viewpoint: Replication in economics. Can J of Econ 40(3):715–733. https://doi.org/10.1111/j.1365-2966.2007.00428.x

Hastie T, Tibshirani R, Friedman JH (2017) The elements of statistical learning: data mining, inference, and prediction. Springer, New York, NY

Heilig L, Hofer J, Lessmann S, Voß S (2016) Data-driven product returns prediction: a cloud-based ensemble selection approach. In: Proceedings of the 24th european conference on information systems

Hess JD, Mayhew GE (1997) Modeling merchandise returns in direct marketing. J Direct Market 11(2):20–35. https://doi.org/10.1002/(SICI)1522-7138(199721)11:2<20:AID-DIR4>3.0.CO;2-#

Hevner A, March S, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75. https://doi.org/10.2307/25148625

Hofmann A, Gwinner F, Fuchs K, Winkelmann A (2020) An industry-agnostic approach for the prediction of return shipments. In: Proceedings of the 26th Americas conference on information systems, pp 1–10

Hong Y, Pavlou PA (2014) Product fit uncertainty in online markets: nature, effects, and antecedents. Inf Syst Res 25(2):328–344. https://doi.org/10.1287/isre.2014.0520

Imran AA, Amin MN (2020) Predicting the return of orders in the e-tail industry accompanying with model interpretation. Procedia Comput Sci 176:1170–1179. https://doi.org/10.1016/j.procs.2020.09.113

Jabareen Y (2009) Building a conceptual framework: philosophy, definitions, and procedure. Int J Qual Methods 8(4):49–62. https://doi.org/10.1177/160940690900800406

John S, Shah BJ, Kartha P (2020) Refund fraud analytics for an online retail purchases. J Bus Anal 3(1):56–66. https://doi.org/10.1080/2573234X.2020.1776164

Joshi T, Mukherjee A, Ippadi G (2018) One size does not fit all: predicting product returns in e-commerce platforms. In: Proceedings of the 10th IEEE/ACM international conference on advances in social networks analysis and mining, pp 926–927. https://doi.org/10.1109/ASONAM.2018.8508486

Kaiser D (2018) Individualized choices and digital nudging: multiple studies in digital retail channels. Karlsruher Institut für Technologie (KIT). https://doi.org/10.5445/IR/1000088341

Karl D, Asdecker B (2021) How does the Covid-19 pandemic affect consumer returns: an exploratory study. In: Proceedings of the 50th european marketing academy conference, vol 50

Karl D, Asdecker B, Feddersen-Arden C (2022) The impact of displaying quantity scarcity and relative discounts on sales and consumer returns in flash sale e-commerce. In: Proceedings of the 55th hawaii international conference on system sciences. https://doi.org/10.24251/HICSS.2022.556

Ketzenberg ME, Abbey JD, Heim GR, Kumar S (2020) Assessing customer return behaviors through data analytics. J Oper Manag 66(6):622–645. https://doi.org/10.1002/joom.1086

Kraus S, Breier M, Lim WM, Dabić M, Kumar S, Kanbach D, Mukherjee D, Corvello V, Piñeiro-Chousa J, Liguori E, Palacios-Marqués D, Schiavone F, Ferraris A, Fernandes C, Ferreira JJ (2022) Literature reviews as independent studies: guidelines for academic practice. Rev Manag Sci 16(8):2577–2595. https://doi.org/10.1007/s11846-022-00588-8

Lantz B, Hjort K (2013) Real e-customer behavioural responses to free delivery and free returns. Electron Commer Res 13(2):183–198. https://doi.org/10.1007/s10660-013-9125-0

Leitch G, Tanner JE (1991) Economic forecast evaluation: profits versus the conventional error measures. Am Econ Rev 81(3):580–590

Li X, Zhuang Y, Fu Y, He X (2019) A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping. Sci China Inf Sci 62(5). https://doi.org/10.1007/s11432-018-9511-1

Li J, He J, Zhu Y (2018) E-tail product return prediction via hypergraph-based local graph cut. In: Proceedings of the 24th ACM sigkdd international conference on knowledge discovery & data mining, New York, NY, pp 519–527. https://doi.org/10.1145/3219819.3219829

Melacini M, Perotti S, Rasini M, Tappia E (2018) E-fulfilment and distribution in omni-channel retailing: a systematic literature review. Int Jnl Phys Dist Log Manage 48(4):391–414. https://doi.org/10.1108/IJPDLM-02-2017-0101

