literature review e supply chain

Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models

International Journal of Physical Distribution & Logistics Management

ISSN : 0960-0035

Article publication date: 14 March 2018

Issue publication date: 22 March 2018

The purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML’s diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics.

Design/methodology/approach

The systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a “snowballing” technique, a total of 47 articles form the basis of the review.

The literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions.

Research limitations/implications

To circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models.

Practical implications

The design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution.

Social implications

Improved LML performance would have positive societal impacts in terms of service and resource efficiency.

Originality/value

This paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions.

• Literature review
• Omnichannel
• Digital supply chains

Lim, S.F.W.T. , Jin, X. and Srai, J.S. (2018), "Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models", International Journal of Physical Distribution & Logistics Management , Vol. 48 No. 3, pp. 308-332. https://doi.org/10.1108/IJPDLM-02-2017-0081

Emerald Publishing Limited

Copyright © 2018, Stanley Frederick W.T. Lim, Xin Jin and Jagjit Singh Srai

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Last-mile delivery has become a critical source for market differentiation, motivating retailers to invest in a myriad of consumer delivery innovations, such as buy-online-pickup-in-store, autonomous delivery solutions, lockers, and free delivery upon minimum purchase levels ( Lim et al. , 2017 ). Consumers care about last-mile delivery because it offers convenience and flexibility. For these reasons, same-day and on-demand delivery services are gaining traction for groceries (e.g. Deliv Fresh, Instacart), pre-prepared meals (e.g. Sun Basket), and retail purchases (e.g. Dropoff, Amazon Prime Now) ( Lopez, 2017 ). To meet customer needs, parcel carriers are increasing investments into urban and automated distribution hubs ( McKevitt, 2017 ). However, there is a lack of understanding as to how best to design last-mile delivery models with retailers turning to experimentations that, at times, attract scepticism from industry observers (e.g. Cassidy, 2017 ). For example, Sainsbury’s, Somerfield, and Asda established innovative pick centres, but closed them down within a few years ( Fernie et al. , 2010 ). eBay launched its eBay Now same-day delivery service in 2012, but in July 2015, it announced the closure of this programme. Google, likewise, opened and then closed its two delivery hubs for Google Express in 2013 and 2015, respectively ( O’Brien, 2015 ).

The development of these experimental last-mile logistics (LML) models, not surprisingly, created uncertainty within increasingly complicated and fragmented distribution networks. Without sustainable delivery economics, last-mile service provision will struggle to survive (as was the experience of Sainsbury’s, Somerfield, Asda, eBay, Google, and Webvan) with retailers increasingly challenged to find an optimal balance between pricing, consumer expectations for innovative new channels, and service levels ( Lopez, 2017 ; McKevitt, 2017 ).

Although several contributions have been made in the LML domain, the literature on LML models remains relatively fragmented, thus hindering a comprehensive and holistic understanding of the topic to direct research efforts. Hitherto, existing studies provide limited or no guidance on how contingency variables influence the selection of LML configurations ( Agatz et al. , 2008 ; Fernie et al. , 2010 ; Mangiaracina et al. , 2015 ; Lagorio et al. , 2016 ; Savelsbergh and Van Woensel, 2016 ). Our paper addresses this knowledge deficiency by reviewing the disparate academic literature to capture key contingency and structural variables characterizing the different forms of last-mile distribution. We then theoretically establish the connection between these variables thereby providing a design framework for LML models. Our corpus is comprised of 47 papers published in 16 selected peer-reviewed journals during the period from 2000 to 2017. The review is performed from the standpoint of retailers operating LML. As such, some LML research streams are deliberately excluded, including issues related to public policy, urban traffic regulations, logistics infrastructure, urban sustainability, and environment.

This paper is structured as follows. First, we provide a working definition for LML and introduce relevant terms. Second, we set out the research methodology and conduct descriptive analyses of the corpus. The substantive part of the paper is an analysis of the literature on LML models and the development of a design framework for LML. The framework synthesises a set of structural and contingency variables and explicates their interrelationships, shedding light on how these interactions influence LML design. Finally, we highlight the key gaps in the extant literature and propose future research opportunities.

Defining LML

The term “last-mile” originated in the telecommunications industry and refers to the final leg of a network. Today, LML denotes the last segment of a delivery process, which is often regarded as the most expensive, least efficient aspect of a supply chain and with the most pressing environmental concerns ( Gevaers et al. , 2011 ). Early definitions of LML were narrowly stated as the “extension of supply chains directly to the end consumer”; that is, a home delivery service for consumers ( Punakivi et al. , 2001 ; Kull et al. , 2007 ). Several synonyms, such as last-mile supply chain, last-mile, final-mile, home delivery, business-to-consumer distribution, and grocery delivery, have also been used.

Despite their nuances, existing LML definitions converge on a common understanding that refers to the last part of a delivery process. However, existing definitions (details available from the authors) appear incomplete in capturing the complexities driven by e-commerce, such as omission in defining an origin ( Esper et al. , 2003 ; Kull et al. , 2007 ; Gevaers et al. , 2011 ; Ehmke and Mattfeld, 2012 ; Dablanc et al. , 2013 ; Harrington et al. , 2016 ); exclusion of in-store order fulfilment processes as a fulfilment option ( Hübner, Kuhn and Wollenburg, 2016 ); and/or non-specification of the destination (or end point), including failure to capture the collection delivery point (CDP) as a reception option ( Esper et al. , 2003 ; Kull et al. , 2007 ). Without a consistent and robust definition of LML, the design of LML models is problematic.

For the purpose of this review, we examine existing terminology on last-mile delivery systems in order to create a working definition for LML. As part of this definition, we introduce the concept of an “order penetration point” ( Fernie and Sparks, 2009 ) as a way of defining the origin of the last-mile. The order penetration point refers to an inventory location (e.g. fulfilment centre, manufacturer site, or retail store) where a fulfilment process is activated by a consumer order. After this point, products are uniquely assigned to the consumers who ordered them, making the order penetration point a natural starting point for LML. The destination point is commonly dictated by the consumer, hence we use “final consignee’s preferred destination point” as the terminology to indicate where an order is delivered. The choice of destination point could be a home/office, a reception box (RB), or a pre-designated CDP.

Last-mile logistics is the last stretch of a business-to-consumer (B2C) parcel delivery service. It takes place from the order penetration point to the final consignee’s preferred destination point.

Extending the above definition, we reference Bowersox et al. ’s (2012) view of a supply chain as a series of “cycles”, with half the cycle being the product/order flow and the other, information flow. We also reference the Supply Chain Operations Reference model ( Supply Chain Council, 2010 ) recognising that LML operates within a broader supply network. In particular, the LML cycle coincides with the consumer service cycle, interfacing direct-to-consumer-goods manufacturers, wholesalers, retailers with the end consumer ( Bowersox et al. , 2012 ). The process may be divided into three sub-processes, namely source, make, and deliver.

These three sub-processes set the focus for this review and they align with the delivery order phase in LML, namely picking, packing, and delivery. This model is consistent with Campbell and Savelsbergh’s (2005) view of the business-to-consumer process comprising order sourcing, order assembly, and order delivery. Accordingly, this review focuses on the examination of LML models: LML distribution structures and the contingency variables associated with these structures. The term “distribution structure” covers the stages from order fulfilment to delivery to the final consignee’s preferred destination point. It includes the modes of picking (e.g. warehouse or store-based), transportation (e.g. direct delivery by the retailer’s own fleet), and reception (e.g. consumer-pickup) ( Kämäräinen et al. , 2001 ). The associated contingency variables provide guidance for decision support by highlighting the key characteristics of each distribution structure for the design and selection, matching product, and consumer attributes ( Boyer and Hult, 2005 ).

Research methodology

A structured literature review aims to identify the conceptual content of a rapidly growing field of knowledge, as well as to provide guidance on theory development and future research direction ( Meredith, 1993 ; Easterby-Smith et al. , 2002 ; Rousseau et al. , 2008 ). Structured reviews differ from more narrative-based reviews because of the requirement to provide a detailed description of the review procedure in order to reduce bias; this requirement thereby increases transparency and replicability ( Tranfield et al. , 2003 ). Therefore, undertaking a structured review ensures the fidelity, completeness, and rigour of the review itself ( Greenhalgh and Peacock, 2005 ).

Our review provides a snapshot of the diversity of theoretical approaches present in LML literature. It does not pretend to cover the entirety of the literature, but rather offers an informative and focused evaluation of purposefully selected literature to answer specific research questions. In the following sections, we discuss the execution of the four main steps (planning, searching, screening, and extraction/synthesis/reporting) as outlined by Tranfield et al. (2003) . We also incorporate literature review guidelines suggested by Saenz and Koufteros (2015) . Our study uses key research questions identified by an expert panel and we reference the Association of Business Schools journal ranking 2015 to decide which journals to include in this scholarship ( Cremer et al. , 2015 ). Our review includes the classification of contributions across methodological domains. In later sections, we utilise insights from the literature review to develop an LML design framework that captures the relationships between distribution structures via a set of structural and associated contingency variables.

What is the current state of research and practice on LML distribution in the e-commerce context?

What are the associated contingency variables that can influence the selection of LML distribution structures?

How can the contingency variables identified in RQ2 be used to inform the selection of LML distribution structures?

The academic material in this study covers the period from 2000 to 2017. This period coincides with critical industry events, such as the emergence and subsequent demise of the online grocer, Webvan. The review is limited to peer-reviewed publications to ensure the quality of the corpus ( Saenz and Koufteros, 2015 ) and considers 16 journals, including one practitioner journal ( MIT Sloan Management Review ), to capture theoretical perspectives on industry best practices. Only articles from the selected journals have been included in this review, with one exception, where we included the article by Wang et al. (2014) , published in Mathematical Problems in Engineering . The article was deemed critical as it represents the only piece of work to date that connects and extends prior research on the evaluation of CDPs.

The 16 journals were selected based on their primary focus on empirical and conceptual development, rather than on their discussion of analytical modelling. Although we appreciate that there are significant research studies in this area (e.g. operations research), the focus of this review led us to primarily consider how scholars conceptualise LML distribution structures and apply theoretical variables to LML design through quantitative, qualitative, or conceptual approaches, rather than through mathematic-based models. The mathematic-based model literature focuses on the development of stylised and optimisation models in areas of multi-echelon distribution systems, vehicle routing problems ( Savelsbergh and Van Woensel, 2016 ), buy-online-pick-up-in-store services ( Gao and Su, 2017 ), pricing and delivery choice, inventory-pricing, delivery service levels, discrete location-allocation, channel design, and optimal order quantities via newsvendor formulation for different fulfilment options ( Agatz et al. , 2008 ), amongst others. These studies typically employ a series of assumptions to simplify real-world operations in order to provide closed-form or heuristic-based prescriptive solutions ( Agatz et al. , 2008 ; Savelsbergh and Van Woensel, 2016 ). Therefore, this review excluded journals with a primarily mathematical modelling or operations research focus. However, it included relevant mathematical modelling articles – published in any of the 16 selected journals – as long as they explicated types of LML distribution structure and/or the associated contingency variables. Finally, this study also excluded general management journals in order to fit the operational focus of this research.

The literature search was conducted on the following databases: ISI Web of Science, Science Direct, Scopus, and ABI/Inform Global. Two search rounds were undertaken to maximise inclusion of all relevant articles. The first literature probe was performed using the following search terms: “urban logistics” OR “city logistics” OR “last-mile logistics” OR “last mile logistics”. To extend the corpus, we incorporated the “snowballing” technique of tracing citations backward and forward to locate leads to other related articles; this study used this process in the second round to supplement a protocol-driven methodology. This approach resulted in new search terms and scholar identification to refine the search strategy as the study unfolded ( Greenhalgh and Peacock, 2005 ). The following new search terms were identified: “home delivery”, “B2C distribution”, “extended supply chain”, “final mile”, “distribution network”, “distribution structure”, and “grocery delivery”. These new keywords were then used to create additional search strings with Boolean connectors (AND, OR, AND NOT). Finally, in order to cross-check the searches, we consulted with a supply chain professor from Arizona State University and one from the University of Cambridge. It is therefore posited that the review coverage is reasonably comprehensive.

Exclusion criteria: paper titles bearing the terms “urban logistics”, “city logistics”, “last-mile logistics”, or “last-mile” but with limited coverage on distribution structures and the associated design variables were excluded (e.g. public policy, urban traffic regulations, logistics infrastructure, urban sustainability, environment), as were editorial opinions, conference proceedings, textbooks, book reviews, dissertations, and unpublished working papers.

Inclusion criteria: papers with coverage of distribution structures and design variables in the e-commerce context were included, regardless of their actual study focus. We included multiple research methods to have both established findings as well as emergent theorising.

During the search phase, we identified 425 articles referencing our subject terms. We eliminated duplicates based on titles and name of authors and rejected articles matching the exclusion criteria. For example, while the paper by Gary et al. (2015) holds the keyword “urban logistics” in the title, it focuses on logistics prototyping, rather than LML models, as a method to engage stakeholders. This paper, therefore, was excluded. The elimination stage resulted in 100 articles being considered relevant for further review. Results were exported to reference management software, EndNote version X8, for further review and to facilitate data management. We then adopted the inclusion criteria to select the final articles. Finally, we grouped the articles into two categories: LML distribution structures and the associated contingency variables. Ultimately, a total of 47 journal articles form the corpus of this review.

Extraction, synthesis, and reporting

Following an initial review of the 47 articles, a summary of each article was prepared using a spreadsheet format organised under descriptive (year, journal, title), methodology (article type, theoretical lens, sampling protocol), and thematic categories (article purpose, context, LML distribution structures, design variables, others) as adapted from Pilbeam et al. (2012) .

Accordingly, we conducted three analyses: descriptive, methodological, and thematic ( Richard and Beverly, 2014 ). The descriptive analysis summarises the research development over the period of interest, and the distribution statistics of the journals. The methodological analysis highlights the research methods employed in the domain, while the thematic analysis synthesises the main outcomes from the extracted literature and provides an overview of the review structure. Reporting structures were organised in a manner that sequentially responds to the research questions posed earlier.

Descriptive analysis

Table I provides summary statistics of the papers reviewed, author affiliations c , identifying contributions, as well as those journals where surprisingly contributions have yet to be made.

Methodological analysis

Typology-oriented provision: owing to the recent proliferation of LML models, a typology-oriented approach was particularly conducive for understanding LML practices. Lee and Whang (2001) , Chopra (2003) , Boyer and Hult (2005) , and Vanelslander et al. (2013) each developed LML structural types to assist design under different consumer and product attributes. These studies mostly captured the linearly “chain-centric” LML models prevalent in the pre-digital era.

Literature review and conceptual studies: several reviews have contributed in this domain. Some papers focused on specific areas, such as the evolution of British retailing ( Fernie et al. , 2010 ) and distribution network design ( Mangiaracina et al. , 2015 ), whereas others discussed several topics at once ( Agatz et al. , 2008 ; Lagorio et al. , 2016 ; Savelsbergh and Van Woensel, 2016 ). Narrowly focused papers identified limited LML structural types or variables influencing distribution network design, while more broadly focused papers examined wider issues in urban, city, or multichannel logistics. Conceptual studies typically provided guidance on the selection of LML “types” based on certain performance criteria (e.g. Punakivi and Saranen, 2001 ; Chopra, 2003 ), or logistics service quality (e.g. Yuan and David, 2006 ).

