Achieving Operational Excellence: Paradigm Shift with Machine Learning-Driven Optimization


  • Pravin Mane Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development, Pune, India.
  • Hema Mirji Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development, Pune, India.
  • Bhavsar Dhananjay Narayan Assistant Professor,Dr. D. Y. Patil Institute of Technology
  • Rahul Manjre Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Abhijit Kadam Institute of Management and Social Sciences, Solapur, India.
  • Pratima Gund Assistant Professor, Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development, Pune, India.
  • Girish Bahirat Research Scholar , Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development, Pune, India.


Machine Learning, Optimization, Paradigm Shift, Data-driven, Efficiency, Automation, Predictive Maintenance, Resource Allocation


Achieving operational excellence has become crucial for organisations trying to stay ahead in the highly competitive business environment of today. Traditional methods must be rethought in order to be effective, and machine learning-driven optimisation stands out as a game-changing approach. The tremendous effects of incorporating machine learning into operational processes are explored in this abstract, which provides a succinct summary of the main ideas and discoveries.The conventional approach to operations management places a significant emphasis on static, rule-based systems. Organisations are able to optimise operations in a variety of areas, including as resource allocation, supply chain management, and customer service, by utilising the power of sophisticated algorithms.This abstract highlights the several benefits of optimisation driven by machine learning. It highlights how new technologies enable businesses to instantly analyse enormous datasets, find undiscovered trends, and take proactive, well-informed action. We demonstrate the real advantages of lower costs, more productivity, and better customer experiences through case studies and examples.Additionally, this abstract explores the difficulties and factors to be taken into account when applying machine learning-driven optimisation, including data privacy, hiring talent, and ethical issues. It highlights the urgent requirement for a comprehensive strategy that combines cutting-edge technology and careful planning.


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How to Cite

Mane, P. ., Mirji, H. ., Dhananjay Narayan, B. ., Manjre, R. ., Gund, P. ., & Bahirat, G. . (2024). Achieving Operational Excellence: Paradigm Shift with Machine Learning-Driven Optimization. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 377–387. Retrieved from



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