Supply chain digitisation and management

Abstract

In the rapidly evolving landscape, digitising the operations and facilities in a supply chain network is essential to make the system autonomous and develop strategies for enhancing resilience, transparency, and efficiency. The COVID-19 pandemic highlights the necessity of sustainable solutions for the hybrid mode of operations. To overcome several challenges, including price optimisation, demand forecasting under uncertainty, supply-demand gap reduction, take into account vulnerability, competitive business environment and risk, the supply chain needs to be streamlined with technology-driven infrastructures incorporating physical and information flow into overall supply chain processes. The digitisation aspect encompasses adopting cutting-edge technologies such as enterprise resource planning (ERP) for supply chain visibility, e-hailing platforms, real-time data analytics, the Internet of Things (IoT) and Internet of Behaviour (IoB), blockchain-driven technology, as well as additive manufacturing, enabling seamless connectivity and communication among diverse stakeholders. This revolution enables strategic integration of various entities and state of the art data-driven decision-making, providing real-time insights into logistics movements, demand forecasting, production planning and inventory levels. Supply chain digitisation and management emphasises collaboration with supply chain partners to identify important factors, optimise costs and enhance overall supply chain resilience. Digitisation and management are technological evolutions and strategic shifts integrating analytical tools, allowing businesses to formulate models to improve performance. The implementation of blockchain-driven technology solidifies trust and safety transactions by creating an immutable and transparent log, mitigating threats and enhancing traceability. Digitisation and management exemplify a transformative journey towards a more connected, data-driven, and agile global supply chain ecosystem.

KEYWORDS:

• Supply chain visibility
• blockchain
• data analytics
• information and digital technologies
• simulation

Introduction

In the contemporary business environment, the transformation towards digitalisation is a fundamental requisite for endowing supply chain networks with autonomy. The global pandemic has accentuated the urgency to explore sustainable solutions to navigate the challenges of a hybrid operational model and address the crisis stemming from resource shortages. To overcome several challenges, including vulnerability, cost fluctuations, risks, demand uncertainty, and complexity, the supply chain needs to be innovative and streamlined with technology-driven infrastructures incorporating physical and data flow into supply chain operations. Supply chain digitisation involves the strategic integration of digital technologies across the entire supply chain ecosystem, revolutionising traditional practices by leveraging advanced tools (Figure 1) such as data analytics, mobile internet, knowledge graph, advanced robotics, blockchain-driven technology, 3D printing, cloud computing, storage capacity, Internet of Things (IoT), Internet of Behaviour (IoB), large language models, transformer models, artificial intelligence and autonomous vehicles.

Figure 1. Technologies of Current Era.

Digital technologies to improve supply chain efficiency.

Therefore, this special issue looks for a new way to re-invent supply chain engineering and management (Dolgui and Proth 2010) from the perspective of digital platforms. The supply chain is going through a digital revolution termed Industry 4.0, equipped with advanced information and digital technologies (IDT) (Ghobakhloo 2020). IDT include the use of e-commerce platforms such as the internet of things (IoT) (Gupta et al. 2023), internet of behaviour (IoB) (Sun et al. 2023), electronic data interchange (EDI), blockchains, and cloud computing and the incorporation of advanced software tools such as computer-aided manufacturing (CAM), computer-aided design (CAD), enterprise resource planning (ERP) and computer-aided engineering (CAE). Digital platforms play a crucial role in transforming supply chains technologically. Through digital platforms, supply chains are evolving toward digital ecosystems and technological networks. Since there is a growing interest in smart contracting, blockchains in supply chain and logistics, exploring the utilisation of digital platforms in supply chain and operations management is required (Queiroz, Telles, and Bonilla 2020; Roeck, Sternberg, and Hofmann 2020). For example, the implementation of digital twins can be facilitated to manage disruptions in the supply chain (Ivanov and Das 2020). To produce a better outcome, supply chain performance can be monitored digitally through blockchain-driven platforms (Manupati et al. 2020).

