Recent cutting-edge technologies such as big data analytics, internet of things (IoT), smart factories and artificial intelligence (AI) are transforming the way people acquire and consume goods, firms manufacture and deliver produce, and logistics networks and society interact (Bibby & Dehe, 2018; Gölzer & Fritzsche, 2017). Together, these new concepts and technologies are said to usher in a Fourth Industrial Revolution (Schwab, 2017), or Industry 4.0 (Fatorachian & Kazemi, 2018). Business models and logistics production systems need to adapt to the new dynamics of production and consumption.
The ubiquity of smartphones and apps is drastically changing the customer experience and expectations, allowing individuals to participate in various stages of the production process. For example, the combination of digital manufacturing, mobile and augmented reality technologies allow customers to provide feedback in a co-creation process (Mourtzis, Gargallis, & Zogopoulos, 2019), while IoT, sensors and data analytics enable the continuous collection of usage data throughout the entire product lifecycle. These new modes of relationship are already impacting the work of operations and supply chain managers.
One of the most promising technologies for contemporary operations and supply chain management (OSCM) is artificial intelligence. AI emerged in the 1960s as "the science of making machines do things that would require intelligence if done by men" (Minsky, 1968). Today, a new generation of AI is being used to work on a vast array of issues including product recommendations and customisation, dynamic pricing, real-time production tracking, prevention of order shipment delays and inventory shortages, customer feedback collection for product development and supplier monitoring to minimise procurement costs (Syam & Sharma, 2018). In addition, a subset of AI known as machine learning is developing methods, (e.g. regression analysis, specific algorithms) and associated technologies (e.g. sensors, APIs) that allow computer systems to “learn” using historical data and act without human intervention.
AI can also potentially be combined with Industry 4.0 cutting-edge technologies, such as big data analytics, blockchain, internet of things and cyber-physical systems. The use of AI “in supply chain ecosystems […] in combination with human behaviour will create a new degree of intelligence, innovation, and collaboration” in organizations (Bienhaus & Haddud, 2018). Today’s operations and supply chain managers need to gain a better understanding about how AI can be applied to OSCM problems.
The use of AI applications within an OSCM context presents considerable managerial and organizational challenges. For example, in the adoption stage managers need to identify the requisite capabilities and potential obstacles to successful AI implementation. The potential impact of AI on operations management, production planning and control, productivity and performance also need to be investigated. Managers need to understand how AI initiatives affect the interplay of business, logistics and production systems at individual, organisational and supply chain levels. For example, AI can support individual worker’s activities by performing repetitive tasks: AI commanded robots can audit manufacturing processes; robots can minimize the idleness of production systems when integrated with customers and suppliers; and AI can be used for predictive maintenance when combined with IoT and machine learning. Behind each opportunity lies a challenge for managers to successfully capture the benefits from AI. Little is known for instance about the contribution of AI driven robots to production systems. From an operations management perspective, a major challenge is how to use AI to gain insights for demand forecasting and production planning.
More research is required into: strategies of AI use within organizations for existing OSCM problems (e.g. production planning and control, demand forecasting, operations management optimisation, distribution management); the impact of AI on production processes throughout the value chain; the drivers, enablers and obstacles to AI adopation and use; the development of new business models; and into the implications of AI for operations management practice.
This special issue aims to explore the role of AI in OSCM, and especially how AI creates value in a digital age when combined with other Industry 4.0 cutting-edge technologies. Our objective is to stimulate research and debate both around how managers are using or could use AI to improve OSCM practice and performance, and create competitive advantage, as well as the enablers and inhibitors to adoption, integration and use. This special issue invites scholars, managers and practitioners to use case studies or other empirical methods to report in-depth on AI applications in operations and supply chain management.
This special issue calls for contributions to:
Papers concerning these and other related critical issues in operations and supply chain challenges are encouraged. The special issue aims to sharpen the focus on, and raise the awareness of these critical issues, especially those facing developing economies as well as advanced industrial economies, and to promote research, both theoretical and empirical, on specific digitalisation related problems and innovative practices to address these problems.
For more information please visit: https://www.journals.elsevier.com/technological-forecasting-and-social-change/call-for-papers/tension-in-the-data-environment.