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Digital Transformation Pharma
What are the common pitfalls of improving Overall Equipment Efficiency and how to avoid them?
By
Rajarshi February 22, 2024 -
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Most enterprises, be it in biopharmaceuticals or other industries will naturally strive to enhance their overall equipment effectiveness/efficiency (OEE) in a bid to stay competitive in a fast-changing global environment. However, there are several OEE optimisation mistakes that are avoidable for Biopharma and other industrial players. Knowledge of the right OEE fundamentals is also necessary for proper implementation and optimisation alike. Here’s taking a closer look.
OEE fundamentals at a glance
Here are some key points on overall equipment effectiveness/efficiency (OEE) that are worth noting.
- A score of 100% OEE is regarded as perfect production, which means that quality products are being manufactured with zero downtime.
- A score of 85% indicates top performance for discrete manufacturing enterprises and is a long-term objective worth aspiring for.
- A score of 60% indicates major scope for improvements.
- A score of 40% is taken to be low, although it is not as uncommon as you may think. It may sometimes be enhanced via measures that are easier to apply.
- Some key metrics in this case include production targets in real-time, efficiency (ratio of actual to target), actual (true count of production), and downtime (unplanned time on stoppages in each shift which is calculated on a real-time basis).
- Some other benchmarks include quality (parts which do not require implementation of quality control measures, especially those that have to be reworked. The calculation is Quality = Good Count/Total Count).
- Performance is equal to Ideal Cycle Time x Total Count/Run Time, meaning the number of time that there are production stoppages or slowdown.
- Availability is the calculation of Run Time/Planned Production Time which considers sudden and unplanned stoppages.
Now that you have a basic grasp of the OEE fundamentals, it is time to look at how you can avoid common mistakes within the fold of your optimisation efforts.
OEE Optimization- Mistakes to Avoid
Let us look at a few common overall equipment effectiveness optimisation mistakes that Biopharma companies often end up making.
- Automated systems are not the be-all or end-all- While OEE-tracking systems can help you detect losses, they do not offer total visibility on your packaging and other operations at times. Human activities may lead to major changes in production lines which automated systems often do not account for. Hence, you should aim at offering more context and training for these models in order to get the full picture.
- External losses- You may face losses arising from elsewhere and not your production line. This can be glitches in IT systems, suppliers, company departments, and more. High variability in terms of product dimensions or packaging measurements may be an issue or even missing documentation of any batch due to scheduling errors can also be seen at times. While striving to optimize OEE, Biopharma companies should adopt a more holistic approach with a view towards first identifying the root causes behind losses and cultivating a lean manufacturing philosophy within the organization itself.
- Track what counts- What you can comfortably measure due to regular sensor-based tracking with historical data and insights is not always effective. Inputs for OEE calculations often derive from the latest machines which may not be the ones causing slowdowns on the production line. You should look at spreading the information net wider without simply going in for available inputs based on what is already working well.
- Benchmarks should be assessed carefully- While an increase in OEE is always nice, it may not always be a hard and fast thing. The setup time may be impacted in some cases, with small batches enabling companies to adjust to variable demand but lowering OEE. Frequent changes in products also indicate higher time expenditure on activities which are not productive. Relying more on comparative analyses is the need of the hour, especially between similar production lines and products. These can also be done for the same production line on a before and after basis once the process change or technological setup has been implemented.
- Disruptive events are sometimes skipped- Historical data and insights for analyses may not always take disruptive events into account. You should proceed by undertaking a process failure mode and effects analysis of the whole process, including automation, scheduling, packaging, and batch closure. Risk evaluation should be evaluated from analytical/historical insights although exceptional failure scenarios should be covered as much as possible.
As can be seen, while striving to enhance OEE is always desirable, it is important to set realistic benchmarks and look at surrounding issues that your system may not always help you detect. Avoiding these mistakes will undoubtedly be beneficial for Biopharma companies in the long run.
FAQs
What risks are associated with neglecting the impact of external factors, such as supply chain disruptions, on OEE in the Biopharma industry?
There are several risks that are associated with neglecting the sheer impact of external factors on OEE like disruptions in the supply chain. Biopharma players can face risks like improper forecasting and risk management, inventory management woes, higher loss ratios, poor delivery of requirements, and even quality drops.
How can a lack of standardized metrics and benchmarks hinder OEE improvement efforts in the Biopharma industry?
The absence of standardised benchmarks and metrics will naturally bog down OEE improvement initiatives in the Biopharma industry. There will be no clarity on what to measure and fix with most companies calculating OEE in the wrong way as a result.
What risks are associated with setting unrealistic OEE improvement goals in the Biopharma sector?
There are associated risks for Biopharma companies setting OEE improvement goals that are unrealistic. These include insufficient visibility, decisions based on wrong or limited analytics, not accounting for actual issues in the calculation, and focusing on the wrong metrics at the outset.
How does Equipment Utilisation impact time-to-market for biopharmaceutical products?
Equipment utilisation has a huge impact on the time-to-market threshold for biopharmaceutical products. Proper utilisation and productivity will help combat unplanned downtime and sudden disruptions, while enabling smoother delivery as per targets without frequent changeovers or higher occurrences of rejects. Faster time-to-market is a necessity for staying competitive in the current scenario and suitably utilizing equipment is necessary for this purpose.
How does a lack of scalability in OEE improvement solutions pose challenges for growing Biopharma companies?
Non-scalable solutions for OEE improvement may pose various challenges for growing Biopharma entities. They may be bogged down by issues like improper risk management and higher loss ratios along with poor inventory management and real-time performance tracking.
