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Decision-making in the manufacturing environment using a value-risk graph

Article dans une revue avec comité de lecture
Author
SHAH, Liaqat Ali
21439 Institut de Recherche en Communications et en Cybernétique de Nantes [IRCCyN]
VERNADAT, François
49775 Unit for e-Commission, Interoperability, Architecture and Methods
124174 Unit B2, Interoperability, Architecture and Methodologies
ccSIADAT, Ali
107452 Laboratoire de Conception Fabrication Commande [LCFC]
ccETIENNE, Alain

URI
http://hdl.handle.net/10985/9953
DOI
10.1007/s10845-014-0895-6
Date
2014
Journal
Journal of Intelligent Manufacturing

Abstract

A value-risk based decision-making tool is proposed for performance assessment of manufacturing scenarios. For this purpose, values (i.e. qualitative objective statements) and concerns (i.e. qualitative risk statements) of stakeholders in any given manufacturing scenario are first identified and are made explicit via objective and risk modeling. Next, performance and risk measures are derived from the corresponding objective and risk models to evaluate the scenario under study. After that, upper and lower bounds, and target value is defined for each measure in order to determine goals and constraints for the given scenario. Because of the multidimensionality nature of performance, the identified objectives and risks, and so, their corresponding measures are usually numerous and heterogeneous in nature. These measures are therefore consolidated to obtain a global performance indicator of value and global indicator of risk while keeping in views the inter-criteria influences. Finally, the global indicators are employed to develop minimum acceptable value and maximum acceptable risk for the scenario under study and plotted on the VR-Graph to demarcate zones of “highly desirable”, “feasible”, “and risky” as well as the “unacceptable” one. The global scores of the indicators: (value-risk) pair of the actual scenario is then plotted on the defined VR-Graph to facilitate decision-making by rendering the scenarios’ performance more visible and clearer. The proposed decision-making tool is illustrated with an example from manufacturing setup in the process context but it can be extended to product or systems evaluation.

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