Digital transformation in supply chains: Challenges, Strategies, and Implementations
Submissions open: 1 October 2021
Author Submission Deadline: 31 December 2021
Digital transformation (DT) has become an increasingly popular topic for both researchers and practitioners (Choi et al., 2018). DT can be defined as “a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies” (Vial et al., 2019, p.118). The term “entity” may refer to an organization, a system or a supply chain. DT aims to improve an entity’s operational performance by impacting its products, business processes, sales channels, and supply chains (Matt et al., 2015). For instance, IBM, SAP, Oracle, and Microsoft have developed software applications in analytics, demand planning, warehouse, inventory management, transportation and distribution operations and a few retailers like Walmart and Carrefour trace the flow of food from farmers to retailers using blockchain technology (Stanley, 2018; Dimitrov, 2019). The application of Industry 4.0 technologies for rebuilding and reconfiguring global supply chains, cannot be ignored. Extant recent literature on digital transformation including big data analytics, artificial intelligence, blockchain technologies, and internet of things (IoTs) are advancing knowledge around the application of Industry 4.0 technologies and digital transformation in the supply chain domain (Hartley and Sawaya, 2019; Wamba and Queiroz, 2020; Pournader et al., 2019).
The literature has discussed some aspirational goals or potential benefits of digital transformation (projects) in supply chains. For example, DT aims to facilitate seamless supply chain collaboration to reduce cost and to improve performance (Stank et al., 2019). Aided by big data, DT may help develop solutions that perform real-time supply chain management and provide proactive, customized data-based solutions (Handfield et al., 2019). An experiment to determine whether high-resolution, item-level visibility of products inside a retail store can be achieved by using RFID through a combination of information and communication (Morenza-Cinos et al., 2019). Higher supply chain quality management through reducing mass manufacturing variability can arguably be achieved by using 3D printing (Sasson and Johnson, 2016). Supply chain agility could be improved by applying rapid prototyping and rapid tooling (Dwivedi et al., 2017) and additive manufacturing that directly integrates customers and manufacturers in decentralized, local manufacturing (Durach et al., 2017). It is easier to find statements about the potential benefits of all these new technologies, than to find efforts to conceptualize and theorize different mechanisms used to realize such benefits.
Research of DT in supply chains is at the nascent stage in many aspects. There are questions about definition and understanding of DT strategies; characteristics of DT; characteristics and maturity of different DT technologies, relationships between different entities, capabilities, uncertainty and risks when implementing DT initiatives; addressing reluctance to change; and the mechanisms used to achieve sustainability, competitiveness, performance and create value when implementing DT. Despite extensive research in the recent past, research of digital transformation in supply chains lacks diversity, theoretical applications and explanations that can fully answer the above questions.
This special issue recognizes supply chain digital transformation journey is neither easy nor without challenges. The possibility of entire supply chain integration using digital technologies is still at a distance (Preindl et al., 2020). DT failure risk is regarded as the first concern for practitioners as 70 percent of all DT initiatives do not reach their goals (Tabrizi et al., 2019). Why DT initiatives fail is not clear. It could be because of the technologies, the people, or the transformation processes. If people and digital technologies do not integrate well, DT can magnify the flaws of the disconnection (Tabrizi et al., 2019). While some past studies have provided a list of barriers and challenges, this special issue does not aim to repeat such a list. It is more important to create a deeper understanding of what goes on in managers’ minds and how they address different challenge, strategy and implementation issues.
This special issue focuses on exploring and understanding at least three main challenges facing logistics and supply chain managers when they consider or implement DT in supply chains. The first challenge is to determine whether DT in supply chains is the right option (i.e. to be digital or not). DT is not about technology; it should be guided by a broader strategy and a mindset of change (Tabrizi et al., 2019). This is related to applying the right strategy to match DT and focal firms, supply chain actors (such as multi-tier suppliers, multi-tier customers) based on different approaches at different development stages. This means identifying which supply chain capabilities are required and which DT technologies and existing technologies can be combined to build new capabilities and making the choice and appropriate use of the right DT technologies for each type of supply chain. Another issue is whether all DT technologies are readily purchased and adopted. Some are not. Some are still in their early technology readiness stage. This raises the question of whether DT is about technology acceptance, adoption, diffusion or innovation. This requires the identification of which DT technologies require further development, and whether it is better to be the pioneer developer or wait until the technologies are proven or developed by other members of the supply chain.
