Adapting to the new dynamics of digital supply chains

From the January-February 2025 edition of Supply Chain Management Review.

January-February 2025

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The pandemic and Red Sea crisis have once again revealed how disruptions in one part of the world can ripple through the entire system, exposing significant vulnerabilities within the global supply chain network. As supply chains are elevated to a critical component of national security and rise to the forefront of CEOs’ strategic priorities, a renewed emphasis is placed on transparency, resilience, diversification, and technological innovation.

Industry 4.0 technologies, including the Industrial Internet of Things (IIoT), blockchain, machine vision, robotics, artificial intelligence (AI), and machine learning, are at the heart of this shift. These transformative technologies are fundamentally reshaping how supply chains function, offering organizations unparalleled opportunities to bolster efficiency, transparency, and resilience through predictive analytics, automation, enhanced traceability, and real-time data integration.

For CEOs, integrating these innovative technologies into their supply chain strategies serves as both a risk mitigation strategy and a means to capitalize on efficiency improvements to secure a competitive edge. In this article, we examine the current applications of Industry 4.0 technologies in supply chain management, their implications for supply chain security and risk management, and the resulting shifts in skillsets and competencies required for digital supply chains. Insights and recommendations are provided for practitioners and policymakers navigating this rapidly changing landscape.

Embracing Industry 4.0: Supply chain digital revolution

In 2023, we surveyed 361 manufacturers across a range of industries, from traditional sectors like textile mills, food manufacturing, and chemical manufacturing, to high-tech fields such as computer and electronic product manufacturing. We asked supply chain managers, plant managers, logistics managers, operations managers, etc., about their adoption of Industry 4.0 technologies, specifically IIoT, blockchain, machine vision, robotics, and AI. Remarkably, about 97% of organizations had implemented at least one of these technologies,
and 76% had adopted all five to some extent.
This result signifies that the integration of advanced technologies in manufacturing is no longer a mere trend but a widespread practice reshaping the industry.

Among these technologies, machine vision and IIoT stand out with a 90% adoption rate, underscoring their critical role in enhancing quality control and operational efficiency. AI and blockchain follow closely at 87%, reflecting their growing importance in proactive planning and secure, transparent transactions. Robotics, with an 86% adoption rate, remains essential for automating repetitive tasks and improving precision in manufacturing processes. In the following section, we explore how companies are leveraging these technologies to improve efficiency, streamline operations, and strengthen supply chain resilience.

Machine vision is a cornerstone technology in modern manufacturing. The majority of manufacturers surveyed have incorporated machine vision throughout their production cycles for defect detection, from raw materials inspection to intermediate component evaluation, and ultimately, finished product examination. A small number of manufacturers, however, limit its use to specific stages within their operations.

Beyond defect detection, 73% of manufacturers use this technology to monitor variability in production processes, ensuring consistent quality and steady output through real-time oversight. Machine vision is also widely employed to monitor environmental conditions during production, with 79% of manufacturers utilizing it to manage key factors such as lighting, dust and particle levels, and vibration, which are crucial for maintaining both product quality and production efficiency.

IIoT serves as the digital nerve center of modern manufacturing, connecting machines, products, and people. Deployed throughout the manufacturing process and across the supply chain, IIoT enables real-time monitoring and data exchange. From our survey, 77% of manufacturers have increased the number of sensors in their production processes to monitor and collect data, with the vast majority analyzing and acting on these data automatically. Notably, 68% of manufacturers have implemented real-time optimization to some degree in their manufacturing and supply chain networks, taking advantage of IIoT capabilities. 

 

One key strategic application of IIoT is accelerating new product introductions. By enabling real-time data collection and analysis, IIoT allows manufacturers to predict maintenance needs and synchronize supply chains, which, in turn, shortens the time required to bring new products from concept to market. Along similar lines, IIoT empowers organizations to respond dynamically to changing product demands by swiftly adjusting operations, production lines, and supply chains. As a result, the majority of manufacturers surveyed report utilizing IIoT to expedite new product launches, adapt to shifts in market needs and consumer preferences, or both.

AI, as the analytical engine of the manufacturing sector, drives advanced supply chain oversight, real-time responses to production and logistical variances, and preemptive strategies aligned with changing market needs and supplier dynamics. The manufacturers surveyed utilize AI to oversee suppliers’ overall production, monitor the progress of their specific orders, and track upstream supply conditions.

In logistics management, 70% of manufacturers leverage AI to monitor port of entry capacities and critical points in goods movement (e.g., from ships to trucks); 69% use it to track upstream logistics providers’ capacities such as addressing driver shortages to prevent delays; and 72% apply it to manage their own logistics operations.

