Suffice to say that long-established businesses have their own challenges. Furthermore, accurate systematic methods and tools for managing risks in the context of industry 4.0 are lacking or less efficient, spreading unrealistic awareness of risk (or situational awareness) in various domains where risk management is needed. Conventional methods have their own limits and might not identify all aspects that influence system safety. Once traditional industry challenges are combined with emerging risks along with new systemic and organizational risks as well as cognitive and motivational biases in human logic, there will be the necessity of building thorough Asset Management and Decision Support approaches accounting both for conventional and emerging risk safety management. Hence, innovative, and efficient approaches that can investigate issues from a broad systemic perspective to support asset management practitioners to deal with those threats associated with the complexity of socio-technical systems are of interest. On these grounds, this paper focuses on identifying and analyzing components of risk management approaches especially for new emerging safety risks within industry 4.0 (emerging technology-related risks), as well as the rising of extreme, rare, and disruptive events, at a time of continued uncertainty in the global economy, in conjunction with the highly insecure political situation caused by recent armed conflicts (for e.g., Russia vs Ukraine), and the coronavirus disease pandemic (COVID-19) that might create fatal disturbance of the performance of organizations. We opt for the relatively new methods that How to cite this paper: Diop, I., Abdul-Nour, G., & Komljenovic, D.
Lockout/tagout (LOTO) is practiced in manufacturing facilities to ensure safety during machinery maintenance procedures. In flexible manufacturing systems, human error (HE) is a major source of accidents and process deviations. Special measures are needed to minimize occupational risk and increase operational efficiency. In this article, we study a production planning problem involving a failure-prone production system meeting two types of demand and we discuss the associated decision-making process. The aim is to develop an optimal, robust and flexible control strategy that facilitates the integration of LOTO into corrective maintenance (CM) and ultimately into production. The influence of HE on flexible manufacturing systems (FMS) is viewed in terms of production and maintenance planning. The frequency of machine repair depends largely on HE. The intrinsic costs of shortage, inventory and CM are optimized over an unbounded planning horizon. Analytical formalism is combined with discrete event simulation, as well as design of experiments (DOE) and a genetic algorithm (GAs) to identify the optimal planning of production and CM with mandatory LOTO. An illustration and sensitivity analysis are proposed to express, in quantitative terms, the usefulness and efficiency of the proposed approach.
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