A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information

• We model the experience a decision-maker would recall of previous manufacturing technology decisions.
• A fuzzy-decision-tree mining algorithm is proposed to justify new manufacturing decision investment projects using an experience-based approach.
• The methodology considers experience and intuition formed through historical cases.
• The model was applied to an industrial decision problem and showed encouraging results.
• Compared with other advanced knowledge-based decision models, it is not so mathematically complex that managers will have difficulty using them in practice.

Manufacturing technology selection is traditionally a human-driven approach where the trade-off of alternative manufacturing investments is steered by a group of experts. The problem is a semi-structured and subjective-based decision practice influenced by the experience and intuitive feeling of the decision-makers involved. This paper presents a distinct experience-based decision support system that uses factual information of historical decisions to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements. A fuzzy-decision-tree algorithm is applied to provide a more objective approach given the evidence of previous manufacturing technology implementation cases. The model uses the information relationship of key technology decision variables, project requirements of an implemented technology case and the success outcome of a project to support decision problems. An empirical study was conducted at an aircraft manufacturer to support their technology decision for a typical medium complexity assembly investment project. The experimental analysis demonstrated encouraging results and practical viability of the approach.