Citation:

Srikanth Reddy Katta, Sudheer Devaraju, 2021. "Database-Driven Optimization of Production Line Downtime in Pharma Manufacturing Using Hierarchical Models", ESP Journal of Engineering & Technology Advancements 1(2): 293-304.

Abstract:

One of the major issues drawn from manufacturing production lines in the pharmaceutical industry is time loss due to production line halts. Effective downtime management must be supported by a powerful solution that involves information analysis and sophisticated optimization tools. This research work considers a database-oriented solution based on the use of hierarchal models with a view to analyzing and tracking production downtime. To categorize downtime factors into different levels, a nested structure is adopted to obtain high levels of detail that would help identify the appropriate action to address each cause. Analyzing a large big data set, such as a large pharmaceutical plant data set consisting of machine performance details, production plan, and error report, some of the advanced techniques, such as regression analysis, decision tree, and optimization, can be used. The hierarchical model breaks down downtime into three major categories: This is a relative of planned maintenance, unplanned machine failures, and process inefficiencies. Probabilistic tools and forecasting models are used to analyze relations, identify the likely downtime profiles and recommend proper measures. Data available to the authors show that it is possible to achieve up to a 15 per cent decrease in OT and to raise OEE by 10 per cent. This approach enables timely decision-making through real-time data monitoring, prognostics, and health management. They emphasize the need to adopt a database-driven solution to enhance efficiency in the production processes of a pharm manufacturing plant to enhance output while at the same time cutting on time used for repairs and other customized tasks.

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Keywords:

Production Downtime, Pharmaceutical Manufacturing, Hierarchical Models, Machine Learning, Predictive Maintenance, Root Cause Analysis.