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Design of learning environments for Mechanical Engineering

Michel Galaup 1 N. Muller 2 C. Pons Lelardeux 2, 3 D. Panzoli 3, 2 P. Jessel 2 Pierre Lagarrigue 4, 3
2 IRIT-REVA - Real Expression Artificial Life
IRIT - Institut de recherche en informatique de Toulouse
3 SGRL - Serious Game Research Lab
INUC - Institut national universitaire Champollion [Albi]
Abstract : Under the concept of "Industry 4.0", production processes will be pushed to be increasingly interconnected, information based on a real time basis and, necessarily, much more efficient. In this context, capacity optimization goes beyond the traditional aim of capacity maximization, contributing also for organization's profitability and value. Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of maximization. The study of capacity optimization and costing models is an important research topic that deserves contributions from both the practical and theoretical perspectives. This paper presents and discusses a mathematical model for capacity management based on different costing models (ABC and TDABC). A generic model has been developed and it was used to analyze idle capacity and to design strategies towards the maximization of organization's value. The trade-off capacity maximization vs operational efficiency is highlighted and it is shown that capacity optimization might hide operational inefficiency.
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Michel Galaup, N. Muller, C. Pons Lelardeux, D. Panzoli, P. Jessel, et al.. Design of learning environments for Mechanical Engineering. Procedia Manufacturing, Elsevier, 2017, 13, pp.1440 - 1446. ⟨10.1016/j.promfg.2017.09.126⟩. ⟨hal-01630913⟩



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