The digitisation of the supply chain helps meet new consumer needs, supply-side issues, and priorities to improve efficiency. Based on advanced forecasting approaches, such as internal and external data predictive analytics, product distribution approaches reduce the delivery time. Disruptive technologies such as digitalisation and Industry 4.0 drive the emergence of new supply chain management (SCM) paradigms, concepts, and models. Developing digital supply chains and intelligent operations are enabled by the IoT, IoB, cyber-physical networks, and smart, connected goods. Emerging tools, such as Big Data Analytics, advanced robotics, decentralised agent-driven control, tracking, tracing technologies, augmented reality, and additive manufacturing, play an important role (Ben-Daya, Hassini, and Bahroun 2019). These new advanced technologies enable businesses to implement data-led strategies in a digitally-driven manufacturing environment to collect data throughout the item life cycle (Tao et al. 2018) and to improve horizontally and vertically integrated production (Frank, Dalenogare, and Ayala 2019).

In supply chain analytics, the data layer (Figure 2) describes the foundational levels where various kinds of data are collected, stored, and analyzed to support decision-making. The supply chain data layer plays a critical role in enabling businesses to derive insights, make informed decisions, and optimise their overall supply chain. The data collection layer in supply chain analytics refers to the foundational level where various data types are systematically collecting from diverse sources including raw material procurement, manufacturer, supplier, logistics providers, distributors, retailers and customer demands across the supply chain ecosystem. IoT, Sensor, QR code, radio-frequency identification (RFID), enterprise resource planning (ERP) are utilised to capture the data. Through communication, network data is shared and stored in the cloud, which can be employed to perform analytical techniques and make decisions. By systematically accumulating, validating, and connecting diverse data types from across the supply chain, organisations can lay the groundwork for effective decision-making and continuous process optimisation.

Figure 2. Supply Chain Analytics.

Layers from data generation to data analytics.

During the COVID-19 pandemic, the importance of a resilient supply chain is highlighted by Singh et al. (2021) and Queiroz et al. (2023). Singh et al. (2021) simulated a public distribution system (PDS) network to demonstrate disruptions in a food supply chain with varying demands. The ripple effect of a pandemic on supply chains, risk-mitigating measures and possible ways for improvement are analysed by Ivanov and Dolgui (2021). Digital supply chain platforms often serve as the basic framework to clarify the relationship between digital technology and sustainability. Digital systems facilitate information management across the supply chain, allowing data to accumulate from each phase. At the same time, data access can be given to any actor involved, including the end-user. A product or service can eventually be initiated with a digital twin, i.e., a virtual counterpart, to collect data and information from design and manufacture to usage and final disposal over the lifecycle.

To summarise, the studies on digital platforms for supply chain and logistics are still understudied in the literature. We thus organised this special issue for the International Journal of Production Research on the challenges and solutions of supply chain digitisation and management in the new era.

Summary of papers in this special issue

A summary of articles that address several aspects and benefits associated with supply chain digitisation and management is presented next. Different categories to which the papers belong and various elements contributing to the successful integration of digital technologies into the supply chain context are elaborated next.

Supply chain visibility

Real-time visibility is crucial in supply chain management, particularly during crises. Insights from studies involving several global supply chain experts emphasise the superiority of real-time visibility over forecasting in enhancing predictability. Factors influencing supply chain visibility (SCV), its content, and implications underscore the potential pitfalls of limited SCV, such as forecast errors, bullwhip effects, and disruptions. Digital technologies like AI, blockchain (Wang, Chen, and Zghari-Sales 2021), cloud systems (Giannakis, Spanaki, and Dubey 2019), knowledge graph (Mitra et al. 2023) and RFID (van Hoek 2019) are recommended for enhanced SCV, however managerial perspectives are often overlooked. Earlier studies categorised SCV into automational, informational, and transformational characteristics, with IoT and AI highlighted for addressing challenges related to organisational factors more than technological ones.

Factors influencing SCV include internal and external tracking, inter-organisational collaboration, trust, information sharing, and technical considerations like cloud-based systems (Agrawal et al. 2022). IT infrastructure compatibility, RFID, and blockchain are crucial for SCV, with higher investments leading to improved visibility and subsequent benefits in cost management, sustainability, and customer service. There is a need to balance accuracy and resolution in information sharing for effective SCV. Trust emerges as a key enabler, while barriers include lack of standardisation, supply chain complexity, IT infrastructure issues, and hesitancy to share data. SCV proves beneficial for managing supply chain complexity, offering insights into capabilities, performance effects, and risk management.

