Benchmarking maintenance performance in select agro-based industry

PurposeThis paper demonstrates a multi-criteria analytic hierarchy process (AHP) framework for evaluating and benchmarking maintenance performance in the select agro-based industry.Design/methodology/approachInitially, 20 maintenance practices (criteria) have been identified after a detailed literature review and discussion with the agro-based industry (sugar, textile and dairy industry) executives. These are then grouped into six maintenance management areas referred to as group criteria. The multi-criteria methodology consists of three steps: criteria identification, hierarchical modeling and data collection and maintenance performance evaluation, and benchmarking. The multi-criteria methodology proposed in this work facilitates two ways of carrying out benchmarking: (1) within the agro-based industry and (2) between the agro-based industry. The methodology has been explained by taking a case example of 45 agro-based industries (18 dairy, 13 sugar and 14 textile) from the western region of India. The sensitivity analysis of the model has been performed to ascertain the robustness of the results.FindingsThere is a difference in the maintenance performance across the agro-based industries due to different maintenance practices perceived differently.Research limitations/implicationsThe outcome of the model is mainly given by the judgments of the agro-based industry executives. It is also sensitive to any change in the relative importance to the evaluation criteria or the perception about the maintenance performance.Practical implicationsThe study contributes in identifying the weakness, if any, by comparing the agro-based industry under investigation with the benchmark factory at three levels, namely, overall performance (factory level), group criteria (maintenance management area level) and criteria (maintenance practice level) allowing further improvement.Originality/valueThe methodology assists in better decision-making and in improving maintenance performance.