Towards Effective Maintenance Planning
Manufacturing industry spend a great deal of their capital on equipment maintenance for their production systems. Hence, the cost of improper maintenance management is something that the industry would like to avoid. Industrial maintenance activities are directly linked to the profitability of industries and affects the performance, dependability, and sustainability of production systems. Naturally, the equipment downtime directly affects the system performance, but the rippling effects (idle and blocked states of equipment) are also prime contributors to the inefficiency of production systems by constraining the flow of products in the production system. In addition to inefficiency, studies suggest that 30% of the energy consumption in manufacturing industry is wasted on stand still equipment. Therefore, effective maintenance planning for the resources is an extremely important activity.
The aim of this thesis is to create effective maintenance planning principles which will enable the production system to operate efficiently. Due to the discrete and complex nature of production systems, certain resources become critical that reduces system inefficiency the most. The focus of the thesis is to identify the critical resources and create continuous awareness of them in order to reduce its criticality through effective maintenance.
The thesis identifies the critical resource of the production system that affects production efficiency the most. This criticality is identified by studying the gaps in current industrial state practices. Through this study, the purpose of establishing criticality from a holistic perspective is obtained to assist maintenance planning accordingly. The research methods performed include descriptive surveys, questionnaires, and structured and semi-structured interviews.
Moreover, the gaps in current industrial state practices for maintenance planning of critical resources and the usage of criticality classification are also studied. In addition, solutions to reduce the criticality of these resources through maintenance planning are identified and tested using simulation studies of two automotive industrial cases. The reduction in criticality is shown by reducing the active period of critical resource and increasing its utilization. In order to achieve this solution, the maintenance planning principles used include throughput-critical resource identification, maintenance prioritization, and maintenance staff utilization. Reducing the criticality of these resources proved to increase the system throughput potential and further balance production lines.
Applying these maintenance planning principles to industries has the potential for making production systems function more efficiently, which was shown through obtaining 5% throughput increase on average in one of the case studies. However, effective maintenance planning requires continuous awareness of the critical resource, which requires robust decision support tools which constantly monitor the production system. Future work includes data-driven analysis for decision support tools for effective maintenance planning, which includes dynamic production system changes.
Keywords: Maintenance planning, criticality classification, bottleneck analysis, maintenance prioritization