Supply chain risk management gains prominence due to various challenges, with SCV identified as a solution to identify deviations early on. Data elements like deliveries, deviations, inventory levels, and capacity are important. SCV plays a significant role in economic, environmental, and social sustainability. Delphi experts rate customer service as paramount for performance effects. Despite challenges like lack of standardisation and supply chain complexities, SCV is considered crucial for companies to develop resilience. Real-time visibility facilitates better decision support, resource utilisation, productivity, and profitability, particularly in high-complexity global supply chains. SCV may be less necessary in low-complexity contexts but remains implementable. Cultural aspects, innovation, digitalisation, and governmental policies influence SCV, advocating for more research on extended supply chains, empirical studies, case research, and best practices to enhance understanding and implementation of SCV systems.

Lateral cost-sharing

The adoption of customer reward programmes (CRPs) is widespread in the domain of modern marketing to boost sales and strengthen customer loyalty. Among the popular customer reward programmes (CRPs), the consumption point initiative is prominent. Retailers allocate points to customer accounts based on their payments and predefined point generation ratios, allowing customers to gather redeemable points for complimentary products or discounts on future purchases. Real-world platforms are adopting universal point schemes (UPSs), which serve as expanded interpretations of consumption point programmes. The UPS is explored with multiple retailers and a platform implementing points within a channel (Feng et al. 2022). This strategy examines different control modes, such as decentralisation and centralisation, and finds that retailers prefer lower point conversion ratios under lateral cost-sharing. The nuanced impact of cost spillover on channel members’ preferences is explored, particularly in scenarios involving the addition of a new retailer to the channel (Acimovic and Graves 2017).

A proposition of a buyback contract as a more effective alternative to the wholesale price contract for point management is made (Wang, Gurnani, and Subramanian 2021). Optimal contract parameters maximise the channel's profit and achieve a flexible profit split. The decision-making process of retailers and the platform within a fixed group of retailers is elucidated using the Hessian matrix. The findings underscore that, under decentralised control and lateral cost-sharing, the lowest point conversion ratios are inclined to be set by retailers compared to other control modes. Additionally, it is highlighted that the optimal conversion ratio of a retailer under decentralised control surpasses that under centralised control when double marginalisation and cost spillover are negligible. Overall, the strategy helps to determine the complexities of channel dynamics and practical solutions for optimising profit and achieving equilibrium within the channel.

E-hailing platform

In the last decades, the need for taxi services has constantly increased in the e-hailing platforms. Matching such demand for taxi providers requires a crucial plan because of the dynamic nature of demand patterns. These planning and approaches are countermeasures against the changing nature of this work, which is to meet passenger needs, traffic situations and other external influences. Taxi drivers use reactive strategies when dealing with different scenarios and challenges that may arise while working.

Maruthasalam, Roy, and Venkateshan (2021) investigated whether a driver can be profitable by accounting for the reactive strategy in e-hailing platforms. The study proposed a platform to govern how it enables drivers to stimulate suitable reactive strategies. Four operating modes resulted from selecting a combination of the two reactive strategies. The proposed study is conducted in two phases: multi-taxi and single-taxi models. The first step is to assess the multi-taxi system in which taxis contend for requests, with refusals passing on another driver, and examine the counter effect behaviour of taxis by an agent-based simulation model. In next stage, analytical approach is used to compare how much profit a taxi driver incurred while operating within an area of service without competing with other taxis. This kind of relaxation allows one to understand the structure of location dependencies between consecutive trips and analyzes driver reactions in operating mode. To depict the relevance of our work, obtain taxi trip data from Ahmedabad, India, with discrete demand points. The derivative-free optimisation method profit maximisation reactive strategy. The total profit is predicted for each operation mode and topology within service regions.

