Proceeding 6th International Conference on Operations and Supply Chain Management (AN INTEGRATED MODELING OF HUMAN, MACHINE, AND ENVIRONMENTAL ASPECTS IN SUPPLY CHAIN PLANNING AND OPERATIONS USING FUZZY LOGIC )

Dewa, Parama Kartika and Pujawan, I Nyoman and Vanany, Iwan (2014) Proceeding 6th International Conference on Operations and Supply Chain Management (AN INTEGRATED MODELING OF HUMAN, MACHINE, AND ENVIRONMENTAL ASPECTS IN SUPPLY CHAIN PLANNING AND OPERATIONS USING FUZZY LOGIC ). AN INTEGRATED MODELING OF HUMAN, MACHINE, AND ENVIRONMENTAL ASPECTS IN SUPPLY CHAIN PLANNING AND OPERATIONS USING FUZZY LOGIC . pp. 1119-1128.

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Abstract

Supply chain planning and operations is deeply dependent on human endeavor. The performance of a supply chain is determined by the human that is involved in the process of planning and operation. Supply chain planning involves activities such as demand forecasting, developing various plans that includes production plan, procurement plan, and distribution plan. Supply chain operations are essentially executing such supply chain processes such as procurement, production, transportation, and warehousing. In all of the above processes, the roles of human are critical, although the specific roles played from one process to another are different. Human performance problems identified in real operational events often involve operators performing actions that are not required for accident response. Analyses of the major failure/accidents during recent decades have concluded that human errors on part of operators, designers or managers have played a major role. On the other hand, the effectiveness of human in planning as well as operations of a supply chain is affected by two other factors, namely the tools used and the working environment. In this paper we present a simulation modeling that establish a linkage between human, tools, and working environments in supply chain planning and operations to reduce or eliminate human error. The analysis of these relations is complex, involving vagueness and uncertainty data. Fuzzy Logics (FL) provides a mathematical framework for the systematic treatment of vagueness and imprecision data. This paper presents a simulation modeling using fuzzy logics in reducing human error.

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