Overall equipment effectiveness (OEE) is top of mind across the pharmaceutical landscape. And there’s a good reason why.
OEE is a comprehensive metric that considers equipment availability, throughput and product quality in its calculation. While quality is job one in life sciences, OEE provides a snapshot of manufacturing performance – and a framework to improve it.
The good news? Thanks to process analytical technologies (PAT) and other advanced analytics, life sciences companies now have tools that can provide constant oversight of processes that directly impact OEE. Measurement is a big part of PAT, but this blog is focused on the “big A” at the heart of PAT – “Analysis.”
In other words, the latest applications can analyze manufacturing data, pinpoint deviations – and accelerate resolution by delivering critical information to people who can act on it.
How Advanced Analytics Impact OEE in Life Sciences
Over the years, I’ve often been asked what life sciences applications are best suited to advanced analytics.
My response? Analytics can impact OEE in almost any operation – from improving blender, reactor or fermenter performance to optimizing tablet throughput and sterilization effectiveness.
Analytics – and more informed decisions – start with data. And for any life sciences company, the path to analytic-driven results begins with three questions:
- What is my business objective?
- What data is available?
- How can analytics leverage that data – and deliver information that enables better decision-making?
Advanced Analytics in Action
Let’s take a closer look at how advanced analytics can improve OEE and help meet business objectives.
First, anomaly detection is a classic use case for a streaming analytics platform that can process and analyze large, incoming datasets in real time. Anomaly detection applies to a myriad of processes across a pharmaceutical plant and has a direct impact on product quality, equipment availability and throughput.
Simply put, anomaly detection continuously monitors processes, learns what is normal and then creates alerts when abnormal patterns are detected.
For example, anomaly detection could identify an unusual fluctuation in temperature or an excessive use of a caustic in a batch. With timely information, operators are better equipped to quickly determine the cause of the anomaly – and take steps to resolve it.
Similarly, real-time monitoring and analysis of equipment conditions can deliver critical information to enable predictive maintenance – and improve process performance.
For instance, monitoring the health of an air compressor on a fermenter could detect unusual energy usage or temperatures, which can be a key indicator of impending failure. Armed with that knowledge, workers can make better decisions. And schedule maintenance before compressor performance impacts batch quality – or causes unanticipated downtime on the line.
Steps to Success
For any life science company implementing an advanced analytics project, the ultimate vision is better OEE. And more specifically, better overall quality and performance that will have a direct and positive financial impact.
How can you put your project on the path to success? Here’s some advice based on successful engagements with industry leaders.
- Treat An Analytics Project as You Would Any Manufacturing Project Too often, analytics projects are treated like outliers, with technology or vendor selection being the primary activity. But we all know “let’s just see what the supplier delivers” is never a recipe for success.
Instead, be sure to start with a team composed of key stakeholders at the manufacturing site that will be participating in the project. Develop a comprehensive project plan that includes a business objective – and implementation and validation cycles.
- Thoroughly Research Vendor CapabilitiesNothing beats references – and specifically – vendor references that outline projects similar in scope to yours.
- Consider and Always Pre-Plan for Validation Requirements Remember that analytics are, by their nature, adaptive and designed as human decision support. Think about how to validate mechanisms that alert an operator to potential, but unexpected, deviations or decisions.
- Don’t Fall Victim to “Magical Thinking”The marketing hype around analytics – and especially machine learning and AI – has been significant. I’ll never forget the client who told me they tried predictive maintenance, but were disappointed because it didn’t predict all failures. Quite simply, that’s not a realistic expectation.
Avoid the human inclination for perfection. Instead, verify that you are delivering useful information for planned events that could happen again – not anything that could happen anytime.
The key to success? Resist the hype, set realistic expectations for your project, track your results – and stay focused on your original value proposition.