Mentzer JT, Kahn KB (1995) Forecasting technique familiarity, satisfaction, usage, and application. J Forecast 14(5):465–476. https://doi.org/10.1002/for.3980140506

Micol Policarpo L, da Silveira DE, da Rosa RR, Antunes Stoffel R, da Costa CA, Victória Barbosa JL, Scorsatto R, Arcot T (2021) Machine learning through the lens of e-commerce initiatives: an up-to-date systematic literature review. Comput Sci Rev 41:100414. https://doi.org/10.1016/j.cosrev.2021.100414

Miles MB, Huberman AM, Saldaña J (2020) Qualitative data analysis: A methods sourcebook. Sage, Los Angeles

National Retail Federation/Appriss Retail (2023) Consumer returns in the retail industry 2022. https://nrf.com/research/2022-consumer-returns-retail-industry . Accessed 23 May 2023

Ni J, Neslin SA, Sun B (2012) Database submission the ISMS durable goods data sets. Mark Sci 31(6):1008–1013. https://doi.org/10.1287/mksc.1120.0726

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10:89. https://doi.org/10.1186/s13643-021-01626-4

Pandya R, Pandya J (2015) C5.0 algorithm to improved decision tree with feature selection and reduced error pruning. IJCA 117(16):18–21. https://doi.org/10.5120/20639-3318

Petropoulos F, Apiletti D, Assimakopoulos V, Babai MZ, Barrow DK, Ben Taieb S, Bergmeir C, Bessa RJ, Bijak J, Boylan JE, Browell J, Carnevale C, Castle JL, Cirillo P, Clements MP, Cordeiro C, Cyrino Oliveira FL, de Baets S, Dokumentov A, Ellison J, Fiszeder P, Franses PH, Frazier DT, Gilliland M, Gönül MS, Goodwin P, Grossi L, Grushka-Cockayne Y, Guidolin M, Guidolin M, Gunter U, Guo X, Guseo R, Harvey N, Hendry DF, Hollyman R, Januschowski T, Jeon J, Jose VRR, Kang Y, Koehler AB, Kolassa S, Kourentzes N, Leva S, Li F, Litsiou K, Makridakis S, Martin GM, Martinez AB, Meeran S, Modis T, Nikolopoulos K, Önkal D, Paccagnini A, Panagiotelis A, Panapakidis I, Pavía JM, Pedio M, Pedregal DJ, Pinson P, Ramos P, Rapach DE, Reade JJ, Rostami-Tabar B, Rubaszek M, Sermpinis G, Shang HL, Spiliotis E, Syntetos AA, Talagala PD, Talagala TS, Tashman L, Thomakos D, Thorarinsdottir T, Todini E, Trapero Arenas JR, Wang X, Winkler RL, Yusupova A, Ziel F (2022) Forecasting: theory and practice. Int J Forecast 38(3):705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001

Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21–45. https://doi.org/10.1109/mcas.2006.1688199

Potdar A, Rogers J (2012) Reason-code based model to forecast product returns. Foresight 14(2):105–120. https://doi.org/10.1108/14636681211222393

Pur S, Stahl E, Wittmann M, Wittmann G, Weinfurtner S (2013) Retourenmanagement im Online-Handel–das Beste daraus machen: Daten, Fakten und Status quo. Ibi Research, Regensburg

Rajasekaran V, Priyadarshini R (2021) An e-commerce prototype for predicting the product return phenomenon using optimization and regression techniques. In: Singh M, Tyagi V, Gupta PK, Flusser J, Ören T, Sonawane VR (eds) Advances in computing and data sciences: proceedings of the 5th international conference on advances in computing and data sciences, 1st edn, vol 1441, pp 230–240. https://doi.org/10.1007/978-3-030-88244-0_22

Ravitch SM, Riggan M (2017) Reason and rigor: how conceptual frameworks guide research. Sage, Los Angeles, London, New Delhi, Singapore, Washington DC

Ren S, Chan H-L, Siqin T (2020) Demand forecasting in retail operations for fashionable products: methods, practices, and real case study. Ann Oper Res 291(1–2):761–777. https://doi.org/10.1007/s10479-019-03148-8

Rezaei M, Cribben I, Samorani M (2021) A clustering-based feature selection method for automatically generated relational attributes. Ann Oper Res 303(1–2):233–263. https://doi.org/10.1007/s10479-018-2830-2