Blockchain-driven operation

The financial supply chain (FSC) plays an important role in modern business, providing necessary financing for firms to overcome financial difficulties and uncertainties. Uncertain environments, characterised by high-level financial costs, market uncertainty, and business deception, pose challenges for firms in obtaining financing. The financial supply chain under uncertain environments requires innovative business models (Mitra, Goswami, and Tiwari 2022) to address these challenges and ensure sustainable and healthy development. Blockchain-driven technology (BDT) has emerged as a potential solution to address fraudulent problems in the FSC. It offers a new possibility to enhance the security and transparency of transactions within the supply chain.

The use of BDT reduces the reliance on firms’ reputations in the FSC, as the characteristics of blockchain, such as transparency and trust, contribute to a more secure and efficient system (Zhao, Liu, and Zhang 2023). BDT helps in reducing the transmission effect of price risk and increases the intensity of risk transmission, thereby improving the overall security of the supply chain. By leveraging BDT, the FSC can benefit from increased traceability, reduced costs, and improved efficiency, ultimately enhancing the system's security. The adoption of BDT influences the strategic choices in the FSC. The equilibrium strategy differs based on the choice of business models, such as the core enterprise, third-party service, or platform models. The third-party service model is the equilibrium strategy when choosing between the core enterprise model and the third-party service model, while the platform model is the equilibrium strategy when choosing between the third-party service model and the platform model. The use of BDT can have implications for market stability, price performance, and reputation within the FSC. It can help mitigate fraud and improve the overall efficiency of the supply chain.

The strategic choices in the FSC under uncertain environments, influenced by the adoption of BDT, have significant implications for market stability, profit performance, and risk transmission. Understanding the strategic choices and equilibrium strategies in the FSC under uncertain environments can help firms make optimal decisions and improve overall financial performance.

Information and digital technology

The institutional framework effectively manages the disaster supply chain using an integrated approach of Information and Digital Technology (IDT) in India (Dash and Dixit 2022). According to the Ministry of Health and Family Welfare, Government of India report, the Indian subcontinent is particularly vulnerable to natural calamities. Furthermore, several agencies and governments have referred to the three-step process, such as preparedness, mitigation, and response, to deal with natural calamities (Habibi, Kumar Chakrabortty, and Abbasi 2023). The suggested institutional framework would allow for multi-system information exchange, coordination, and decision-making, and IDT's strong effect on the catastrophe supply chain contributes to improved performance and practices at the operational, planning, and strategic levels.

The mathematical model is useful for lowering the total weighted demand response time of a catastrophe supply chain network and helpful in analysing how the integration of IDT affects the total response time of the institutional framework. Meanwhile, the model is used to ensure successful vaccine distribution for SARS-CoV-2, including monitoring, regulation, and execution. Initially, the model formulation consists of three sub-problems such as facility location problem, commodity flow problem, and vehicle allocation problem. Further, scenario and sensitivity analysis shows the possible solutions to the multiple instances of the problem. Also, the impact of IDT has been evaluated under three scenarios:

• Scenario 1 with delay and no IDT

• Scenario 2 with delay and IDT

• Scenario 3 with IDT failure and human intervention.

Additionally, to assess the model's applicability in the context of COVID-19 using model information flow, as well as the influence of IDT on the total response time of the disaster supply chain, and to examine the impact of IDT using scenario analysis. The scenario analysis findings demonstrate the necessity of IDT adoption in the catastrophic supply chain and its capacity to handle IDT failures more effectively. Finally, an illustrative case study is used to validate the model.

Decision support system for manufacturing technology

Configuring spare parts supply chains is a complex process that involves balancing various factors such as inventory management, manufacturing technology choice, and supply chain design. Managers face the daunting task of deciding whether to centralise or decentralise inventory and selecting either traditional manufacturing methods or more advanced techniques like additive manufacturing (Westerweel et al. 2021). The decision-making process becomes more complex due to the unpredictable and sporadic demand for spare parts. This situation makes it essential but difficult to optimise the supply chain configuration. Advanced methodologies and decision support systems (DSS) are developed to support these complex decisions. The DSS can guide the choice between various supply chain configurations and manufacturing technologies (Cantini et al. 2022).

