Testing the relationship between lean management practices and achieving excellence performance: A sample of workers in the electricity production sector in Illizi, Algeria

Othmane Blal; Abdellah Bendob

doi:10.47243/jos.3167

Authors

Othmane Blal University Center of Illizi / Ageria 0009-0002-3479-5043
Abdellah Bendob University Center of Illizi / Ageria 0000-0002-6500-2854

10.47243/jos.3167

Abstract

Purpose: This study aims to investigate the relationship between lean management practices and achieving excellence performance in the electricity production sector in the state of Illizi, Algeria, with a particular focus on identifying the most influential lean management tools. Method: The study is based on a sample of 180 workers, and data were collected through a structured questionnaire. The analysis was conducted using SPSS, applying Spearman’s rank correlation coefficient and the Wilcoxon Single Sample Classification Test. Additionally, a Multi-Layer Perceptron (MLP) artificial neural network model was employed to evaluate the relative importance of lean management variables and assess prediction accuracy. Findings: The results indicate a statistically significant and positive relationship between lean management practices and excellence performance. A strong correlation was found between multi-functional workers and excellence performance (r = 0.747), while standardized work showed a moderate positive correlation (r = 0.629). The neural network model achieved an accuracy rate of 88.7%, with multi-functional workers identified as the most influential factor (100% importance), followed by standardized work (73.9%). Conclusion: The study contributes to the literature by demonstrating the critical role of lean management practices—particularly human-centered factors—in achieving excellence performance.

Keywords:

Lean Management Excellence Performance Multi-functional Workers Continuous Improvement Electricity Production Sector

Downloads

Download data is not yet available.

References

Abdelali, D., & Djamel, A. (2026). Conceptual foundations and practical implications of e-government in strengthening institutional governance: A case study of the Algerian electricity and gas sector. Science, Education and Innovations in the Context of Modern Problems, 9(2), 1–10. https://doi.org/10.56334/sei/9.2.36

Adam, F. A., Gamal El-Din, M. M., & Mohammed, A. A. (2020). Predicting the exchange rate in Sudan using neural network models during the period (1960–2017). Journal of Economic, Administrative and Legal Sciences, 4(14), 85–100.

Ainous, R. (2022). Measuring patient satisfaction with health care using artificial neural network (ANN) models. Journal of the Institute of Economic Sciences, 25(1), 333–352.

Alzrooq, T., & Bounehas, A. (2025). The impact of green human resource management on service quality: A field study from the perspective of employees at the general electricity company in Bani Walid, Libya. Science, Education and Innovations in the Context of Modern Problems, 8(8), 72–84. https://doi.org/10.56352/sei/8.8.8

Aryadoust, V., & Goh, C. C. (2014). Predicting listening item difficulty with language complexity measures: A comparative data mining study. CaMLA Working Papers.

Benítez, R. R., López, C., & Real, J. C. (2018). The lean and resilient management of the supply chain and its impact on performance. International Journal of Production Economics, 203, 190–202.

Duc, F., Fontaine, R., Hoenen, D., & Wellhoff, S. (2020). Pro en lean. Magnard-Vuibert.

Dung, S. T., Thomas, G., & Oyerinde, Y. (2023, June). Predicting students’ academic performance using artificial neural networks. International Journal of Computer Applications, 185(19), 1–7.

Fresno, P. L. (2012). Contribution of lean management to excellence. Nang Yan Business Journal, 1(1), 89–98.

Hohmann, C. (2010). Guide pratique des 5S et management visuel. Eyrolles.

Hohmann, C. (2012). Lean management. Eyrolles.

Kelendar, H. (2020). Lean thinking from Toyota manufacturing to healthcare sector. Research in Medical & Engineering Sciences, 8(5), 913–923.

Mohammad, M., Mann, R., Grigg, N., & Wagner, J. P. (2011). Business excellence model: An overarching framework for managing and aligning multiple organisational improvement initiatives. Total Quality Management & Business Excellence.

Mora, A. C., García, A. N., & Cristobal, R. P. (2015). Project to improve knowledge management and key business results through the EFQM excellence model. International Journal of Project Management, 33, 1638–1651.

Porter, L., & Tanner, S. (2004). Assessing business excellence: A guide to business excellence and self-assessment (2nd ed.). Elsevier Butterworth-Heinemann.

Punna Rao, G. V., Nallusamy, S., & Narayanan, M. (2017). Augmentation of production level using different lean approaches in medium-scale manufacturing industries. International Journal of Mechanical Engineering and Technology, 8(12), 360–372.

Ratter, E., & Nader, S. (2022). The use of lean management tools in production companies with implemented total quality management (TQM). European Research Studies Journal, 25(3), 357–368.

Ries, E. (2016). Lean startup. Nouveaux Horizons.

Stewart, J. (2011). The Toyota kaizen continuum. CRC Press.

Weiss, R., Karimijafarbigloo, S., Roggenbuck, D., & Rödiger, S. (2022). Applications of neural networks in biomedical data analysis. Biomedicines, 10, 1–30.

Wilson, L. (2010). How to implement lean manufacturing. McGraw-Hill.

Womack, J., & Jones, D. (2009). Système lean (2nd ed.). Pearson Education.

Womack, J., Jones, D., & Roos, D. (1990). The machine that changed the world. Macmillan.

Zacharis, N. Z. (2016, September). Predicting student academic performance in blended learning using artificial neural networks. International Journal of Artificial Intelligence and Applications, 7(5), 17–29.