Rogers DS, Lambert DM, Croxton KL, García-Dastugue SJ (2002) The returns management process. Int J Log Manag 13(2):1–18. https://doi.org/10.1108/09574090210806397

Röllecke FJ, Huchzermeier A, Schröder D (2018) Returning customers: the hidden strategic opportunity of returns management. Calif Manage Rev 60(2):176–203. https://doi.org/10.1177/0008125617741125

Sahoo N, Dellarocas C, Srinivasan S (2018) The impact of online product reviews on product returns. Inf Syst Res 29(3):723–738. https://doi.org/10.1287/isre.2017.0736

Samorani M, Ahmed F, Zaiane OR (2016) Automatic generation of relational attributes: an application to product returns. In: Proceedings of the 4th IEEE international conference on big data, pp 1454–1463

Santoro G, Fiano F, Bertoldi B, Ciampi F (2019) Big data for business management in the retail industry. MD 57(8):1980–1992. https://doi.org/10.1108/MD-07-2018-0829

Shaharudin MR, Zailani S, Tan KC (2015) Barriers to product returns and recovery management in a developing country: investigation using multiple methods. J Clean Prod 96:220–232. https://doi.org/10.1016/j.jclepro.2013.12.071

Shang G, McKie EC, Ferguson ME, Galbreth MR (2020) Using transactions data to improve consumer returns forecasting. J Oper Manag 66(3):326–348. https://doi.org/10.1002/joom.1071

Srivastava SK, Srivastava RK (2006) Managing product returns for reverse logistics. Int Jnl Phys Dist Log Manage 36(7):524–546. https://doi.org/10.1108/09600030610684962

Stock JR, Mulki JP (2009) Product returns processing: an examination of practices of manufacturers, wholesalers/distributors, and retailers. J Bus Logist 30(1):33–62. https://doi.org/10.1002/j.2158-1592.2009.tb00098.x

Stöcker B, Baier D, Brand BM (2021) New insights in online fashion retail returns from a customers’ perspective and their dynamics. J Bus Econ 91(8):1149–1187. https://doi.org/10.1007/s11573-021-01032-1

Sweidan D, Johansson U, Gidenstam A (2020) Predicting returns in men’s fashion. In: Proceedings of the 14th international fuzzy logic and intelligent technologies in nuclear science conference, pp 1506–1513. https://doi.org/10.1142/9789811223334_0180

Thaler RH, Sunstein CR (2009) Nudge: Improving decisions about health, wealth and happiness. Penguin

Tibben-Lembke RS, Rogers DS (2002) Differences between forward and reverse logistics in a retail environment. Supp Chain Mnagmnt 7(5):271–282. https://doi.org/10.1108/13598540210447719

Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x

Toktay LB, van der Laan EA, de Brito MP (2004) Managing product returns: the role of forecasting. In: Dekker R, Fleischmann M, Inderfurth K, van Wassenhove LN (eds) Reverse logistics. Springer, Berlin, Heidelberg, pp 45–64. https://doi.org/10.1007/978-3-540-24803-3_3

Toktay LB, Wein LM, Zenios SA (2000) Inventory management of remanufacturable products. Manage Sci 46(11):1412–142. https://doi.org/10.1287/mnsc.46.11.1412.12082

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag 14(3):207–222. https://doi.org/10.1111/1467-8551.00375

Uman LS (2011) Systematic reviews and meta-analyses. J Can Acad Child Adolesc Psychiatry 20(1):57–59

Urbanke P, Kranz J, Kolbe L (2015) Predicting product returns in e-commerce: the contribution of mahalanobis feature extraction. In: Proceedings of the 14th international conference on computer and information science

Urbanke P, Uhlig A, Kranz J (2017) A customized and interpretable deep neural network for high-dimensional business data–evidence from an e-commerce application. In: Proceedings of the 38th international conference on information systems

Vakulenko Y, Shams P, Hellström D, Hjort K (2019) Service innovation in e-commerce last mile delivery: mapping the e-customer journey. J Bus Res 101:461–468. https://doi.org/10.1016/j.jbusres.2019.01.016

vom Brocke J, Simons A, Niehaves B, Reimer K, Plattfaut R, Cleven A (2009) Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: Proceedings of the 17 th european conference on information systems

von Zahn M, Bauer K, Mihale-Wilson C, Jagow J, Speicher M, Hinz O (2022) The smart green nudge: reducing product returns through enriched digital footprints and causal machine learning. SSRN J. https://doi.org/10.2139/ssrn.4262656

