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Industry 4.0 and the digital supply chain (DSC) are changing how things are made and moved around the world. This change is all about how smart technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain are making supply chains work better. These tools help companies react faster and more clearly to what's needed. By using these new technologies, businesses can get better at guessing what customers want, keeping the right amount of stock, and quickly adjusting to new market trends. With these advanced technologies, companies can see big improvements, like being able to match supply with demand more closely and change their plans fast when things in the market change. It is really important for businesses to get how these tech tools work together as the world of making and selling things keeps changing. This chapter examines the convergence of traditional supply chain systems with Industry 4.0, focusing on the transformative impact of technologies such as the IoT, AI, and blockchain.

Digital technologies (DTs) have emerged as a major driving force, transmuting the ways Supply Chains (SCs) are managed. The integration of DTs in supply chain management (SCM), Digital Supply Chain Management (DSCM), has fundamentally reshaped the SCM landscape, offering new opportunities and challenges for organizations. This chapter provides a comprehensive overview of modern DTs and the way they impact modern SCM. This chapter has twofold objectives. First, it illustrates the major changes that DTs have brought to the supply chain landscape, unraveling their multifaceted implications. Second, it offers readers a deeper and comprehensive understanding of the challenges and opportunities arising from the incorporation of DTs into supply chains. By going through the chapter, readers will be able to have a comprehensive grasp of how DTs are reshaping SCM and how organizations can survive and thrive in the digital age. This chapter commences by shedding light on how DTs have and continue to redefine SCM, improving supply chain resilience, visibility, and sustainability in an increasingly complex and interconnected world. It also highlights the role of DTs in enhancing SC visibility, agility, and customer-centricity. Furthermore, this chapter briefly highlights the challenges related to the adoption (pre and post) of DTs in SCM, elucidating on issues related to talent acquisition, data security, and regulatory compliance. It also highlights the ethical and societal implications of this digital transformation, emphasizing the significance of responsible and sustainable practices. This chapter, with the help of three cases, illustrates how the adoption of DTs in SC can impact the various SC performance indicators.

This chapter investigates the potential of integrating multiple criteria decision-making (MCDM) techniques with decision support systems of digital supply chain management (DSCM) to achieve optimal outcomes. Digital supply chain (DSC) employs digital technologies (DTs) such as artificial intelligence (AI), Internet of Things (IoT), and big data analytics to provide extensive datasets and valuable insights pertaining to supply chain operations. MCDM techniques employ these realizations to facilitate informed decision-making through the assessment of multiple competing criteria. Usually MCDM approaches are used in the academic research with comparatively lesser application in industry. We argue that MCDM methodologies can play an instrumental role in DSCM, specifically in the areas of supplier selection, demand forecasting, and inventory management. Nevertheless, the integration of MCDM like AHP, ANP, DEMATEL, etc., with decision support systems presents several challenges, including concerns regarding the quality of data and the intricate task of assigning weights to various factors.

The convergence of the digital supply chain (DSC) with sustainability presents promising opportunities and notable challenges for today's enterprises. This chapter explores the relationship between digital supply chain management (DSCM) and sustainability within the context of supply chain management. It begins by providing a detailed overview of the digital transformation in supply chain management, emphasizing its rapid evolution and its profound impact on sustainability. The chapter then delves into the various sustainability challenges that manifest within DSCs, including issues related to energy consumption, e-waste management, and environmental impact. It goes beyond environmental considerations, exploring social and ethical dimensions, such as the potential consequences of digitalization on socioeconomic disparities. Moreover, the chapter presents a comprehensive framework of best practices and strategies aimed at navigating these challenges. It highlights the utilization of digital technologies like artificial intelligence (AI), Internet of Things (IoT), and blockchain to enhance transparency, efficiency, and responsible sourcing. Real-world case studies are included to exemplify successful implementations of sustainable practices across diverse industries. In conclusion, the chapter emphasizes the imperative of a holistic, authentically sustainable supply chain transformation, underpinned by digital technologies, to advance sustainability objectives while adeptly addressing emerging challenges in the ever-evolving landscape of supply chain management.

