How Equipment Utilisation can improve Manufacturing Analytics & OEE in Biopharma?
Life Science Pharma

How Equipment Utilisation can improve Manufacturing Analytics & OEE in Biopharma?

By Amit Singha January 29, 2024 - 48 views

Biopharma enterprises are steadily turning their attention towards manufacturing analytics and OEE best practices in sync with equipment utilisation and enhancing overall productivity. In an ideal scenario, a Biopharma entity will be functioning at 100% in the available time due to overall equipment effectiveness. This will be based on the validated performance of the machines in question along with 100% quality product outputs.

How OEE Matters

When it comes to ensuring overall equipment effectiveness (OEE), embracing smart manufacturing is the need of the hour for Biopharma players. With OEE in place, manufacturers can examine the effectiveness levels of their manufacturing operations and overall execution. Root causes behind several problems can be swiftly identified while prompt action can also be taken for improving processes. Machine sensors and subsequent manufacturing analytics can enable the collection of OEE information on a real-time basis, thereby offering higher efficiency in tracking process-linked downtime and making instant improvements in terms of machine performance and efficiency levels.

OEE is the ratio of the utilisation of the pharmaceutical manufacturing facility in comparison to the total output that it was tailored for. It is a productivity measurement that helps calculate the efficiency of equipment that helps manufacture finished products through taking three fundamental aspects into account. These include the following:

 • Availability- It indicates uptime, i.e. the total availability of time for the manufacturing facility. This does not account for sudden downtimes (unscheduled) and maintenance. Hence, this is the planned time of production that may be assigned for production.

• Performance- It is the final output of the plant in the time that it is functioning as compared to the maximum possible output that it may have obtained at its validated speed. Losses can be in the form of slow cycles which indicate how equipment functions slower in comparison to the validated speed. Another issue is micro stops or when equipment stops working for a small duration.

There are more sub-segments for all these aspects in a bid to zero in on the reasons behind downtimes. It helps in planning on improving these parameters or doing away with issues as much as possible. The calculation of OEE in Biopharma manufacturing is necessary for ensuring overall profitability. Manufacturing analytics and OEE data will help in decision-making across aspects like manufacturing infrastructure and operator training investments, lean manufacturing value, ROI calculation, profit gains from procedural and infrastructural improvements, lowering efforts for production performance follow-ups, investment comparisons via ROI data, digitisation of plants and obtaining OEE data quickly. It also helps in deciding how to scale up and secure production output and enable total control over machine performance and operations.

Smart Manufacturing Technologies Worth Embracing

When it comes to smart manufacturing for boosting equipment utilisation, along with machine analytics and insights, here are some aspects worth noting carefully.

• Automation is indispensable for enabling better control. This also depends on data analytics. Data gathered at each development stage should be examined to keep quality and production controls in check. Suitable analytical frameworks are crucial for better automation and opens up newer productivity gains via AI, IoT and ML.

• Purview of Data Analytics- Analytics has a crucial role to play across several Biopharma spheres including biosimilars, platform processes, process intensification, advanced therapies, personalised medicines, CDMOs, and of course, defining the artificial intelligence (AI) strategy.

• Relying on Artificial Intelligence- Usage of AI in the Biopharma industry has now become mainstream. More companies are embracing automated AI-backed procedures that thrive on data-based analytics and insights for decision-making and make use of predictive analytics too. AI is being used to make manufacturing highly efficient while scaling up equipment utilisation levels alongside.

How It Adds Up For Biopharma Companies

Biopharma companies are already facing challenges in terms of staying more competitive in the current scenario. Several pharmaceutical players may not be able to reap the rewards of high-selling patent-safeguarded drugs with higher margins and sales volumes. Generic drugs are already taking up the lion’s share of prescriptions written globally (a whopping 85% in the US alone). As per The Economist, drugs valued up to $170 billion in yearly revenues are not be in the patent-protected category and will face competition from multiple generic versions.

To stay competitive in this fast-changing industry landscape, Biopharma enterprises have to utilise equipment more effectively in a bid to scale up production and cut down on wastage and unnecessary costs/overheads alike. Compliance and quality challenges also have to be tackled more effectively in order to mitigate root causes/issues. Manufacturing analytics should be a firm point of focus in this case. ERP systems can gather raw data (material-based), while the MES (manufacturing execution system) will have details of execution of particular batch manufacturing processes. The key operating parameters will also be stored in specific data management tools while the laboratory management system will have product quality-based data. Incident management systems can gather adverse events and other occurrences alongside.

All this data can be made silo-free and consolidated with easy viewing for a more holistic picture with proper manufacturing analytics and techniques like multivariate analysis and other tools. This will automatically contribute towards superior equipment utilisation as well.


Are there specific best practices for implementing Equipment Utilisation strategies in Biopharma manufacturing?

Some of the best practices that can be ensured include data consolidation for a holistic view and also focusing on manufacturing analytics for data gathering and insights regarding equipment productivity, condition, and other parameters. It will also help in coming up with predictive analytics-based strategies on maximising equipment utilisation.

What key performance indicators (KPIs) should Biopharma companies monitor alongside OEE to gauge overall manufacturing efficiency?

Some of the KPIs (key performance indicators) that Biopharma companies may track along with OEE to assess manufacturing efficiency include customer rejects, downtimes, lead time to customer, inventory turns, and equipment maintenance.

How does a focus on Equipment Utilisation align with sustainability goals in Biopharma manufacturing?
Focusing on equipment utilisation aligns perfectly with sustainability goals in Biopharma production. Operating equipment at its maximum capacity with minimal downtimes and production losses boosts sustainability across the entire ecosystem. It ensures faster output and delivery to the end-consumer, thereby helping eliminate wastage and costs alike.

Are there specialized software solutions tailored for Equipment Utilisation and Manufacturing Analytics in Biopharma?

There are several automation, AI, ML, and IoT-based solutions tailored for equipment utilisation and manufacturing analytics in the biopharmaceutical sector. These help gain valuable data and insights which enable better decision-making on effectively utilising manufacturing equipment for maximum productivity.

How do Biopharma manufacturers gauge the success of Equipment Utilisation optimization initiatives through the lens of improved Overall Equipment Effectiveness (OEE)?

OEE (overall equipment effectiveness) can help greatly in terms of enabling Biopharma manufacturers assess the success of their equipment utilisation and optimisation initiatives. This is majorly through enabling them to evaluate quality, availability and performance.

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