Walsh G, Möhring M (2017) Effectiveness of product return-prevention instruments: empirical evidence. Electron Mark 27(4):341–350. https://doi.org/10.1007/s12525-017-0259-0

Walsh G, Möhring M, Koot C, Schaarschmidt M (2014) Preventive product returns management systems–a review and model. In: Proceedings of the 22nd european conference on information systems

Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2):xiii–xxiii

Winklhofer H, Diamantopoulos A, Witt SF (1996) Forecasting practice: a review of the empirical literature and an agenda for future research. Int J Forecast 12(2):193–221. https://doi.org/10.1016/0169-2070(95)00647-8

Wirth R, Hipp J (2000) CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, vol 1, pp 29–40

Zhao X, Hu S, Meng X (2020) Who should pay for return freight in the online retailing? Retailers or consumers. Electron Commer Res 20(2):427–452. https://doi.org/10.1007/s10660-019-09360-9

Zhu Y, Li J, He J, Quanz BL, Deshpande A (2018) A local algorithm for product return prediction in e-commerce. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3718–3724. https://doi.org/10.24963/ijcai.2018/517

Zotteri G, Kalchschmidt M (2007) Forecasting practices: empirical evidence and a framework for research. Int J Prod Econ 108(1–2):84–99. https://doi.org/10.1016/j.ijpe.2006.12.004

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Appendix: Author-centric content summary (with focus on forecasting issues)

1.1 journal publications.

Hess and Mayhew ( 1997 ) describe a forecasting approach, taking the example of a direct marketer for apparel with a lenient consumer return policy (free returns anytime). The analysis can plausibly be applied to a general retailer, although return time windows are somewhat different. A regression approach and a hazard model are compared. The regression approach itself is split into an OLS estimation of return timing (with poor fit) and a logit model of return propensities, which is in turn used for the split function of the box-cox-hazard approach for estimating the probability of a return over time. The accuracy was measured by fit statistics regarding the absolute deviation from the actual cumulative return proportion, with the split-hazard model outperforming the regression model. Besides price, the importance of fit of the respective product is used as a predictor.

Potdar and Rogers ( 2012 ) propose a method using reason codes combined with consumer behavior data for forecasting returns volume in the consumer electronics industry, aiming at the retailer stage as well as the preceding supply chain stages. The subject of their study is an offline retailer, which allows generalization for e-tailers due to a similar return policy (14 days free returns with no questions asked). In a multi-step approach, the authors are using essential statistical methods (moving averages, correlations, and linear regression), but use sophisticated domain and product knowledge like product features or price in relation to past return numbers, aiming to rank different competing products regarding their quality, and to predict the volume of returns for a given product for each given period of time.

Fu et al. ( 2016 ) derive a framework for the forecasting of product- and consumer-specific return propensities, i.e., the return propensity for individual purchases. Their study is directed at online shopping and is evaluated using the data from an online cosmetic retailer selling via Taobao.com. The predictors are categorized into inconsistencies in the buying and in the shipping phase of a transaction. A latent factor model is introduced for return propensities capturing differences between expectations and performance. This model is extended by product (e.g., warranty) and customer information (e.g., gender, credit score). The model is based on conditional probabilities, and an iterative expectation–maximization approach derives its parameters. MAE and RMSE, precision/recall, and AUC metrics assess the forecast accuracy. As benchmark models, two matrix factorization models and two memory-based models (historical consumer or product return rates) are compared, while the proposed model outperforms the references. Furthermore, this model allows identifying various return reasons, e.g., return abuse and fraud.

Building on the work of Fu et al. ( 2016 ), Li et al. ( 2019 ) investigate underlying reasons for consumer returns, taking the example and data of an online cosmetic retailer via Taobao.com. They examine the customers’ return propensity for product types, aiming at detecting abnormal returns suspecting abuse. Different from purchase decisions, they find customer profile data to be more important predictors for return decisions than product information or transaction details. The authors detect “selfish” or “fraud” consumers based on this rationale. For estimating return propensities for a given consumer and product, they calculate the return behavior depending on the return decision of similar consumers (“trust network”) and the amount of trust in these other consumers. MAE and precision-recall-measures are used to assess the prediction of different random walk models. The employed trust-based random walk model outperforms the other models on most indicators, building the basis for anomaly detection of consumers to cluster them into groups (honest/selfish/fraud) and individually address the return issues of these groups.

