Robust Improvement Planning of Automated Multi-stage Manufacturing Systems

Abstract

Automated multi-stage manufacturing systems serve as the backbone of mass industrial production. Increasing the efficiency of these systems represents a key challenge for manufacturing companies, which continuously cope with planning the sequence of optimal improvement actions according to budget and implementation time constraints. Improvement actions related to different areas, as quality, maintenance, logistics, are usually evaluated independently among each other. Recent developments in data gathering support the evaluation of the effect of improvement actions at local level, i.e. single machine, without accounting for the interactions at system-level among machines. This work presents an optimization method for the sequencing of improvement actions in automated multi-stage manufacturing systems. It combines dynamic programming with a stochastic analytical model for the performance evaluation of manufacturing systems. Results from a real industrial case in the furniture sector prove the usefulness of this novel methodology, compared to traditional bottleneck identification and improvement.