Identifying and Prioritizing the Key Factors Affecting the Efficient Maintenance Management in the Agricultural Industry Based on Empirical Studies

ماشین های کشاورزی

Article 3, Volume 13, Issue 4 - Serial Number 30, December 2023, Pages 427-451   

Document Type: Research Article

DOI: 10.22067/jam.2022.76333.1104

Authors

H. Soltanali^{* 1}; M. Khojastehpour²

¹Postdoctoral Researcher, Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

²Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction: With the emergence of new automation and mechanized technologies in the production and processing of agricultural products in Iran, which aim to accelerate the food supply process, adopting appropriate management models in the field of maintenance becomes inevitable. This is crucial to maintain and enhance the operational reliability of agricultural machinery, tools, and equipment. Furthermore, proper management of various physical assets in the agricultural industry, including operation and maintenance, is one of the most important requirements. This is due to their crucial role in ensuring readiness and high availability during the seasons of planting, cultivating, and harvesting agricultural products. These needs differ from that of other continuous production processes.
 Materials and Methods: To achieve an efficient model in the field of maintenance, the following steps have been investigated:

a) Reviewing and identifying the most important criteria and sub-criteria driving the maintenance management. This is based on the previous literature and the experts’ opinion.
b) Evaluating and prioritizing the main criteria and the interactions between their sub-criteria using the Best-Worst Method (BWM).
c) Providing improved solutions for maintenance management of Iranian agro-industries.

We decided to employ BWM because, compared to similar methods, it (i) provides more reliable pairwise comparisons, (ii) reduces the possible anchoring bias that may occur during the weighting process by respondents, (iii) is the most data-efficient method, and (iv) provides multiple optimal solutions which increase flexibility when accessing the best weight point. The process of weighting by BWM is summarized in five steps:
1) Determine a set of evaluation criteria identified by the experts or decision-makers.
2) Identify the most important (Best) and the least important (Worst) criteria according to the experts or decision-makers, each of which may have their own Best and Worst.
3) Determine the preference of the Best criterion over all the other criteria using a number from 1 to 9 (where 1 represents equal importance and 9 represents extremely more important).
4) Determine the preference of all the decision criteria over the Worst criterion.
5) Compute optimal weights.
 Results and Discussion: According to the preliminary surveys, the most important criteria in the excellence maintenance model were identified as “organizational management”, “human-related factors”, and “organizational aspects”, respectively. The results of the BWM revealed that sub-criteria such as "top management support," "fund allocation and inventory resource management," and "appropriate maintenance strategies" had the greatest impact on maintenance management in agro-industries, with global weights of 0.108, 0.075, and 0.067, respectively. Additionally, these findings were compared to previous research conducted in the field of agricultural and production system maintenance models.
 Conclusion: The findings of this study could assist managers in revising and developing maintenance management models in the agro-industries. Future studies could consider calculating the interactions among the criteria that were omitted in this study to simplify the evaluation process which might improve the accuracy of weighing criteria. This can be achieved through the combination of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modeling.

Keywords

Agro-industry; Best-Worst method; Machinery; Maintenance; Mechanization

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