The second challenge is to determine which solution/technology providers, research institutions, suppliers, customers and other channels are useful sources of new knowledge about DT technologies. How managers can access these knowledge sources and learn from them? How can managers manage the relationships between the DT designers or solution/technology providers and the users before, during, and after DT in supply chains? The strategies and related problems of DT in supply chains are unique for the focal firm and its supply chains. However, DT designers, developers, and solution providers are often experts biased towards technology rather than in aspects of functional domain expertise like supply chain management (Trkman, 2013). Therefore, it is critical to consider how the two parties can be efficiently and effectively involved earlier and integration and collaboration improved dramatically.
The third challenge managers face is in the implementation processes and the performance measurement of DT in supply chains. DT is not a simple IT-enabled organizational transformation; DT in supply chains needs to solve many difficulties, such as understanding how DT technologies work, integrating new and existing technologies, experimentation and testing of different technologies and configurations, alignment of DT with different supply chain members and with organizational strategic objectives, structures, core values; and structuring and implementing supply chain processes reengineering (Wessel et al., 2020). After the strategies and decisions for DT in supply chains have been made, the next decision is how DT is to be implemented and leveraged to revamp operation and supply chain management (Stank et al., 2019). The case study of Adidas Russia/CIS serves as a good example of how IT and supply chain issues are addressed simultaneously (Cordon et al., 2017). In a supply chain setting, the question is how transaction cost, uncertainty, power asymmetric, unwillingness to share information should be addressed during a DT project.
To overcome such challenges, this special issue acknowledges the need to research DT in supply chains on the strategy level. For example, we can explore the firm’s ability to alter or make strategic changes to its business model and the impact of such changes on business performance (Galindo-Martín et al., 2019). Meanwhile, as part of the digital transformation strategy, firms develop DT technologies such as big data and predictive analytics as a tool to transform logistics and supply chain functions with the hope of creating new value. While big data business analytics (BDBA) and supply chain analytics are viewed as strategic assets that must be integrated to achieve strategic benefits (Wang et al., 2016), many organizations are still struggling with data integrity and accuracy issues. On the other hand, research needs to explore the mechanisms used to achieve different potential benefits of integrating digital technologies to transform supply chain management. While studies show blockchain technology is employed to improve traceability and transparency can broadly achieve supply chain social sustainability (Venkatesh et al., 2020), more research is required to distinguish which members of the supply chain actually gain which benefits, and why. There are claims that artificial intelligence (AI) can mitigate supply chain risk and achievement of supply chain resilience under unpredictable situations (Baryannis et al., 2019), but what sort of AI can mitigate unpredictable disruptions?
This special issue encourages research to understand the above-mentioned emerging DT challenges. In this special issue, we encourage scholars/practitioners to conceptualize, theorize and analyze real-world DT initiatives or ongoing efforts in supply chain digital transformation. We invite studies using all kinds of methodologies: qualitative studies, case studies, empirical studies, conceptual (theory formulation), experiments, action research, design science, etc. The journal is not suitable for a research which relies purely on mathematic modelling unless this plays an important supporting role in action research or design science study types. Authors are encouraged to ensure the papers submitted to meet the aims and scope of the journal, that emphasizes research that is “strategically focused, theoretically grounded and fostering meaningful research impact”.
- Peter Shi, Macquarie Business School, Macquarie University, Australia. [email protected]
- Mani Venkatesh, Montpellier Business School, France. [email protected]
- V G Venkatesh, EM Normandie Business School, France. [email protected]
- Samuel Fosso Wamba, Toulouse Business School, France. [email protected]
- Bill Wang, Business Information Systems, Auckland University of Technology, New Zealand, [email protected]
Manuscript submissions: 31 December 2021
Initial (first-round) decisions: 28 February 2022
Final revised paper resubmissions: 31 May 2022
The manuscript should be prepared according to the International Journal of Physical Distribution and Logistics Management’s author guidelines that can be found at https://www.emeraldgrouppublishing.com/journal/ijpdlm#author-guidelines
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