An intelligent decision support system for manufacturing technology investments

Making strategic decision on new manufacturing technology investments is difficult. New technologies are usually costly, affected by numerous factors, and the potential benefits are often hard to justify prior to implementation. Traditionally, decisions are made based upon intuition and past experience, sometimes with the support of multicriteria decision support tools. However, these approaches do not retain and reuse knowledge, thus managers are not able to make effective use of their knowledge and experience of previously completed projects to help with the prioritisation of future projects. In this paper, a hybrid intelligent system integrating case-based reasoning (CBR) and the fuzzy ARTMAP (FAM) neural network model is proposed to support managers in making timely and optimal manufacturing technology investment decisions. The system comprises a case library that holds the details of past technology investment projects. Each project proposal is characterised by a set of features determined by human experts. The FAM network is then employed to match the features of a new proposal with those from historical cases. Similar cases are retrieved and adapted, and information on these cases can be utilised as an input to prioritisation of new projects. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Implications of the proposed approach are discussed, and suggestions for further work are outlined.