digital transformation manufacturing case study

Capturing the true value of Industry 4.0

In the past five years, a select group of companies have started pulling ahead in their efforts to implement Industry 4.0 across their manufacturing networks . Leading manufacturers are now realizing significant value from data and analytics, AI, and machine learning (ML). However, a large majority remain stuck in pilot purgatory, struggling to capture the full potential of their transformation efforts or deliver a satisfactory return on investment.

While digital transformations are notoriously difficult to scale up  across networks of factories, the pressure to succeed is intense. Companies at the front of the pack are capturing benefits across the entire manufacturing value chain—increasing production capacity and reducing material losses, improving customer service and delivery lead times, achieving higher employee satisfaction, and reducing their environmental impact. Scaled across networks, these gains can fundamentally transform a company’s competitive position.

With so much at stake, manufacturers are putting significant time and money behind their digital transformations . These investments are paying off for some, but most remain unable to scale successful pilot programs or fully leverage new tools and technology to see meaningful returns.

This article explores some of the common pitfalls associated with digital transformations and how a more strategic and value-driven approach could help manufacturers in the race to get ahead.

Delivering value across every area of the factory

The digitally enabled factory of today looks very different from the leading factory of ten years ago. Advances in data and analytics, AI, and ML—and the array of technology vendors in the market—mean manufacturers can choose from hundreds of potential solutions and tech applications to improve their ways of working.

Implemented successfully, these solutions deliver irresistible returns. Across a wide range of sectors, it is not uncommon to see 30 to 50 percent reductions in machine downtime, 10 to 30 percent increases in throughput, 15 to 30 percent improvements in labor productivity, and 85 percent more accurate forecasting (Exhibit 1).

While digital transformations are notoriously difficult to scale up across networks of factories, the pressure to succeed is intense. Companies at the front of the pack are capturing benefits across the entire manufacturing value chain.

Digital transformations are revolutionizing all aspects of manufacturing, touching not just processes and productivity but also people. The right applications of technology can lead to more empowered decision making; new opportunities for upskilling, reskilling, and cross-functional collaboration; better talent attraction and retention; and improved workplace safety and employee satisfaction.

Customers see the impacts through reduced manufacturing lead times, right-first-time launch management, and improved customer service and complexity management. And, of course, there are the win–win advantages associated with reduced environmental impact, made possible through lower emissions and reduced waste and more efficient energy, water, and raw-material consumption.

These productivity, process, and people improvements are not easy to accomplish—especially across a network of individual manufacturing sites, each with its own site leadership, IT infrastructure, and workplace culture. It is not uncommon to hear of companies achieving impressive results through pilot programs at one factory site only to find themselves unable to replicate these local wins across their network.

This was the situation at a global industrial company. Facing a considerable ramp-up in demand—volume more than doubled over just three years, which translated to producing more than 50 million additional parts—the business embarked on an ambitious digital transformation at one factory. The goal was to increase overall equipment effectiveness (OEE) by ten percentage points and reduce product unit costs by more than 30 percent.

The project delivered: the factory was admitted to the Global Lighthouse Network , a World Economic Forum initiative, in collaboration with McKinsey, that recognizes leadership in the Fourth Industrial Revolution. The site started welcoming external visitors to showcase its transformation. But despite this achievement, it was unclear to the company how to take this local success story and replicate it across other sites.

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The common pitfalls of scaling digital transformations.

There are five common reasons why manufacturers are not succeeding on this journey.

Siloed implementation. By pursuing digital transformations as a theoretical exercise, many companies unwittingly set up independent delivery teams that are decoupled from business leaders, site operations, manufacturing excellence, and central IT. Others focus too much on replicating a single site experience, failing to appreciate wider network complexities.

Failure to adapt. By deploying a one-size-fits-all approach, manufacturers miss the opportunity to build in the customization and adaptation needed to leverage the unique circumstances, culture, and values of separate factory sites.

Analysis paralysis. Performing a full and deep up-front analysis of an entire network can leave a manufacturer out of steam before a transformation can get off the ground. Instead, robust, accurate-enough insights can be gleaned from a well-developed extrapolation methodology.

Technology-driven rather than value-driven. A technology-first rollout means that solutions are deployed without a clear link to real value opportunities, business challenges, or capability requirements. The result: undermining crucial buy-in from the people charged with making deployment work.

Letting the ‘perfect’ defeat the good. By waiting until a fully fledged, ideal-state data and IT/OT (information technology/operational technology) architecture is defined and implemented before rolling out Industry 4.0 solutions, manufacturers lose out on the shorter time-to-impact made possible through a proven and pragmatic minimal viable architecture.

A technology-first rollout means that solutions are deployed without a clear link to real value opportunities, business challenges, or capability requirements, undermining crucial buy-in from the people charged with making deployment work.

Three company archetypes join the race

Manufacturers playing catch-up to the leading companies generally fall into one of three company archetypes.

The cautious starters. These companies are investigating how to begin their digital-transformation journeys. They need help to identify the full value that Industry 4.0 can bring to their business and to develop a network-wide strategy and deployment road map.

The frustrated experimenters. These companies have started experimenting through pilot programs with some successes. However, they find themselves deploying technologies without a clear understanding of how to achieve financial ROI.

The ready-to-scalers. These companies are deploying solutions and technologies but remain unable to maximize the returns or scale at pace across their networks. They need to recalibrate by focusing on how to capture the full benefits of Industry 4.0 or how to accelerate rollout to respond to shifts in business and customer needs.

Slowing down to go fast

No matter where a company falls on the spectrum of archetypes, there is great value in slowing down and regrouping around a new, more targeted strategy aimed at maximizing the value of a digital transformation.

An important lesson from the few organizations that have succeeded in scaling digital innovations is how they started their impact journey. Before jumping headfirst into procurement and deployment, the leading companies spend time identifying the full potential of Industry 4.0—pinpointing high-leverage areas across the manufacturing value chain—and architecting a laser-focused digital-manufacturing strategy and deployment road map.

The first phase of this approach includes a network scan to identify the value at stake and a priority list of technology use cases, taking into consideration data, IT/OT, and organizational maturity. An accompanying road map can then build on this groundwork, defining the deployment strategy and targeted sites for initial rollout, followed by a network-wide rollout plan to reach scale.

Taking the time up front to perform a network scan to find opportunities for big wins and quick wins can create significant momentum for a digital transformation. As manufacturing sites begin to capture financial and operational value—not to mention the benefits associated with improved organizational capabilities, workforce satisfaction, customer service performance, and environmental impact—these returns can create a virtuous feedback loop where programs become self-funding and initiatives translate more quickly into competitive advantage.

Scaling success

It is this methodology that underpinned the approach taken by the industrial company mentioned earlier. Following its lighthouse success, the business needed to understand how and where to invest to maximize returns across its network. By performing a network scan on a subset of its manufacturing value streams across more than a dozen sites, it identified five sites that together represented around 80 percent of the value at stake. Further, it found that ten out of the 17 identified use cases for technology accounted for 75 percent of the potential impact.

