Selection of modules for mass customisation

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Satisfying customer expectations is of paramount importance in today's markets. A customer expects to receive a product that meets as best as possible his/her expectations. To satisfy diversified requirements companies may focus on product families. For a specific customer the company has to select within a product family what will make a valid product. Then, in a just-in-time environment, the suppliers may have to provide the exact subassemblies corresponding to each product in a predefined time. This integration takes place by designing modules for the supplier. This paper proposes to extract customers' behaviour patterns, in terms of the components required, using data-mining and entropy maximisation when selecting modules to be manufactured. Different methods for selection of modules are proposed. Computational tests are performed to evaluate performance of the selection methods with respect to the specified assembly time/resource level. 1. Introduction Transformation from the 'demand-driven economy' to the 'offer-driven economy' has increased corporate competitiveness. To support the 'offer-driven economy', companies tend to enlarge their product portfolios in pursuit of increased market shares. The challenge is then to determine a trade-off between the cost of offering a wide range of products and the gains that such portfolio provides. Modularity is useful in realising the assemble-to-order (ATO) policy when dealing with a large diversity of products. Indeed, a relatively small stock of modules permits to offer a large portfolio of final (modular) products. The designed modules should enable the best response to the customer needs. The latter can be accomplished by integrating the information about anticipated customer requirements at the product design phase. Customers' behaviour is not easy to elicit. Selection of a particular product by a customer is the result of a cognitive process including its objective …