The integration of various manufacturing technologies, such as conventional manufacturing (CM) and additive manufacturing (AM), in a rapidly evolving field requires a strategic approach. This includes critical decisions on the balance of centralised and decentralised inventory management as well as an assessment of the economic viability of different manufacturing methods for spare parts. The decision-making process, thus, involves assessing various configurations and their impacts on costs, logistics, and customer service levels. The DSS, which uses decision tree algorithms, is useful in this situation. It provides insights into the most cost-effective configurations and manufacturing technologies for specific scenarios.

A decision tree algorithm that uses thorough parametric analysis achieves this. It enables comparisons of various supply chain scenarios. It is regarded as the unique characteristic of different manufacturing technologies. The key findings include a decision tree that provides a structured approach to compare the total costs of supply chains with varying degrees of centralisation and different manufacturing technologies (AM and CM). The system is especially beneficial for managers and practitioners in designing efficient and cost-effective supply chains for spare parts.

Multi-agent framework for container booking

The landscape of businesses is continually shifting with digitalisation, creating new possibilities for cross-border supply chains. Within this context, maritime transportation plays an increasingly vital role in the global supply network. However, the maritime supply chain is still in its early stages of digitalisation (Zeng, Chan, and Pawar 2021). Unlike sectors such as retail, the maritime shipping industry involves multiple stakeholders, including buyers, suppliers, logistics service providers, and various authorities. The complexity of operational interactions among these entities sets the maritime industry apart, making the implementation of container e-booking systems challenging and less explored in academic research.

Zeng, Chan, and Pawar (2020) stand out as pioneers in researching the adoption of inter-organisational information systems in maritime shipping. Their exploration, utilising the Technology-Organization-Environment Framework, investigates the container booking process, analyzing both inter-organisational and intra-organisational factors influencing e-booking adoption in the maritime sector. Their study reveals a gap between the general understanding of information system adoption and the specific use of e-booking systems in the maritime supply chain. Conducting an exploratory case study involving eight companies across different tiers of the maritime supply chain, they recognise the imperative need for e-booking systems.

In a related context, Mandal et al. (2022) contribute to this ongoing discourse by aiming to develop an integrative, adaptive, and intelligent container booking system, coupled with a multi-agent architecture. This proposed architecture seeks to facilitate real-time information exchange between autonomous agents, shippers, freight forwarders, and shipping lines. The article details how agents communicate to resolve inconsistencies and introduces a container slot optimisation problem, considering market segmentation, various booking periods, heterogeneous containers, and port congestion scenarios. The provided multi-agent framework enables managers to determine booking limits for different container types, enabling informed decisions on accepting or rejecting incoming booking requests.

Conclusion

This special issue contributes to the existing literature by introducing digital technologies to enhance real-time visibility, a better decision support model for productivity and profitability, the impact of lateral cost sharing to boost sales and strengthen customer loyalty, reactive strategy in e-hailing platforms, blockchain-driven technology to improve FSC, a mathematical model for disaster SC based on information and digital technology, utilisation of decision tree algorithm to design spare parts SCs, and integrative, adaptive, and intelligent container booking system, coupled with a multi-agent architecture. In conclusion, digitisation and management appear as a transformative force reshaping the landscape of logistics and operational strategies. These solutions will help managers and practitioners boost productivity, adaptability, reliability, and flexibility in complex and competitive business environments.

The collaborative approach in organisations based on digitisation enables seamless communication among supply chain associates. It also optimises costs and facilitates a resilient and agile supply chain capable of steering the complexities of the modern business environment. The importance of blockchain-driven technology within a supply chain cannot be emphasised enough, as it ensures trust, security, and transparency in transactions. By establishing an immutable ledger, BDT reduces the risk of fraud and improves traceability, fostering a reliable foundation among stakeholders. Efficiency and effectiveness in the maritime industry are experiencing remarkable growth with implementing a container slot optimisation model in the multi-agent framework.