Empirical studies: these studies mainly compared LML types or demonstrated the impact of particular variables upon LML. Studies undertaking the former research purpose (contrasting types) employed simulations, field/mail surveys, and econometrics to examine performance or CO 2 emissions (e.g. Punakivi et al. , 2001 ). One paper employed a mixed-method approach (case research and modelling) to understand the organisation of the physical distribution processes in omnichannel supply networks ( Ishfaq et al. , 2016 ). Empirical studies aiming at the latter research purpose (evidencing impact) used field and laboratory experiments and statistical methods on survey data to examine the interplay between operational strategies and consumer behaviour (e.g. Esper et al. , 2003 ; Boyer and Hult, 2005 ; Kull et al. , 2007 ). These studies also employed econometrics to examine the effects of cross-channel interventions (e.g. Forman et al. , 2009 ; Gallino and Moreno, 2014 ). Additionally, a few studies used case research to provide operational guidance via framework development, such as last-mile order fulfilment ( Hübner, Kuhn and Wollenburg, 2016 ) and LML design, to capture the interests of various stakeholders ( Harrington et al. , 2016 ).

Mathematical modelling: studies also employed a variety of mathematical tools and techniques to formulate LML problems and find optimum solutions, mostly for vehicle routing problems ( Campbell and Savelsbergh, 2005 ; McLeod et al. , 2006 ; Aksen and Altinkemer, 2008 ; Crainic et al. , 2009 ; Wang et al. , 2014 ). In their work, Campbell and Savelsbergh (2006) combined optimisation modelling with simulation to demonstrate the value of incentives. Other studies focused on identifying optimum distribution strategies (e.g. Netessine and Rudi, 2006 ; Li et al. , 2015 ), inventory rationing policy ( Ayanso et al. , 2006 ), delivery time slot pricing ( Yang and Strauss, 2017 ), and formulating new models to capture emerging practices, such as crowd-sourced delivery ( Wang et al. , 2016 ).

Thematic analysis

The grounded theory approach ( Glasser and Strauss, 1967 ) was used to code and classify emerging repeated concepts and terminologies via the qualitative data analysis software, MAXQDA version 12. The classification was based on the two categories defining LML models: LML distribution structures and their associated contingency variables. Coding of the data was conducted independently by two authors. The distinguishing terms and concepts were documented in a codebook; where their views differed, the issues were discussed until consensus was reached. Terminologies relating to each classification level were either derived from the extant literature or introduced in this paper to unify key concepts.

For the first category, the types of LML distribution structure are classified based on different levels of effort required by vendor and consumer: push-centric, pull-centric, and hybrid. A push-centric system requires the vendor to wholly undertake the distribution functions required to deliver the ordered product(s) to the consumer’s doorstep; a pull-centric system requires the consumer to wholly undertake the collection and transporting function; and a hybrid system requires some effort on the parts of both the vendor and consumer and is varied by the location of the decoupling point. A further breakdown divided the push-centric distribution system into modes of picking (manufacturer-based, distribution centre (DC)-based, and local brick-and-mortar (B&M) store-based); the pull-centric distribution system was divided into modes of collection from fulfilment point (local B&M store and information store); and the hybrid distribution system was divided into modes of CDP (attended collection delivery point (CDP-A) and unattended collection delivery point (CDP-U)).

The second category captures the associated contingency variables commonly used in existing studies. This study created a list of 13 variables that influence the structural forms of last-mile distribution: consumer geographical density, consumer physical convenience, consumer time convenience, demand volume, order response time, order visibility, product availability, product variety, product customisability, product freshness, product margin, product returnability, and service capacity. These variables determine the manner in which, or the efficiency with which, a distribution structure fulfils consumer needs while relating to the idiosyncrasies of product types.

These classifications facilitate the understanding of LML models and enabled future structural variables to be consistently categorised. Figure 1 serves to present a structural overview of the LML models reported in the literature.

Review of LML distribution structures

push: product “sent” to consumer’s postcode by someone other than the consumer;

pull: product “fetched” from product source by the consumer; and

hybrid: product “sent” to an intermediate site, from which the product is “fetched” by the consumer.

Table II summarises the corpus on LML distribution structures.

Push-centric system: n -tier direct to home

This study found that the push-centric system is the most commonly adopted distribution form. It typically comprises a number of intermediate stages ( n -tier) between the source and destination in order to create distribution efficiencies. The literature classifies three picking variants according to fulfilment (inventory) location: manufacturer-based (or “drop-shipping”), DC-based, or local B&M store-based (i.e. retailer’s intermediate warehouse or store). The destination can either be consumers’ homes or, increasingly, their workplaces ( McKinnon and Tallam, 2003 ). The mode of delivery can be in-sourced (using retailer’s own vehicle fleet), outsourced to a third-party logistics provider (3PL) ( Boyer and Hult, 2005 ), or crowd-sourced using independent contractors ( Wang et al. , 2016 ).

When selecting a distribution channel, retailers need to trade-off between fulfilment capabilities, inventory levels ( Netessine and Rudi, 2006 ), product availability and variety ( Agatz et al. , 2008 ), transportation cost ( Rabinovich et al. , 2008 ), and responsiveness ( Chopra, 2003 ). The nearer the picking site is to the consumer segment, the more responsive is the channel. However, this responsiveness comes at the expense of lower-level inventory aggregation and higher risks associated with stock-outs ( Netessine and Rudi, 2006 ).

Pull-centric system: consumer self-help

The literature also discussed two variants of the pull-centric system. Both variants require consumers to participate (or self-help) throughout the transaction process, from order fulfilment to order transportation. The first variant represents the traditional way of shopping at a local B&M store, with consumers performing the last-mile “delivery”. The second “information store” variant adopts a concept known as “dematerialisation”, substituting information flow for material flow ( Lee and Whang, 2001 ). This variant recognises that material or physical flows are typically more expensive than information flows due to the costs of (un)loading, handling, warehousing, shipping, and product returns.

This study found that despite the popularity of online shopping, there are still occasions where consumers favour traditional offline shopping. Perceived or actual difficulties with inspecting non-digital products, the product returns process, or slow and expensive shipping can deter consumers from online shopping ( Forman et al. , 2009 ). This study also demonstrates other benefits of a pull-centric system, including lower capital investments and possible carry-over (or halo) effects into in-store sales ( Johnson and Whang, 2002 ).

Hybrid system: n -tier to consumer self-help location

The rich literature here mainly compared different modes of reception. Variants typically entailed a part-push and part-pull configuration. For instance, the problem associated with “not-at-home” responses within attended home delivery (AHD) can be mitigated by delivering the product to a CDP for consumers to pick up. The literature discussed two CDP variants: CDP-A and CDP-U. It found that retailers establish CDP-A through developing new infrastructure development, through utilising existing facilities, or establishing partnerships with a third party ( Wang et al. , 2014 ). Other terminologies associated with CDP-A include “click-and-collect”, “pickup centre”, “click-and-mortar”, and “buy-online-pickup-in-store”. The literature showed that retailers establish CDP-U (or unattended reception) through independent RBs equipped with a docking mechanism, or shared RBs, whose locations range from private homes to public sites (e.g. petrol kiosks and train stations) accessible by multiple users ( McLeod et al. , 2006 ).

These CDP-A and CDP-U strategies are commonly adopted by multi/omnichannel retailers to exploit their existing store networks, to provide convenience to consumers through ancillary delivery services, and to expedite returns handling ( Yrjölä, 2001 ). Moreover, the research showed that integrating online technologies with physical infrastructures enables retailers to achieve synergies in cost savings, improved brand differentiation, enhanced consumer trust, and market extension ( Fernie et al. , 2010 ). Studies have also investigated the cost advantage and operational efficiencies of using CDP-U over AHD and CDP-A (e.g. Wang et al. , 2014 ). CDP-U reduces home delivery costs by up to 60 per cent ( Punakivi et al. , 2001 ), primarily by exploiting time window benefit ( Kämäräinen et al. , 2001 ).

Development of LML design framework

This section addresses the second and third research questions by developing a framework that contributes to LML design practice. The development process is governed by contingency theory ( Lawrence and Lorsch, 1967 ), in which “fit” is a central concept. The contingency theory maintains that structural, contextual, and environmental variables should fit with one another to produce organisational effectiveness. The management literature conceptualises fit as profile deviation (e.g. Jauch and Osborn, 1981 ; Doty et al. , 1993 ) in terms of the degree of consistency across multiple dimensions of organisational design and context. The probability of organisational effectiveness increases as the fit between the different types of variables increases ( Jauch and Osborn, 1981 ; Doty et al. , 1993 ). In this paper, the environmental and contextual variables are jointly branded as contingency variables since the object was to examine how these variables impact the structural form of LML distribution.

We developed the LML design framework in two steps. First, we synthesised a set of LML structural and contingency variables and established the relationship between these through a review of the LML literature. Second, we reformulated the descriptive (i.e. science-mode) knowledge obtained via the first step into prescriptive (i.e. design-mode) knowledge. We adopted the contingency perspective in combination with Romme’s (2003) approach to inform knowledge reformulation.

Synthesising LML structural variables

Product source refers to the location where products are stored when an order is accepted; it coincides with the start point of an LML network. It can be contextualised as a supply network member entity (manufacturer, distributor, or retailer). To illustrate, the computer manufacturer Dell (customisation services), online grocer Ocado (home delivery services), and the UK’s leading supermarket chain Tesco (click-and-collect services) source their products from manufacturer, distributor, and retailer sites, respectively.

Geographical scope concerns the distance separating the start point (product source) and the end point (final consignee’s preferred destination point) of an LML network. An LML network can be classified as centrally based (e.g. Dell Services) or locally based (e.g. Tesco’s click-and-collect).

Mode of distribution describes the delivery mode from the point where an order is fully fulfilled to the end point; it can be classified into three types: self-delivery (e.g. Tesco’s self-owned fleet for home deliveries), 3PL delivery including crowdsourcing (e.g. Dell Services), and consumer-pickup (e.g. Tesco’s click-and-collect services).

Number of nodes concerns the operations in which products are “stationary”, residing in a facility for processing or storage. As opposed to nodes, links represent movements between nodes. There are two variations in respect to this variable: two-node and multiple-node. For example, a two-node structure can be found in Dell’s direct-to-consumer distribution channel, where computers are assembled and orders fulfilled at the factory prior to direct home delivery. In contrast, multiple-node structures are reflected in “in-transit merge” structure where an order comprising components sourced from multiple locations are assembled at a common node. As a case in point, when consumer order a computer processing unit (CPU) from Dell along with a Sony monitor, a parcel carrier would pick up the CPU from a Dell factory and the monitor from a Sony factory, then would merge the two into a single shipment at a hub prior to delivery ( Chopra, 2003 ).

Synthesising LML contingency variables

Consumer geographical density: the number of consumers per unit area ( Boyer and Hult, 2005 ; Boyer et al. , 2009 ; Mangiaracina et al. , 2015 ).

Consumer physical convenience: the effort consumers exert to receive orders ( Chopra, 2003 ; Harrington et al. , 2016 ).

Consumer time convenience: the time committed by consumers for the reception of orders. This variable fluctuates according to the structural form of last-mile distribution ( Rabinovich and Bailey, 2004 ; Yuan and David, 2006).

Demand volume: the number of products ordered by consumers relative to the distribution structure ( Chopra, 2003 ; Boyer and Hult, 2005 ).

Order response time: the time difference between order placement and order delivery ( Kämäräinen et al. , 2001 ; Mangiaracina et al. , 2015 ).

Order visibility: the ability of consumers to track their order from placement to delivery ( Chopra, 2003 ; Harrington et al. , 2016 ).

Product availability and product variety: product availability is the probability of having products in stock when a consumer order arrives ( Chopra, 2003 ; Yuan and David, 2006).

Product variety is the number of unique products (or stock keeping units) offered to consumers ( Punakivi et al. , 2001 ; Punakivi and Saranen, 2001 ).

Product customisability: the ability for products to be adapted to consumer specifications ( Boyer and Hult, 2005 ).

Product freshness: the time elapsed from the moment a product is fully manufactured to the moment when it arrives at the consumption point ( Boyer and Hult, 2005 ).

Product margin: the net income divided by revenue ( Boyer and Hult, 2005 ; Campbell and Savelsbergh, 2005 ).

Product returnability: the ease with which consumers can return unsatisfactory products ( Chopra, 2003 ; Yuan and David, 2006).

Service capacity: the ability of an LML system to provide the intended delivery service and to match consumer demand at any given point in time ( Rabinovich and Bailey, 2004 ; Yuan and David, 2006).

Synthesising the relationship between LML structural and contingency variables

Firms that target customers who can tolerate a large response time require few locations that may be far from the customer and can focus on increasing the capacity of each location. On the other hand, firms that target customers who value short response times need to locate close to them.

This statement identifies the association between a structural variable, namely “geographical scope”, and a contingency variable, namely “order response time”. Within the literature, two variations emerged for each variable: centralised vs localised network for geographical scope and long vs short delivery period for order response time; i.e. centralised geographical scope corresponds to long response time, while localised scope is more responsive. As such, the findings demonstrate that by identifying connecting rationales and the variations at different levels for each variable, we can capture correlations between two sets of variables (i.e. structural and contingency). Continuing this procedure across relevant statements found in our corpus, Table IV summarises the outputs.

Reformulation from science-mode into design-mode knowledge

We adopted the approach by Romme (2003) to reformulate the descriptive knowledge (i.e. science-mode, developed in the previous section) into prescriptive (i.e. design-mode) knowledge so that the latter becomes more accessible to guide practitioners in their LML design thinking. This approach has previously been used to contextualise various design scenarios (e.g. Zott and Amit, 2007 ; Holloway et al. , 2016 ; Busse et al. , 2017 ). For example, Busse et al. (2017) employed a variant of the approach to investigate how buying firms facing low supply chain visibility can utilise their stakeholder network to identify salient supply chain sustainability risks.

if necessary, redefine descriptive (properties of) variables into imperative ones (e.g. actions to be taken);

redefine the probabilistic nature of a hypothesis into an action-oriented design proposition;

add any missing context-specific conditions and variables (drawing on other research findings obtained in science- or design-mode); and

in case of any interdependencies between hypotheses/propositions, formulate a set of propositions.

[If order response time delivered by an LML network is short, then the geographical scope of the LML network should be localised].

[For an LML network to achieve short order response time, localise the geographical scope].

Following similar procedures, the science-mode knowledge describing the relationships between structural and contingency variables can be reformulated to the design-mode shown in Table V . Collectively, the resulting design-mode knowledge constitutes a set of design guidelines for LML practitioners.

Main research issues, gaps, and future lines of research

Although the literature covered in this study thoroughly addresses LML structures, the extant literature has limitations. Based on this study’s findings, there are four main areas that require future study.

