Overall Equipment Effectiveness (OEE) and Its Application in Pharma/Biopharma Manufacturing
Part 3 of 4: Applying OEE: Approaches and Supporting Technologies
Introduction
Our prior eletters outlined the definition of OEE and its components, where it has been employed in the
various types of plants in the pharmaceutical industry from API to Packaging, and why it should be part
of an Operations Excellence program. The objective of this eletter is to identify how to capture the
information necessary to measure and report OEE, and the overall design/technological architecture
needed to maintain the resulting system.
Phased Approach to Implementation
As with any implementation, a phased approach that allows a top-down implementation will usually
provide early experience and provide a basis for improvement as the reporting evolves. A first step
should be to start with basics of what is important to the operation, what will OEE tell us, and what is
actionable to improve the operation. For example, a good first check might be to calculate what the
ideal 24/7 output of an operation could be for a year using the throughput rates of critical constraints of
the operation, and comparing that to the quantity of good product the operation has produced.
This would typically lead to a discussion of what the Planned OEE should be for an operation, before the
Actual OEE is even measured. Different types of plants and the product mix will affect the Planned OEE;
e.g., a high mix plant will have more changeovers/cleanings, and affect Availability.
Data Sources
Plants have a variety of data repositories and reporting systems including ERP, scheduling,
manufacturing execution, robotics, packaging line operations themselves, and a variety of spreadsheets.
OEE reporting requires the integration of these to capture the elements needed to calculate actual
performance. The source for each of the following elements must be determined:
OEE Component
Typical Source System
Storing Actual Data
Reference for Planned OEE
Actual Performance
Product Rate
Demonstrated optimal rates
Production by time period
ERP; MES
Quality Rate
Process capability
Availability
Work shifts/hours per week
Planned downtime
Changeover/cleaning
standards
Rejected lots
Scrap – yield loss
Recycled product
Unplanned Downtime
Changeover times
ERP; MES
ERP; MES
MES; LIMS
MES
Maintenance logs
Once the sources of data are identified, a sustainable system for regularly reporting them should be
developed for each work center. For a highly automated work center such as packaging, all of the data
might be available in an automated system. For bulk finishing and API manufacturing, the data may
have to collected and compiled periodically. Using a phased approach, the early phases may be
prototyped in spreadsheets and/or available reporting software; as the measurement becomes refined
and the real drivers of performance become more obvious, investment in additional software might
make sense.
Types of Tools Available
In the more automated operations such as packaging and filling, some OEE reporting may be included
where automated motion control systems are installed. In the less automated API and bulk
manufacturing operations, standalone systems may be required.
The best SCADA (supervisory control and data acquisition) software includes this functionality without
requiring additional modules or add-ons to be purchased. In top SCADA software the tools necessary for
OEE management are built-in. There are also some stand alone devices that have more functionality
than spreadsheets but may not require a full SCADA system.
In planning for future IT in manufacturing operations, OMAC-compliant (Organization for Machine
Automation and Control ) open architecture standards for packaging and filling provide for integration of
equipment and supports 21 CFR Part 2 electronic record documentation for production data. As PAT
systems are tested and installed, they typically incorporate OPC standards (Object Linking and
Embedding for Process Control) for both real time data acquisition and historical data access.
Modeling and Predictive Analysis
Simulation is a powerful tool to analyze both the root causes of problems and the effects of
improvements on OEE. A model of a biopharmaceutical operation must be detailed enough to show the
interrelationship of production schedules, lead times, on-time delivery service level,
cleanouts/changeovers and inventory levels. The model must be at the customer order and SKU level of
detail and operate over a period of time that would include production cycles of all the smaller volume
products as well. Rhythm cycles, i.e., product campaign scheduling intervals, have a big impact on OEE
in all aspects of pharmaceutical manufacturing, and can be optimized with simulation.
With such a complete simulation model, the impacts of lean demand pull can be used to manage
product scheduling vs. demand. Measurement of both service level and OEE can then be achieved.
Simulation is unique in that it can incorporate statistical variability in all the areas it exists: changeover
times, downtimes, rate of quality rejects, losses due to speed, short stops, speed imbalances on the
lines, etc. It identifies where crucial bottlenecks exist and the resulting effects on OEE metrics.
Statistical distributions for these variability data can be derived from the actual historical data discussed
above. In most cases, the variability follows more skewed patterns rather than normal bell curves, so
this type of more accurate simulation is important for predictive OEE estimation.
Management Methodology and Summary
A phased approach to implementation allows for early actionable results, then growing over time to
more sophisticated tools and techniques. Early focus should be on the Planned OEE metrics, which do
not require substantial computer system support for actual data collection. Then, to begin collecting
actual data, start at the work center rather than machine level if data are not readily available. Focus
the staff time and systems investment to collect actual data on those areas where there are known
bottlenecks and issues.
To Follow
Part 4: Experience of Implementing OEE – Identifying and overcoming barriers