A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. (2018)

• Record Type: Journal Article
• Authors: Ivanov, Dmitry; Sethi, Suresh; Dolgui, Alexandre; Sokolov, Boris
• Is Part Of: Annual reviews in control. Volume 46(2018)
• Page Start: 134
• Page End: 147
• Publication Date: 2018
• Subjects: Contol -- Supply Chain -- Operations -- Industry 4.0 -- Dynamics -- Planning -- Scheduling -- Optimal program control -- Model-predictive control -- Adaptation -- Resilience -- Digital Supply Chain; Automatic control -- Periodicals; Periodicals; 629.805
• DOI: 10.1016/j.arcontrol.2018.10.014
• Languages: English
• ISSNs: 1367-5788

Abstract

Abstract: Modern production and logistics systems, supply chains, and Industry 4.0 networks are challenged by increased uncertainty and risks, multiple feedback cycles, and dynamics. Control theory is an interesting research avenue which contributes to further insights concerning the management of the given challenges in operations and supply chain management. In this paper, the applicability of control theory to engineering and management problems in supply chain operations is investigated. Our analysis bridges the fundamentals of control and systems theory to supply chain and operations management. This study extends our previous survey in the Annual Reviews in Control (Ivanov et al. 2012) by including new literature published in 2012–2018, identifying two new directions of control theory applications (i.e., ripple effect analysis in the supply chains and scheduling in Industry 4.0) and analysis towards the digital technology use in control theoretic models. It describes important issues and perspectives that delineate dynamics in supply chains, operations, and Industry 4.0 networks and identifies and systemizes different streams in the application of control theory to operations and supply chain management and engineering in the period from 1960–2018. It updates the existing applications and classifications, performs a critical analysis, and discusses further research avenues. Further development of interdisciplinary approaches to supply chain optimization is argued. An extended cooperation between control engineers and supply chain experts may have the potential to introduce more realism to dynamic planning and models, and improve performance in production and logistics systems, supply chains, and Industry 4.0 networks. Finally, we analyze the trends towards the intellectualization of control and its development towards supply chain control analytics.