The purpose of this paper is to compute reliability, availability and maintainability (RAM) indices to measure and improve the performance of an automated croissant production line under real working conditions. Based on this study, the authors demonstrate how RAM analysis is very useful for deciding maintenance intervals and for planning and organizing the adequate maintenance strategy.
The present work is carried out by analyzing failure and repair data based on statistical techniques. Descriptive statistics of the failure and repair data at workstation and line level were carried out. Trend and serial correlation tests validated the assumption of independence and identical distribution of the failure data were conducted. Moreover, the reliability and maintainability of both the croissant production line and its workstations have been estimated at different mission times with their best fit distribution.
The main objectives of the applied method are to understand the nature of the failure patterns, and to estimate the reliability and maintainability characteristics of the croissant production system in precise quantitative terms. The analysis identifies the critical points of the production line that require further improvement through effective maintenance strategy.
This study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis and its effect on the performance of the system that works under real conditions. The advantage of the methodology is the continuous monitoring of the production process through appropriate indices, the utilization of which leads to a continuous cycle of improvement.
The author wishes to acknowledge financial support provided by the Research Committee of the Technological Education Institute of Central Macedonia Greece, under Grant No. SMF/LG/151117-200/08.
Tsarouhas, P. (2019), "Statistical analysis of failure data for estimating reliability, availability and maintainability of an automated croissant production line", Journal of Quality in Maintenance Engineering, Vol. 25 No. 3, pp. 452-475. https://doi.org/10.1108/JQME-04-2018-0029
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