As digital technologies continue to evolve, the ongoing advancements in generative AI, transformer, knowledge graph, and automation hold the promise of further efficiencies and innovations. The trajectory of supply chain management is set to embrace adaptability, responsiveness, and enhanced performance, positioning advanced digital technologies as an integral component in the future of logistics. In adopting this digital revolution, organisations optimise their current supply chain operations and lay the groundwork for a future-ready and resilient supply chain ecosystem.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Manoj Kumar Tiwari

Manoj Kumar Tiwari is the Director of Indian Institute of Management Mumbai. He is also serving as the Vice-Chancellor of Tata Institute of Social Sciences as an additional charge. He is a Professor on lien with a Higher Academic Grade (HAG) in the Department of Industrial and Systems Engineering at the Indian Institute of Technology, Kharagpur. He is actively involved in research relevant to the application of optimization, modelling, decision support systems, and data mining in logistics, supply chain management, and manufacturing research domains. He is associate/senior editor of several renowned journals like IJPR, POMS, IISE, ESWA, IJPE, JIM, IEEE-SMCA, INS, and CAIE. He has led numerous projects and consultancy assignments with prominent industry and government organizations in India, including the Indian Air Force, Procter and Gamble, TATA Hitachi, and ports located in eastern India. Prof. Tiwari is an author of more than 360 articles in leading international journals with an H index of 84. He is a fellow of the National Academy of Sciences India (NASI), the Indian National Academy of Engineering (INAE), the West Bengal Academy of Science and Technology (WAST), the Indian Academy of Sciences (IASc), the Institute of Industrial and Systems Engineers (IISE), USA, and also recognized with the David F. Baker Distinguished Research Award (IISE, USA).

Bopaya Bidanda

Bopaya Bidanda is the Ernest Roth Professor and former chair of the Department of Industrial Engineering at the University of Pittsburgh, PA. He is the Immediate Past-President of the Institute of Industrial & Systems Engineers (IISE). He was previously a Commissioner with the Engineering Accreditation Commission (EAC of ABET) and currently serves on ABET Board of Delegates. He has also consulted with abroad range of industries across the world. His research focuses on manufacturing systems, production systems, reverse engineering, computer integrated manufacturing, rapid prototyping, and project management. Prof. Bidanda has published 12 books and over 100 papers in international journals and conference proceedings.

Joseph Geunes

Joseph Geunes is a professor in the Wm Michael Barnes'64 Department of the Industrial & Systems Engineering at the Texas A&M University. He is Marilyn and L. David Black Faculty Fellow of Industrial & Systems Engineering (IISE). He received a PhD degree in business administration and operations research from Penn State in 1999. His research focuses on Production and logistics planning, supply chain management and operations research.

Kiran Fernandes

Kieran Jude Fernandes is the Associate Pro Vice-Chancellor (Development and Engagement) and Professor of Operations Management at the Durham University Business School. Prior to this role, he held the roles of Executive Dean (Interim), Associate Dean and Head of Department (Management & Marketing) at the Durham University Business School. Professor Fernandes is a Fellow of the Academy of Social Sciences, the Executive Director of the Northern Powerhouse Innovation Observatory, Fellow of University College Durham, and the Wolfson Research Institute. He held academic positions at the Universities of Warwick and York and as a consulting Professor at the Cabinet Office's Civil Contingencies Secretariat. He was appointed by the UK Secretary of State for International Development as a Non-Executive Director of the UK National Commission (UKNC) for UNESCO with special responsibility for Higher Education, and in 2017 was elected vice-chair of the UKNC. He is a Director of the Global Gateway's Federation and Governor at several schools. He currently sits on a range of advisory panels covering various aspects of Operations and Innovation Management and is on the Academic Advisory Council member of the Chartered Management Institute.

Alexandre Dolgui

Alexandre Dolgui is an IISE Fellow, Distinguished Professor, and the Head of Automation, Production and Computer Sciences Department at the IMT Atlantique, France. His research focuses on manufacturing line design, production planning and supply chain optimisation. His main results are based on the exact mathematical programming methods and their intelligent coupling with heuristics and metaheuristics algorithms. He is the coauthor of 5 books, the co-editor of 20 books or conference proceedings, the author of 253 refereed journal papers, 30 editorials and 31 book chapters. He is the Editor-in-Chief of the International Journal of Production Research (IJPR).