With a sound value-capture deployment strategy in place, and after structurally investing in the required capabilities, the company was able to replicate the network scan approach across the rest of its manufacturing network and scale to other business areas. A senior stakeholder in the company said: “We essentially wrote the playbook for how to scale this into our other sites and are making great progress in these places—not only across our downstream production network but also within our upstream production sites, leveraging digital to reduce human interventions and increase compliance.”

Focus on real business needs and current performance challenges, and follow a “strengths upward” approach, building on solutions that have already worked well at individual sites and can be rolled out pragmatically across the network.

In another example, a global consumer company had been piloting digital innovations in a number of business units for some time, but with few ideas achieving much impact beyond the individual line or site. Company leaders recognized the need to clarify which digital solutions could contribute to overall business needs and priorities, and where to focus transformation efforts to implement solutions at scale.

Following two months of up-front analysis focused on eight prioritized sites from a network of more than 40 factories in Europe and North America, the company realized that about 20 sites accounted for 80 percent of the total savings potential. It also identified a prioritized portfolio of digital solutions, with about two dozen use cases having relevance across the entire network, and a dozen identified as “no regrets” priorities.

Crucially, the process has enabled the company to understand the level of readiness of its data and technology infrastructure and the investment required in technical, managerial, and transformational capabilities. The company came out of the two months with an aligned and value-oriented road map for rolling out a digital transformation across its network. The plan integrated both digital and traditional lean or Six Sigma improvements, accounted for resources and technology requirements, and reflected a clear strategy for building capabilities at scale. The company went on to deploy at scale across multiple sites, pursuing more than $100 million of identified savings.

The seven golden principles for getting the best out of Industry 4.0

Whether manufacturers are starting out on their digital-transformation journeys—or recalibrating their approach after false starts or failed attempts—the approaches adopted by leading companies point to seven golden principles for scaling a successful digital transformation.

Communicate well and often. Establish an effective engagement plan and regular communication with critical senior stakeholders, site leaders, and a cross-functional core team.

Lighthouses reveal a playbook for responsible industry transformation

Be specific. Focus on real business needs and current performance challenges, and follow a “strengths upward” approach, building on solutions that have already worked well at individual sites and can be rolled out pragmatically across the network.

Segment, select, and syndicate. Segment the manufacturing network and select representative sites for an up-front network scan. Syndicate the extrapolation methodology up front to indicate how focused insights will be scaled to derive a networkwide analysis.

Formalize the value at stake. In each assessed site, describe the actual value at stake by linking the most applicable Industry 4.0 solutions or use cases with current digital readiness, data availability, and IT/OT architecture.

Develop a three- to five-year vision for the network. Describe the total value at stake from prioritized bundles of use cases to align business leaders on the ambition, and form a compelling change story  for the broader organization. An engaging visual representation of the key solutions can help to engage the broader organization with the vision (Exhibit 2).

Design a digital-manufacturing road map. Develop a prioritized rollout plan with a clear scaling strategy and articulation of the value to capture over time, integrating enablement of data and IT/OT architecture as well as resourcing requirements, capabilities, and change management.

Syndicate the vision and secure leadership buy-in. Circulate the business case and requirements with key stakeholders, aiming for a clear mandate from top leadership and close engagement on target setting and execution from site leaders.

Whether stuck in pilot purgatory or under mounting pressure to demonstrate returns, companies can become dispirited and discouraged. However, by taking just one or two months to slow down and develop a robust manufacturing strategy and deployment road map, companies can accelerate their Industry 4.0 transformations and chart a clear journey forward for the next few years.

Ewelina Gregolinska is an associate partner in McKinsey’s London office, where Rehana Khanam is a partner and Prashanth Parthasarathy is a senior expert; Frédéric Lefort is a partner in the Gothenburg office.

The authors wish to thank Søren Fritzen, Sven Houthuys, Regis Peylet, Mikhail Razhev, and Hariharan Vijaykumar for their contributions to this article.

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Developing a digital transformation process in the manufacturing sector: Egyptian case study

• Original Article
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• Published: 22 June 2022
• Volume 20 , pages 613–630, ( 2022 )

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• Yasser Omar Abdallah   ORCID: orcid.org/0000-0001-5425-7598 1 , 3 ,
• Essam Shehab 2 &
• Ahmed Al-Ashaab 1  

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Digital transformation is of crucial importance in the manufacturing industry, especially after the COVID-19 pandemic because of the increasing need for remote working and socially distanced workplaces. However, there is a lack of a clear and well-defined process to implement digital transformation in manufacturing. This paper aims to identify the most critical stages to implementing digital transformation in the manufacturing sector. Twenty-one structured interviews with experienced specialists in digitalisation in the manufacturing sector in the Egyptian economy were held and used the Best–Worst Method to analyse the data as an analysis tool for a multiple criteria decision making (MCDM) approach. The digital transformation process comprises eight stages covering technology, management, communications, and customer elements. The main contribution of this work stage is the balance between the different elements of digital transformation—digital technologies, leadership and strategy, people and business processes—to create an integrated 8-step process of digital transformation in the manufacturing sector of developing economies such as the Egyptian economy.

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1 Introduction

With the exponential expansion of digital technologies and their widespread use in various sectors and industries, businesses are adapting their business models to achieve their ultimate objectives. This transformation has been accelerated by the coronavirus disease pandemic of 2019 (COVID-19) and its impact on the corporate sector and how customer needs are shaped (Müller et al. 2018 ).

This change has an impact on how businesses function both internally and externally connect with their consumers to deliver products and services. Manufacturing, being one of the most critical sectors of any economy, has been strongly impacted by the digitalisation of manufacturing processes through the development of new technologies that may be used to boost manufacturing operations productivity (Grabowska 2020 ).

In the manufacturing industry, digital transformation (DT) is vital for organisations to remain competitive since digitalisation of corporate operations is the only path to ensure sustainability in today’s competitive marketplaces (Mohelska and Sokolova 2018 ). This demonstrates the crucial significance of DT in enabling established firms to compete with native digital organisations that have arisen during the past 10 to 15 years. While academic research has highlighted DT in recent years, there is still debate regarding the term’s definition, context and implications, notably in the manufacturing industry (Bahlooq et al. 2020 ; Carolis et al. 2017 ; Ismagilova et al. 2019 ; Hartley and Sawaya 2019 ).

As DT is a very generic term, there is a lack of a well-defined definition of DT; moreover, those who attempt to explain DT frequently do so from their own perspectives and backgrounds (e.g., financial sector and government domain) (Culot et al. 2020 ). There is a lack of a clear and in-depth process of how the manufacturing industry can implement DT successfully. Because of this, most DT initiatives in the manufacturing industry fail due to a lack of understanding of the concept and requirements for implementing DT successfully.

