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Predictive Maintenance in Pharma

Identify the probability of machine breakdowns and increase your product quality

Challenges

  • Leading pharmaceutical company

  • Extremely high packing standards mean product spoilage is very costly

  • Machine breakdowns substantially reduce overall plant efficiency

  • Client wanted to predict probability of machine breakdowns and quality defects

Solutions

  • Combined data from production line sensors with the Bill of Materials (BOM), input-material quality measures and machine operator team

  • Built predictive Machine Learning models that successfully explained the probability of a machine breakdown and quality defect

  • Developed combinatorial optimization method, inspired by genetic algorithms, providing the machine operator with the optimal machine parameters for the given product

  • Built an app to visualise the optimal machine parameters directly on the operator’s tablet

Values

  • Team running the production and packaging lines gain a high degree of confidence in the machine parameters suggested by the app

  • Significantly reduced machine breakdowns and quality defects with the algorithm continuing to learn from new equipment, products and materials

  • Gained additional insights on the ideal composition of skills for the employees operating the production line

Roles

Machine Learning Engineer

Technologies

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Sectors

Health & Pharma, Manufacturing

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