In addition, more than 65% of organizations deploy AI tools to navigate the complexities of mergers and acquisitions involving their suppliers and monitor their financial stability. AI is also used to analyze supplier cost drivers and predict price fluctuations, enabling manufacturers to refine their procurement tactics ahead of time.

Blockchain is revolutionizing trust and transparency in supply chain management by providing secure and immutable records and transactions that enable the complete tracing of products from raw materials to final delivery. In transaction management, while 65% of respondents use blockchain to securely record transaction data, over 67% recognize its benefits in reducing the time and costs associated with complex transactions. This reflects a growing awareness of blockchain’s inherent strengths in providing a tamper-proof ledger system and enabling more streamlined, transparent, and efficient transaction processes across the supply chain.

 

In the area of security and privacy enhancement, 69% of manufacturers deploy blockchain to strengthen information system security, and 72% leverage it to enhance privacy. This highlights the rising need to protect data integrity in today’s interconnected, data-driven supply chains. Blockchain’s ability to encrypt and decentralize data storage is used to safeguard sensitive information and foster trust among supply chain partners.

In addition to its security features, blockchain is increasingly employed to streamline operations, ensure compliance, and facilitate agile business practices. About 73% of manufacturers affirm the use of blockchain for improving auditability, and a notable 76% for improving operational efficiency overall.

 

 

Robotics technology is a crucial component of manufacturing innovation, executing tasks with precision and consistency while elevating productivity on both the factory floor and throughout the supply chain. A substantial percentage of manufacturers have adopted robotics at various stages of their production processes. Some respondents have implemented a comprehensive automation strategy, spanning from the receipt of raw materials to the packaging and delivery of finished products, while others have focused robotics on specific functions. The rapid development and deployment of robotics are accelerating the evolution toward a more automated, precise, and interconnected manufacturing ecosystem.

At the core of the advances is the integrated implementation of these technologies, which together create a synergistic ecosystem where each one complements and enhances the capabilities of the others. For instance, IIoT and AI form a powerful combination, with IIoT devices gathering vast amounts of data from various stages of production, while AI processes this data to enable predictive maintenance, optimize production schedules, and enhance quality control. The real-time data provided by IIoT makes AI algorithms more accurate and effective. Similarly, machine vision and robotics work in tandem, with machine vision providing critical visual feedback to robotic systems. In tasks such as quality inspection or precision assembly, machine vision guides robots to identify defects, align parts correctly, and perform complex tasks with high precision. AI further empowers robots to interpret data, learn from past experiences, and execute tasks with cognitive awareness that mimics human understanding. Such intelligent robotics systems are capable of performing intricate assembly tasks, navigating unpredictable terrains, and providing services once considered too complex for automated systems. Additionally, robotics integrated
with IIoT leads to more responsive and adaptive manufacturing processes, where robots receive real-time updates and instructions, allowing them to respond to changing conditions dynamically. Finally, the integration of blockchain with IIoT and AI ensures secure data storage and management, offering data integrity and traceability essential for quality assurance, compliance, and supply
chain transparency.

Using a structural modeling methodology, our analysis of data from 361 manufacturers also reveals statistically significant interconnections among the five key Industry 4.0 technologies. Adoption of these technologies collectively bolsters the resilience of firms, enhancing their ability to anticipate, navigate, and recover from various threats and unforeseen disruptive events.

 

Navigating the paradox: Enhancing security while managing new risks

In our survey, 70% of manufacturers have established specialized departments to manage supply chain risks, and 80% utilize information systems to support these efforts.
The integration of Industry 4.0 technologies into supply chain management presents a double-edged sword: while these technologies enhance security and mitigate traditional risks, they also introduce new vulnerabilities that must be carefully managed.

Consider a high-tech manufacturing facility that produces advanced medical devices, an industry where precision and reliability are non-negotiable due to the potential impact on human health. Implementing machine vision, AI, IIoT, blockchain, and robotics technologies can create an ecosystem that drastically improves product quality, supply chain security, and risk management.

• Machine vision is integrated into assembly lines, performing real-time inspections for structural integrity, correct component placement, and surface defects. This rigorous process ensures that each device meets stringent quality standards, reducing the risk of operational errors that could lead to product recalls or legal issues.
• AI algorithms analyze data collected from machine vision and other sensors to identify patterns that may predict potential defects or failures. AI also optimizes production processes by learning from historical data, improving quality control measures, and reducing waste.
• IIoT sensors throughout the supply chain provide real-time data on inventory levels, environmental conditions during transport, and equipment status. This network allows for predictive analytics that can foresee and manage disruptions, such as machine breakdowns or delays in material shipments, ensuring continuous operations.
• Blockchain technology creates a traceable record for every component that goes into a medical device. From the provenance of raw materials to the delivery of the final product to healthcare facilities, blockchain ensures transactional integrity, combats counterfeiting, and provides an immutable audit trail for compliance purposes.
• Robotic arms and automated guided vehicles perform repetitive and precision tasks alongside human operators. They quickly respond to changing production needs, reduce human errors, and ensure the manufacturing process is agile and precise.

