STUDY ON THE IMPLEMENTATION EFFECTIVENESS OF A MACHINE LEARNING SOLUTION COST-EFFECTIVE PRODUCT QUALITY OPTIMIZATION IN MANUFACTURING LEADERSHIP

Manoj KUMAR, Nicolae BALC

Abstract


This study explores the implementation effectiveness of machine learning (ML) solutions aimed at cost-effective product quality optimization within manufacturing leadership. Through field data collected from multiple global manufacturing plants, the author identifies deployment challenges, revealing strategic weakness in the three out of ten chosen parameters—particularly strategic triggers, scalability planning, and process justification—scored significantly low. These insights highlight the gap between ML potential and real-world execution. The paper proposes a structured framework integrating CRISP-DM and Agile methodologies to address these weaknesses. Key strategies include aligning ML initiatives with business goals, enhancing scalability planning, and embedding ML into core process controls to drive sustainable quality leadership.


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