Originally published by Quantzig: How We Helped Pharmaceutical Manufacturer Set Up Predictive Maintenance For Equipment
Introduction:
In the rapidly evolving landscape of pharmaceutical manufacturing, optimizing equipment performance is paramount to maintaining efficiency and profitability. Predictive maintenance, powered by advanced technologies, has emerged as a crucial strategy to achieve these objectives. By harnessing industrial IoT sensors, analytics platforms, and AI solutions, pharmaceutical manufacturers can enhance operational efficiency, minimize downtime, and ensure peak performance. This case study delves into how our strategic methodologies and digitalization efforts empowered a pharmaceutical manufacturer to implement a robust predictive maintenance system, leveraging advanced AI and machine learning capabilities for a resilient pharmaceutical value chain.
Summary:
Client Details: A multinational pharmaceutical manufacturer with over $2 billion in revenues.
Challenges: The client encountered frequent ad-hoc machine failures across their plant locations, leading to costly breakdowns, production losses, and high maintenance costs. To mitigate these challenges, the client sought a proactive approach to reduce failure risks and enhance operational efficiency.
Solutions:
Quantzig assisted the client in implementing predictive maintenance using sensor-based IoT data. By integrating sensor data into a central data lake, our team conducted risk identification analysis using models like random forests, Hidden Markov Models (HMM), and neural networks. Interactive dashboards were developed to provide real-time alerts for maintenance schedules and historical performance deviations.
Impact Delivered:
- 45% reduction in maintenance and breakdown costs
- 70%+ failure prediction accuracy
- 20% reduction in inventory holding cost for spare parts
Industry Overview:
The pharmaceutical industry is characterized by stringent regulations and a focus on research and development to discover innovative treatments for various diseases. Predictive maintenance models play a significant role in optimizing equipment performance, reducing downtime, and ensuring uninterrupted production.
The Power of Predictive Maintenance for Pharma:
- Data-Driven Approach and Industrial IoT Sensors: Industrial IoT sensors provide real-time insights into equipment performance, enabling early detection of potential issues.
- AI-Based Predictive Maintenance and Machine Learning: AI-driven systems predict equipment failures, allowing for proactive maintenance and risk reduction.
- Total Productive Maintenance (TPM) and Advanced Data Analytics: TPM principles and advanced analytics optimize maintenance activities, increase productivity, and mitigate machine failures.
- Purpose-Built Software Platform: Purpose-built platforms integrate predictive maintenance software and machine learning algorithms to optimize maintenance lifecycle.
Benefits of Predictive Maintenance in Pharmaceutical Industry:
- Enhanced Operational Efficiency: Real-time insights minimize unplanned downtime, optimizing overall production operations.
- Cost Reduction and Resource Optimization: AI-based maintenance strategies optimize resource allocation, reducing operational costs.
- Increased Productivity and Peak Performance: Early detection of machine failures ensures equipment operates at peak performance levels, minimizing production impact.
- Sustainable Competitive Advantage: Purpose-built software platforms ensure optimized maintenance, enhancing the pharmaceutical supply chain’s resilience.
About the Client:
A multinational pharmaceutical manufacturer with more than $2 billion in revenues sought to implement predictive maintenance to minimize production profit margin impact due to frequent machine failures.
Challenges:
The client faced challenges in consolidating sensor-based data, leading to difficulties in accessing and analyzing data for business insights. Lack of centralized data hindered timely alerts for potential machine failures, resulting in production losses and increased maintenance expenses.
Solutions:
Quantzig integrated sensor data into a central data lake and applied predictive maintenance models to predict device failure stages. Interactive dashboards provided real-time alerts, enhancing operational efficiency and reducing risks of machine failures.
How Quantzig Helps Achieve Predictive Maintenance in Pharma:
- Advanced Analytics and Machine Learning: Our team integrates advanced analytics and machine learning capabilities to facilitate early detection of potential machine failures.
- Proactive Asset Maintenance and TPM: We adopt a proactive approach to asset maintenance, aligning with TPM principles to optimize routine maintenance activities.
- Comprehensive Analysis of Operational Costs: Quantzig conducts thorough analysis to optimize resources and increase productivity, ensuring a competitive edge in the market.
- Tailored Solutions for Competitive Advantage: We provide tailored solutions that contribute to sustainable competitive advantage, positioning pharmaceutical companies for success in a competitive market.
Conclusion:
Our predictive maintenance solution revolutionized routine maintenance in pharmaceutical manufacturing, showcasing the transformative power of industrial digital transformation. By strategically leveraging cutting-edge technologies, our client experienced increased productivity, operational efficiency, and cost savings, ensuring a resilient and optimized future for their production operations.
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