To fill this gap, the present paper aims to propose and rank clear and well-defined stages for the manufacturing industry to implement DT successfully with validation of case studies from the Egyptian manufacturing sector as one of the most prominent developing economies. This will help any manufacturing organisation grasp the stages and procedures needed to commence the DT journey. The paper presents a literature review in developing DT process in the manufacturing sector and the main elements of DT implementation in the manufacturing industry, followed by the data collections methods and analysis. Finally, it demonstrates the research findings and formulates a DT implementation process in the manufacturing industry.

2 Literature review

This section discusses a working definition of DT in the manufacturing industry. In addition, the main elements of DT that contribute to the success of any DT efforts are discussed to gain further insight into the stages needed to develop a clear DT process in the manufacturing industry.

According to the literature, few publications specifically define DT, particularly in the context of manufacturing, and definitions of DT and other related concepts, such as Industry 4.0 or digital disruption, often contradict (Mariani and Borghi 2019 ).

After conducting a study on the idea and analysing various definitions, a conceptual working definition of DT with a focus on the manufacturing industry has been developed (Abdallah et al. 2021a ):

Digital transformation is a customer-centric mechanism that enables continuous improvement in the productivity of the manufacturing processes using advanced digital technologies, such as cloud computing, the Internet of Things (IoT), big data analytics, digital twins and artificial intelligence, in all aspects of the organisation.

While this field of research has lately gained prominence in academia, there is a dearth of evaluations of industrial organisations’ readiness to conduct DT procedures. Additionally, existing models in the literature place a heavy emphasis on Industry 4.0 technologies, with just a few models emphasising DT as a comprehensive overview of the process (Jones et al. 2021 ; Brozzi et al. 2020 ; Chirumalla 2021 ).

The scientific community and consultancy businesses are both heavily involved in DT research. The theoretical and methodological underpinnings of strategy selection, on the other hand, are relatively unclear. Typically, the theoretical grounds for choosing a DT method are addressed for specific domains. Additionally, larger organisations have made more significant advances in DT than smaller ones (de Jesus and Lima 2020 ). This is due to the substantial financial resources needed to incorporate digital technologies in all aspects of the organisation.

Although there is research efforts on digital transformation in recent years, few suggested a comprehensive list of factors that could contribute to the successful implementation of DT, especially in the manufacturing sector (Paryanto et al. 2020 ; Zaoui and Souissi 2020 ). That is why in an initial phase of this ongoing research, the main elements that contribute to the success of DT processes in the manufacturing industry were listed. These are people , leadership and strategy , enabling technologies and tools and business processes (Abdallah et al. 2021b ). As Fig.  1 shows, DT is not only about technical capabilities and infrastructure in the organisation; it should involve changing the organisational strategy and direction (Jones et al. 2021 ), capacity building activities towards digitalisation for the human factor and integrating all of the stakeholders around the organisation into it (Mihardjo et al. 2019 ; Oberer and Erkollar 2018 ).

Digital transformation framework (Abdallah et al. 2021b )

Human capital is the foundation of any organisational change endeavour. DT necessitates the development of specialised capabilities in an organisation’s employees and workers (Paryanto et al. 2020 ; Dhanpat et al. 2020 ; Giang et al. 2021 ). The first stage in improving an organisation’s human capital is identifying the gap in digital capabilities in the organisation. Team building and management are also important aspects of human capital. DT is a collaborative effort that necessitates the integration of several departments and pieces to produce strong DT results. Typically a DT team includes (a) the digital transformation leader, who controls the whole process and resolves any conflicts that arise during a project’s execution; (b) the change agent, who encourages other members to adapt to change and be flexible in their job duties and work methods; (c) a business specialist with experience in marketing and distribution channel operations to provide insight into the project; (d) the data architect, who analyses and provides reports to assist senior management in making timely decisions, as this is a fast-paced process that necessitates flexibility and making the correct judgments at the appropriate time; (e) the financial analyst, who does the project’s cost–benefit analysis and manages the project’s financial budget; and (f) the UX specialist, who can reflect the customers’ voice and make the solutions provided by the organisation user-friendly. These team members work together to integrate DT efforts successfully (Chirumalla 2021 ; Jafari-Sadeghi et al. 2021 ).

As previously stated, the suggested definition of DT is a continuous process that must be integrated into an organisation’s strategy. Top management should foster an organisational culture that promotes DT activities and provide internal incentives to encourage employees to participate (Ismagilova et al. 2019 ).

The role of the leader is critical in DT. The most appropriate DT project is changing their leadership style to fit the scenario (Situational Leadership Style) (Tekic and Koroteev 2019 ). To be effective in the process, a leader needs to possess the following characteristics:

Flexibility: Being open to change necessitates an entrepreneurial mindset.

Diversified knowledge: This is acquired by seeing what is going on in other industries and determining what is working and relevant to their own.

Priority and results focus: These are must-win processes that define success or failure and are focused on increasing the company’s market performance.

Ownership and responsibility: People value bravery and accountability. To effectively lead, leaders and managers must hold themselves accountable for the overall performance of their teams (Krishnan et al. 2021 ).

Digital technologies are a crucial factor in the success of the DT process. Many studies mention various technologies employed in the DT process based on the operations field of an organisation as well as the technological infrastructure that existed in the economy, and all of these Industry 4.0 technologies are connected together to produce a more efficient DT process.

Artificial intelligence (AI) and its numerous applications are among the most important technologies that allow DT within any organisation. With AI and machine learning, machinery becomes more efficient and effective in its operations, as the machine itself has a better understanding of the processes and how to self-learn and fix any problems that arise within the manufacturing processes (Mittal 2020 ).

Businesses may use IoT technology to connect the physical and digital worlds. It enables manufacturing organisations to collect more data from machines and equipment, which aids in understanding production issues and how to address them more efficiently and run more productive operations (Olsen and Tomlin 2020 ).

Furthermore, one of the critical components in the effective implementation of DT is the security of the organisation’s digital world. With the rising number of cyberattacks occurring every day, cybersecurity technology has become critical in every organisation (Radanliev et al. 2020 ). Manufacturing companies must establish a robust cyber system to make data more accessible while also making it safe enough to withstand digital attacks.

Cloud computing is also a cornerstone of DT. With agile applications and the requirement to access data at any time and from any location, an organisation needs a strong cloud system that allows workers to interact with it and access the data they need to accomplish their operations (Butt 2020a ). Machine intelligence and IoT generate massive volumes of data on everything that occurs in a factory. Big data analytics can give amazing insights into this data and how to gather, analyse and organise large volumes of data to assist management in making timely choices and providing new ways of innovation.

Robots are one of the modern technologies employed inside manufacturing organisations to undertake complicated duties to prevent worker fatigue and improve workplace health and safety standards (Bongomin et al. 2020 ). However, an industrial organisation must maintain the proper balance between robots and human labour to ensure the agility of the processes and make the most of the firm’s machines.