Operational challenges in executing last-mile operations

The extant literature has focused on the planning aspect of LML, rather than exploring operational challenges. Consequently, research often takes a simplistic chain-level perspective of LML in order to develop simplistic design prescriptions for practitioners. While this approach seems suitable in the pre-digital era, it is inadequate to capture the complexities of last-mile operations in the omnichannel environment ( Lim et al. , 2017 ). The focus on LML nodes as solely unifunctional is also inadequate ( Vanelslander et al. , 2013 ). Not acknowledging the multi-functionality of individual nodes limits understanding of how this variant works.

To address the limitations of extant research, we propose extending the current research from addressing linear point-to-point LML “chains” (e.g. Chopra, 2003 ; Boyer and Hult, 2005 ) to also addressing the “networks” context, where multiple chains are intertwined and more widely practised in the industry. A study of LML systems using 3PL shared by multiple companies is an example of necessary future research. We also recommend future research to address the multi-functionality of individual nodes in an LML system. A study that addresses the ability of an LML node to simultaneously be a manufacturer and a distributor introduces more structural variance and needs to be theoretically addressed.

Additionally, existing literature typically focuses on comparing structural variants’ performance outcomes and their corresponding consumer and product attributes. However, we argue that such focus limits our understanding of how LML distribution structures interact as part of the broader omnichannel system. Accordingly, an avenue for future research would employ configuration perspectives ( Miller, 1986 ; Lim and Srai, 2018 ) to complement the traditional reductionist approaches (e.g. Boyer et al., 2009 ) in order to more holistically examine LML models. Future studies could consider the structural interactions with relational governance of supply network entities, in order to promote information sharing and enhancing visibility, which are critical in omnichannel retailing ( Lim et al. , 2016 ).

Finally, while recent articles have started to examine the effects of online and offline channel integrations (e.g. Gallino and Moreno, 2014 ), limited contributions have been made to date to understand how retailers integrate their online and offline operations and resources to deliver a seamless experience for consumers ( Piotrowicz and Cuthbertson, 2014 ; Hübner, Kuhn and Wollenburg, 2016 ). We propose revisiting the pull-centric system variants in the context of active consumer participation to understand the approaches retailers can use to attract consumers to their stores. In this regard, the subject can benefit from insightful case studies to advance our understanding of the challenges retailers face, as well as the operational processes retailers adopt to meet these challenges.

Intersection between last-mile operations and “sharing economy” models

With the exception of one paper ( Wang et al. , 2016 ), the majority of the extant literature discusses conventional LML models. Given the rapidly growing sharing economy that generates innovative business models (e.g. Airbnb, Uber, Amazon Prime Now) in several sectors (e.g. housing, transportation, and logistics, respectively) and exploits collaborative consumption ( Hamari et al. , 2016 , p. 2047) and logistics ( Savelsbergh and Van Woensel, 2016 ), there is an immense research scope at the intersection between LML and sharing economy models. First, we propose empirical studies to examine how retailers can effectively employ crowdsourcing models for the last-mile and to show how they can effectively integrate these models into their existing last-mile operations, such as combining in-store fulfilment through delivery using “Uber-type” solutions. This type of study is critical for understanding the impact of crowdsourcing models on retail operations and for promoting their adoption. Second, papers addressing omnichannel issues ( Hübner, Kuhn and Wollenburg, 2016 ; Hübner, Wollenburg and Holzapfel, 2016 ; Ishfaq et al. , 2016 ) are emerging. The emergence of new omnichannel distribution models demands theoretical development and the identification of new design variables. These models include on-demand delivery model (e.g. Instacart), distribution-as-a-service (e.g. Amazon, Ocado), “showroom” concept stores (e.g. Bonobos.com, Warby Parker), in-store digital walls (e.g. Adidas U.S. adiVerse), unmanned delivery (e.g. drones, ground robots), and additive printing (e.g. The UPS store 3D print). Increasingly, we also observe the growing convergence of roles and functions between online and traditional B&M retailers, which suggests new integrated LML models. These new roles and functions demand future research. Finally, while collaborative logistics enable the sharing of assets and capacities in order to increase utilisation and reduce freight, its success rests on developing a logistics ecosystem of relevant stakeholders (including institutions). Consequently, exciting research opportunities exist to explore new design variables that capture key stakeholders’ interests at various levels ( Harrington et al. , 2016 ).

Data harmonisation and analytics: collection and sharing platforms

The literature review revealed that, to date, there has been a tendency towards geographical-based studies and the use of simulated data. For example, this review reports studies based in Finland ( Punakivi and Saranen, 2001 ), Scotland ( McKinnon and Tallam, 2003 ), the USA ( Boyer et al. , 2009 ), England ( McLeod et al. , 2006 ), Germany ( Wollenburg et al. , 2017 ), and Brazil ( Wanke, 2012 ), amongst others. While these studies contribute to generating a useful library of contexts, they are difficult to compare, given differences in geography and geographically based data collection and analysis methods. Moreover, the majority of the studies in this review (41.30 per cent) were based on modelling and simulated data with limited application to real-world data sets, which might suggest a lack of quality data sets. Simulated data limit the advancement of domain knowledge, thus the development of real-world data sets could significantly fuel progress. As such, more attention should be focused on developing data sets, e.g. through the use of transaction and consumer-level data, to gain insights into last-mile behaviours and to design more effective LML models.

Additionally, future studies should standardise data collection in order to address current trends in urbanisation and omnichannel retailing, which are changing retail landscapes and consumer shopping behaviours. This study recommends establishing a data collection framework to guide scholars in LML design, with scholars developing new competences in data mining analytics to exploit large-scale data sets.

Moving from prescriptive to predictive last-mile distribution design

Extant studies have derived correlations between variation of independent variables (e.g. order response time) and variation of dependent variables (e.g. degree of centralisation) to provide prescriptive solutions to the design of last-mile distribution structures. However, these relationships (both linear and non-linear) are often confounded by other factors due to the real-world complexities and they inherently face multicollinearity and endogeneity issues, including the omitted variable bias problem, which leads to biased conclusions. Moreover, model complexity increases as more variables are included, potentially causing overfitting. Given these complexities, researchers usually find immense challenges in untangling these relationships. In this regard, we offer several valuable future lines of research leveraging more advanced techniques for the design of last-mile distribution.

First, our review captured 13 contingency variables that influence the design of last-mile distribution. Future research could discuss other contingency variables and investigate the use of statistical machine and deep learning techniques to identify the most critical contingency variables and uncover hidden relationships to develop predictive models. Second, as urbanisation trends continue, more institutional attention is required on urban logistics focused on negative externalities (congestion and carbon emissions) driven by the intensification of urban freight. According to our review, there is insufficient attention paid to urban freight delivery, and we propose exploring archetyping of urban areas for the development of predictive models to guide the design of urban last-mile distribution systems.

Third, the developed design framework is based on the assumption that only one last-mile distribution structure may be adopted for a given scenario. As we observed in the omnichannel setting, it is common for retailers to concurrently operate multiple distribution structures. The interrelationships between the various structural combinations under the management of a single LML operator also present a potential future research direction.

Last, there is room for a combination of methods to more appropriately tackle the increasingly complicated and fragmented distribution networks in the omnichannel environment. Indeed, this research revealed only two papers in the corpus that have employed a mixed-method approach. Ishfaq et al. (2016) used case research and classification-tree analysis to understand the organisation of distribution processes in omnichannel supply networks, while Campbell and Savelsbergh (2006) combined analytical modelling with simulation to demonstrate the value of incentives in influencing consumer behaviour to reduce delivery costs.

Conclusions

This paper offers the first comprehensive review and analysis of literature regarding e-commerce LML distribution structures and their associated contingency variables. Specifically, the study offers value by using a design framework to explicate the relationship between a broad set of contingency variables and the operational characteristics of LML configuration via a set of structural variables with clearly defined boundaries. The connection between contingency variables and structural variables is critical for understanding LML configuration choices; without understanding this connection, extant knowledge is non-actionable, leaving practitioners with an overwhelming number of seemingly relevant variables that have vague relationships with the structural forms of last-mile distribution.

From a theoretical contribution perspective, this paper identifies attributes of delivery performance linked to product-market segments and the system dynamics that underpin them. This understanding of the interrelationships between LML dimensions enables us to classify prior work, which is somewhat fragmented, to provide insights on emerging business models. The reclassification of LML structures helps practitioners understand the three dominant system dynamics (push-centric, pull-centric, and hybrid) and their related contingency variables. Synthesising structural and contingency variables, the network design framework ( Table IV ) sets out the connections, which when reformulated ( Table V ), provide practitioners design prescriptions under varying LML contexts.

Accordingly, the literature review demonstrates that push-centric LML models driven by order visibility performance are ideally suited to variety-seeking market segments where consumers prioritise time convenience over physical convenience. Conversely, it shows that pull-centric LML models favour order response time, order visibility, and product returnability performance, which are widely observed in markets where consumers desire high physical convenience, low product customisability, and high product variety. Most interestingly of all, this study explains the emergent hybrid systems, where service capacity performance excellence is delivered through multiple clusters of contingency variables, which suits availability-sensitive markets and markets where consumers prioritise physical (over time) convenience.

This paper identifies four areas for further research: operational challenges in executing last-mile operations; intersection between last-mile operations and sharing economy models; data harmonisation and analytics; and moving from prescriptive to predictive last-mile distribution design. Research in these areas could contribute to consolidating the body of knowledge on LML models while maintaining the essential multidisciplinary character. We hope that this review will serve as a foundation to current research efforts, stimulate suggested lines of future research, and assist practitioners to design enhanced LML models in a changing digital e-commerce landscape.

Classification of literature review on LML models

Journal pool for reviewed papers

LML design framework

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1 Introduction

2 method of study, 3 literature classification and review, 4 findings and discussion, 5 conclusion and future research directions, conflict of interest, data availability statement, author contribution statement.

• List of tables
• List of figures

Digital supply chain: literature review of seven related technologies

Shuo Zhang , Qianhui Yu , Shuwei Wan , Hanyue Cao and Yun Huang

Macau University of Science and Technology, MACAO, Macau, China

* e-mail: [email protected] ** All the authors contribute equally to this paper.

Received: 10 December 2023 Accepted: 16 February 2024

This paper systematically reviews literature related with digital supply chains (DSC) and investigates the application status and development trend of different digital technologies in supply chain management. The review is conducted from the perspective of seven key digital supply chain technologies, i.e. Internet of Things (IoT) & Radio Frequency Identification (RFID), 5th Generation Mobile Communication Technology (5G), 3D Printing, Big data (BD), Blockchain, Digital Twins (DT), and Intelligent autonomous vehicles (IAVs). It highlights the main limitations and opportunities of the various DSC technologies, provides an overview of prior studies, and identifies knowledge gaps by outlining the advantages, weaknesses and restrictions of individual technology. The paper also aims at providing a development framework as a roadmap for the match of different digital technologies with different strategic goals.

Key words: Digital supply chain (DSC) / internet of things / RFID / 5G / 3D printing / big data / blockchain / digital twins / intelligent autonomous vehicles

© S. Zhang et al., Published by EDP Sciences 2024

The introduction of the idea of “digitalization” over the past few decades has resulted in numerous changes and advancements in a variety of fields. The effective use of digital technologies in supply chain management (SCM) has given rise to the concept of a “digital supply chain” (DSC), which transforms and enhances the established supply chain in numerous ways [ 1 ]. As businesses search for new methods to deliver products quickly, one of the most important DSC pillars will be the swift matching of information and suitable suppliers [ 2 ]. According to a Gunasekaran et al. [ 3 ] survey, 82% of CEOs in sectors with active supply chains want to boost corporate spending on digital capabilities. It is anticipated that the whole digital supply chain market will reach $13.679 million by 2030, creating a compound annual growth rate of 13.2% because of the COVID-19 pandemic's pressure on the supply chain sector to accelerate its move toward digital transformation [ 4 ]. According to Future of Supply Chain Survey [ 3 ], 61% respondents said technology was a source of competitive advantage, and 81% of chief supply chain officers planned to implement but had not yet begun actively to figure a digital supply chain roadmap. It is a challenging problem for businesses to find an appropriate transformation path and economical technology at the early stage of the supply chain's digital transformation.

A large number of researchers have summarized and reviewed existing research in the field of DSC, predicted future developments and challenges, and provided researchers with different perspectives on this issue. For example, Frank et al. [ 5 ] and Barata [ 6 ] concentrated on supply chain management (SCM) studies in the age of Industry 4.0. These papers offered recommendations for future study while summarizing the trends of Industry 4.0 technology in manufacturing firms. Bongomin et al. [ 7 ] and Meindl et al. [ 8 ] investigated the applications of Industry 4.0 technologies in the industrial sectors and the SCM areas. However, most of these studies focused on the manufacturing industry, and there were few literature reviews on other business fields, such as the medicine sector and food field. Raj and Sharma [ 9 ]investigated different aspects of the digital supply network. One of the most crucial DSC pillars was the capability to react quickly to demand as businesses search for faster methods to deliver goods and overcome fictitious delivery obstacles. Additionally, DSC has the capacity to achieve operational agility by making efficient use of the data gathered and models to quickly adapt to shifting environmental conditions. Through sensor arrays or other cutting-edge technologies, DSC offers ways to improve warehouse management and constantly monitor inventory levels to guarantee the right amount of inventory available to satisfy demand and anticipates future demands for products and services and purchasing trends [ 10 , 11 ]. These studies summarized certain characteristics of DSC. However, the literature summarizing the multiple technologies of DSC in a comprehensive way is insufficient.

The emerging scenarios of technology applications in future DSC demand high coverage, high data transmission rate, low latency, high security, high reliability and multi-device connectivity [ 12 ]. According to Tan and Sidhu [ 13 ], RFID and IoT played an important role in meeting customer needs in the supply chain. Farajpour et al. [ 1 ] stated that they had reviewed a large body of literature on pragmatic approaches to the implementation and utilization of 3D printing, digital twins, RFID and Intelligent autonomous vehicles. Brinch [ 14 ] proved that big data was an important innovation in DSC. Taboada and Shee [ 15 ] and Musigmann et al. [ 16 ] summarized that 5G and blockchain technologies had a wide range of applications in supply chain management. Hofmann and Rüsch (2017) highlighted that future research should investigate and explore the availability of technology for different business sectors or areas of application to comply with Industry 4.0. Another important study conducted by Ben-Daya et al. [ 17 ] reviewed recent literature on digital technology applications in SCM and found the following gaps: a lack of clear guidelines for IoT and cyber-physical system (CPS) adoption in a supply chain context, a lack of a roadmap that addresses supply chain problems in a new technological environment, and a number of obstacles to implementation. Therefore, this paper fills the gap by carrying out a systematic review, summarizing the advantages, characteristics, and challenges and integrating the current state, limitations, and future trends of the seven technologies, i.e., Internet of Things (IoT) & Radio Frequency Identification (RFID), 5th Generation Mobile Communication Technology (5G), 3D Printing, Big data (BD), Blockchain, Digital Twins (DT), Intelligent autonomous vehicles (IAVs), in manufacturing industry and some other business sectors. The following research questions are proposed.

RQ1. What are the main features of the technologies of DSC?

RQ2. What are the current states of research and practices of the technologies of DSC?

RQ3. What are the trends and limitations of the DSC technologies?

