Multi-criteria decision methods (MCDMs) are used as an effective tool to support decision makers (DMs) in critical decision processes. These methods are used in several fields of application by analyzing static decision-making problems in which it is assumed that the decision is made at a precise moment. By increasing the complexity of decision-making problems and operating in increasingly competitive production sectors, very often analyzing a decision-making problem in a static way is not enough. This paper deals with considering the temporal variable in the construction of a dynamic MCDM, which takes into account historical and current data in order to learn from the past; and prospective also allowing to have a forecasting perspective of future data through the use of techniques that work in this sense. Our approach was tested in a multinational company in the manufacturing sector. The results show that the use of dynamic approaches allows DMs to obtain more precise alternative rankings given the information they exploit from the past; furthermore, the use of the prospective model, integrated with the dynamic one, makes it possible to provide greater detail on the possible future rankings of the alternatives that update their positions based on the feedback received. The approach allows for drawing advantages from a management point of view as it defines a complete decision support tool for the choices related to the planning and control of production processes. Our approach can be implemented in corporate information systems. Furthermore, the involvement of the DM in the construction of the model helps to define a learning process that feeds the decision-making process by generating greater awareness of the DM on the choices to be made.

A New Dynamic and Perspective Parsimonious AHP Model for Improving Industrial Frameworks

Fattoruso, Gerarda
;
2022-01-01

Abstract

Multi-criteria decision methods (MCDMs) are used as an effective tool to support decision makers (DMs) in critical decision processes. These methods are used in several fields of application by analyzing static decision-making problems in which it is assumed that the decision is made at a precise moment. By increasing the complexity of decision-making problems and operating in increasingly competitive production sectors, very often analyzing a decision-making problem in a static way is not enough. This paper deals with considering the temporal variable in the construction of a dynamic MCDM, which takes into account historical and current data in order to learn from the past; and prospective also allowing to have a forecasting perspective of future data through the use of techniques that work in this sense. Our approach was tested in a multinational company in the manufacturing sector. The results show that the use of dynamic approaches allows DMs to obtain more precise alternative rankings given the information they exploit from the past; furthermore, the use of the prospective model, integrated with the dynamic one, makes it possible to provide greater detail on the possible future rankings of the alternatives that update their positions based on the feedback received. The approach allows for drawing advantages from a management point of view as it defines a complete decision support tool for the choices related to the planning and control of production processes. Our approach can be implemented in corporate information systems. Furthermore, the involvement of the DM in the construction of the model helps to define a learning process that feeds the decision-making process by generating greater awareness of the DM on the choices to be made.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11369/444427
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