Enterprise Resource Planning (ERP) is a software toolbox that allows an organisation to connect all of its divisions to move information across large distances in real-time (Moeuf et al. 2018 ). Cloud ERP with a strong cloud system is more efficient in terms of data privacy and data transmission velocity than traditional ERP software.

These, among other examples of digital technologies, are the backbone of any DT initiatives in the manufacturing industry. Any organisation should choose the right combination of digital technologies suitable for its needs and financial capabilities (Mittal et al. 2018 ).

Connected business processes are critical, from supply chain integration with suppliers and their many layers to warehousing and quality control to customer relationship management and listening to consumer input in the DT process. Linking all activities across the value chain is critical because single initiatives will fail unless other processes from various departments are integrated (Butt 2020a ).

This complexity of suppliers should be controlled collaboratively to deliver and store raw materials using supplier relationship management software utilising IoT and big data analytics to ensure that raw materials for industrial processes are not in short supply (Götz and Jankowska 2020 ). As a result, this must be meticulously monitored and coordinated with the procurement department.

Using sophisticated DT technologies, such as robots/cobots via advanced robotics management, in an organisation’s production process will increase productivity and allow the organisation to expand its products and services constantly.

A comprehensive literature review indicated that little effort was made in developing a balanced process for implementing DT in the manufacturing sector that focuses on DT's four dimensions. The major limitations of the existing DT processes is that it is primarily focused on adopting digital technologies. They also lacked how to overcome the challenges of implementing DT process resulting from human resistance to change and the development of communication plans for the integration between all stakeholders.

To overcome the above drawbacks, this research project has developed an integrated digital transformation process for the manufacturing industry. The developed process has taken into consideration all the aspects of the entire digital transformation process.

3 Methodology

This study followed an integrated approach to determining the stages necessary for DT in Egypt’s manufacturing industry, which included stakeholder interviews and literature reviews. The quantitative approach was taken by assigning weights to the pre-identified stages under each criterion using the best–worst multi-criteria decision-making method. Figure  2 illustrates the adopted research methodology.

Methodology adopted for the study

Multi-criteria decision-making (MCDM) is a well-established technique for resolving complex situations, especially when several factors affect an objective (Wankhede and Vinodh 2020 ). It is a subset of decision theory. MCDM issues can be classified into two types according to their solution space, namely discrete and continuous. Multi-attribute decision-making (MADM) approaches can be used to handle discrete problems, whereas multi-objective decision-making (MODM) methods can be used to tackle continuous problems. MCDM has been used in a variety of ways over the years, including the Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution, Analytic Network Process, and Preference Ranking Organization Method for Enrichment Evaluations, among others (Rezaei 2015 ).

The best–worst method (BWM) was adopted in this research. According to Rezaei ( 2015 ), the BWM is another effective MCDM tool that, due to its particular properties, may be utilised to study complex problems such as the one discussed in this paper: (a) the BWM can be used alone or in conjunction with other MCDM approaches; (b) the BWM’s consistency in comparisons makes it extremely trustworthy; (c) the BWM makes use of integers for convenience; and (4) in comparison to the AHP approach, the BWM approach requires fewer pairwise comparisons. This is why the BWM technique was used in this study to weigh the variety of aspects evaluated.

The BWM can be calculated using the following stages (Rezaei 2015 ):

The first step involves the creation of the decision criteria set: { C 1 , C 2 , C 3 ... Cn }.

The second step involves the determination of the best (i.e., most important) and worst criteria (least important).

Step three is the determination of the favourite of the best criterion over every other criterion by assigning a number from 1 to 9. In this case, the vector for the resulting Best-to-Others would be as indicated in Eq. ( 1 ):

where aBj denotes the preference of the best criterion B over the j criterion, and aBB = 1.

The next step is to determine the preference of the various criteria over the worst criterion by assigning any number between 1 and 9. In this instance, the vector for the Others-to-Worst would be as indicated in Eq. ( 2 ):

where the preference of criterion j over the worst criterion w is denoted by ajW , where aWW = 1.

The calculation of optimal weights are done at this step \(\left({w}_{1}^{*},{w}_{2}^{*},\dots ,{w}_{n}^{*}\right)\) . In assessing the optimal weight for the criteria (each pair of \({w}_{B}/{w}_{j}\) and wj/ww ), we have \({w}_{B}/{w}_{j}={a}_{Bj}\) and \({w}_{j}/{w}_{w}={a}_{jw}\) . To meet these conditions for the entire j , one must identify a solution in which the maximum absolute differences \(\left|\frac{{w}_{B}}{{w}_{j}}-{a}_{Bj}\right|\text{ and }\left|\frac{{w}_{j}}{{w}_{w}}-{a}_{jw}\right|\) for entire j is minimised. This results in Eq. ( 3 ) when factoring the non-negativity and sum conditions for the weights.

The optimal weights \(\left({w}_{1}^{*},{w}_{2}^{*},\dots ,{w}_{n}^{*}\right)\text{ and }{\xi }^{*}\) would be obtained by solving Eq. ( 3 ). For the purposes of ensuring consistency, a consistency ratio (CR) also known as the Ksi value is assessed using \({\xi }^{*}\) . The bigger the \({\xi }^{*}\) value, the less reliable the CR and comparisons become. The comparison becomes more reliable when the \({\xi }^{*}\) value is closer to zero.

The judgements for the analysis of the most critical stages in the manufacturing sector’s DT process were acquired from industry experts with a minimum of 10 years of experience in operations and production management roles and from different manufacturing sectors such as home appliance, food processing, clothing, pharmaceuticals and furniture. They were contacted via structured face-to-face interviews. In total, 28 experts were contacted, but only 21 responses were deemed worthy of consideration for the analysis following data cleaning. The rejected responses had not completely answered all of the questions to provide an accurate assessment of the interview.

4 Results and data analysis

This section presents an overview of the most important stages in the DT process in the manufacturing industry, which cuts across all the sectors (i.e., managerial, economic, human and technical aspects). The quantitative results are also presented in this section.

4.1 Quantitative analysis

The results of the BWM used to assign the various weights to the identified stages of the DT process in the manufacturing sector, as presented in Table 1 , are discussed in this section. A total of eight stages were identified and ranked by experts. These stages shall be discussed in detail in subsequent sections in this study.

After assessing the various inputs from the consulted experts, invest in the right technologies and tools (S1) was selected as the most important step in the DT process, whereas communicate plans with customers (S8) was selected as the least important step in the DT process. As mentioned earlier in the introduction, all eight stages are crucial in the DT process, and in this paper, they are ranked according to their relative importance. In this example, S8 is least important compared to S1, but this does not mean it is not important.