Figure 1 shows the distribution of published literature from 2003 to 2023. Research on digital supply chains began to grow significantly year on year from 2017, perhaps due to the popularity of integration of Internet and computer with business and industry. There will be over 800 published articles in 2022, which is certainly an emerging field. This search revealed that although practitioners widely identify and discuss, the concept of DSC with different technologies are still in the early stages of study in academics.

Figure 2 ranks the subthemes of DSC in order of popularity. The most popular theme is management, with 641 publications (19.055%). This is followed by the combination of DSC and engineering, including engineering electrical electronics and industrial engineering. In addition, scholars have focused a great deal of their attention on the fields of sustainability and computer science, which suggests the active role of DSC research in this area.

This study uses a systematic literature review (SLR) approach, consisting of three stages: planning, screening, and reporting, to conduct a qualitative literature assessment of the relevant literature [ 18 ]. The planning step includes developing search criteria and scoping the database. Relevant publications are located with the help of a detailed online search to collect, organize, and synthesize existing DSC literature with different technologies. The following major online databases were employed: Web of Science, Elsevier's Scopus, ScienceDirect (Elsevier), ProQuest (ABI/INFORM), and IEEE Xplore. The second step is screening. The relevant literature are obtained and filtered, classified, and analysed. This study classifies the literature into seven areas, including emerging technologies that are widely discussed in the DSC field, i.e. IoT & RFID, 5G, 3D Printing, Big data, Blockchain, Digital Twins, and Intelligent autonomous vehicles, which were not predetermined before the search but they had gradually emerged during the comprehensive reading process that took place while drafting this study. The reporting step involves a literature review that meets the requirements to facilitate a quick overview of the field for other scholars.

Currently, digital supply chains can solve the disruption problem caused by information asymmetry, by data analytics to improve efficiency, sustainability, traceability, and customer responsiveness. Digitalization also brings challenges to traditional supply chains, such as uncertainty in the application of technology, infrastructure development, and the organization's ability to control costs and risks. However, digital supply chains will surpass traditional supply chain management strategies in the future, enhancing communication between companies and their suppliers, and the ability to deal with unforeseen events [ 9 ]. After analysing articles on the DSC literature, the following review is based on the seven main technologies mentioned above.

3.1 IoT and RFID

3.1.1 overview.

With the development of the Internet, enterprises need to face increasing amount of information, and how to address information asymmetry and achieve effective supply chain coordination. The Internet of Things (IoT) connects all objects to facilitate interaction with each other, forming an interconnected network [ 19 ]. IoT enables the virtualisation of supply chains and brings several capabilities to improve supply chain management, such as product tracking, inventory accuracy and cost-saving [ 20 ]. Sensing layer of IoT, integrating different types of ‘things’, such as RFID tags, sensors, actuators, can collect supply chain-related data, which will increase the efficiency of supply chain management [ 21 ]. Supply chains embedded with IoT offer potential opportunities for Industry 4.0 transformation to improve operational efficiency and fulfil the requirements of the fourth industrial revolution [ 22 ].

3.1.2 Application of IoT and RFID

Most reviews for IoT technology have focused on the food and manufacturing supply chains. This section takes a look at IoT technologies with a focus on impacts on supply chain management in different application areas. IoT enables digitalization in agriculture. Ruiz-Garcia and Lunadei [ 19 ] argued that IoT-related technologies, such as radio frequency identification (RFID), had the potential to help transform numerous agricultural operations, which offered excellent chances for agricultural study, development, and innovation. Tzounis et al. [ 23 ] discussed the ongoing challenges and prospects of IoT data management in the agricultural supply chain. Yan et al. [ 24 ] introduced IoT into smart production and growth of fresh agriculture produce (FAP), to manage cold chain logistics during FAP transportation, monitor the quality, offer technical support for locating and tracking, and coordinate FAP supply chain.

IoT has also applied in the field of food supply chain management (SCM). It was suggested to use an IoT-based prepackaged food supply chain management platform, which tracked the prepackaged food supply chain in real time and eventually guaranteed a safe and secure food consumption environment [ 12 ]. During COVID-19, food shortages occurred in several South African countries, and implementing IoT is essential to resolve this issue and establish a sustainable food supply chain. IoT is compatible with other business processes and systems of supermarkets to predict the demand for food more accurately [ 25 ].

In the medical industry, with the emergence of epidemics, DSC has become crucial in the vaccine industry. To address concerns with demand forecasting, vaccination quality, and stakeholder trust in the vaccine supply chain, an intelligent system for vaccine monitoring was created. IoT technology was used by this system to track vaccine quality throughout the supply chain (Hu et al., 2023).

RFID technology may have an effect on the application of IoT-related technologies in SCM. Jangirala et al. [ 26 ] developed an RFID authentication protocol for the SCM utilizing a portable blockchain that offered a better trade-off between security and functional features, communication and computational costs for the 5G mobile edge computing environment. The use of IoT made it possible to gather a lot of data on the shop floor, and a comprehensive big data strategy was recommended to frequent trajectories from many RFID-supported shop floor logistics data [ 27 ].

RFID technology is often highlighted as a solution to one of the major inconsistencies between inventory and demand, as the complete transparency of inventory. For example, Heese [ 28 , 29 ] found that supply chain coordination was enhanced and inventory record inconsistencies were reduced due to RFID technology. Additionally, it can save lead times, increase ordering accuracy, improve inventory losses, and lower error rates. Fan et al. (2015) concluded that RFID can adjust the order quantity to reduce cost and increase inventory availability. They also found that retailers should focus more on tag prices and the percentage of fixed RFID expenses by the newsvendor model. In addition, RFID could be applied to production, planning, and scheduling. The movement of materials might be tracked in real time once RFID has rationalized the logistics within manufacturing sites such as warehouses and workshops [ 30 ]. Zhong et al. [ 31 ] applied RFID technology to the manufacturing sector, acquiring more accurate and logical assessments and eventually achieving real-time advanced collective intelligence. Lu et al. (2016) argued that the positioning of automated guided vehicles, widely used in manufacturing and supply chain management, could be enhanced by RFID technology.

A mass of theoretical models were proposed to better apply RFID technology in SCM. In the context of production and logistics, Wamba and Chatfield [ 32 ] proposed a contingency model that analysed five weighted factors and created value in a RFID-enabled SCM. Sari [ 33 ] found that integrating RFID technology in the supply chain could provide more advantages when participants engaged in more extensive collaboration. These advantages were more pronounced when market demand was less uncertain and delivery periods were longer. A framework was proposed for considering RFID applications from the perspective of location identification and remanufacturing process optimization [ 34 ].

3.1.3 Future trends and problems faced by IoT and RFID in DSC

There are some constraints to apply IoT and RFID. Companies might face technical or economic problems if tagged on individual items [ 35 ]. To make it more economically or technically sound, examples include increasing the readability of RFID tags, properly integrating RFID data collection and decision support tools, extending the life of active tag batteries, improving processing capabilities, and developing low-cost RFID tags. It is also noted that innovation will be the driving force behind RFID adoption, rather than merely cost reduction [ 36 ]. The deployment of IoT imposed different requirements on enhancing security in various domains [ 37 ]. These include, for instance, viruses and hackers launching malicious assaults and losing control over information. Future research could focus on improving data analysis tools, establishing an early warning system. Existed problems related to IoT and RFID are classified in Table 1 .

Problems description in IoT&RFID literature.

3.2 Big data (BD)

3.2.1 overview.

Big data (BD) describes a way of collecting, managing and analysing large amounts of data. BD is mostly referenced with the four Vs, i.e. volume, velocity, variety and veracity (Dietrich et al., 2014; Sathi, 2014). Volume describes the increasing size of data and data bases. Variety relates to the various forms of data: text, sound, video, multimedia, structured and unstructured, etc. Velocity represents the large amounts of data that arrive in real-time irregularly. If a further usage is necessary, the data arriving fast has to be handled. Veracity characterizes the data quality and accuracy, which determines both the credibility and suitability of the data [ 38 ].

Big data analytics (BDA) is the ideal way for decision-makers to cope with problems associated with huge volumes of data in today's competitive environment. Applications of BD in the supply chain are concentrated on managing complexity and assisting decision-making by optimizing supply chain visibility to handle risks and interruptions. The BDA and supply chain sectors should collaborate to create new, efficient models and methodologies as the complexity of global supply chain networks rises (Awwad et al., 2018). For example, companies use big data (BD) to control inventory and optimize and improve production processes, which helps them reduce internal costs associated with all processes [ 39 ].

3.2.2 Applications of BD

Giannakis and Louis [ 40 , 41 ] firstly advocated to combine BD with semantic web services in agent society to support the creation of multiagent-based management (MAS) system. Singh and Singh [ 42 ] constructed a theoretical framework and demonstrated that a company's past success in handling supply chain interruptions did not necessarily mean its future success in handling disruptions. Therefore, businesses can actively improve supply chain risk resilience within their organizations by investing in big data analytics capabilities [ 43 ].

By fusing sustainable supply chain concerns with BDA, Kaur and Singh [ 44 ] suggested an ecologically friendly purchasing and logistics model, which incorporated BD techniques into supply chain modelling to enable businesses to make the best choices between economic revenue and environmental responsibility by minimizing procurement and carbon emissions costs. Mageto [ 45 ] used the Toulmin argument model to establish a link between BDA and sustainable supply chains in manufacturing supply chains. This assisted business managers in choosing the best BDA tools for monitoring sustainable supply chain activities and enhancing competitiveness, performance, and productivity. Circular supply networks were additionally suggested by Choi and Chen [ 46 ], which focused on how large-scale group decision-making might materialize and promote circular supply chains in the age of BD.

It is believed that big data and predictive analytics (BDPA) are great tools for maximizing enterprise value and improving business performance (Gunasekaran, 2017). Alshawabkeh [ 47 ] discovered that the performance of the supply chain was greatly and favorably influenced by BA using a supply chain operations reference model. As a result, businesses can use big data's distinctive indicators, such as volume, speed, diversity, accuracy, and value, to enhance the efficiency of their supply chains. Dev et al. [ 48 ] proposed a heuristic method that can quickly process unstructured supply chain key performance indicator (KPI) data derived from simulation results and combined discrete event simulation, fuzzy analytical network processes, and the technique for order preference by similarity in a BDA environment to help find critical KPIs throughout the supply chain to guide managers in decision making. Integrating BDA into information quantifying and generation can support decisions making in new product development. Bag et al. [ 49 ] revealed that BDA management competence had a strong and considerable influence on the creation of new green products, and a weak but significant impact on sustainable supply chain outcomes and employee development.

3.2.3 Future trends and problems faced by BD in DSC

There are some advantages to BD technologies adoption to improve supply chain performance, build sustainable supply chains, and handle supply chain risk challenges [ 40 , 48 ]. However, the main challenges faced by BDA at the supply chain level are governance and compliance, integration and cooperation, information, IT capabilities, cybersecurity and infrastructure. Future trends in this area will focus on finding solutions to these four major issues so that BDA can raise the supply chain's value to the company. Existing literature related to BD and supply chain are listed in Table 2 .

Problem description in BD related literature.

3.3 Blockchain

3.3.1 overview.

In the supply chain, the information created during operation is opaque and retained in separate systems, reducing the efficiency of the entire supply chain. Blockchain technology (BCT) can effectively address the issue of information silos, provide more sources of information and higher-quality data information, lower the risk of data leakage, and ensure the security and effectiveness of the supply chain based on BD analysis (Behl, 2022). For instance, the food and pharmaceutical industries have started using blockchain technology to ensure the quality and safety of their products, which safeguards businesses' reputations and the safety of their clients. In the context of operations and supply chain management, the block may contain data or trigger a smart contract. The development of the block is shown from requesting a new transaction, transaction broadcasted to the P2P Network, verification to the completed block being appended to the chain. Through a simple buyer-supplier example as Figure 3 , the details in the block are shown about the data recorded at each stage and how the smart contract increases value to the process [ 50 ].

3.3.2 Applications of Blockchain

Blockchain in a SUPPLY CHAIN setting has been covered in an expanding corpus of literature [ 51 , 52 ]. This section focuses on three main applications of BCT, i.e. supply chain finance, traceability and security, and intelligent contract management.

An increasing number of businesses are starting to use BCT to support supply chain financing [ 53 ]. Supply chain finance problems are solved by blockchain for the transparency features in some different sectors. The fabric BCT platform was developed for logistics businesses in finance, where the private information of logistics firms was encrypted while smart contracts are created to simplify the loan and payback procedure for the companies (Fu et al., 2022). Additionally, Blockchain technology could help small- and medium-sized enterprises (SMEs), addressing their time-consuming, costly, and finance constrained issues. Su et al. (2022) used evolutionary game theory to create a three-party game model of SMEs and showed the dynamic developmental route of supply chain financing techniques with BCT.

Security and traceability are two primary applications of blockchain. Many instances of food fraud, contamination, and adulteration are documented every day in numerous nations or regions, highlighting the urgent need to modernize the decentralized supply chain paradigm. From farm to table, blockchain enables the tracking of products' basic materials and origin to ensure food quality for consumers [ 54 ]. Khanna et al. [ 55 ] developed a platform for supply chain in dairy industry using BCT, which could guarantee the security and traceability of dairy products across the supply chain. Li et al. (2022) proposed a new BCT-based model for quality and safety traceability management of traditional Chinese medicine supply chain. In short, distinct blockchain solutions have been studied and put into practice to address the issues of traceability and security, depending on the characteristics of different industries.

Smart contracts are another important blockchain application in the supply chain. BCT can assist in offering a digital solution and guaranteeing immutable and real-time tracking of all supply chain transactions, accompanied by conflicts of interest [ 56 ]. In addition, BCT can lower transaction costs by minimizing the number of intermediate proofs [ 57 ]. Chang et al. [ 58 ] suggested a blockchain-based framework with smart contracts. This facilitated the creation of multilateral collaboration networks among supply chain participants in addition to making it simpler to share and synchronise tracking data. To manage group purchasing organization (GPO) contracts in the healthcare supply chain, Omar et al. [ 59 ] created a blockchain-based system, given a cost analysis and a security study. To protect and manage participant data and automate the purchasing process of the oil supply chain, Haque et al. [ 60 ] proposed the blockchain Hyperledger concept, regarding the supply chain's upstream operations in transactions and smart contracts.

3.3.3 Future trends and problems faced by blockchain in DSC

BCT has a lot of potential in the supply chain industry, however there are still certain challenges and limitations. Since not all businesses are interested in pursuing openness, therefore, future research should focus on choosing or improving a consensus mechanism that is appropriate for BCT to distribute advantages within the system and achieve consistency. The constraints on the data format, make it challenging to store and perform traceability queries on unstructured data types, like video. In addition, BCT and IoT technology can be used together to assure the acquisition and reliability of data and achieve complete data authenticity. The literature related to blockchain are summarized in Table 3 .

Problem description in blockchain literature

3.4.1 Overview

Mobile communication technology plays an indispensable role in human production and life and has now reached its fifth generation, namely, 5th Generation Mobile Networks (5G) [ 61 ]. The fifth generation of mobile, cellular technologies, networks and solutions − 5G, has the potential to deliver at 10 Gbps data rates, less than 1 ms latency, improved network capacity supporting billions of devices, high levels of security and reliability, and substantial energy savings [ 62 , 63 ]. The global market for 5G technology is predicted to reach $277 billion by 2025, which is a bright prospect [ 64 , 65 ]. The New Radio Network enables the New Radio (NR) and the 5G Core Network (5GC) that are the two components of the 5G architecture [ 66 ]. 5G allows to digitalise many local processes in the supply chain, such as manufacturing, warehousing, and transportation. Local digitalization can lead to fully digitalized supply chains by facilitating digital processes at the network level [ 67 ].