 In an integrated setup where all five technologies coalesce, the following scenario could unfold: machine vision detects a slight discrepancy in a device component, the AI system analyzes this information, correlates it with patterns of potential defects, and automatically adjusts the robotics-operated assembly line. Simultaneously, IIoT devices update inventory systems to order necessary replacements, while blockchain entries verify the integrity of the new components. This seamless interplay not only secures the production line from immediate risks but also equips the supply chain with robust mechanisms to prevent and respond to future challenges.

 

However, while these technologies offer significant advantages, they also introduce new dimensions of risk that require careful management.

• Cybersecurity risks. As IIoT devices and AI systems are interconnected and often rely on internet connectivity, they increase the attack surface for cyber threats. Hackers could potentially gain unauthorized access to sensitive data, manipulate the operations of manufacturing systems, or even cause shutdowns.
• Data privacy concerns. AI systems process vast amounts of data, some of which may be sensitive. Mishandling or exposure of this data, whether intentional or not, could result in privacy violations and legal consequences.
• Blockchain vulnerabilities. While blockchain is secure by design, its security depends on proper implementation and infrastructure. Flaws in smart contracts or the potential for 51% attacks on smaller blockchains could be exploited.
• Complex system integration. Seamless integration of these advanced technologies requires sophisticated systems engineering which is inherently prone to errors. Integration errors may cause unexpected system behavior, leading to operational disruptions
• or safety issues.
• Reliance on technology. Heavy reliance on automated systems may reduce human oversight, potentially missing opportunities for manual intervention to prevent or mitigate errors. Over-reliance on technology can also erode the critical skills of the workforce, making it more difficult to resolve issues without the aid of automated systems.
• Machine vision limitations. Machine vision systems are only as good as the algorithms and data they are trained on. They may not always correctly interpret every scenario, leading to false positives or negatives in quality control, which could result in defective products being overlooked or unnecessary waste of good products.
• Robotic malfunctions. Robotics, if not properly maintained or if they malfunction, can cause accidents that may injure workers or damage production facilities.

In a nutshell, Industry 4.0 technologies offer considerable advantages for improving supply chain efficiency and agility and they hold great potential for mitigating traditional supply chain risks. However, these technologies also introduce new vulnerabilities that necessitate comprehensive risk management strategies. Recognizing and understanding these emerging security risks is the first step toward securing the digital supply chain. Manufacturers must proactively address these risks by implementing robust cybersecurity protocols, developing contingency plans for technological disruptions, and providing adequate training for employees to competently manage and collaborate with these advanced technologies. 

Evolving skillsets: Integrating traditional skills with technological innovation

Broadly speaking, the skillsets required for supply chain professionals fall into three categories: core supply chain skills, technical and digital proficiencies, and soft skills including adaptability. In the Industry 4.0 era, many traditional skills are not being replaced but rather augmented by new technological competencies. Supply chain management professionals must now be technologically savvy, data-literate, and adaptable to change—while maintaining a strong foundation in traditional supply chain principles.

 

This blend of traditional expertise and technological innovation is particularly evident in core supply chain skills, which are evolving to meet the demands of the digital landscape. For instance, in logistics and operations management, professionals are expected to integrate technologies such as IIoT and robotics to track shipments and inventory and also to optimize workflows and reduce downtime. Similarly, in procurement and supplier relationship management, the use of blockchain for contract management and traceability demands new technical expertise, and effective communication with suppliers now includes the ability to collaborate on technology integration, such as sharing data through IIoT platforms. In inventory management, AI enables predictive analytics, which necessitates a skillset for interpreting and acting on data-driven insights to optimize stock levels. In lean management and process improvement, traditional principles are now applied in conjunction with AI and data analytics to further reduce waste and improve efficiency; thus, SCM professionals must understand how to leverage these technologies to achieve lean objectives and drive process improvements. From the perspective of compliance and quality assurance, supply chain professionals not only need the skills to interpret data from advanced systems and make informed decisions, but also must ensure that technology-driven processes comply with regulatory standards.

