Modeling and Sensitivity Analysis of Total Cost in Preventive Maintenance and Equipment Quality Control

Seyed Jamal  Ashrafisaadat; Kamaleddin Rahmani; Soleiman  Iranzadeh; Nasser  Feghi Farahmand; Houshang  Taghizadeh

Authors

Seyed Jamal Ashrafisaadat Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran Author https://orcid.org/0009-0005-2898-3349
Kamaleddin Rahmani Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran Corresponding author https://orcid.org/0009-0002-0749-9206
Soleiman Iranzadeh Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran Author https://orcid.org/0000-0002-4190-0115
Nasser Feghi Farahmand Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran Author https://orcid.org/0000-0001-6263-4787
Houshang Taghizadeh Department of Management, Ta.C., Islamic Azad University, Tabriz, Iran Author https://orcid.org/0000-0002-0963-0653

Keywords:

Modeling; Sensitivity Analysis; Preventive Maintenance; Equipment Quality Control

Abstract

The objective of this study was to model and conduct a sensitivity analysis of total cost in preventive maintenance and equipment quality control. In terms of purpose, this research was applied; in terms of implementation method, it was descriptive and survey-based; and in terms of analytical approach, it was analytical–mathematical. The statistical population used for testing the model consisted of the wastewater treatment plant of the Urmia Industrial Estate, and the entire statistical population was examined. The instruments used for data collection and measurement of the variables included maintenance records and documents, quality-related information, cost and time data, and other relevant operational data. Mathematical modeling, numerical solution using Maple 11 software, the paired-samples t test, and sensitivity analysis were employed to analyze the research data. In this study, an integrated model was developed to simultaneously determine the optimal preventive maintenance intervals and quality control parameters in order to minimize the total system cost. The results obtained from solving the model indicated that the optimal values of the decision variables were (11, 1.91, 7, 658), while the objective function reached 116 monetary units. Furthermore, the results of the paired-samples t test over a 12-month period demonstrated a significant difference between costs before and after implementation of the model, indicating that implementation of the model significantly reduced costs. The sensitivity analysis also showed that the parameters associated with transition of the process to an out-of-control state and deviation of the quality characteristic exerted the greatest effects on the total cost function and decision variables. Overall, the proposed model demonstrated high efficiency in reducing the integrated costs of maintenance, repair, and quality control.

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