Based on the preferences provided by the consulted stakeholders for each step as provided in “Appendix 1”, further calculations were done to ascertain the weights for the various factors. A Ksi* value (CR) of 0.079 was determined, close to zero. Remember that in the BWM, the closer the CR is to zero, the more consistent the experts' comparisons. The weights for the various stages are presented in Fig.  3 . It reflects the respondents’ views on the various identified factors that are considered important stages in the DT process in the manufacturing industry and that have the potential to positively affect the development of DT efforts in the manufacturing industry in a developing economy such as the Egyptian economy.

DT process steps in the manufacturing sector

According to the results, invest in the right technologies (S1) was assigned the highest weight of 25.8%, followed by invest in staff training (S3), which was assigned 18.8%. Draw up a comprehensive yet flexible/adaptable budget (S4) comes close to S3 with a weight of 17.4%. This reflects the importance of financial resources in the DT process in the manufacturing industry. Involve all departments in developing a strategy (S2) comes next with a weight of 11.3%, which reflects the connected-departments factor in the DT definition. This is followed by pilot the project in one part of the business first (S6) with a weight of 9.7%. Next comes communicate strategy and goals with employees (S7) with a weight of 7.6%. Communicating the strategy among employees reduces the resistance to change significantly, especially when employees feel that they are part of the decision-making process. The two least important stages are assign a board-level or C-level sponsor to the project (S5) with a weight of 5% and communicate plans with customers (S8) with approximately the same weight of 4.5%.

4.2 Formulating DT process stages in the manufacturing industry

This section will discuss the implementation stages and best practices of DT in the manufacturing sector after the readiness assessment of an organisation. Assessing the readiness of the organisation's capabilities helps the decision-maker get an overall insight into the DT processes needed for the organisation to move forward in the DT journey. As mentioned earlier, this process needs to be continuous and needs to be further updated regularly to cope with the advancement of digital technologies and the shift in customer needs and wants. Figure  4 summarises all the stages in the DT process ranked based on the BWM after conducting the interviews.

DT process in the manufacturing industry

4.2.1 Invest in the right technologies and tools

The right understanding of these technologies and how the manufacturing organisation could use them properly in their daily activities is crucial to the success of any DT process. These digital technologies range from Artificial Intelligence (AI), Internet of Things (IoT), big data analytics, robots, and many other tools. Investing in the right technologies that suit an organisation’s structure and size will help the organisation best utilise these technologies to get the most return on investment in the shortest period of time (Benitez et al. 2020 ). This practice will differ from one manufacturing sector to another and will depend on many factors such as the organisation’s financial and human capabilities (Dalmarco et al. 2019 ). It is very important to emphasise investing in technologies that are right for the organisation, not the best technologies existing in the market, as these best technologies may not be suitable for the organisation.

4.2.2 Invest in staff training

One of the main challenges of DT is the lack of skills necessary to cope with this transformation, either intellectual skills or technical skills (Butt 2020b ). This is due to a lack of training programs provided by an organisation to their employees as well as the demographic nature of the workforce in some economies. Investing in training to build technical skills to cope with the advancement of digital technologies is crucial to the success of the DT process. Figure  5 summarises the essential digital skills needed for implementing DT.

Essential digital skills for DT

Not only are technical skills necessary, but intellectual and human skills are also very important in the DT process (Chirumalla 2021 ). Investing in training programs, such as innovation management or managing change, especially for middle managers, is of great importance to help managers understand the resistance of change for their employees and know ways to cope with this resistance and overcome it.

4.2.3 Draw up a comprehensive yet flexible/adaptable budget

Financial capabilities are one of the main criteria for a decision-maker to take into consideration when setting a DT strategy. Developing a budget that suits the needs of the manufacturing organisation to achieve its goals from DT is of great importance. This will also determine the level of digital technologies advancement that the organisation should invest in Ing et al. ( 2019 ). When setting a budget for DT, several factors need to be taken into consideration—for example, the cost–benefit analysis as well as the expected return of investment and the project payback period. Every manufacturing organisation should allocate a specified percentage of its profits to DT processes each year to cope with the developments of digital technologies, as this a continuous process.

4.2.4 Involve all departments in developing a strategy

As mentioned above, DT is not only about operation or production departments, but it should also happen across all departments of an organisation (Chirumalla 2021 ). When developing a strategy for DT, the decision-makers should involve all departments in the organisation as well as the stakeholders most affected by this change—for example, the suppliers. Many organisations go beyond that and develop a co-strategy with their main suppliers or main distributors to ensure that their DT efforts are well organised so they get the most out of this change (Ciano et al. 2021 ).

Involving all departments in developing the strategy will significantly reduce the resistance to change when implementing these change strategies, and this will enhance the efficiency of the processes and reduce any efforts to make this change fail (Horváth and Szabó 2019 ).

4.2.5 Pilot the project in one part of the business first

DT is a huge project in terms of scale and scope. Implementing the project in all the businesses inside an organisation in the first stages of the project would bear a great risk both financially and administratively. For this reason, one of the best tactics to initiate a DT project is to pilot it first on one the business units of the organisation. This pilot may take place in one of the production lines or one of the production processes inside the production line. The scale of the piloting will depend on the financial capabilities of the organisation as well as the skills already equipped in its labour. Moreover, it will depend on how far the organisation wants to reach in adapting DT projects and processes.

4.2.6 Communicate strategy and goals with employees

Communication is one of the most important procedures in any projects that aims to make significant change (Kovaitė et al. 2020 ). Top management should communicate the goals of implementing DT to the working labour of the organisation. This will help reduce any fear from the employees that digital technologies will eliminate their jobs and convince them that applying digital technologies will reshape their jobs, not replace them (Horváth and Szabó 2019 ). Another benefit from communication is that it opens up space to invite any innovative ideas that employees might have in implementing these kinds of projects. This will increase the sense of participation in forming this strategy and make them more committed to achieving the goals of this strategy.

4.2.7 Assign a board-level or C-level sponsor to the project

Top management support is one of the key success practices from previous experiences in manufacturing organisations (Krishnan et al. 2021 ). This support has a great influence on reshaping the organisational culture to be more digital-oriented and help the labour understand the significance of DT in their daily activities. This support also encourages middle managers to take initiative in this direction and showcase their digital processes within their departments to achieve a more digital-oriented workflow.

4.2.8 Communicate plans with customers

The final best practice is to communicate your plans with the customers. As the customers are the heart of any organisation, they should be aware of any change in the plans of a company, especially if it relates to the user experience or communication channels with the customers (Zaoui and Souissi 2020 ). For example, making the customer more aware of new chatbot feature on the organisation’s website and how to use this feature will maximise the use of this feature.

5 Implications

5.1 theoretical implications.

Although there is an extensive literature for DT implementation, few gave a detailed process for DT implementation in the manufacturing sector (Jafari-Sadeghi et al. 2021 ; Anderson and Ellerby 2018 ; Luthra et al. 2020 ; Stoianova et al. 2020 ; Balog and Knapčíková 2021 ). This research adds to the literature by developing a prioritised DT implementation process that is specifically tailored for the manufacturing industry using data collected from top experts in the manufacturing field in an evolving economy that is focused on the manufacturing industry like the Egyptian economy. Moreover, this research considers most of the aspects in the DT process implementation from technology to human factor and leadership support.

