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تعداد صفحات این فایل: ۲۴ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Dynamic MCDM with future knowledge for supplier selection~~en~~

Abstract

Dynamic multi-criteria decision making (DMCDM) is an emerging subject in the decision-making area and in the last decade the challenge to consider time as an important variable has become important. Some frameworks already exist in this area but when compared with other types of decision-making models, DMCDM needs more work to be applicable in real industrial problems. In this work we extend a dynamic spatial-temporal framework, designed to deal with historical data (feedback), to address the problem of considering future information/knowledge (feed-forward). The main objective is to enrich dynamic decision-making models with explicit knowledge (existing historical data) and tacit knowledge (e.g. expert predictions) in time-evolving problems, such as supplier selection. Considering supplier-predicted information for future situations (e.g. investments in capacity) and, simultaneously, learning from historical data can help a company to find less risky and consistent alternatives. The proposed model is successfully implemented in a real case study for supplier selection in one automotive industry to demonstrate the capability and applicability of the model.

۱ Introduction

Most organisations consider neither available knowledge about suppliers’ past behaviour nor tacit knowledge (e.g. from experts) about their future investments or trends, in their strategic decisions about supplier or business partner selection. There is a need for flexible decision models that contemplate including available knowledge (past and future) in the process of tactical and strategic decision-making, and supplier selection is a good example. Extending a dynamic multi-criteria decision-making model with future knowledge – the topic of this paper – is a good contribution to tackle that need. Multi-criteria decision making models are commonly used in organisations to rationalise the process of decision-making (Figueira, Greco, & Ehrgott, 2005; Triantaphyllou, 2000). Usually the first assumption to simplify this type of problem is to assume that both criteria and alternatives are fixed a priori and that the decision occurs only once, i.e. no spatial or temporal considerations are included in the model. There is no doubt that with this assumption the validity of the result is rather limited, specifically when the values change over time and the decision matrix is not fixed or static. Moreover, since this work focuses on medium- or long-term decisions (tactical or strategic), spatial-temporal factors are crucial to ensure up-to-date and informed decisions. The challenge is really how to model knowledge in the decision process (Richards, 2002). Recently, Campanella and Ribeiro (2011) proposed a general dynamic multi-criteria decision making (MCDM) model that combines feedback information (historical data) with current information, for each alternative, in a spatial-temporal decision process. Further, the dynamic decision model was adapted for a business-to-business general supplier selection process, with multiple inputs and multiple outputs (Campanella, Pereira, Ribeiro, & Varela, 2012), but without any consideration about future knowledge. However, this dynamic model only addressed past (historic) information, and in this work we advocate that future knowledge should also be considered, particularly for tactical or strategic decisions. Hence, in this work, the above dynamic MCDM model (Campanella & Ribeiro, 2011) is extended to deal with future data. The future knowledge, which can also be called predicted information, can either be captured using prediction models or could be estimated based on expert knowledge or other available sources. Many decisions in companies are strategic decisions for the future, and these have been criticised for not considering future predictions, thus resulting in unrealistic decisions (de Boer, Labro, & Morlacchi, 2001; Ho, Xu, & Dey, 2010). Decision-making, using just current data, is a paradoxical challenge in management, whilst unstructured strategic decision-making requires a wider perspective, which deals with past and future data. In summary, this work addresses the problematic of considering time as a basic variable in the decisionmaking process, by including feedback and feedforward information in the classic MCDM problem. The feedback represents the past knowledge about suppliers’ behaviour, and feed-forward represents the knowledge about future investments, trends, etc. Both can be merged to improve strategic decision-making processes in organisations. Finally, the main aim of this work is to enrich dynamic decision-making models with explicit knowledge (existing historical data) and tacit knowledge (e.g. experts’ predictions) in time-evolving problems, such as supplier selection. The paper is organised as follows. In the second section, related works about multicriteria decision making for supplier selection are discussed. The third section introduces an extension for a dynamic MCDM model using both past and future information. In the fourth section the complete model is applied to a real supplier selection case study in the auto industry, to illustrate the versatility of the new approach, and, finally, in the last section the conclusion is presented.

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