Suppliers Selection in Consideration of Risks by a Neural Network

Abstract

Faced with the dynamic demands of a changing market, companies are facing fierce competition, which forces them to consider more and more new approaches to improve quality, reduce costs, produce on time, control their risks and remain successful in the face of any disruption. It is clear that the choice of appropriate suppliers is one of the key factors in increasing the competitiveness of companies. Thus, suppliers selection has a very important impact on the control of risks throughout the supply chain and on increase of its performance. Therefore, it is important for managers to realize the long-term impact of their supplier selection strategies on the benefits and effective functioning of the organization. To minimize supply and demand risks, this work presents a generic supplier selection model based on artificial neural networks (ANNs) to help manufacturers to choose the most efficient suppliers and monitor their performance. The results showed that ANNs are very well adapted to our problem since they have provided a very considerable efficiency in terms of the results obtained. Indeed, the application of the ANN will avoid the difficulty of desiging an algorithm to solve our problem, it is through the expertise of the managers in the purchasing department that our ANN will learn to be efficient and serve as a tool to help a decision makers to choose the best suppliers.