3.4.2 Application of 5G

Although some 5G applications, such as cloud gaming and amusement video streaming, have a stronghold in the market for consumers, they have not yet gained widespread adoption in the industry, especially in logistics systems [ 68 ]. The main applications of Industry 4.0 in the logistics sector include identification and traceability, robots and autonomous systems for material handling, and decision support tools [ 69 ]. Khatib and Barco [ 70 ] developed a model to upgrade traditional logistics using 5G networks with the objective to satisfy support needs while optimizing the distribution of available resources for various types of traffic. Additionally, 5G can be combined with the radio real-time-locating system (RTLS). After interviewing twenty-eight industry experts, Küpper et al. [ 71 ] argued that 5G had high accuracy and could be developed into a worldwide universal positioning system. 5G is currently in the early stages of development due to the higher requirements for infrastructure development, including long-lasting battery life and low-latency networks. Therefore, 5G positioning technology is a future research trend to enable 5G technology to assist the logistics and manufacturing industries to improve efficiency and reduce costs in complex environments in the real world.

3.4.3 Future trends and problems faced with 5G in DSC

As an emerging technology, 5G has a significant impact on supply chain digitalization [ 67 ]. However, there still exist some unresolved issues. Since 5G technology is universal, it is necessary to enhance cross-industry collaboration between the manufacturing industry and the upstream and downstream entities of the supply chains. Besides, technical facilities need to be improved, including device battery life and network latency [ 72 ]. 5G, as a communications technology, is extremely data-intensive, which can improve the accuracy of predictions [ 73 ]. For researchers, model testing of complex environments in the field can be conducted to further determine its stability. The detailed challenges of 5G literature in the digital supply chain are shown in Table 4 .

Problem description in 5G-related literature.

3.5 3D printing

3.5.1 overview.

3D printing technology is also known as additive manufacturing (AM), which produces parts by adding material layer by layer onto a 3D solid computer model. Fixtures, cutting tools, coolants, and other auxiliary resources are not necessary. 3D printing technology is cost-effective and the higher the production volume is, the lower the average cost. Once applied on a large scale, it will inevitably reduce energy consumption and resource requirements, thus driving the digitization of traditional supply chains (Gebler et al., 2014). 3D printing can have remarkable impacts on downstream segments of the supply chain, such as manufacturing and distribution, as its integration with the supply chain is crucial in fulfilling the demands of customers of low cost and customization [ 74 , 75 ]. In particular, 3D printers make the supply chain more agile and flexible to react to changes in the marketplace, which reduce transport costs, holding costs and reduce waste in factories when demand is uncertain [ 76 ].

3.5.2 Application of 3D printing

There is great potential for 3D printing applications in the areas of manufacturing supply chain, medical product customization and environmental sustainability. Scholars tended to combine 3D printing technology with the medical field, such as designing customized medical implants [ 77 , 78 ]. In recent years, 3D printing technology has become more integrated with environmental sustainability and the circular economy in the manufacturing industry [ 79 , 80 ]. More academics and business managers are investing more in AM to achieve Industry 4.0 and smart factories.

Agnusdei and Del Prete [ 81 ] conducted a literature review that currently divided 3D printing technology into three research categories, i.e. technologies and materials, additive manufacture for sustainability, and additive manufacture for design. Each of these three categories can be linked to the digital supply chain [ 82 ]. Beltagui et al. [ 83 ] described the impact of AM technologies in the supply chain, from internal operations to society. They provided a model outlining the various levels of AM adoption and contended that consistency should be guaranteed at all levels, including the operational level, strategic level, and contextual level, to accomplish AM's contribution to the organization, supply chain, market, and society. Scholars have empirically studied the performance of AM in SMEs [ 84 , 85 ]. They recognized local production of highly customized goods by AM had significant advantages for SMEs, including increased flexibility, easier logistics management, and lower production costs.

Regarding environment and sustainability, 3D printing technology has become very useful in waste management. Thomas and Mishra [ 86 ] proposed a circular sustainable circular economy system in the plastic industry that helped alleviate the problem of carbon emissions and maximize profit by reducing waste and ordering costs. Customers could refer to circular indexes when choosing commodities. There is a great potential opportunity for 3D printing technology to drive the development of the “reverse supply chain”.

Sun et al. [ 87 ] implemented 3D printing technology in the food supply chain, providing means for tailoring and modifying foods processing based on customer-specific requirements, thus enabling food manufacturing processes wherever necessary.

3.5.3 Future trends and problems faced by 3D printing in DSC

The gradual expansion of 3D printing technology from medical applications to manufacturing, logistics and transportation has brought opportunities as well as challenges to the supply chain. Firstly, 3D printing technology has been extensively used in circular supply chains or SMEs, while it may not be advantageous to non-manufacturers or large firms. Another barrier for companies to use this technology is the high cost of materials, equipment, operation, purchase, depreciation, and maintenance. Besides, the industry-wide unreliability of quality assurance procedures is another concern. In AM, there is a general lack of appropriately trained workers, and little chances for cooperation and idea exploitation [ 88 ]. Lastly, it is worth noting that the various sectors of business, as well as the government, need to legislate on the intellectual property rights of 3D printing technology and develop norms and guidelines to address various issues in a timely manner [ 89 ]. The problems faced by 3D printing technology are shown in Table 5 .

Problem description in 3D printing literature

3.6 Digital twins (DT)

3.6.1 overview.

The digital twins (DT) concept was first developed based on Product Lifecycle Management in aerospace engineering, but it has become a booming area since incorporated with other fields [ 90 ]. The DT concept typically involves the following three components: (1) a physical object; (2) its ‘digital’ or ‘virtual’ representation; and (3) the way in which the two and DTs are connected. The concept of a “digital twin” is broader than just a virtual digital representation of a system. It seeks to real-time digitally record the essential elements of a dynamic physical system. A digital replica of an actual logistics system reflects the whole supply chain network in real time at any given time [ 91 ]. The digital twins supply chain (DTSC) may replicate past, present, and future events using historical data. Decision-makers can simulate a supply chain before making a choice, increasing operational efficiency, by giving a thorough understanding of the real-time activities of all pertinent entities, such as inventories, purchasing, and sales [ 90 , 92 ]. The digital model and the physical status of a DTSC are frequently synchronized, real-time, system-level instantaneous optimization of available information (Olsen and Tomlin, 2020). A basic decision scheme in a DTSC is shown as Figure 4 .

3.6.2 Application of DT

DT can be used in sectors including circular supply chains, food supply chains, and international port management, etc. The most notable application for DT is production planning and control. Others include shop floor management, vehicle scheduling, warehouse management, freight load planning, etc. [ 93 ]. This section mainly reviews the DT-related literature in food supply chain and the pharmaceutical SC.

Sharma et al. [ 94 ] created DT for robotic work cells which utilize a robotic drive system and robot simulation software tools, for food retail supply chain during the epidemic. Binsfeld and Gerlach [ 95 ] developed a quantitative technique to evaluate the benefits of DTs and assess the impact of DT on supply chain management and logistics performance in multi-echelon inventory management of an organic food SC.

In pharmaceutical SC, Spindler et al. [ 96 ] investigated the benefits of a simulation-based model and evaluated the potential for the adoption in a scalable Digital Twins system. Park et al. [ 97 ] suggested a distributed DT simulation-based cyber physical production systems to reduce the differences among assets and develop a production plan based on the results of DT simulation.

3.6.3 Future trends and problems faced by DT in DSC

The supply chain sector is thriving with the digital twins, but there still lack of a common understanding of the word and literature to explore its potential application areas [ 98 ]. Future research is necessary to address the fact that DT and digital supply chain twins are not consistently defined academically. DT is rarely employed in service sectors of SC, such as purchasing, logistics, distribution, and retail, even though existing DT is predominantly used in manufacturing [ 99 ]. Additionally, since DT will affect multistructural composition of the supply chain network, such as changes in organizational structure, financial situation, and information flow [ 100 ], logistics executives are hesitant to implement DT because it is difficult to compare the effects of use in a reasonable cost-benefit manner. Table 6 lists the DT-related literature.

Problem description in IAVs literature.

Problem description in DT literature.

3.7 Intelligent autonomous vehicles

3.7.1 overview.

Intelligent autonomous vehicles (IAVs), also known as Internet of Vehicles (or Vehicles of Tomorrow), are completely computer-controlled depending on their surroundings and decision-making, and can run independently without human supervision [ 101 , 102 ]. As Figure 5 , the intelligent automated guidance can be achieved by: first, awareness of surrounding context, through radars, cameras or other embedded sensors; second, interpretation of the sensory data retrieved into potential manoeuvres, through analysing and compiling a list of possible actions [ 104 ]. Compared with manual or traditional vehicles, IAVs provide intrinsic value to flexible supply chains and have advantages of enhanced safety, faster delivery times, less traffic congestion overall, and lower CO 2 emissions [ 105 ]. As a result, this will break restrictions on staff workers availability and work schedule control [ 106 ]).

3.7.2 Application of IAVs

IAVs can be well integrated with digital supply chains and incorporated into all aspects of the supply chain, such as flexible and sustainable supply chains. According to Tsolakis et al. [ 107 ], simulation tools and real IAV test beds are preferred for validating the design of digital supply chains.

Flexible supply chains are playing an increasingly significant role in the manufacturing sector. IAVs are seen as a good solution for flexible manufacturing systems (FMSs) to reduce the repetitive and labor-intensive manual handling operations in manufacturing processes [ 108 ]. This performance is widely used by fresh agricultural products (FAP) supply chain to demonstrate great agricultural achievements such as intelligent farming, mechanical weeding, fertilization and fruit and vegetable harvesting [ 109 ]. Cronin et al. [ 110 ] suggested a plan for an integrated AIVs material handling system based on user requirement specifications and function requirement specifications. This system demonstrated the potential opportunity for AIVs applications in the supply chain to enable low-cost, autonomous material handling processes.

In addition, IAVs can be used in connection with sustainable supply chain networks, as they can improve the economic, environmental, and societal sustainability aspects of supply chain systems [ 111 ]. By matching the vehicle characteristics with a software framework that establishes vehicle characteristics as membership variables in the simulation model, business managers and academics can incorporate commercial IAVs into the supply chain ecosystem. Vehicle navigation, planning, and scheduling tasks can be further implemented at the control level, enabling an improved sustainable performance [ 101 ].

3.7.3 Future trends and problems faced by IAVs in DSC

The desire to boost pertinent accuracy and efficiency is still a major driving force behind the expanding trend of IAVs supply chain adoption [ 112 ]. Due to the advantages of IAVs, they have received much attention from the manufacturing industry. However, IAVs are not used on a large scale because there are still some problems to be solved. First, since the majority of the models have only undergone laboratory testing, it is still necessary to verify their stability in complex environments. Second, the construction of infrastructure needs to be improved. For instance, there may not be enough charging piles in some impoverished regions, which could cause management challenges subsequently. In addition, the high cost is also a barrier for enterprises to use IAVs, such as hardware, software installation, and staff training. Businesses need to closely follow regulations regarding IAVs and upgrade their computer programs. All the problems with intelligent autonomous vehicles in the supply chain are listed in Table 7 .

In this section, we provide a number of observations regarding the application of seven technologies in digital supply chain management and identify the gaps in the literature with respect to the potential of the technologies in helping address supply chain management challenges.

One of the major concerns in digitizing the supply chain with the technologies is the high costs that include database, staff training, hardware, software installation, and supporting infrastructure, etc. In the usage process, there are system maintenance and upgrading costs, which are not negligible. Attracting and retaining the proper personnel is key to maximize the company's technology investments. However, budget constraints and staff turnover are the barriers to adopt the digital technologies for the supply chain.

Another concern in supply chain digitization is standardization. The lack of standardization for global digital transformation has severely restricted the digital transformation for the enterprise in supply chains, which has pushed the governments and relevant international organizations to further establish a clear reference framework and guidelines for enterprises to digitalize their supply chain [ 113 ]. Through standardization, information interoperability and data exchange and sharing can be achieved. A unified standard can link the systems of one company with other systems when collaboration with other parties is required to reach the highest level of efficiency within the supply chain. The establishment of a standardized DSC management platform for different digital technologies is also one of the future trends.

The large amount of data sharing and exchange in digital supply chain applications brings security and privacy issues. Nevertheless, the industry is currently concentrated on using high-precision data analysis algorithms, and the application of data privacy protection algorithms is still in its infancy [ 114 ]. The problems of an untrustworthy transaction payment environment, easy data loss, and difficult traceability have largely restricted the further development and implement of digital supply chain technologies. Therefore, developing a privacy assurance system to guarantee the integrity and confidentiality of data is one of the key research directions in the future.

The application of digital technologies to the agri-food supply chain is another area of great interest to both academics and practitioners. From a management perspective, the agri-food supply chain presents enormous challenges since it deals with perishable goods, safety is an important concern, and there are many actors involved in the chain. This market niche for healthier products, especially fruits and vegetables, has increased amounts of agri-food surplus, waste, and loss (SWL) generated during production, shipping, storage, and processing [ 115 ]. It is estimated that approximately 33% of the food produced globally is lost or wasted annually, with agri-food SWL from fruits and vegetables accounting for about 22% of this loss [ 116 ]. Furthermore, agri-food supply chains are an essential component of all economies and cannot be offshored. Thus, preventing avoidable agri-food SWL throughout the supply chain is a compelling potential of the application of digital technologies and research in this area is expected to grow.

Moreover, scalability is the ability of a system or software to increase its capacity and maintain operational stability in response to user demand [ 117 ]. Rare literature in digital supply chains has focused on scalability. Scalable supply chain can leverage digital technologies to improve visibility, expedite decision-making processes, enhance real-time communication [ 118 ]. Additionally, majority of the research activities are in two of the supply chain processes in digitization, namely make and deliver and isolate digital technology. To extend the supply chain with more activities and digital technologies is the future trend.

This paper reviews latest research articles in the application of digital technologies to areas of supply chain management and various supply chain processes. As such, we explored IoT & RFID, 5G, 3D Printing, BD, Blockchain, Digital Twins, and Intelligent autonomous vehicles in an SCM context, presented its main technology enablers and current status. We organized the seven digital technologies applications around key supply chain processes. We identified the gaps in the literature with respect to the potential of the seven technologies to help supply chain managers better understand the status, applications, benefits, and drawbacks of digital technologies. The aim is to provide an informative overview of the latest development in this emerging and growing area, which is of interest to both researchers and practitioners. We conclude this paper by pointing out several limitations for future research to address.

Due to the rapid and mature development of emerging technologies in the last decade, this paper focuses primarily on the review of pertinent papers from the last decade and less on literature from a decade ago.

Most of the papers in this literature review are based on mainstream academic journals in the field of supply chain and related technologies because the papers published in such journals are more authoritative, but this may lead to some other important types of research being neglected.