5.2 Practical implications

The suggested model help manufacturing firms in the developing economy to formulate a comprehensive strategy to implement DT, especially for those manufacturing firms that are in the beginning of their digital transformation journey. By offering this process, manufacturing firms can understand and prioritise their business objectives to suit their capabilities in order to achieve a successful digital transformation. As stated earlier, digital transformation is not only a one-time process, rather, it is a continuous process that needs the integration of all aspects of the organisation. That is why organisation need to adapt their processes and prioritise them according the dynamic factors that can affect their digital transformation journey. That is why this suggested framework for a DT process offers the most important eight steps of the DT and the flexibility to prioritise them according to the manufacturing firm capabilities and their surrounding business environment.

6 Conclusions

This paper aims to identify and determine the most important stages for DT implementation in the manufacturing sector after assessing the readiness of the manufacturing organisation to begin this process. After studying the literature review, we determined eight stages and ranked them according to their importance. Twenty-one structured interviews with manufacturing experts in the Egyptian economy were held and analysed using the BWM to rank the stages according to the interviewees’ perspectives. The stages in the DT process are as follows, according to importance: (1) invest in the right technologies and tools, (2) invest in staff training, (3) draw up a comprehensive yet flexible/adaptable budget, (4) involve all departments in developing a strategy, (5) pilot the project in one part of the business first, (6) communicate strategy and goals with employees, (7) assign a board-level or C-level sponsor to the project, and (8) communicate plans with customers. Understanding the dimensions and factors of each of these eight stages will help manufacturing organisations implement DT successfully, especially when they have yet to start their DT process.

This research has limitations. First, this study was applied to the Egyptian economy, which has developing economy, which has different characteristics from the developed economics, even in adopting different digital technologies. Second, the purpose of this research is to address the implementation phase of the DT process; other post-implementation phases may include measurement and control, feedback and improvement.

Future research may include applying the same process in a specific manufacturing industry and seeing how changing the manufacturing industry impacts the process. Other future research may include applying the same DT process in a developed economy and making a comparative analysis between both processes or applying the same process in a different sector and measuring the differences in the process.

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Abdallah, Y.O., Shehab, E. & Al-Ashaab, A. Developing a digital transformation process in the manufacturing sector: Egyptian case study. Inf Syst E-Bus Manage 20 , 613–630 (2022). https://doi.org/10.1007/s10257-022-00558-3

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Digital transformation in manufacturing: An overview

Manufacturers are turning to digital solutions to streamline their operations, increase efficiency, and gather valuable data and insights..

The digital transformation in manufacturing represents a significant opportunity to improve operational efficiency, reduce costs, and enhance the customer experience. The manufacturing industry is undergoing a profound transformation driven by rapid advances in technology and the growing demand for more efficient, sustainable, and data-driven operations. This transformation often referred to as Industry 4.0 , marks the beginning of a new era of digitalization, data-driven decision-making, and smart automation. The adoption of Industry 4.0 technologies and practices is critical for manufacturers looking to remain competitive in a rapidly changing business environment.

In an era where data is becoming the new currency, manufacturers that can leverage data and analytics to drive innovation and improve their operations will be the ones that succeed. The digital transformation of manufacturing also presents a critical solution for addressing the global challenges posed by climate change, as it enables the creation of more sustainable and environmentally friendly production processes.

The importance of digital transformation in manufacturing

Manufacturing has long been a vital part of the global economy, and the industry has come a long way in recent decades. However, with the advent of new technologies and the increasing demand for customization and efficiency, the manufacturing sector is undergoing a significant transformation. Digital transformation is at the forefront of this change, offering manufacturers a wealth of opportunities to improve their operations, meet the demands of customers, and remain competitive in a rapidly evolving market.

A brief overview of the current state of manufacturing and the benefits of digital transformation

The current state of manufacturing is characterized by a growing need for customization and personalization, as well as increased pressure to reduce costs, improve quality, and speed up delivery times. To meet these demands, manufacturers are turning to digital solutions to streamline their operations, increase efficiency, and gather valuable data and insights. From Industry 4.0 technologies and practices to investments in data and analytics infrastructure to the adoption of smart automation and connected systems, digital transformation is enabling manufacturers to stay ahead of the curve and thrive in today’s fast-paced, global market.

The benefits of digital transformation in manufacturing are numerous and wide-ranging. By adopting digital solutions, manufacturers can improve their operations in a number of ways, including increased efficiency and productivity, reduced costs, improved quality control, and enhanced customer experience. Additionally, digital transformation can provide manufacturers with valuable insights into their operations, enabling them to make data-driven decisions, optimize their processes, and stay ahead of the competition.

The drivers of digital transformation in manufacturing

The manufacturing industry is undergoing a significant transformation, driven by a number of factors, including technological advancements and increased competition. These drivers are driving manufacturers to embrace digital solutions and transform their operations in order to remain competitive in the rapidly evolving market.

The role of technology advancements and increased competition

The role of technological advancements in digital transformation in manufacturing cannot be overstated. Advances in areas such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing have enabled manufacturers to collect and analyze vast amounts of data, automate processes, and streamline operations. This has allowed manufacturers to become more efficient, reduce costs, and improve the quality of their products and services.

Increased competition is another key driver of digital transformation in manufacturing. With the rise of global competition, manufacturers are facing increased pressure to improve their operations and offer unique and innovative products and services. By embracing digital solutions, manufacturers can not only stay ahead of the competition but also differentiate themselves in the market and better meet the demands of their customers.

Fostering a culture of innovation through digital maturity

The impact of changing customer demands and market trends

Changing customer demands and market trends are also driving the need for digital transformation in manufacturing. Consumers are increasingly demanding customized and personalized products and services and are expecting faster delivery times and improved quality. Manufacturers must respond to these demands by embracing digital solutions that allow them to streamline their operations, improve efficiency, and meet the needs of their customers.

The key components of a successful digital transformation in manufacturing

The manufacturing industry has undergone a significant transformation in recent years, with the rise of Industry 4.0 and the increasing adoption of digital technologies. The successful implementation of a digital transformation in manufacturing requires a combination of the right technology, a supportive culture, and a focus on data and analytics.

Adoption of Industry 4.0 technologies and practices

Industry 4.0 refers to the fourth industrial revolution, characterized by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and robotics into the manufacturing process. The adoption of these technologies is a key component of a successful digital transformation in manufacturing. By implementing these technologies, manufacturers can improve efficiency, reduce costs, and enhance the overall quality of their products.

Investment in data and analytics infrastructure

Data is at the heart of a successful digital transformation in manufacturing. Investing in a robust data and analytics infrastructure is critical for manufacturers looking to take advantage of the opportunities presented by Industry 4.0. This infrastructure should include the collection, storage, and analysis of data from a variety of sources, including IoT-connected devices, machines, and production lines. The insights generated from this data can help manufacturers make informed decisions, improve their processes, and stay ahead of the competition.