Although the paper from Cui et al. ( 2020 ) aims at product return forecasts from the perspective of the manufacturer, their case can be generalized for classic e-tailers, as the manufacturer is responsible for the return handling in their scenario—a task often performed by the retailer. They used a comprehensive data set from an automotive accessories manufacturer aiming to forecast return volume for sales channels and different products. The observed return rates lower than 1% are uncommonly low, and therefore the results must be interpreted with caution. First, a hierarchical OLS regression step-by-step incorporates up to 40 predictors regarding sales, time, product type, sales channel, and product details, including return history. The full model shows a significantly increased performance measured by a more than 50% decrease of MSE, which was used as the primary performance measure. Interestingly, relatively small differences in model quality (R 2 ) led to overproportional changes in the MSE. Using a machine-learning approach for predictor selection (“LASSO”), another MSE reduction of about 10% was achieved. Data Mining approaches (random forest, gradient boosting) could not outperform the LASSO approach. Forecasting performance was strongly dependent on the variation of the data. The two best predictors for return volume were past sales volume and lagged return statistics. The authors were wondering about the importance of lagged return information, failing to acknowledge that this predictor includes the consumer reaction to detailed product information, which has not been a significant predictor.

Ketzenberg et al. ( 2020 ) segment customers and target detecting the small number of abusive returners, as these are unprofitable for the retailer and generate significant losses over a long time. In general, high-returning customers are usually more profitable. The data used for this study is from a department store retailer with various product groups in the assortment. Predictors are transactional data and customer attributes. For classification, different algorithms like logit, Support Vector Machines (SVM), Random Forests (RF), Neural Networks (NN) are used in combination with different shrinkage methods like LASSO, ridge regression, and elastic net. Random Forests and especially Neural Networks outperform the other algorithms, assessed by sensitivity, precision, and AUC. In conclusion, a low rate of false positives could assure retailers of using abuse detection systems.

Shang et al. (Shang et al. 2020 ) developed a predict-aggregate (P-A) model adaptable both for retailers and manufacturers for forecasting return volume in a continuous timeframe, in contrast to commonly used aggregate-predict (A-P) models. Instead of aggregating data first (i.e., sales volume and returns volume), they first aggregate product-specific return probabilities and then aggregate the purchases by addition of the individual probabilities. As predictors, they only use timestamps and lagged return information. They tune and assess their models on two datasets from an offline electronics and an online jewelry retailer. ARIMA and lagged return models known from end-of-life forecasting (de Brito et al. 2005 ) are used as benchmarks, using RMSE as an assessment criterion. The authors show that even a basic version of their approach outperforms the benchmark models in almost all observed cases by up to 19%, though using only lagged returns and timestamps as input. Different extensions, e.g., including more predictor variables, can easily be integrated and are shown to further improve the forecasting performance.

John et al. ( 2020 ) try to predict the rare event of return fraud from customer representatives that make use of exactly knowing the e-commerce company’s return policy framework and buying and returning items fraudulently. Therefore, predictors range from transaction details to customer service agent attributes. A penalized likelihood logit model was chosen by the authors and was evaluated by precision and recall, focussing on maximizing recall and minimizing false negatives. The most important predictors were communication type and reason for interaction.

The paper by Rezaei et al. ( 2021 ) introduces a new algorithm to automatically select attributes from high-dimensional databases for forecasting purposes. As a demonstration sample, they use simulated data as well as the publicly available ISMS Durable Goods dataset (Ni et al. 2012 ) for consumer electronics. The results are assessed by AUC, precision, recall, and f1-score. They compare different configurations. For the simulated data, LASSO as shrinkage method generally works best, outperforming RF and BaggedTrees. For real-world data, based on a forecast with a logit model, they show that the proposed selection algorithm performs similar or better compared to LASSO, SVM, and RF, while the complexity of the chosen variables is lower.

1.2 Conference publications

Urbanke et al. ( 2015 ) describe a decision support system to better direct return-reducing interventions at e-commerce purchases with highly likely returns. They compare different approaches for extracting input variables for return propensity forecasting. They use a large dataset from a fashion e-tailer, aiming to reduce the input variables regarding consumer profile, product profile, and basket information from over 5,000 binary variables to 10 numeric variables by different algorithms (e.g., principal component analysis, non-negative matrix factorization, etc.). The results are then used to predict return propensities with a wide variety of state-of-the-art algorithms (AdaBoost, CART, ERT, GB, LDA, LR, RF, SVM), thus also revealing both feature selection and prediction performance. The proposed Mahalanobis feature extraction algorithm used as input for AdaBoost outperforms all other combinations presented, while interestingly, a logit model with all original inputs delivers relatively precise forecasts.

