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AI and Automation: Building Resilient and Sustainable Supply Chains in Uncertain Times

This chapter discusses how the Covid-19 crisis laid systemic weaknesses in global supply chains and drove action from efficiency-based to resilience-based models. It gives a holistic approach to building resilient and sustainable supply chains, built on flexibility, agility and redundancy while underpinned by the latest technologies including AI/automation/blockchain. The chapter compares old-style with new approaches to risk management, drawing on what we believe are major lessons from the pandemic, and emphasizes the growing need for transparency, sustainability and strategic diversification. Practical recommendations are provided to help organizations prepare for future disruptions and build adaptive, future-ready supply chains. In addition to technological advancements, the chapter describes how sustainability principles such as circular economy models, ethical sourcing and carbon footprint reduction must be incorporated into supply chain strategies. With cases and examples from around the world, it offers a model that shows how digital innovation combined with sustainability systems can help organizations respond faster to change, more effectively manage risks and develop innovative approaches long-term competitive advantage in- or out-of-industry.

1.1. Introduction

In a post-pandemic world, resilience is no longer just the capacity to bounce back from shock (Leach et al. 2021). It is also the ability to foresee, absorb, adjust to and recover from a broad range of threats that are often unexpected, whether from pandemics or geopolitical tension or natural and cyber disaster (Nandi et al. 2021; Thakur-Weigold and Miroudot 2024). The pandemic hastened a shift in the paradigm, forcing companies to rebalance their priorities away from just-in-time efficiency and towards just-in-case preparedness. At the heart of this transformation lies the integration of disruptive technologies, such as artificial intelligence (AI), automation and blockchain (Drissi Elbouzidi et al. 2023).

This chapter provides a thorough overview of resilience in the supply chain from the perspectives of technology and sustainability. It starts by clarifying the term resilience for supply chain scenarios, and then describes the three dimensions of resilience (flexibility, agility and redundancy) and its measurable indicators. It then discusses risk management frameworks, comparing traditional ones to AI-enabled predictive analytics. Based on lessons from the pandemic and other global crises, the chapter focuses on structural vulnerabilities and uses case studies to show what were important failures and important strategies. Finally, it offers practical ways to develop future-ready supply chains that are resilient, sustainable and tech-enabled to survive the vagaries of a fast-evolving world.

1.2. Understanding supply chain resilience

The resilience of a supply chain is no longer viewed as a reactive response, but rather as a systemic characteristic, a dynamic feature incorporated into the design, the governance and the technological infrastructure of supply chain networks. In an era of black swan events, geopolitical instability, climate shocks and cyber threats, supply chains must both run efficiently in regular principal terms and act robustly under stresses and adapt themselves during recovery. Resilience, then, is not the act of avoiding disruption, but having the capability to withstand, adjust and grow through it (Rana and Bhambri 2024b).

Fundamentally, supply chain resilience is a combination of three interconnected elements: flexibility, agility and redundancy (Kamalahmadi et al. 2022). These three things together create a core triad that lets organizations respond to disruption as a condition to be designed for not an existential threat which brings a problem we can solve for you (Garavelli 2003).

1.3. Risk management frameworks

In a supply chain, risk management is to identify, assess and prioritize the expected risk and disruptions that may have an effect on the continuity of supply chain operations (Olufunke Olawale et al. 2024).

The risk management can be improved with the help of AI by analyzing the potential impact of various risks related to financial, operational, environmental or political risk.

1.4. Impact of the pandemic on supply chain vulnerabilities

1.4.1. Disruptions caused by global crises

The Covid-19 crisis acted as an earthquake to the world supply chain in highlighting the fragility of the conventional supply chain practices and significantly hindering the movement of goods. Factories, transport networks and international borders were locked down, abruptly and globally. Factory shutdowns, especially in China, led to shortages in core raw materials and parts that left impacts throughout the electronics and auto sectors (Liu et al. 2022). At the same time, transportation slowdowns particularly in air and ocean freight left backlogs and starved product inventories, causing significant product delays. Labor shortages from sickness, quarantines and lockdowns worsened the situation. Companies had to operate with limited staff, leading to reduced output and slowdowns at various stages of the supply chain (Ganesan et al. 2021).

1.4.2. The cascading effects on global supply networks

1.4.2.1. Domino effect of disruptions

The aftershocks of the Covid-19 pandemic reverberated through global supply chains, as one trouble spot in the supply chain frequently led to subsequent delays and shortages further downstream. For example, shortages of semiconductors, as a result of plants shutting in Asia, hit the automotive sector particularly hard. The factories, which produced the chips, closed.

1.4.2.2. Vulnerabilities in just-in-time (JIT) inventory systems

The Covid-19 pandemic has exposed stark weaknesses in JIT (Just-In-Time) inventory systems that value cost efficiency over supply chain resilience. Such systems are optimized to reduce stock and holding costs and do not have buffer stock that can be used in the event of a shock. With firms experiencing sudden surges or breaks in the demand for products, many realized the folly of relying on JIT strategies for critical products. Witness the automobile industry as an example, relying on JIT inventory systems. Shortages, particularly of critical components such as semiconductors, led to long delays in production, underscoring the challenges that lean inventory models can run into in times of crisis (Mohammad et al. 2022).

1.4.2.3. Supply chain complexity

During the Covid-19 pandemic, however, the real nature of the challenges implicit in global supply chains became far more readily apparent. At that time, businesses had a long and stretched tie with the suppliers who could not quickly adapt to disruptions. This complexity not only made it difficult to troubleshoot issues quickly, but was also slow to change. No one was immune, even businesses with supposedly mighty technological engines such as Walmart and Amazon saw big disruptions (Rose et al. 2023).

1.4.3. Case studies of major supply chain disruptions during the pandemic

1.4.3.1. Case study: Amazon and inventory shortages during the pandemic

The global supply chain was thrown into disarray by the Covid-19 pandemic, especially e-commerce giants such as Amazon. With the pandemic leading to factory closures and delays in transportation across the globe, Amazon had great difficulty in keeping stock of its essential goods (Rana and Bhambri 2024a). With factories shut down and significant disruptions in shipping routes resulting from factories and ports closing and labor shortages, the company experienced widespread out-of-stocks (Jyani 2024).

In early 2020, Amazon faced shortages of items such as sanitizers, cleaning products and nonperishable foods. After demand for these items skyrocketed, the company quickly realized it could not meet demand, and that demand had been accelerated by the health crisis and the migration of people shopping online. To solve the problem, Amazon deprioritized nonessential goods, adjusted its logistics to ship critical items first and hired 175,000 new workers (Strauss-Wieder 2023).

1.4.3.2. Case study: 3M and the N95 mask shortage

The healthcare and pharmaceutical supply chains were put under huge stress by the Covid-19 pandemic, which came with an unprecedented surge in demand for life-saving medical equipment, including PPE, ventilators and test kits (Mathew and Mathew 2020). This was exacerbated by production slowdowns and plant closures led by lockdowns, as well as dislocations in global logistics networks. One prime example is 3M, but they are far from alone; the N95 mask manufacturer simply could not keep pace with the explosive demand for personal protective equipment (PPE) when the pandemic began. As hospitals worldwide experienced dire shortages of these critical supplies, 3M had difficulty meeting demand if it boosted production to meet global, rather than simply domestic, needs. The company was in a bind. Its own manufacturing capacity was limited, and global supply chains were in disarray. This resulted in a lag of distribution of N95s to the front-line health care workers as well as health catastrophe. At the same time, drug makers such as Pfizer were pushed to...