Simulation-based planning of maintenance activities in the automotive industry

Abstract

Factories world-wide do not utilize their existing capacity to a satisfactory level. Several studies indicate an average Overall Equipment Efficiency (OEE) of around 55% in manufacturing industry. One major reason is machine downtime leading to substantial system losses culminating in production plans with unsatisfactory robustness. This paper discusses an approach to integrate maintenance strategies into a production planning approach using discrete event simulation. The aim is to investigate how and where in the planning process maintenance strategies can be integrated and how different maintenance strategies influence production performance and the overall robustness of production plans. The approach is exemplified in an automotive case study, integrating strategies for reactive maintenance in a simulation model to support decision making on how repair orders should be prioritized to increase production performance. The results show that introducing priority-based planning of maintenance activities has a potential to increase productivity by approximately 5%.

Data-driven algorithm for throughput bottleneck analysis of production systems

“…For example, in the case of a Producing bottleneck (Chiang, Kuo, & Meerkov, 2001), as shown in the Test study 1 in Section 5.1, cycle time reduction activities can be carried out or variations in processing times further analysed and reduced. Reactive maintenance activities can also be prioritised, especially for cycle-time bottlenecks (Gopalakrishnan et al, 2013). For machines which constitute downtime bottlenecks, corresponding sensor-level information from their components can be further analysed to explain any abnormal behaviour.…”

“…Since production resources (machines, robots, operators and so on) are usually scarce, they must be used efficiently to increase system throughput (Li, Ambani, & Ni, 2009). In maximising throughput, it is essential to identify bottleneck machines in a production system so that maintenance (and other production improvement activities) can be focused on these (Gopalakrishnan, Skoogh, & Christoph, 2013;Wedel, Von Hacht, Hieber, Metternich, & Abele, 2015;Guner, Chinnam, & Murat, 2016). A system-level decision support tool is therefore needed, to analyse these bottleneck machines (Jin, Weiss, Siegel, & Lee, 2016).…”

Risk Based Maintenance in the Hydroelectric Power Plants

“…For this reason, researchers have solved the problem of complex maintenance planning/scheduling for multi-objective maintenance optimization problems by using particle pile optimization [14][15][16], Monte Carlo simulation [17,18], Bayesian network [19], simulated annealing [12], and genetic algorithm [20][21][22]. In addition, the problem of maintenance planning has been addressed in many sectors from the construction [23] sector to the automotive [24] sector, from the textile [25] industry to the chemical [26,27] industry. There are many studies conducted in the energy sector in which this study is carried out.…”

Planning of Maintenance Activities – A Current State Mapping in Industry

“…A prior study shows that through strategic planning of maintenance activities, productivity can be increased by about 5% [4]. Therefore maintenance activities planning is an integral decision making aspect for maintenance engineers, requiring support from modern methodologies, data analysis approaches, and Information and Communication Tools (ICT).…”

“…Criticality analysis needs to be continuously updated every day to prioritize maintenance activities [10] With complex production, prioritization of maintenance work-orders becomes crucial and challenging [11]. Throughput improvement can be achieved through prioritizing maintenance activities for the static and dynamic bottleneck machines [4]. The highest criticality is given to the equipment that is most important for a specific purpose, which normally is production.…”