A unique mathematical programming algorithm for performance optimization of organizational indicators in manufacturing sector

ABSTRACT

This study presents an integrated algorithm for the evaluation and optimization of manufacturing systems by considering managerial and organizational performance indicators. The proposed algorithm is composed of data envelopment analysis (DEA), fuzzy DEA and statistical methods. In order to achieve the goals of this study, a set of 12 criteria were chosen to indicate the application of the integrated method. The results showed DEA results have lower mean absolute percentage error (MAPE) than that of the fuzzy DEA. This study also analyzes and weights the indicators, and the results showed “research and development investment to production value” and “education and training investment per employee” indicators are the most effective indicators. This is the first study that introduces a unique algorithm for managerial and organizational factors. Second, it can handle data uncertainty due to existence of fuzzy mathematical programming in the algorithm. Third, weights of indicators are identified through robust statistical algorithm.

KEYWORDS:

• Manufacturing sector
• organizational indicators
• performance optimization
• Data Envelopment Analysis (DEA)
• Fuzzy DEA (FDEA)

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

M. Merati

M. Merati is an industrial engineer. He graduated from University of Tehran, Iran. Mojtaba is interested in performance assessment as well as optimization in organizations and the ERP systems.

V. Salehi

V. Salehi is a PhD candidate at Memorial University of Newfoundland, Canada. His research areas include resilience engineering, human factors, safety II, complex systems analysis, and multi-criteria decision-making. He has published more than 25 papers in well-known journals.

A. Rafiei

A. Rafiei is an industrial engineer. She graduated in bachelor’s program from University of Tehran, Iran and graduated in master’s program from Northeastern University in Masters. Aysan’s interests include optimization in logistics problems and value chains improvement.