• Subscribe

Maximizing efficiency in pharma: unleashing the power of OEE

Synthesize more data from more sources for highly accurate overall equipment effectiveness (OEE) calculations

Overall equipment effectiveness (OEE) is a critical measure of equipment and process health, providing a single number that gives a representative estimate of how well equipment is performing. Pharma manufacturers can use OEE calculations to identify patterns of performance and areas of improvement, allowing them to reduce downtimes, enhance yield or performance, and optimize quality. However, despite its critical importance as a metric, most pharma manufacturers currently calculate overall equipment effectiveness manually via Excel spreadsheets. These manual offline systems produce outdated data that can’t be used for real-time adjustments and are prone to calculation and input errors. Some manufacturers use more automated systems, but only extract data from a single source system like a manufacturing execution system (MES). This reduces the measurement accuracy of the equipment state and often lacks the context to understand root causes for any downtime (where the data may be in a quality system). As such, while pharma has recognized the need to accurately quantify current performance using OEE calculations, today that is a difficult task.

Compounding this issue of automated data collection and synthesis is that there is no single consistent definition for OEE. Even within a single manufacturing facility, variations in how the OEE calculation is performed for a single piece of equipment, as well as the key definitions or assumptions that go into the calculation, reduce the ability for teams to compare numbers across pieces of equipment. What’s needed is a better way to automate the collection of all the data around a piece of equipment and its utilization, as well as a consistent way to calculate OEE that can scale across all the process units in a facility. That requires an approach that can synthesize multiple data sources and collate and contextualize the data in a way that is simple to visualize and act on.

SmartFactory Rx is an example of a system that has taken a holistic approach to understanding OEE. We do this by using multiple data sources and contextualizing and integrating them together to provide a single unified view of equipment usage. Integration of data from multiple available sources provides the context necessary to determine not only what is happening in real-time, but the contributing factors behind the OEE calculation. For example, to determine the rate at which the equipment is running, data from the plant information system (e.g., OSIsoft PI) may be needed. Identifying the quantity of material produced may require data from the MES. Data associated with maintenance is usually obtained from the computerized maintenance management system (CMMS), including whether it was a planned maintenance or down-time caused by an unplanned event on the line. All these systems together provide the data needed to accurately account for overall OEE, and the cause of any low efficiency (or trends).

We use an equipment performance tracking model to build a set of statistics that represent all the activities performed by a piece of equipment, as well as the processes that it runs. To develop this model, we look at a number of levels of process performance. Figure 1 illustrates the entire process, starting with the innermost layer and moving outward:
  • At level 1, data from a real-time historian (such as OSIsoft PI) is used to derive if the equipment is currently producing, stopped, waiting, or is in a fault position.
  • At level two, this data is integrated with batch-context data (e.g., whether there is a batch in progress and, if so, whether it is a new batch, continuation of the existing batch or a re-process).
  • At level three (the outermost level), we provide even greater context as we look to identify root causes for the undesired equipment state. For instance, we check the equipment upstream and downstream to see if the problem can be attributed to an issue elsewhere on the manufacturing line.
It’s also possible the low efficiency may be attributed to delayed maintenance activities. The accurate identification of root causes is possible since SmartFactory Rx connects to multiple data sources and can effectively integrate them in real-time.
Figure 1: Process of developing an equipment performance tracking (EPT) model
Figure 1: Process of developing an equipment performance tracking (EPT) model

This kind of automated equipment performance tracking enables effective visualization of activities and issues on the line that can be demonstrated in a Gantt chart, as in Figure 2. In this example, you can see a piece of equipment that has not been producing (shown as gray) because of a line clearance being performed. However, that took up 30% of this equipment’s day. Can this be minimized? Additionally, for more than 50% of the day, part of the equipment was broken down. From there, we can provide insights into opportunities for performance improvement and set key performance indicators (KPIs) based on more realistic OEE.

Figure 2: Example of Gantt chart visualization of equipment state
Figure 2: Example of Gantt chart visualization of equipment state
Consistent KPIs will help engineering, maintenance and operations teams focus on the critical pieces of equipment requiring the most improvement. Since SmartFactory Rx uses a standardized model to calculate OEE, manufacturers can take data from other lines and across different sites. This means, for instance, teams can compare the performance of a bioreactor on one site, to a similar bioreactor at another site. Such data provides valuable insights for teams to continuously improve performance based on real data. SmartFactory Rx has proven a critical tool for manufacturers to accurately identify root causes for downtimes and delays and improve equipment efficiency.

About the Author

Picture of Nirupaplava Metta
Nirupaplava Metta
The SmartFactory Rx Team develops integrated automation solutions for process manufacturing to harness the power of data, reduce development time and improve productivity to optimize high value manufacturing. It increases throughput, decreases risk, and accelerates time to market for new products. For more details, connect with us on LinkedIn.