Building a digital-first culture and mindset

A digital-first culture and mindset are essential for the successful implementation of a digital transformation in manufacturing. This means that manufacturers need to adopt a customer-centric approach and embrace the change brought about by digital technologies. Companies must empower their employees with the right tools, training, and support to fully leverage these technologies and cultivate a culture that values innovation and continuous improvement.

Implementation of smart automation and connected systems

Smart automation and connected systems play a crucial role in the digital transformation of manufacturing. By automating repetitive tasks and connecting systems and devices, manufacturers can reduce the risk of human error and improve the overall efficiency of their operations. Additionally, the data generated by these connected systems can provide valuable insights into the production process, allowing manufacturers to identify areas for improvement and optimize their operations.

Developing a digital transformation roadmap for manufacturing

A digital transformation roadmap is a strategic plan that outlines the steps a company must take to achieve its digital transformation goals. In the context of manufacturing, a digital transformation roadmap can help companies embrace new technologies, modernize their operations, and stay ahead of the competition. In this section, we’ll discuss the key components of a successful digital transformation roadmap for manufacturing.

Step 1: Assessing current state and defining goals

The first step in developing a digital transformation roadmap is to assess the current state of the company and define clear, measurable goals for the transformation. This should include an analysis of the company’s technology and data infrastructure, processes, and culture. The goals should be aligned with the company’s overall business strategy and should take into account both short-term and long-term needs.

Step 2: Identifying priorities and developing a plan

Once the current state and goals have been defined, the next step is to identify the priorities for the transformation. This should be done in consultation with key stakeholders and should take into account the resources available, the timeline for the transformation, and the potential impact on the business.

Based on the priorities, a detailed plan for the transformation should be developed. This plan should include specific actions and timelines for implementation, as well as a clear understanding of the resources required and the roles and responsibilities of the various stakeholders.

Step 3: Implementing and monitoring progress

The implementation phase is where the plan for the digital transformation is put into action. This will likely involve a significant amount of change management as employees and processes are adapted to the new technologies and approaches. It is important to communicate the benefits of the transformation clearly and effectively and to provide support and training as needed.

Finally, progress should be regularly monitored and evaluated, and the roadmap should be adjusted as necessary. This will help ensure that the transformation remains on track and that the goals are achieved.

Case studies of digital transformation in manufacturing

The successful implementation of a digital transformation in manufacturing can have a profound impact on a company’s operations, competitiveness, and bottom line. In this section, we’ll take a closer look at some real-world examples of digital transformation in the manufacturing industry.

Digital transformation examples in manufacturing

One prominent example of digital transformation in manufacturing is the automotive industry. Automakers have invested heavily in Industry 4.0 technologies and data-driven processes, leading to significant improvements in efficiency and productivity. Another example is the aerospace industry, where digital technologies have been leveraged to improve supply chain management, streamline the design process, and reduce the time it takes to bring a new product to market.

Transforming your business with data observability in the era of digitization

Discussion of the key elements that led to their successful transformations

The successful transformations in the automotive and aerospace industries were driven by a number of key elements, including investment in advanced technologies, the adoption of data-driven processes, and a focus on building a digital-first culture. In each case, the companies were able to leverage digital technologies to improve their operations, reduce costs, and stay ahead of the competition. The key to their success was a combination of technology, process, and culture, working in harmony to deliver tangible results.

Challenges and best practices for digital transformation in manufacturing

While digital transformation in manufacturing offers many benefits, it is not without its challenges. In this section, we’ll discuss some of the common obstacles manufacturers face when undergoing a digital transformation and provide best practices for overcoming them.

Common obstacles and how to overcome them

One of the biggest challenges in digital transformation in manufacturing is resistance to change. Many employees may be skeptical of new technologies and processes and may be unwilling to adopt them. To overcome this challenge, manufacturers need to provide their employees with training and support and communicate the benefits of the transformation clearly and effectively.

Another challenge is a lack of investment in technology and data infrastructure. Manufacturers must invest in the right technology and data infrastructure if they want to reap the benefits of digital transformation. This includes not only the technology itself but also the personnel and resources needed to manage it effectively.

Benefits of digital transformation in manufacturing

The benefits of digital transformation in manufacturing are many and varied. By adopting Industry 4.0 technologies and data-driven processes, manufacturers can improve efficiency, reduce costs, enhance the quality of their products, and better meet the needs of their customers. Additionally, digital transformation can help manufacturers stay ahead of the competition and remain relevant in an increasingly digital world.

Strategies for ensuring long-term success

To ensure long-term success, manufacturers must develop a clear and comprehensive digital transformation strategy. This strategy should be built around the specific needs and goals of the company and should take into account the technology, process, and culture elements discussed earlier. Manufacturers should also be prepared to continually evaluate and adjust their strategy as needed in response to changing market conditions and technology trends.

Another important factor in long-term success is the development of a strong data and analytics culture. Manufacturers should encourage the collection and analysis of data from all aspects of their operations and should use these insights to drive continuous improvement. Finally, manufacturers should foster a culture of innovation and continuous learning, empowering their employees to take advantage of new technologies and processes as they emerge.

The future of digital transformation in manufacturing

The manufacturing industry is undergoing a rapid transformation driven by digital technologies, and the pace of change is only expected to accelerate in the coming years. In this section, we’ll examine some of the emerging technologies and trends shaping the future of digital transformation in manufacturing and discuss how manufacturers can prepare for what’s to come.

Discussion of emerging technologies and trends

One of the key trends shaping the future of digital transformation in manufacturing is the increasing use of artificial intelligence (AI) and machine learning (ML). These technologies have the potential to revolutionize the way manufacturers approach operations by enabling them to make better use of data, automate repetitive tasks, and optimize decision-making.

Another important trend is the growing adoption of the Internet of Things (IoT) in manufacturing. The IoT refers to the network of connected devices that can communicate and share data with one another and has the potential to create more efficient, interconnected, and responsive manufacturing operations.

AI and big data are the driving forces behind Industry 4.0

Predictions for the future of the industry and how manufacturers can prepare

In the years to come, it is likely that the use of AI, ML, and IoT will become increasingly widespread in the manufacturing industry. Manufacturers that embrace these technologies and incorporate them into their operations will be well-positioned to stay ahead of the competition and continue to grow.

To prepare for the future, manufacturers must invest in the development of their data and analytics capabilities and actively seek out new technologies and best practices. They should also be proactive in developing their human capital by providing their employees with the skills and training they need to succeed in a rapidly evolving digital landscape.