Using input keywords, the aforementioned databases were searched to produce the findings of this review. Studies with marginally different inputs may have gone unnoticed because searches are so sensitive to these keywords.

Augmented Reality, Cloud Computing, Nanotechnology, Omni Channel, and other technologies are not covered in this paper. Future research could make supplement of these literature to digital supply chain.

The research is funded by Macau University of Science and Technology (Project No. FRG-22-107-MSB).

The authors declare that they have no competing interests.

All data generated and analyzed during this study are included in this article.

Shuo Zhang, Qianhui Yu, Shuwei Wan, Hanyue Cao contributed to writing—original draft preparation; Yun Huang proposed the method and reviewed, revised; and acquired the funding.

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Cite this article as : Shuo Zhang, Qianhui Yu, Shuwei Wan, Hanyue Cao, Yun Huang, Digital supply chain: literature review of seven related technologies, Manufacturing Rev. 11 , 8 (2024)

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Open Access

Peer-reviewed

Research Article

Remanufacturing and channel strategies in e-commerce closed-loop supply chain

Roles Conceptualization, Data curation, Methodology, Project administration, Writing – original draft

Affiliation China Telecom Research Institute, Guangzhou, 510630, China

Roles Formal analysis, Methodology, Software, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

Roles Resources, Supervision, Validation, Writing – review & editing

Affiliations School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China, School of Modern Post (School of Automation), Beijing University of Posts and Telecommunications, Beijing, 100876, China

• Ying Shi, 
• Rong Ma, 
• Tianjian Yang

• Published: May 16, 2024
• https://doi.org/10.1371/journal.pone.0303447
• Reader Comments

This paper studies the recycling and remanufacturing mode and sales channel issues in the closed-loop supply chain. Specifically, this study establishes an e-commerce closed-loop supply chain consisting of a manufacturer and an e-commerce platform, and divides the recycling model into recycling by the manufacturer or recycling by the platform. Considering two common sales models in e-commerce platforms: the resale model and agency model, combined with the recycling model, four different research scenarios are formed. We use backward induction to solve the Stackelberg game problem and explore the remanufacturing and channel strategies of the manufacturer and the e-commerce platform. The research results show that for the manufacturer, under the same recycling model, when consumers’ preference for remanufactured products and the sensitivity of recycling volume to recycling prices are low, he will prefer the resale model. Under the same sales model, the manufacturer always prefers the recycling model in which he is responsible for recycling. However, the choice of platform is contrary to that of the manufacturer. In the resale model, both the manufacturer and the platform will choose to recycle by themselves, which cannot achieve a win-win situation. Under the agency model, when consumers’ preference for remanufactured products is high and the sensitivity coefficient of recycling volume to recycling price is low, supply chain members can achieve a win-win situation, and the scope of the win-win situation decreases as the unit production cost of new products increases. In addition, rising consumer preference for remanufactured products will lead to lower consumer surplus.

Citation: Shi Y, Ma R, Yang T (2024) Remanufacturing and channel strategies in e-commerce closed-loop supply chain. PLoS ONE 19(5): e0303447. https://doi.org/10.1371/journal.pone.0303447

Editor: Mohammad Masukujjaman, Management and Science University Faculty of Business Management and Professional Studies, MALAYSIA

Received: January 24, 2024; Accepted: April 24, 2024; Published: May 16, 2024

Copyright: © 2024 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Research on supply chains has long had a strong foundation in sustainable development, and many studies have been conducted based on closed-loop supply chains. A closed-loop supply chain (CLSC) refers to the recycling of goods from consumers and the reuse of all or part of the goods to obtain added value. Guide and Van Wassenhove define a closed-loop supply chain from a business perspective as: a system designed, controlled and operated to maximize value creation throughout a product’s lifecycle and to dynamically recover value from different types and quantities of returns over time [ 1 ]. Recently, the "China Renewable Resource Recycling Industry Development Report (2023)" released by the China National Resources Recycling Association pointed out that the total volume of renewable resource recycling in China in 2022 was approximately 371 million tons [ 2 ]. This number highlights the importance of renewable resource recycling and its huge potential for the sustainable development of our country.

The continuous development of the Internet has promoted the development of the e-commerce market. According to the "China E-Commerce Report (2022)" released by the Ministry of Commerce, data from the National Bureau of Statistics of China show that in 2022, the national e-commerce transaction volume reached 43.83 trillion yuan, which is an increase of 3.5% over the previous year. The discussion on CLSC has expanded to include the context of e-commerce due to its rapid growth. Product recycling and the sale of remanufactured products are common in e-commerce, and there are different recycling entities. On the one hand, e-platforms can be responsible for recycling. For example, large e-commerce platforms JD.com and Taobao both provide recycling services to consumers, including door-to-door pickup and in-person quality inspection services. There are also specialized second-hand product recycling platforms, such as Xianyu and Zhuanzhuan. On the other hand, as the main body of product manufacturing and sales, manufacturers may also serve for recycling. For example, Bright Milk recycles milk cartons, and Uniqlo also announced product recycling and reuse activities on its official website. Moreover, to encourage consumers to recycle products, some manufacturers have also developed recycling incentive mechanisms. For example, the German water purifier brand BRITA recycles filter elements from consumers. Scores can be accumulated based on the number of recycling filters, which can be used to redeem new water purifier filter elements. It can be seen that different recycling models are both common in CLSC. Trade-in services are also a common recycling incentive mechanism, where consumers can exchange old (used) products for new ones [ 3 ].

In the e-commerce platform supply chain, in addition to the selection of recycling and remanufacturing entities, there is also an important business strategy, that is, the selection of sales modes. Existing studies have discussed the CLSC decision-making issues of different recycling entities, but did not consider the different sales modes in e-commerce platforms [ 4 ]. The resale mode and agency sales mode are the two main sales models on e-commerce platforms. As an online retailer, the platform purchases goods wholesale from manufacturers and resells them to customers. This is a resale mode, such as JD.com ’s self-operated sales. The e-commerce platform operates as an online market, offering customers and manufacturers a trading platform and charging manufacturers commissions. This is an agency mode, such as the Taobao platform. In the agency mode, producers can interact directly with customers, whereas in the resale mode, the only role is to produce goods and wholesale them to the platform. What impact will different distances from consumers have on the choice of recycling mode? Is there a relationship between the way manufacturers sell their products and their choice of recycling mode? There is no clear conclusion yet on this issue. China’s two leading e-commerce platforms, JD.com and Taobao platforms both provide product recycling services to consumers. There is a lack of discussion on how different recycling models perform under different sales modes, and what impact different model combinations have on the profitability of supply chain members.

This study seeks to answer the following questions in light of the aforementioned theoretical and practical motivations:

(1) In the e-commerce closed-loop supply chain, what are the optimal recycling models for the manufacturer and the e-commerce platform under various sales models?

(2) Faced with the same recycling model, how do supply chain participants make channel strategies?

(3) Is there a strategy acceptable to both supply chain members to create a win-win scenario?

(4) What impact will different strategies have on consumer surplus?

To solve these problems, this paper establishes an e-commerce closed-loop supply chain model consisting of an e-commerce platform and a manufacturer. Considering two recycling models, the manufacturer is responsible for recycling and the e-commerce platform is responsible for recycling, as well as the two sales models of resale and agency on the e-commerce platform. Combining different strategies, four research scenarios are constructed. We establish the profit functions of the manufacturer and the e-commerce platform in each scenario respectively, and obtained the equilibrium solutions in each scenario by solving the Stackelberg game problem. By comparing the equilibrium solutions of different scenarios, the strategic choices of supply chain members are obtained. This study discusses the different sales models of the e-commerce platform from the perspective of different recycling entities in the closed-loop supply chain, supplementing the shortcomings of the existing literature by offering management insights into the operational choices made by participants in the closed-loop supply chain for e-commerce.

The remainder of this paper includes the following. Section 2 summarizes the literature relevant to this study. Section 3 explains the establishment of the model. The four models are discussed in detail in Section 4. Section 5 discusses the strategies of supply chain members. Section 6 compares the consumer surpluses in four models. The study findings are summarized in Section 7.

2. Literature review

The literature related to this study is mainly about the closed-loop supply chain and e-commerce sales modes.

2.1. Closed-loop supply chain

Savaskan et al. studied the closed-loop supply chain problem considering product remanufacturing, considered three different recycling modes and designed a coordination mechanism [ 5 ]. Many studies on CLSC are based on this. The closed-loop supply chain was defined by Guide and Van Wassenhove [ 1 ] and how it was developing from a commercial standpoint was summed up.

Scholars have discussed CLSC issues in a competitive environment. When there are competing retailers in the supply chain, supply chain members have different choices for direct and indirect recycling modes in decentralized and closed-loop supply chain structures [ 6 ]. Manufacturers have an incentive to remanufacture when there are competitors in the supply chain, but when competition declines, this incentive will diminish. In contrast, service competition hurts both competing manufacturers, while price competition might boost remanufacturers’ profitability [ 7 ]. A three-level CLSC network model with competition was developed by Qiang et al., and the effects of competition intensity and other variables on equilibrium were explored [ 8 ]. Both direct horizontal competition between two manufacturers and vertical competition between manufacturers and retailers were examined by Patare and Venkataraman [ 9 ]. They discovered that a higher level of competition can raise product quality.

As an important part of CLSC, consumer behavior has also attracted the attention of scholars. To optimize their utility, consumers may have varied preferences for different products due to the differences between new and remanufactured products. Scholars have done some studies taking into account customer preferences. The ideal price for both new and remanufactured goods was investigated by Abbey et al. using data from large-scale trials conducted in consumer preference models [ 10 ]. A rise in customer demand for remanufactured goods can boost supply chain efficiency overall and positively affect both product and price points. However, remanufactured products will suffer if manufacturers penalize e-commerce platforms given that they are concerned about fairness [ 11 ]. The proportion of green consumers among total consumers and their green preferences are also beneficial to manufacturers’ economic benefits and have an important impact on manufacturers’ green product segmentation strategies [ 12 ]. Due to the existence of consumer preferences, the factors that affect consumers’ willingness to purchase remanufactured products are also a question worth studying. Agrawal et al. conducted a behavioral experiment [ 13 ]. Hazen et al. examined customers’ propensity to switch from buying new items to buying remanufactured ones by combining macro-level pricing, government incentives, and environmental benefit variables with the moderating effects of micro-level consumer attitudes [ 14 ]. Consumers’ propensity to buy remanufactured products is greatly influenced by several factors, including perceived risks, costs, and moral obligations and responsibilities [ 15 ].

Due to the supervision of corporate social responsibility and the pursuit of sustainable social and economic development, the government also plays an important role in CLSC, which has attracted the attention of scholars [ 16 , 17 ]. The government’s management of CLSC mainly has two aspects, one is supervision and the other is subsidies. In terms of government regulation, the influence of recycling regulations on the remanufacturing business was examined by Esenduran et al. [ 18 ]. The trading supervision of carbon emission reductions by the government influences the decisions made in CLSC. By controlling the price of carbon trading, the government can have an impact on supply chain choices [ 19 ]. Carbon taxes are also a way for governments to regulate in order to promote sustainable development. Ma and Liu [ 20 ], and Li et al. [ 21 ] both discussed the carbon tax issue. Wang and Hong analyzed the best pricing and recycling practices in a CLSC with two recycling channels where the government provides subsidies to the manufacturer or two recyclers [ 22 ]. They found that subsidy policies stimulate consumption and increase the profits of supply chain members. Furthermore, government subsidies can regulate CLSC’s profit distribution. The effect of the subsidy will depend on the subsidy rate as well as the subsidy ceiling [ 23 ].

An essential component of CLSC is returns. Its objective is to recycle waste or products that don’t satisfy customer expectations from customers to a specific point in the supply chain where they can be processed. Researchers have studied return policies in great detail [ 24 , 25 ]. Additional shipping charges are frequently associated with the return process and are typically covered by the customer or retailer. It makes sense for customers to shoulder the cost when considering the real number of returns—that is, whether the volume of returns is high or the percentage of non-defective products among the returned products is low [ 26 ]. Zhang et al. conducted a study on the issue of defective products and waste returns. They concluded that, to lower the rate of defective product returns, it is not desirable to incur significant costs to improve product quality [ 27 ]. The best return policies vary depending on the supply contract. Comparative analysis revealed that buy-back and wholesale pricing contracts have stricter return policies than quantity discount contracts, which also result in higher demand and return requirements [ 28 ]. In an effort to encourage consumers to adopt sustainable practices, managers have created various programs for recycling used goods. To confirm the efficacy of these programs, Taleizadeh et al. set up various scenarios for examination. They discovered that acceptable return rules can accomplish supply chain coordination as well as environmental protection [ 29 ].

The various entities in charge of recycling and remanufacturing in CLSC have been covered in studies. Recycling activities can be carried out in a CLSC under cap-and-trade regulation by retailers, manufacturers, or other parties, and the carbon emissions under various regimes vary [ 30 ]. Manufacturers or sellers are able to remanufacture waste WEEE products under the WEEE CLSC. The best options for remanufacturing companies vary depending on the funding policies of the government [ 31 ].

Scholars have also addressed many aspects of the e-commerce CLSC, including platforms that offer extra warranty services, low-carbon e-commerce closed-loop supply chains, and differential pricing [ 11 , 32 , 33 ]. Blockchain technology is closely related to e-commerce. Ma and Hu talked about how platforms could maximize the synergy between "blockchain + sales formats" to enhance CLSC’s ESS (economic, social, and environmental) performance [ 34 ]. Liu et al. discussed the recycling channel structure selection problem of the e-commerce CLSC. The difference from this study is that they discussed three different recycling models, but did not consider different sales models [ 35 ].

Although studies related to CLSC have considered different recycling and manufacturing entities and have also studied channel issues, they haven’t looked at the topic of e-commerce-related sales channel strategies.

2.2. E-commerce sales mode

The resale model and agency model are the two main sales models in e-commerce. How to choose the optimal model is an important strategic issue faced by supply chain members. Abhishek et al. constructed a stylized theoretical framework to solve a crucial issue for e-tailers: When should an agency sales strategy be employed rather than a more traditional resale approach [ 36 ]? Based on this, many studies on e-commerce sales modes have been carried out. Tian et al. compared the performance of different sales modes in the e-commerce supply chain, offering guidance for mode selection and important references for subsequent research [ 37 ]. Diverse aspects have been incorporated by scholars into the channel models research. Manufacturers tend to select the agency model when there are detrimental effects from online channels to offline channels and the online channels are not competitive [ 38 ]. The choice of channels by manufacturers is significantly influenced by the presence of the secondary market. One important consideration is the price differential between new and old products. Manufacturers who want to sell expensive products will opt for the reselling model if there is a secondary market [ 39 ]. Hu et al. further studied the interaction between suppliers’ strategies for introducing market channels and e-tailers’ sales mode choices, they found that channel competition also affects the choice of sales model [ 40 ].