Building on some parts of this study, the paper of Urbanke et al. ( 2017 ) presents a return decision forecasting approach and aims at two targets, (1) high predictive accuracy and (2) interpretability of the model. Based on real-world data of a fashion and sports e-tailer, they first hand-craft 18 input variables and then use NN to extract more features and compare this approach to other feature extraction algorithms based on different forecasting algorithms. For assessment, they measure correlations between out-of-sample-predictions and class labels and AUC. The best performing classifier was AdaBoost, while the contribution of NN-based feature extraction shows interpretability as well as superior predictive performance.

Ahmed et al. ( 2016 ) focus on the automatic aggregation and integration of different data sources to generate input variables (features). They use return forecasting just as an exemplary classification problem for their data preparation approach, using various ML algorithms, e.g., RF, NN, DT-based algorithms, to detect returned purchases of an electronics retailer. Based on AUC measure, the results of their GARP-approach are superior to not using aggregations while generating an extensive amount of features with no pruning approach. In general, SVM and RF work best in combination with the proposed GARP approach. The data is based on the publicly available ISMS durable goods data sets (Ni et al. 2012 ).

A similar group of authors published another paper (Samorani et al. 2016 ), again using the aforementioned ISMS dataset as an example for data preparation and automatic attribute generation. Besides forecasting performance, in this paper, they want to generate knowledge about important return predictors; e.g., a higher price is associated with more returns, but only as long price levels are below a 1,500$ threshold. AUC is used to assess different levels of data integration, confirming that overfitting might happen when too many attributes are used.

Heilig et al. ( 2016 ) describe a Forecasting Support System (FSS) to predict return decisions in a real environment. First, they compare different forecasting approaches for data from a fashion e-tailer, assessed by AUC and accuracy metrics. The ensemble selection approach outperforms all other classifiers, with RF being the closest competitor. Computational times grow exponentially when using more data. Based on these results, they secondly describe a cloud framework for implementing such ensemble models for live use in a real shop environment.

Ding et al. ( 2016 ) present an approach to predict the daily return rate of an e-commerce company based on sentiment analysis of tweets regarding this company in the categories of news, experience, products, and service. Therefore, they use sophisticated text mining technologies, while the forecasting approach of an econometric vector autoregression is more or less common. The emotion of posts regarding different variables (news, product, service) impacts the returns rate negatively, while the emotion of purchasing experience impacts it positively, showing that the prediction accuracy enhances through classifying social network posts.

Drechsler and Lasch ( 2015 ) aim at forecasting the volume of fraudulent returns in e-commerce over several periods of time. They present different approaches multiplying the sales volume and the relative return rate, the first referring to Potdar and Rogers ( 2012 ), estimating the rate of misused returns directly based on time-lag-specific return rates. In a second approach referring to Toktay et al. ( 2000 ), they estimate the overall returns rate and multiply it by the time-specific ratio of fraudulent returns. The return rates were forecasted by moving averages and exponential smoothing techniques. Assessment criteria for performance comparison based on simulated data were MAE, MAPE, and TIC, showing the first approach to be superior, but both methods are not sufficiently robust. Therefore, the authors include further time-specific information (like promotions or special events, which could foster fraudulent returns) in a model using a Holt-Winters approach, showing superior performance. All of the models are highly dependent on low fluctuation in return rates, showing a shortcoming of these more or less naive forecasting techniques.

Asdecker and Karl ( 2018 ) compare the performance of different algorithms for forecasting binary return decisions: logit, linear discriminant analysis, neuronal networks, and a decision-tree-based algorithm (C5.0). Their analysis is based on the data of a fashion e-tailer, including price, consumer information, and shipment information (number of articles in shipment, delivery time). For the assessment of different algorithms, they use the total absolut error (TAE) and relative error. An ensemble learning approach performs best and similar to the C5.0 algorithm. Though, differences in performance are relatively small, while only about 68% of return decisions are forecasted correctly.

