A decision making framework to achieve prescriptive maintenance in the FMCG production industry

Abstract

In recent years, technologies such as artificial intelligence have enabled computer systems to provide decision support for maintenance planning. Using advanced analytics to determine optimal maintenance actions and moments is called prescriptive maintenance. Multiple prescriptive maintenance models have been proposed for different industries. However, none of these models or frameworks
were designed for use in the FMCG industry. This industry seems to be lagging compared to the oil & gas, process and transport industries for example. The goal of this research is to define a framework that can achieve prescriptive maintenance at a FMCG production plant. This research focuses on the decision making for maintenance on multi-component systems using technologies such as machine learning. The proposed framework uses an intelligent agent-based approach to optimize maintenance planning on a component and system level. The agents act on both levels and negotiate to find an optimal system level result. A life-cycle cost approach was used, where all impact of maintenance
was translated to costs and thereafter minimized. As prescriptive maintenance aims to increase operational efficiency and reduce costs by artificial decision making, the effect of the framework on these KPIs was simulated and compared in a FMCG case study. This case study was done using a discretetime simulation of a cooling compressor system in a beverage production facility. It was found that, given a high-quality prediction of the remaining useful life, this prescriptive maintenance framework can increase a systems operational efficiency and reduce maintenance costs. However, the largest improvement could be seen in the overall production time of the system, as maintenance was planned in such way that loss of functionality was prevented.