Planning of manufacturing networks using an intelligent probabilistic approach for mass customised products
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Manufacturers around the globe are presented with the evident need to successfully capture and efficiently satisfy the increasing demand towards highly customised products. The trend for higher levels of customisation increases the operational costs, affects delivery times and worsens the environmental footprint of production. Moreover, the feasible alternative manufacturing network configurations increase together with the exploding product variety and the large pool of cooperating suppliers. The proposed research work describes an intelligent method that utilises three adjustable control parameters and can be used for the identification of efficient globalised manufacturing network configurations capable of carrying out the production of mass customised products. The decision support system presented allows the generation of alternative manufacturing network configurations and their evaluation, through a set of multiple conflicting user-defined criteria of cost, time, quality and environmental impact. The suggested approach, which is implemented into a web-based software tool, is investigated through a probabilistic analysis for guiding the decision-maker when selecting the values of the adjustable control parameters, in order to obtain high-quality manufacturing network designs. The applicability of the method is validated through a real-life pilot case, using data acquired from an automotive manufacturer.
KeywordsManufacturing network design Mass customisation Decision-making Intelligent algorithm Decentralised manufacturing
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