Related research has also extended to the situation with more supply chain members. In this case, there is generally horizontal competition between manufacturers or retailers. Researchers looked at the case of an e-commerce platform with several competitors and concluded that it makes more sense for manufacturers to choose resale on the platform rather than sell directly to customers. Simultaneously, if a manufacturer just opts for direct sales channels, then another manufacturer ought to run both platform resale channels and direct sales channels [ 41 , 42 ]. In contrast to platforms, Wang et al. talked about the emergence of independent internet retailers. The findings of the study indicate that when direct sales are low-cost, manufacturers will opt for them. Otherwise, commissions and competition have an impact on the choice of agency and resale models [ 43 ]. The above studies are all based on information symmetry, and scholars have also discussed scenarios of information asymmetry [ 44 , 45 ]. Sun et al. investigated the influence of sales form [ 46 ]. Under different sales structures, e-commerce platforms have varying incentives to reveal demand information to upstream. For instance, under the agency model, information regarding negative demand should not be shared, and information about strong market demand should not be disclosed during resale [ 47 ].

Hot issues such as logistics services and live sales in e-commerce are inseparable from discussions combined with sales models [ 48 ]. The platform’s choice of sales model will be influenced by its level of logistics. The hybrid sales model is the ideal option when the platform’s degree of logistics significantly surpasses that of third parties [ 49 ]. The platform’s decision to establish market channels is also affected by the level of logistics services. With high logistics service sales, platforms are more willing to introduce market channels [ 50 ]. In addition, as an emerging type of e-commerce, the issue of live streaming sales and e-commerce sales modes has also attracted the attention of scholars. Supply chain participants will select the agency model simultaneously when evaluating the competition and spillover impacts of live broadcasting under suitable commissions due to negative competition effects and low competition intensity [ 51 ].

Cao et al. studied the trade-in problem, considered the competition between the third-party seller and platform self-operation store, analyzed different trade-in models, and found the optimal trading strategy of the platform [ 52 ]. Yang et al. examined the issue of firms providing dual-channel trade-in programs and analyzed the impact of the profitability of dual-channel firms in the trade-in operation model [ 3 ].

Existing research lacks the issue of sales mode selection in e-commerce CLSC. The discrepancies between this research and relevant literature are presented in Table 1 . As can be seen from the table, our research is close to that of Yang et al. [ 3 ], but they studied the issue of trade-in for old products and did not consider the sales of remanufactured products, nor did they discuss the combination of different sales models and recycling models. This study supplements the deficiencies of related research. We integrate the approaches of the channel model and the recycling model, considering the inclinations of consumers towards remanufactured goods. Based on the decision-making of supply chain members, win-win issues are discussed.

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https://doi.org/10.1371/journal.pone.0303447.t001

3. Problem description and assumptions

As shown in Fig 1 , We construct a closed-loop e-commerce supply chain that consists of an e-commerce platform (she) and a manufacturer (he). In the illustration, the forward product circulation direction is represented by the solid line, and the reverse product circulation direction is shown by the dotted line, and the green color represents the supply chain member responsible for recycling. The market offers both brand-new and remanufactured products. Based on different collection models and channel structures, four different scenarios are established: (a) Model MR: e-commerce platform resale model, the collection subject is the manufacturer. As shown in the figure, the manufacturer sells new and remanufactured products to the e-commerce platform at wholesale prices w n and w r , and then the platform sells the products to consumers at resale prices p n and p r . The manufacturer recycles the product to the consumer at price f . (b) Model MA: agency sales model, the collection subject is the manufacturer. The manufacturer sells the new and remanufactured products directly to consumers at resale prices p n and p r , and the platform charges a certain percentage of commission α . The manufacturer recycles the product to the consumer at price f . (c) Model ER: e-commerce platform resale model, the collection subject is the e-commerce platform. The manufacturer sells the new product to the e-commerce platform at a wholesale price w n , and then the platform sells the new product to consumers at a resale price p n . The e-commerce platform recycles products to consumers at price f and sells remanufactured products directly to consumers at price p r . (d) Model EA: agency sales model, the collection subject is the e-commerce platform. The manufacturer sells the new products directly to consumers at resale prices p n , and the platform charges a certain percentage of commission α . The e-commerce platform recycles products to consumers at price f and sells remanufactured products directly to consumers at price p r .

https://doi.org/10.1371/journal.pone.0303447.g001

3.1. Consumer demand

3.2. Production cost

The manufacturer’s unit costs of producing new and remanufactured goods are c n and c r , respectively. Based on Zheng et al. we assume that c n > c r = 0. That is, a new product’s unit cost is more than a remanufactured product’s [ 54 , 58 ].

3.3. Recycling cost

All notations and explanations are given in Table 2 .

https://doi.org/10.1371/journal.pone.0303447.t002

4. Model and analyse

For the four different scenarios, we establish the profit function separately, and apply the Stackelberg game-theoretic model to obtain the equilibrium solutions by backward derivation and analyze the results.

4.1. Model MR

Corollary 1 states that the wholesale price and sales price of the new product and the remanufactured product are positively correlated with the unit production cost of the new product. The impact of an increase in new product production costs on new product prices is in line with pricing strategies. To achieve profitability, the wholesale price set by the manufacturer will rise, and this will impact the platform retailer’s sales pricing. The increase in the price of the new product requires the manufacturer to adjust that of the remanufactured good at the same time to keep the competitive relationship between the two products in the market. The wholesale and sales prices of the remanufactured product are positively correlated with consumer preference for the remanufactured product, and negatively related to the sensitivity coefficient of recycling volume to recycling price. It makes sense for customer choices to have an influence on product prices. A higher value of λ results in a lower recycling price for the remanufactured product. This lowers the cost of the remanufacturing process for the producer and ultimately lowers the remanufactured product’s wholesale price. The remanufactured product’s sales price decreases as well.

4.2. Model MA

Corollary 2 points out that under the agency model, the price at which the manufacturer sells the new and remanufactured product is positively related to the new product’s unit cost. The sales price of the remanufactured product is positively correlated with consumer preference for the remanufactured product, and negatively related to the sensitivity coefficient of recycling quantity to recycling price. This is consistent with scene MR. In scenario MA, the manufacturer has to pay a commission to the platform. The commission has a positive relationship with the sales price of both goods. This happens as a result of the manufacturer’s profit margins being reduced by increasing the fee. To improve profits, the manufacturer must raise prices.

4.3. Model ER

According to Corollary 3, the unit cost of manufacture of a new product has a positive correlation with all prices. This conclusion is consistent with Proposition 1. Consumer preferences for remanufactured products negatively affect the sales price of the new product and positively affect that of the remanufactured one. This is easily explained. Remanufactured items become more competitive and customers are more prepared to embrace them as their awareness of environmental issues grows. The remanufactured product’s price increases while the new product’s price decreases. The selling prices of both new and remanufactured goods are negatively correlated with the sensitivity coefficient of recycling quantity to recycling price. This is also consistent with the conclusion of Corollary 1. As λ increases, the platform’s recycling cost decreases, leading to a decrease in the selling price.

4.4. Model EA

As demonstrated by Corollary 4, the influence of the new product’s unit production cost and the product recycling quantity’s sensitivity coefficient to the recycling price on the equilibrium price in model EA are in line with the previous findings. That is, whether it is manufacturing costs or recycling costs, rising expenses will always result in higher pricing. Furthermore, there exists a positive correlation between commission rates and the sales price of both new and remanufactured goods. The product pricing structure on the market shifts as a new product’s price goes up with commissions. The platform will raise the sales price in order to stay competitive if it thinks that the market can handle higher pricing and that customers are still eager to purchase.

4.5. Pricing analysis

Through the equilibrium solutions of the wholesale and sales prices of new and remanufactured products under different scenarios, the following findings are reached.

Proposition 1 The wholesale prices in the four scenarios have the following relationships:

Proposition 1 states that in the resale model, remanufactured items have a lower wholesale price than new products. The wholesale price of new products is not affected by the recycling entity. That is, regardless of whether the manufacturer or the platform is in charge of recycling and remanufacturing goods, the wholesale price of new products stays the same. The difference in wholesale prices is determined by production costs. Based on the hypothesis of the study, compared to remanufactured items, new products have a greater unit production cost, so there will be higher wholesale prices. The manufacturing and sales of remanufactured products are impacted by changes in the recycling system, while the production and selling of new products are unaffected, meaning that the wholesale costs of new products remain unchanged. To display the comparison between equilibrium prices more intuitively, we conducted numerical simulations. Numerical simulation methods have been widely used in the study of decision optimization. The numerical simulation in this article is based on the theoretical and practical basis of previous research, and the relevant parameters are assigned values [ 11 , 58 ].

Fig 2 illustrates how consumer desire for the remanufactured product causes the difference between the wholesale costs of the new product and the remanufactured product to close. This is consistent with cognition. The demand for the remanufactured product will rise due to the increased customer preference, which will also raise its price and reduce the difference between it and the new product.

https://doi.org/10.1371/journal.pone.0303447.g002

Proposition 2. The selling prices in the four scenarios have the following relationships:

As seen in Fig 3 , Proposition 2 indicates that the link between product sales prices and the new product’s unit manufacturing cost ( c n ) is significant. When c n is small, the sales price of items under the resale model is higher than that of the agency model, according to a comparison of the prices of new and remanufactured goods under the same recycling structure. Comparing the prices under the same sales model, it is found that when c n is small, the sales price of the product when recycled by the manufacturer is always greater than the price when recycled by the e-commerce platform. When new products are recycled by the manufacturer rather than the e-commerce platform, the sales price of the former is always higher under the resale model. The higher price under the resale model than the agency model can be explained by double marginalization. Under the resale model, the platform sets the retail price and sells the goods to the customer after the manufacturer sets the wholesale price. Both have profit maximization as their goal, and product pricing is subject to double marginalization, which is higher than in a model where the manufacturer sells at a direct price.

https://doi.org/10.1371/journal.pone.0303447.g003

Whoever is in charge of recycling and remanufacturing does not affect the manufacturing and marketing process for the new product. However, a change in the recycling entity affects the remanufactured product. When the manufacturer carries out the recycling, the sales price of the new and remanufactured products are decided by the same subject, and when the platform carries out recycling, the manufacturer and the platform decide on the sales prices respectively. Since there is no way to observe the pricing of the other product, the price of the new product will be set in such a way as to avoid overpricing and losing the market. Simultaneous pricing provides better control over the sales price and market conditions. For the remanufactured goods, when c n is low, the cost difference between the two products is small and the sales price of the product is the same as that of the new product. When c n is high, the new product’s production costs are high. As a result, the manufacturer recycling model will customarily lower wholesale prices for the remanufactured product in order to maintain profit margins. As a result, the remanufactured product’s resale price will be lower than in the platform recycling scenario. Fig 3 illustrates how the price of both products rises in tandem with the new product’s unit cost of production. The price of a new product when recycled by the manufacturer under the resale model is always higher than the price via the recycling model on the e-commerce platform. Comparing Fig 3A–3D respectively, it can be seen that when consumers’ preference for the remanufactured product increases, for the new product, the sales price does not change much under the platform recycling model, but increases in the manufacturer recycling mode. For the remanufactured product, the reduction in β significantly reduces the sales price. This is due to reduced product preference leading to reduced product demand, thus sellers lower prices to attract consumers.

5. Recycling and channel strategies

In this section, we discuss the strategies of the supply chain members. After discussing the manufacturer’s and the e-commerce platform’s respective strategies, we go over win-win situations for both sides.

5.1. The manufacturer’s strategies

To study the manufacturer’s optimal sales model under different recycling models and the optimal recycling model under different sales models, we compare the manufacturer’s profit under different scenarios, and come up with the following conclusions.

Proposition 3 shows that in the model where the manufacturer is responsible for recycling, when λ is small the manufacturer chooses the resale model, otherwise the manufacturer chooses the agency model. In the model where the platform is responsible for recycling, when c n is small, if the commission is high the manufacturer chooses the resale model, otherwise he chooses the agency model. In the product recycling framework where the manufacturer has the responsibility for recycling, the cost of recycling is high when λ is low and decreases as λ rises. The agency model requires the manufacturer to pay the commission. Due to cost structure considerations, the manufacturer chooses the agency model when the recycling cost is low to gain more profit. The conclusion of Proposition 3(ii) seems to contradict common sense, and we offer the following explanation. From (i) and (ii) of Proposition 1, when c n is small, new products and remanufactured goods are offered at higher prices in the resale model; when c n is large, new and remanufactured goods are offered at greater prices in the agency model. Meanwhile, the wholesale price increases as the unit cost of new products rises. So for the manufacturer, when new items have low unit production costs, the profit margin under the agency model becomes low as the commission rises, so he will choose the resale model. The manufacturer will select the agency model as α increases because the selling price of the product under the agency model is higher at this time and is positively correlated with the commission, which is necessary to obtain a larger profit margin when the unit cost of production of the new product is high.

We use numerical analysis to provide a graphic representation of the relationship between the manufacturer’s profit and customer demand for remanufactured products, as well as the sensitivity of recycling volume to recycling pricing. As shown in Fig 4 , the manufacturer’s profit is higher in the resale model than in the agency model when the recycling quantity is less sensitive to the recycling price.

Manufacturer’s channel strategy in (a) the model with manufacturer recycling; (b) the model with e-commerce platform recycling ( c n = 0.9, α = 0.075).

https://doi.org/10.1371/journal.pone.0303447.g004

Proposition 4 shows that in both the resale model and the agency mode, the manufacturer chooses to recycle himself, that is, given any sales model, the manufacturer is willing to be responsible for product recycling and remanufacturing himself. Under the resale model, the manufacturer doesn’t face consumers directly, making new products and selling them to the platform retailer at wholesale prices. If the manufacturer is responsible for recycling and remanufacturing and then wholesaling to the platform, he can better control the cost, realize the distribution of products and reduce the intensity of competition in the market. Under the agency mode, the manufacturer faces consumers directly. If he is also responsible for recycling, he can have better control over the entire CLSC process and can adjust his own pricing and profits. It can be seen that for the manufacturer, no matter what sales mode he is in, it is more profitable to take on the role of recycling and remanufacturing. As seen in Fig 5 , the manufacturer’s profit difference (orange part in the figure) under the two recycling modes increases as consumers’ preference for remanufactured products increases. A rise in β increases the market demand for the remanufactured product, hence increasing the manufacturer’s profitability from recycling and producing the remanufactured product.

https://doi.org/10.1371/journal.pone.0303447.g005

5.2. The E-commerce platform’s strategies

To study the platform’s optimal sales model under different recycling models and the optimal recycling model under different sales models, we compare the e-platform’s profit under various scenarios and come up with the following conclusions.

Proposition 5(i)shows that in the scenario when the recycling is conducted by the manufacturer, the platform chooses the resale model when the sensitivity of the recycling volume to the recycling price ( λ ) is high, and the platform chooses the agency model when λ is low. This is the opposite of the manufacturer’s choice in Proposition 3. A rise in λ represents a decrease in the cost of recycling. When the manufacturer’s recycling cost is low, the wholesale price is also lowered, and the platform can make more profit by wholesaling the product from the manufacturer and then re-selling it through the resale model. As the cost of recycling increases, the platform is unable to make higher profits through wholesale and then re-sale, so she prefers to adopt the agency model of charging a commission to the manufacturer.

