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

Abstract

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. 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.

Designing and exploring risk matrices with MACBETH

“…Nevertheless, it may be relevant to make use of other procedures developed with MACBETH to face variants of the`assignment problematique' 91 in several decision-aid contexts. 73,75,92 For instance, other procedures can be adventurously explored in the framework of IRIS to de¯ne coloring schemes, based on intrinsic references of impact, such as acceptable and critical levels, on each dimension. Furthermore, for some risk evaluation problems/contexts it may be worth investigating the appropriateness of noncompensatory multi-criteria models and classi¯cation procedures, for instance exploring techniques described in Ref.…”

Decision Making for Third Party Logistics Supplier Selection in Semiconductor Manufacturing Industry: A Nonadditive Fuzzy Integral Approach

“…In fuzzy set theory, the linguistic variables are for expressing decision makers' vague and imprecise semantics. Linguistic variable is very useful in dealing with complex situations that are difficult to be reasonably described in conventional quantitative expressions [66,67]. Applying the linguistic variables, the nonadditive fuzzy integral approach enables researchers to handle the vague and imprecise semantics and solve the interdependence among criteria to eliminate subjective judgment problems involved in a decision-making complex.…”

“…Based on the fuzzy logic, linguistic variables are used to determine the importance degree of decision factors. The concept of linguistic variable was presented by Zadeh [66,67]. Making a decision in a multicriterion situation, decision makers' perceptions of criteria vary in terms of the evaluations of linguistic variables with respect to importance weighting on decision factors.…”

The Contribution concept for the control of a manufacturing multi-criteria performance improvement

“…The main PMS functionalities concern the quantification of both the efficiency and effectiveness of the improvement actions (Neely et al 1995) and the performance processes monitoring, in addition to the accounting, the performance diagnosis, the team motivation enhancement and communications improvement (Waggoner et al 1999). Among the numerous PMS's frameworks and models that have been proposed in the literature (Nudurupati et al 2011;Shah et al 2016), we consider hereafter the propositions that are more or less concerned with the improvement of overall performances. Knowing that all these propositions handle the overall, multi-criteria and interacting aspects of the performance, a summarised description of each PMS will allow us to have an idea of the pieces of information or functionalities that are provided for the overall performance improvement.…”