Design of a Data-Driven Performance Evaluation System for Productivity Enhancement in Continuous Manufacturing Industries

Document Type : Research Note

Authors

Department of Industrial Engineering, School of Industrial Engineering, Yazd University.

Abstract

In today's industrial landscape, enhancing productivity in manufacturing sectors, particularly in automated continuous industries, has gained paramount importance. This paper introduces an innovative data-driven reward and penalty system designed to boost productivity. The proposed approach targets agile and team-based performance evaluation, focusing on key indicators such as downtime adjustments, stoppages, rework, and process non-conformities. This methodology is founded on systems thinking and a team-oriented approach, aiming to create a holistic view of operational efficiency. The core of this system lies in its unique integration of statistical methods, specifically entropy and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) techniques. This combination allows for dynamic weighting of performance indicators in each time period, enabling rapid identification of performance fluctuations and highlighting areas requiring process improvement or correction. By employing this data-driven approach, the system provides a responsive and adaptive framework for continuous improvement. To validate its effectiveness, the system was implemented in a heating radiator manufacturing plant. The implementation process was carefully structured, with an initial three-month period followed by a three-month validation phase. The system's direct impact on human capital was evident, fostering team cohesion and promoting a collective mindset among employees. The results of this implementation were significant and multifaceted. After the six-month trial period, the plant witnessed a remarkable 22% increase in production output. This substantial boost in productivity was accompanied by a 20% reduction in failure rates, indicating improved process reliability and efficiency. Furthermore, the rate of defective products decreased by 1.5%, reflecting enhanced quality control measures. Perhaps most notably, the plant's process audit score saw a significant improvement of 21.5%, demonstrating a comprehensive enhancement in overall operational standards. These impressive outcomes underscore the effectiveness of the proposed system in driving tangible improvements across multiple facets of manufacturing operations. The success of this implementation not only validates the theoretical framework but also provides a practical blueprint for other industries seeking to optimize their productivity and quality metrics. By leveraging data-driven insights and fostering a team-oriented culture, this innovative approach presents a promising solution for manufacturers aiming to stay competitive in an increasingly demanding industrial landscape.

Keywords

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References

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Sharif Journal of Industrial Engineering & Management
Volume 41, Issue 2 - Serial Number 2
February 2026
Pages 153-165

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