Li et al. ( 2018 ) propose a hypergraph representation of historical purchase and return information combined with a random-walk-based local graph cut algorithm to forecast return decisions on order (basket) level as well as on product level. By this, they aim to detect the underlying return causes. They use data from two omnichannel fashion e-tailers from the US and Europe to assess the performance of their approach, using precision/recall/F 0.5 /AUC metrics while arguing that precision is the most important indicator for targeted interventions. Three similarity-based approaches (e.g., a k-Nearest Neighbor model) are used as reference. The proposed approach performs best regarding AUC, precision, and F 0.5 metrics.

Zhu et al. ( 2018 ) developed a weighted hybrid graph algorithm representing historical customer behavior and customer/product similarity, combined with a random-walk-based algorithm for predicting customer/product combinations that will be returned. They report an experiment based on data from a European fashion e-tailer suffering from return rates as high as 50%. For assessment, they use precision, recall, and F 0.5 metrics. Their approach is superior to two reference competitors (similarity-based and a bipartite graph algorithm). As predictors, they use product similarities and historical return information, while their approach can be enriched with detailed customer attributes.

Joshi et al. ( 2018 ) model the return decisions based on the data of an Indian e-commerce company, especially dealing with returns for apparel due to fit issues. In a two-step approach, they first model return probabilities using concepts from network science based on a customer’s historical purchase and return decisions, and secondly use a SVM implementation with return probabilities as a single input to classify for the return decision. Assessed by F 1 /precision/recall scores, their approach is superior to a reference random-walk baseline model.

Imran and Amin ( 2020 ) compare different forecasting algorithms (XGBoost, CatBoost, LightGBM, TabNet) for return classification based on the data of a general e-commerce retailer from Bangladesh. As input variables, only order attributes, including payment method and order medium, are used. For evaluation, they use metrics like true negative rate, false-positive rate, false-negative rate, true positive rate, AUC, F 2 -score, precision, and accuracy. In the end, they chose TPR, AUC, and F 2 -score, claiming that misclassifying high return probability objects were the first thing to avoid. According to these metrics, TabNet as a deep learning algorithm outperforms the other models. The most important predictors were payment method, order location, and promotional orders.

As returns are most prominent in fashion e-commerce, most of the forecasting papers take this industry as an example, as forecasting models are more precise when returns are more frequent. Hofmann et al. ( 2020 ) develop a more generalized order-based return decision forecasting approach, appropriate for different industries and suitable also for low return rates. For their analysis, they use a dataset from a german technical wholesaler with a return rate as low as 5%. Input variables were just basket composition and return information. For assessment, they used precision and recall metrics. RF did not perform superior to a statistical baseline approach, nor with oversampling as data preparation, to deal with the group imbalance. The DART algorithm makes use of the group imbalance correction by random oversampling. In general, gradient boosting performs best with imbalanced groups, also without oversampling, but forecasting quality is lower than with more specialized forecasting approaches as described for fashion. Furthermore, results were more accurate on basket level than on single-item level.

Fuchs and Lutz ( 2021 ) use Design Science Research (DSR) principles to design a meta-model for the real-time prediction of returns. The goal is to influence consumer decisions by triggering a feedback system based on the basket composition and its return probability. For forecasting, which is not the primary focus of their paper, they build upon a gradient boosting model taken from existing research (Hofmann et al. 2020 ) and describe possible implementations into an ERP system regarding asynchronous communication requirements and possible architecture.

The paper by Sweidan et al. ( 2020 ) evaluates the forecasting performance of a random forest model for a shipment-based return decision, using real-world data of a fashion e-tailer. For their model, they use customer (e.g., lagged return rate) and order information as inputs. They find that predictions with high confidence are very precise (i.e., low false-positive rate). Thus, interventions can be targeted at such orders already when the items are in the consumers’ basket without risk of a misdirected intervention. For assessment, accuracy, AUC, precision, recall and specificity are used. Regarding the predictors, they note that selection orders (a product in different sizes) are the best predictor for order-based returns.

Rajasekaran and Priyadarshini ( 2021 ) develop a metaheuristic for forecasting the product-based return probabilities. In the first step, they determine return probabilities based on product feedback, time, and product attributes regarding manufacturer return statistics. Secondly, they compare different algorithms (OLS, RF, Gradient Boosting) by MAE, MSE, and RMSE metrics. Interestingly, linear regression performs best in all metrics, but no explanation and a misinterpretation regarding the best algorithm are given.