In conclusion, the digital transformation of manufacturing is an essential step for companies looking to remain competitive in the rapidly changing business landscape. By embracing Industry 4.0 technologies and practices, manufacturers can improve their operations, reduce costs, enhance the customer experience, and address the global challenges posed by climate change. The future of manufacturing is digital, and manufacturers that can effectively navigate the transformation will be the ones that thrive. The digital transformation of manufacturing represents a significant opportunity for growth and innovation, and those companies that embrace it will be well-positioned to succeed in the era of data and digitization.

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30+ Digital Transformation Case Studies & Success Stories [2024]

Digital transformation has been on the executive agenda for the past decade and ~ 90% of companies have already initiated their first digital strategy. However, given the increasing pace of technological innovation, there are numerous areas to focus on. A lack of focus leads to failed initiatives. Digital transformation leaders need to focus their efforts but they are not clear about in which areas to focus their digital transformation initiatives.

We see that digital transformation projects focusing on customer service and operations tend to be more heavily featured in case studies and we recommend enterprises to initially focus on digitally transforming these areas.

Research findings:

• Outsourcing is an important strategy for many companies’ digital transformation initiatives.
• Most successful digital transformation projects focus on customer service and operations

Michelin-EFFIFUEL

Michelin, a global tire manufacturer, launched its EFFIFUEL initiative in 2013 to reduce the fuel consumption of trucks. In this context, vehicles were equipped with telematics systems that collect and process data on the trucks, tires, drivers habits and fuel consumption conditions. By analyzing this data, fleet managers and executives at the trucking companies were able to make adjustments to reduce oil consumption.

• Business challenge : Inability to improve customer retention rates to target levels, due to trucks’ fuel consumption and CO2 emissions .
• Target customers : Fleet managers and operations managers at truck companies in Europe
• Line of business function : Customer success management and sales.
• Solution : By using smart devices, truck and tire performance degradation is detected and maintained from the start. The solution also nudges truck drivers into more cost and environmentally friendly driving.
• Business result : Enhanced customer retention and satisfaction. EFFIFUEL has brought fuel savings of 2.5 liters per 100 kilometers per truck. The company also reduced the environmental costs of transportation activities. According to Michellin, if all European trucking companies had been using the EFFIFUEL initiative, it would have caused a 9 tons of CO2 emission reduction.

Schneider Electric-Box

Schneider Electric is a global company with employees all over the world. Prior to the Box initiative , which is a cloud-based solution, business processes were relatively slow because it is difficult to process the same documents from different locations at the same time. Schneider Electric also needed a way to provide data management and security for its globally dispersed workforce. So Schneider Electric outsourced its own custom cloud environment that integrates with Microsoft Office applications to Box. The platform also ensures tight control of corporate data with granular permissions, content controls and the use of shared links.  Thanks to this initiative, the company has moved from 80% of its content hosted on-premises to 90% in the cloud and has a more flexible workforce.

• Business challenge : Inability to increase operational efficiency of the global workforce without capitulating to data security.
• Solution : Outsourcing company’s cloud-based platform to Box, that ensures data security and integration with Microsoft Office programs to ensure ease of doing business.
• Business result : Schneider Electric connects its 142.000 workers within one platform which hosts 90% of its documents.

Thomas Pink-Fits.me

British shirt maker Thomas Pink, part of the Louis Vuitton Moet Hennessey group, has outsourced the development of its online sales platform to Fits.me Virtual Fitting Room . The aim of the initiative was to gain a competitive advantage over its competitors in e-commerce. Thanks to the online platform developed, customers can determine how well the shirt they are buying fits them by entering their body size.

The platform also helps Fits.me gain better customer insight as previously unknown customer data, including body measurements and fit preferences, becomes available. In this way, the platform can offer customers the clothes that fit them better.

• Business challenge : Lack of visibility into  online sales and customers’ preferences.
• Target customers : Online buyers and users.
• Line of business function : Sales and customer success management.
• Solution : Outsourcing the development of the online platform to Fits.me Virtual Fitting Room .
• Business result : Improved customer satisfaction and engagement. Thomas Pink reports that customers who enter the virtual fitting room are more likely to purchase a product than those who do not. There are many successful digital transformation projects from different industries, but we won’t go into every case study. Therefore, we provide you with a sortable list of 31 successful case studies. We categorized them as:
• System Improvement : changing the way existing businesses work by introducing new technologies.
• Innovation : creating new business practices, based on the latest technology.

If you are ready to start you digital transformation journey,  you can check our data-driven and comprehensive list of digital transformation consultant companies .

To find out more about digital transformation, you can also read our digital transformation best practices , digital transformation roadmap and digital transformation culture articles.

You can also check our sustainability case studies article which include ESG related success stories.

For any further assistance please contact us:

This article was drafted by former AIMultiple industry analyst Görkem Gençer.

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Please note you do not have access to teaching notes, digital transformation maturity model development framework based on design science: case studies in manufacturing industry.

Journal of Manufacturing Technology Management

ISSN : 1741-038X

Article publication date: 30 June 2022

Issue publication date: 22 September 2022

This study aims to propose a novel maturity model development framework based on design science theory utilizing qualitative and quantitative methods for empirical evidence and develops a descriptive digital transformation maturity model by using the proposed framework.

Design/methodology/approach

Design science theory is deeply explored and extended to propose a novel maturity model development approach, including robust and rigorous validation processes. Thus, three consecutive discussion sessions and evaluations with experts are carried out iteratively to evolve and saturate the efficiency and utility of the maturity model, and consensus among experts at each session is validated by the intra-class correlation (ICC) coefficient. Furthermore, the Wilcoxon signed rank test is utilized to test whether there is a difference between consecutive sessions. Finally, prototype testing as a pilot study and two case studies in the manufacturing industry are carried out to validate the applicability of the developed maturity model.

A 3-phase maturity model development framework that includes the activities and outcomes in each phase emerge based on the design science theory. The comparative literature analysis and discussion sessions resulted in six dimensions, ten sub-dimensions, 39-capability items that circumscribe the digital transformation concept and five maturity levels that demonstrate conceptual consistency and a measurement tool for self-assessment. In addition, prototype testing and case studies show that the developed maturity model can measure the company's maturity level. Finally, it is proven that the digital transformation maturity model is developed by following the proposed maturity model development framework.

Practical implications

The maturity model draws a framework for practitioners that facilitate an initial roadmap and enhance the adoption rate, and it motivates the practitioners for frequent and efficient assessments, thus helping the continuous improvement of the digital transformation journey.

Originality/value

The originality of this paper lies in proposing a novel maturity model development framework based on design science and presents the activities and validation methods for this purpose. Furthermore, a comprehensive and rigorously validated digital transformation maturity model is developed based on the proposed framework.

• Digital transformation
• Digitalization
• Industry 4.0
• Maturity model
• Design science
• Digital maturity

Kırmızı, M. and Kocaoglu, B. (2022), "Digital transformation maturity model development framework based on design science: case studies in manufacturing industry", Journal of Manufacturing Technology Management , Vol. 33 No. 7, pp. 1319-1346. https://doi.org/10.1108/JMTM-11-2021-0476

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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