 

With the growing reliance on technology, technical and digital proficiencies have become essential for supply chain professionals. The use of AI and machine learning demands a higher level of proficiency in data analytics and the ability to build and interpret complex models. As supply chains become more interconnected and vulnerable to cyberattacks, professionals must also be well-versed in cybersecurity measures to protect systems from these risks. Risk management, which has always been a core component of supply chain management, now requires professionals to be adept at identifying and managing risks associated with digital integration. Additionally, expertise in ERP and SCM software is critical, as modern systems incorporate advanced features for real-time tracking and analytics, which demand deeper technical skills to take full advantage of the software.

Furthermore, soft skills and adaptability are rising to a new level of importance in digital supply chains, as the pace of change and the complexity of systems demand broader interpersonal and strategic capabilities. Communication and collaboration now extend to coordinating across different technological platforms and working with people from various technical backgrounds. Supply chain professionals must also possess strong change management skills in order to lead and manage the transition toward technology-enhanced supply chains. Problem-solving and critical thinking now involve analyzing large amounts of data, making real-time decisions, optimizing operations, and forecasting and developing contingency plans for previously unknown risks. Leadership and strategic skills have shifted as well, requiring supply chain leaders to think beyond traditional operational challenges and strategize around technology integration, data utilization, and innovation. Finally, learning agility—the ability to quickly learn, adapt, and apply knowledge in new and unfamiliar situations—has become a fundamental capability for keeping up with constant technological advancements and the evolving business environment. 

In essence, this evolution in skillsets is necessary for organizations to harness the full potential of new technologies and maintain a competitive edge. However, it is also important to recognize that while Industry 4.0 technologies offer innovative tools for managing supply chains, the human element remains critical. To truly succeed in digital supply chains, comprehensive training programs must be developed to equip employees with advanced skills, integrating foundational supply chain management principles with the technical know-how required to effectively implement and fully leverage new technologies.

Next steps

A supply chain is inherently an interconnected environment between upstream and downstream partners. We propose that within these environments, the linkages among various technologies form a system that can significantly enhance performance. Some of these technologies focus on information acquisition (e.g., IIoT, AI), verification (e.g., blockchain), and interpretation (e.g., AI), while others emphasize efficiency and resiliency (e.g., robotics, machine vision). These technologies, when effectively integrated, can improve supply chain performance in relatively stable environments where disruptions, such as port worker strikes, are somewhat predictable. They also enable rapid responses to “black swan” events such as COVID-19 and the Red Sea crisis, which are largely unpredictable.

Achieving these advantages hinges on several key factors: (a) seamless integration among the technologies and the organization’s management systems; (b) timely acquisition and analysis of reliable information shared throughout the supply chain; and (c) coordinated actions among supply chain partners, guided by the information and capabilities provided by these technologies.

 

As organizations continue to navigate the digital transformation ushered in by Industry 4.0, the journey ahead requires a strategic and thoughtful approach. The widespread adoption of digital technologies has already begun to reshape the supply chain landscape, offering enhanced efficiency and new capabilities. Yet, as we have seen, this evolution is not without its challenges. The double-edged nature of these advancements necessitates a vigilant stance on supply chain security and risk management, ensuring that the very tools that empower us do not become our vulnerabilities.

Looking forward, it is crucial that businesses not only adapt to these changes but also anticipate and shape the future trajectory of their supply chains. This involves a proactive investment in upskilling the workforce, embracing the dual challenge of maintaining traditional skillsets while cultivating new technological competencies. Equally important is the establishment of a robust and responsive IT infrastructure, capable of supporting the evolving and advanced demands of Industry 4.0 solutions.

Innovation should be at the heart of these next steps, with continuous improvement as a guiding principle. Organizations must foster a culture of agility and learning, using insights from data to drive the decision-making process. Collaboration across departments and with external partners will be key in developing an integrated supply chain ecosystem that is both resilient and dynamic.

As we chart the course forward, the emphasis must also be on sustainability and ethical considerations. The integration of smart technologies should align with environmental objectives and social responsibility, ensuring that advances in supply chain management contribute positively to the broader community and the planet.

Acknowledgements

The authors would like to thank the Institute for Homeland Security at Sam Houston State University for funding our survey as well as Christian Adams and Sumath Krish Prasad Pulipati of Employstats for providing data on skillsets. Their support has been essential to our project.

About the authors

Dr. Willow Yang is an assistant professor of supply chain management at Sam Houston State University. She can be reached at [email protected].

Dr. Pamela Zelbst is a Regent’s Professor and director of the Center for Innovation, Technology & Entrepreneurship at Sam Houston State University. She can be reached at [email protected].

Dr. Milo Berg is an associate professor of economics at Sam Houston State University. 

Dr. Kenneth Green is the LeMay Professor of Technology at Southern Arkansas University

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