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Karl, D. Forecasting e-commerce consumer returns: a systematic literature review. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00436-x

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TM65.3 Embodied carbon in building services: logistics centres

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We are currently experiencing issues with certain downloads. If this publication does not appear immediately on your MyDownloads page, you should receive an email with the PDF within the next two hours. If you do not receive this or need it more quickly, please email [email protected] .

This document discusses the embodied carbon impact of different material handling equipment (MHE) and mechanical, electrical and plumbing (MEP) equipment typically used in logistics centres. Embodied carbon refers to the greenhouse gas emissions associated with materials and construction processes in modules A1 to A5 (product and construction), B1 to B5 (in use) and C1 to C4 (end of life) as defined in BS EN 15978:2011. The embodied carbon calculations in this document follow the methodology outlined in CIBSE TM65: Embodied carbon in building services: a calculation methodology (2021). The life cycle stages that are in scope for this study are A1–A4, B1–B4 and C1–C4.

This document is intended for anyone who is involved in the design, construction, operation or maintenance of logistics centres, as well as those who are interested in reducing the environmental impact of the logistics industry. This includes logistics centre owners, manufacturers, retailers, e-commerce companies, architects, engineers, contractors, policymakers, researchers and students.

The research for this document was conducted by Introba, sponsored by and in partnership with Amazon. It was made possible thanks to the help of many manufacturers who shared information about their products, which enabled the calculation of generic embodied carbon coefficients at the product level. For the building typologies examined, this project is a first step towards understanding the embodied carbon implications of logistics centres with different functions. This study aims to help designers make data-driven decisions early in the design process. However, it is important to note that Environmental Product Declarations (EPDs) are the gold standard for environmental product data as they are produced via a more

comprehensive life cycle assessment and are third-party verified. Additionally, as more manufacturer data is disclosed and more EPDs are created, the results of this study will evolve over time.

Introduction

1.1 Embodied carbon in logistics centres

1.2 Aim and scope

1.3 Terminology and abbreviations

1.4 Methodology

1.5 MHE scenarios

1.6 MEP equipment scenarios

2 Product level: MHE key findings

2.1 Embodied carbon of MHE products by weight

3 Product level: MEP equipment key findings

3.1 Embodied carbon of MEP products by capacity

3.2 Embodied carbon of MEP products by weight

3.3 Refrigerant leakage

4 System-level results: MHE

4.1 Key findings

4.2 Traditional racking centre

4.3 Cross docking centre

4.4 Automated fulfilment centre

4.5 Loop sort centre

4.6 Automated distribution centre

5 System-level results: MEP

5.1 Key findings

5.2 Scenario 1: rooftop unit

5.3 Scenario 2: all-air system

5.4 Scenario 3: fan coil unit

6 Conclusions

6.1 General conclusions

6.2 Detailed conclusions (MHE)

6.3 Detailed conclusions (MEP)

6.4 Practical applications

6.5 Limitations

6.6 Further work

Appendix A: Detailed scope

Appendix B: Detailed methodology

Appendix C: Detailed assumptions

Appendix D: Functional units

Appendix E: Material coefficients and scale-up factors

Appendix F: Worked example

Appendix G: Product-level results

Appendix H: Detailed MEP system-level results

Appendix I: Lever studies

Appendix J: Detailed taxonomy

Lead authors : Jack Pearce (Introba), Will Bury (Introba)

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Peer reviewers: Rowan Bell-Bentley (Arup), Maria Benazzo (Arup), Sarah Bousquet (Arup), Rob Griffiths (Atkins), Roger Hitchin (independent consultant), Fabrizio Varriale (RICS)

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Including an exceptionally warm Northern Hemisphere (NH) summer 1 ,2 , 2023 has been reported as the hottest year on record 3-5 . Contextualizing recent anthropogenic warming against past natural variability is nontrivial, however, because the sparse 19 th century meteorological records tend to be too warm 6 . Here, we combine observed and reconstructed June-August (JJA) surface air temperatures to show that 2023 was the warmest NH extra-tropical summer over the past 2000 years exceeding the 95% confidence range of natural climate variability by more than half a degree Celsius. Comparison of the 2023 JJA warming against the coldest reconstructed summer in 536 CE reveals a maximum range of pre-Anthropocene-to-2023 temperatures of 3.93°C. Although 2023 is consistent with a greenhouse gases-induced warming trend 7 that is amplified by an unfolding El Niño event 8 , this extreme emphasizes the urgency to implement international agreements for carbon emission reduction.

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