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Abstract
The increasing need towards higher product customisation in combination with demand volatility require efficient ways to design manufacturing network configurations. The vast number of alternative design configurations, however, affects production planners that cannot longer rely on experience in order to plan the network. This article presents a method for supporting decision-making in realistic manufacturing network design problems, which investigates the performance and viability of centralised and decentralised production networks under heavy product customisation. Simulation models of automotive networks are developed and their performance is evaluated. Two methods are used in the decision-making process, namely an exhaustive search and an intelligent search algorithm. Multiple conflicting user-defined criteria are used for the evaluation of the alternative manufacturing and transportation schemes, including lead time, production cost, flexibility, annual production volume and environmental impact. In addition, the performance of the intelligent search method is investigated using statistical design of experiments (SDoE). Moreover, a calibration procedure for the intelligent algorithm is presented. An assessment of the examined approaches, with respect to their responsiveness and suitability for highly customer-driven environments, is provided and can be used as a guideline for the manufacturing network planning. The proposed method is validated by utilising realistic data provided from a European automotive manufacturer.