Originally Published on: QuantzigHow We Helped Pharmaceutical Manufacturer Set Up Predictive Maintenance For Equipment

##Elevating Efficiency for a Global Pharma Powerhouse

Embark on a transformative journey with a multinational pharmaceutical giant, boasting a revenue exceeding $2 billion. Struggling with recurring ad-hoc machine failures, the company collaborated with Quantzig, a premier analytics and data management firm, to pioneer predictive maintenance through IoT-driven data.

###Client Challenges: Profit Margin Maintenance: Frequent machine failures incurred substantial costs, impacting production profit margins. Lack of Proactivity: Recognizing the necessity for a proactive strategy to mitigate failure risks and enhance operational efficiency.

###Quantzig's Innovative Solutions: Quantzig spearheaded the implementation of predictive maintenance, leveraging IoT-generated sensor data. Installation of multiple sensors across processes generated millions of data points, integrated into a central data lake. Models like random forests, Hidden Markov Models (HMM), and neural networks predicted device failure stages and identified main causes.

###Impact Delivered: Strategic interventions resulted in significant outcomes:

45% Reduction in Maintenance and Breakdown Costs: Proactive measures led to substantial cost savings. 70%+ Failure Prediction Accuracy: High accuracy achieved in anticipating machine failures. 20% Reduction in Inventory Holding Cost: Optimization of maintenance schedules decreased spare parts inventory costs.

###Industry Dynamics: In the pharmaceutical sector, predictive maintenance optimizes equipment performance, minimizing machine failure risks. Leveraging sensor data and advanced analytics, these models proactively identify potential equipment issues, ensuring uninterrupted production, improving efficiency, and enhancing productivity.

###Client's Journey: A global pharmaceutical giant, grappling with production profit margin challenges due to machine failures, sought to implement predictive maintenance. The objective was to detect potential equipment issues proactively, ensuring a cost-effective approach.

###Client's Predicaments: Data Dispersion Challenge: Despite generating valuable sensor data, challenges arose from dispersed data across various databases. Lack of Real-time Alerts: Absence of real-time alerts for potential machine failures resulted in unexpected breakdowns and production losses.

###Quantzig's Strategic Approach: Data Lake Integration: Consolidating sensor-based data into a central data lake facilitated downstream analysis. Predictive Models Application: Advanced analytics models, including random forests, HMM, and neural networks, predicted failure stages and identified causes. Interactive Dashboards: Real-time alerts through interactive dashboards offered insights into maintenance schedules, warranty expirations, and performance deviations.

###Culmination: Quantzig's tailored solution not only achieved a 45% reduction in maintenance and breakdown costs but also empowered the client to proactively address potential machine failures. The proactive approach ensures sustained operational efficiency and competitiveness in the pharmaceutical manufacturing industry.

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