Proposition 5(ii) also concludes the opposite of Proposition 3. The platform’s decision is influenced by the new product’s unit manufacturing cost in the scenario where the platform is in charge of recycling. When c n is small, the platform chooses resale when alpha is small, and when c n is large, the platform chooses agency when alpha is small. When c n is small, the price of the product is low, and the platform cannot make more profit from the agency model when α is low, so she chooses the resale model. When c n is large, we know from Proposition 2 that the sales price under the agency model is greater. Meanwhile, as α increases, the price rises in both channels under the agent mode, and when α is too large, the excessively high price will have a dampening effect on demand and will have an impact on the platform’s profit, so she will shift to choose the resale mode. Similarly, we use numerical analysis to show in Fig 6 the impact of β and λ on platform profits. As Fig 6 illustrates, when the recycling amount is less sensitive to the recycling price, the manufacturer’s profit is larger in the resale model than in the agency model. Additionally, the manufacturer makes greater earnings in the resale model than in the agency model when consumer desire for the remanufactured product increases.

E-commerce platform’s channel strategy in (a) the model with manufacturer recycling; (b) the model with e-commerce platform recycling ( c n = 0.9, α = 0.075).

https://doi.org/10.1371/journal.pone.0303447.g006

Proposition 6 suggests that in the resale model, the platform prefers to recycle by herself rather than the manufacturer. This finding suggests that the platform’s choice is opposite to that of the manufacturer (Proposition 4). The new product is manufactured by the manufacturer and then wholesaled to the platform under the resale model; the platform is not in charge of the new product’s manufacturing process. When the platform is in charge of recycling the product and remanufacturing and selling it, she has control over the remanufactured product and increases her competitiveness. The platform is thus able to make more profit. As stated in Proposition 6’s conclusion, the resale model does not present a situation in which both the manufacturer and the e-commerce platform benefit. The agency model’s platform profit comparison is complicated and comes from a numerical study.

As shown in Fig 7 , in the agency model, when customer desire for the remanufactured product is low, the e-commerce platform chooses to perform product recycling herself. When the sensitivity of recycling volume to recycling price is low, the platform chooses the model in which the manufacturer conducts recycling. In addition, the cost of producing a new product rises per unit, the platform chooses the model where she conducts product recycling herself. Remanufactured products are in low demand when there is little customer desire for them. In the agency model, the platform has no sales revenue for new products, only commission revenue. Therefore, the platform’s profit margin is low if the manufacturer recycles the product and sells it to the platform in quantity. Similarly, when the unit production cost is high, the profit margin of selling the new product becomes small, and the commission that the platform can get becomes less. The platform prefers that the manufacturer shoulder the expense of recycling when λ is small because of its high cost.

https://doi.org/10.1371/journal.pone.0303447.g007

Because both the manufacturer and the e-commerce platform desire to recycle the goods themselves, Proposition 6 demonstrates that a win-win scenario cannot be achieved under the resale model. Under the agency model, as can be seen in Fig 8 , when β is high, a win-win scenario can be achieved. When β is high, as Fig 7 illustrates, the e-commerce platform also wants to carry out product recycling and sales of remanufactured products by the manufacturer, so a win-win situation is achieved at this point. When c n rises, the win-win space becomes smaller (the orange area in the figure shrinks from the black to the blue line). The same effect occurs when the sensitivity coefficient of the recycling quantity to the recycling price becomes larger. As analyzed earlier, an increase in both makes the platform more willing to choose the model of having herself carry out the recycling, which is the opposite of the manufacturer’s choice, so it will lead to a smaller win-win scope.

https://doi.org/10.1371/journal.pone.0303447.g008

6. Consumer surplus

In this section, we analyze consumer surplus under different scenarios. In green supply chains, consumer surplus is an important indicator for evaluating corporate social responsibility and sustainable development. Consumer surplus represents the residual utility of consumers after purchasing a product, and is usually calculated through the maximum product sales price acceptable to consumers and the actual sales price [ 60 , 61 ].

https://doi.org/10.1371/journal.pone.0303447.g009

7. Conclusions

In this study, a closed-loop e-commerce supply chain is established, and the issue of selecting sales and recycling models for the manufacturer and e-commerce platform is examined. We investigate the resale and agency models in e-commerce, as well as two recycling models wherein the responsibility for recycling and selling remanufactured products lies with the manufacturer and the e-commerce platform, respectively. By merging the models, we create four research scenarios. We analyzed the equilibrium solutions under each model and examined the strategies of manufacturers and e-commerce platforms. The conclusions and management takeaways are listed below.

7.1. Main findings

This study establishes an e-commerce closed-loop supply chain research model based on existing research, supplements the research conclusions in this field, and has theoretical significance.

Savaskan et al. [ 5 ] earlier studied the problem of different recycling entities in the closed-loop supply chain. Comparing the models of manufacturer recycling, retailer recycling and third-party recycling respectively, they found that under decentralized decision-making, the optimal recycling method for the manufacturer is retailer recycling. By establishing a combination of different sales models and recycling models in a closed-loop supply chain in the context of e-commerce, and comparing equilibrium solutions in different scenarios, this study not only obtained the manufacturer’s optimal strategy, but also obtained the e-commerce platform’s strategy selection.

First, for the manufacturer, in the scenario when recycling is his responsibility, when there is little customer interest in remanufactured goods or when there is little correlation between the price and quantity of recycling, the resale model is selected. In the scenario when recycling is the platform’s responsibility, when the unit production cost is small, the manufacturer with large commissions will choose the resale model, otherwise, he will choose the agency model. Cost is clearly a major factor in the manufacturer’s choice of sales strategy under various recycling scenarios. Within a given sales model, the manufacturer has the same recycling model choices. Specifically, when it comes to agency or resale modes, the manufacturer will always decide to recycle on his own. In addition, whatever the recycling strategy, the manufacturer’s profit under the agency model tends to surpass that under the resale model as consumers’ interest in remanufactured goods increases.

Secondly, for the platform, in the model where the manufacturer is responsible for recycling, in situations when recycling volume and recycling price are highly correlated, she usually selects the resale model and vice versa. The choice of platform is influenced by the unit production cost of new goods under the model where the platform is in charge of recycling products. In instances when costs are minimal, the platform selects an agency as commission levels rise. The platform selects resale when the cost is high and the commission rises. Under a given sales model, the e-commerce platform prefers to handle recycling herself rather than the manufacturer in the resale model. Since the strategy differs from the manufacturer’s, the resale model cannot result in a win-win scenario. When customers have little interest in remanufactured goods, the e-commerce platform opts to recycle the products herself under the agency model. The platform favors the manufacturer’s recycling model when the amount of recycling is less sensitive to the price of recycling. In addition, the platform chooses to recycle herself when the cost of producing new goods rises per unit.

Since the decision-making goals of both parties in the game are to maximize their own interests, conflicts will inevitably exist when choosing between different modes. Zhang et al. [ 31 ] showed in their study that when there is no government-funded coordination, the remanufacturing model that is the responsibility of the manufacturer will harm the retailer’s profits. Quan et al. [ 53 ] compared the models in which the manufacturer is responsible for recycling and the retailer is responsible for recycling and found that in most cases, the manufacturer and retailer prefer to be responsible for recycling themselves. In addition, they studied the conditions under which both parties can reach an agreement (i.e., a win-win situation), that is, when the market potential of the two periods meets certain conditions, a win-win situation can be achieved. Our study also analyzes the conditions for achieving a win-win situation for supply chain members by comparing the equilibrium strategies of the manufacturer and the e-commerce platform.

By examining the strategies used by supply chain participants, we discover that in the agency model, the platform will choose a recycling model within a specific range, which is consistent with the manufacturer’s choice, so it can achieve a win-win situation. Customers’ strong desire for the remanufactured product or a low sensitivity of recycling volume to recycling pricing is the prerequisite. A larger win-win scope can be achieved when the unit production cost of new products is low. Since both parties’ choices in the reselling model are opposing ones, a win-win scenario is not possible.

In the study of the closed-loop supply chain, consumer surplus has been fully discussed by scholars. Mu et al. [ 12 ] discussed the consumer surplus problem in the green supply chain and analyzed the impact of green product segmentation strategies. Jia and Li [ 57 ] discussed the consumer surplus under four distribution models in the closed-loop supply chain and found that there were different results under different order fulfillment costs and platform commission rates. In the trade-in problem, different models have different impacts on consumer surplus. If the waiting cost of consumers is high, the cooperation model between the enterprise and the platform can bring a higher consumer surplus than the self-built model [ 3 ].

Our study compares consumer surplus under different combinations of recycling and sales models in closed-loop supply management in the context of e-commerce and finds that consumer surplus is highest under the agency model where the platform is responsible for recycling, at which point double marginalization is alleviated. In addition, the reduction in consumer preference for remanufactured products and the reduction in recycling costs of remanufactured products will lead to an increase in consumer surplus.

7.2. Managerial implecations

Through the analysis of the theoretical model, we provide some management implications for supply chain members.

Manufacturers in closed-loop supply chains need to fully consider consumer preferences for remanufactured products when making decisions. From the conclusion, we see that consumer preferences for remanufactured products will affect manufacturers’ profits under different recycling and sales models. Categories with high consumer acceptance of remanufactured products are usually those with higher value, shorter life cycles, longer service life, and relatively affordable prices. Acceptance of remanufacturing is generally lower for products that require a high degree of hygiene, safety and performance, and for those that are closely related to individual tastes and preferences. Based on this conclusion, manufacturers can decide based on product category when choosing a sales mode. For a given recycling model, the manufacturers can prioritize the resale sales model for remanufactured products with low consumer preference, such as medical devices. Furthermore, given the sales model, manufacturers should give priority to recycling products themselves. Because from conclusion we see that higher profits can be achieved by recycling by the manufacturer himself. In other words, manufacturers should proactively pursue the important role of recyclers in CLSC.

There will be distinct equilibria since the manufacturer’s and the platform’s best options differ. Since the optimal choices of platforms and manufacturers are not entirely consistent, this means that when one party has more say, the interests of the other party may be harmed. This also means that different equilibrium situations will occur under different power structures. When the platform can make relevant decisions, as mentioned earlier, consumers’ preference of the remanufactured products should be taken into consideration. For example, under the agency model, for the remanufactured products that aren’t welcomed by consumers, the platform can proactively provide recycling services and act as a recycler to obtain higher profits. When the manufacturer is the formulation of decision-making, the platform can improve the profitability in the corresponding model, such as by increasing commissions.

Since a win-win situation can only be achieved under certain conditions, if the e-commerce platform and the manufacturer only make decisions from their own perspectives, no matter what kind of power structure they are under, one party may be harmed. Such a cooperation model is bound to not be a stable structure and is not conducive to the long-term stable development of the supply chain. When a win-win situation cannot be reached, both supply chain members should strengthen communication, reduce conflicts, and avoid damage to the profits. In a given sales model, the manufacturer hopes to recycle by itself, but the platform has different optimal choices in the resale mode. Therefore, to meet the optimal conditions, manufacturers should give priority to choosing an agency model to promote the realization of a win-win. Furthermore, compared to the resale model, the agency model has a larger consumer surplus. In order to assume corporate responsibility and improve social welfare, supply chain members should also work together to achieve a win-win situation and bring more utility to consumers under the agency model.

This study also has certain implications for government management decisions. On the one hand, given the conflicts that may arise in CLSC, the government should assume the responsibility for coordination. The government can establish a market coordination mechanism to improve the communication efficiency of supply chain members. When there is a decision-making conflict between the two sides of the supply chain, the government can coordinate and maintain the stability of the closed-loop supply chain. On the other hand, from the perspective of environmental sustainability and consumer welfare, the government should improve the public’s environmental awareness and encourage consumers to participate in environmental protection practices through publicity and science popularization. To avoid the reduction of utility caused by the increase in consumers’ environmental awareness, the government should regulate the price of remanufactured products. At the same time, to encourage supply chain members to participate in environmental protection practices, appropriate subsidies can be provided to reduce the cost of recycling and remanufacturing for supply chain members to increase their enthusiasm.

For consumers, we see from the research results that as consumers increase their preference for remanufactured products, consumer surplus will decrease. On the one hand, consumers should increase their awareness of environmental protection and voluntarily participate in environmentally sustainable practices. On the other hand, to protect their rights and interests, consumers should actively participate in the government’s supervision of remanufactured product pricing and jointly promote the good operation of the closed-loop supply chain.

7.3. Limitations

This study seeks to address the issue of channel and recycling mode selection in the e-commerce closed-loop supply chain, but there are still some limitations. For example, remanufacturing and recycling are the responsibility of the same supply chain member in this study. Future research may consider situations where remanufacturing and recycling responsibilities are separated, such as the presence of third-party corporate recycling, and discuss more scenarios. Further, scenarios of government subsidies or regulations can also be considered to solve more practical problems.

Supporting information

S1 appendix. proofs of propositions..

https://doi.org/10.1371/journal.pone.0303447.s001

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A conceptual framework proposed through literature review to determine the dimensions of social transparency in global supply chains

• Published: 16 May 2024

Cite this article

• Preethi Raja 1 &
• Usha Mohan   ORCID: orcid.org/0000-0003-2161-7600 1  

The current focus in supply chain management (SCM) research revolves around the relationship between sustainability and supply chain transparency (SCT). Despite the three pillars of sustainability – environmental, social, and economic- the limited and scattered analysis is on the social part, and the least is on socially responsible supply chain management (SR-SCM). SCT plays a significant role in elevating the sustainability of the supply chain. This review paper emphasizes the integration of SCT and sustainable supply chain, especially the social aspect as SR-SCM, and coining the new term social transparency (ST). ST is openness to communicating details about the impact of business on people, their well-being, and compliance with social sustainability standards and policies. This paper establishes a conceptual framework using three research methods. systematic literature review, content analysis-based literature review, and framework development. By locating studies in databases like EBSCO, Scopus, and Web of Science, 273 peer-reviewed articles were identified in the intersection of social sustainability, supply chains, and transparency. Finally, the framework proposes five dimensions: tracking and tracing suppliers till provenance, product and process specifications, financial transaction information, social sustainability policies and compliance, and performance assessment to determine ST in global supply chains.

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Data availability.

The data that supports the findings of this systematic literature review and content analysis are either included in this manuscript or are publicly available in the referenced sources. All included studies and their respective citations are provided in the reference section. Any additional data or materials used for this review can be obtained upon request from the corresponding author.

Abbreviations

Supply chain management

Socially responsible supply chain management

Supply Chain Transparency

Social Transparency

Multinational Corporations

Code of Conduct

Corporate Social Responsibility

Preferred Reporting Items for Systematic Reviews and Meta-analyses

Radio frequency Identification

Internet of Things

Sustainable Supply Chain Management

Supply Chain

Textile Standard Certification

Worldwide Responsible Accredited Production

Global Organic Textile Standard

Global Recycled Standard

Registration, Evaluation, Authorization and Restriction on the use of Chemicals

Social Accountability International Certification

Indian Standards Institution Mark

Bureau of Indian Standards

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Preethi Raja & Usha Mohan

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Raja, P., Mohan, U. A conceptual framework proposed through literature review to determine the dimensions of social transparency in global supply chains. Manag Rev Q (2024). https://doi.org/10.1007/s11301-024-00440-1

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Received : 21 July 2023

Accepted : 26 April 2024

Published : 16 May 2024

DOI : https://doi.org/10.1007/s11301-024-00440-1

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