The present chapter discusses evolution, definitions, dimensions, capabilities, and the present state of the art of digital supply chain management (DSCM). The objective of the chapter is to offer a detailed understanding of DSCM, by shedding light on its historical development, exploring its multipronged definitions, and highlighting its core dimensions and capabilities in the contemporary business landscape. The evolution of DSCM appears as a central theme, rooted in the background of industrial revolutions. It starts by relooking at the First Industrial Revolution (IR) with its mechanization and steam power, progresses through the Second IR with electrification and mass production, and arrives at the Third IR, characterized by the rise of computers and the internet. The pivoting transition into the Fourth IR, also called Industry 4.0, marks the start of DSCM with its fusion of digital technologies (DTs) in the supply chain (SC) processes. Analysis of key definitions of DSCMs unveils their role as an enabler of SC collaboration, customer-centric nature, having overarching reliance on DTs. Moreover, the chapter explores the core dimensions of DSCM, exposing its ability to improve SC resilience, sustainability, visibility, efficiency, and agility. These capabilities stem from seamlessly woven DT developments into SC: artificial intelligence (AI), machine learning, the Internet of Things (IoT), and advanced analytics. The chapter concludes by highlighting the present state of the art in DSCM, reflecting its indispensable role in the contemporary turbulent business dynamics. In short, this chapter offers a synthesized view of DSCM's definitions, dimensions, evolution, capabilities, and present status within the larger context of supply chain management (SCM) literature.

The supply chain is undergoing a significant digital transformation to adapt to the increasingly digitalized and globalized business environment. To remain competitive in this evolving market, businesses must seamlessly integrate digital technologies throughout the supply chain, spanning all stages from procurement to distribution. This chapter delves into models and methodologies critical to digital supply chain (DSC) transformation, with a focus on advanced techniques such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and data analytics to boost the resilience and agility of supply chain operations. By leveraging practical examples and case studies, the chapter highlights the myriad enhancements digital transformation can introduce across diverse supply chain stages, including sourcing and after-sales service. Additionally, the chapter examines the complexities of cybersecurity, data integrity, and change management within the digital transformation framework, proposing strategies to address these challenges. The insights offered in this chapter will serve as a thorough guide for both practitioners and scholars in the supply chain field, equipping them to adeptly navigate the multifaceted arena of digital transformation.

This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

The advent of the digital technologies (DTs), coincided with the pandemic and global conflicts, has proven to be an unprecedented and transformative era for supply chain management (SCM). DTs are reshaping the way organizations plan, execute, and optimize their SC operations. Throughout this book, we posit that the adoption of digital supply chain management (DSCM) has become essential for staying competitive and responsive in a rapidly evolving business environment. However, amid technological advancements and digital solutions, there exists a critical factor that often goes overlooked – the significance of intangible assets, specifically intellectual capital (IC). This chapter comprehensively explores the role of an organization's IC in the adoption and performance of DSCM. We employ a comprehensive analytical approach, drawing upon existing literature from various sources to elucidate the relationship between IC and DSCM. Synthesizing insights from the literature, the chapter shows how each constituent of IC contributes to the adoption, operation, and performance improvement of DSCM. The discussion in the chapter shows that human capital (HC) forms foundations, as the knowledge, skills, and abilities (KSAs) of the employees are prerequisites essential for understanding, adopting, and capitalizing on DTs in SCM. The analysis also reveals that SC, which represents organizational processes, digital tools, and knowledge repositories, supports the seamless integration of DTs within SCs. Similarly, RC, by nurturing trust, open communication, and collaborative networks, plays an instrumental role in establishing ecosystems that help the adoption and effective functioning of DSCM. This chapter makes a convincing case to consider IC as the strategic component while DSCM adoption and performance.

This final chapter delves into the future of digital supply chain management (DSCM) amid today's dynamic business environment, shaped by technological advancements and factors like automation, artificial intelligence (AI), sustainability, and resilience. It emphasizes the crucial role of digital technologies (DTs) such as AI, the Internet of Things (IoT), Industrial IoT, and the Internet of Everything (IoET), along with blockchain, in revolutionizing supply chain operations. These technologies enable agility, flexibility, efficiency, and responsiveness, crucial for supply chains to proactively adapt to market changes. The chapter explores the trends in DSCs, focusing on real-time data analytics, end-to-end visibility, sustainability, and resilience. It highlights the growing importance of transparency in supply chains, driven by consumer demand for sustainable practices and product origins. DSCM is identified as pivotal for prioritizing sustainability, leading organizations toward green practices. Despite the opportunities in DSCM, challenges like cybersecurity, data management complexities, geopolitical uncertainties, and talent shortages are acknowledged. To overcome these, the chapter stresses strategic foresight in DSCM and the importance of robust process management, risk management, and talent development. The future-readiness of supply chain professionals is discussed, highlighting the need for change management, development of social and deep work skills, collaboration, and ethical practices. The chapter concludes by underscoring the transformative potential of DTs in the digital era, urging organizations to embrace innovation, transparency, and sustainability in their supply chains, recognizing that the future of DSC is an imminent reality.