DECISION MAKING LINKED TO AVAILABILITY AND EFFECTIVE COST ANALYSIS ON INDUSTRIAL MULTISTAGE MACHINES. A NOVEL STUDY

Abstract

Multi-stage manufacturing systems require the highest possible level of availability. Due to the large batch size required for proper manufacturing, it is necessary to certify failure-free manufacturing time. Specific multi-stage machines must be characterized to calculate availability. Furthermore, preventive maintenance has been studied in several cases, thus avoiding real non-production costs due to component failures. OEE and TPM standards allow to study this problem at single-stage machines. This article presents two Novel methods for decision-making and studying the effect of unexpected failures in multi-stage machines on availability optimization and penalty costs. The first explores the real penalty cost, and the second, the impact on availability. Both methods require prior characterization of the multi-stage machine and time-dependent condition adjustment of all components. A real-life case is presented to test both methods. The first allows for a more in-depth risk analysis of specific components and enables decision-making before manufacturing systems are designed by leveraging the Internet of Things and secure remote virtual access to multi-stage manufacturing systems. In the second case, the result in terms of availability appears to be lower, but the economic improvement significantly influences the end user’s decision-making. The study link to OEE standards to fix a point for further research.