Optimizing Preventive Maintenance Frequency and Duration Using Mathematical Model and Fuzzy Logic in Flour Mills

Abstract

Maintenance planning is one of the most important tasks carried out by a factory. Preventive maintenance is one type of maintenance that prevents major breakdowns from occurring. Increasing preventive maintenance duration reduces corrective maintenance duration. However, conduction of preventive maintenance causes downtime and incurs costs too. Several studies have been conducted to establish a tradeoff between these two types of maintenance to reduce the overall downtime. Models that minimize costs are preferred by industries. Besides, models requiring complex datasets are not readily adopted by most of the industries for regular use. This paper represents three different methods for optimizing preventive maintenance duration and frequency in a renowned flour mill factory of Bangladesh. The first method uses an established mathematical model which determines the optimum frequency of preventive maintenance per month, maximizing profit. This model is comparatively easier to adopt in flour mills since it does not use reliability concepts for different components, rather uses simple costs and downtime data, assuming constant failure rate. The second method modifies the existing model to optimize the duration of each PM inspection as well as frequency, which is an important parameter for flour mill maintenance. The third method uses three maintenance KPIs as inputs to determine the total duration of PM per month for the flour mills. The reason for not adopting PM models in factories is the lack of data availability and the difficulty in aligning the model with the existing maintenance plan. Besides, flour mills have components in continuous production lines which, if maintained separately, would not be profitable, rather a single PM activity should be carried out for some set hours, set by the maintenance department. The rationale behind this paper is to develop easily adaptable PM models for flour mills.