Application of Overall Equipment Effectiveness to Biopharmaceutical Manufacturing - Overall equipment effectiveness is an ideal measure for capital equipment-intensive businesses such as biopharmaceut


Application of Overall Equipment Effectiveness to Biopharmaceutical Manufacturing
Overall equipment effectiveness is an ideal measure for capital equipment-intensive businesses such as biopharmaceutical manufacturing.

BioPharm International
Volume 22, Issue 5


Total Productive Maintenance

Total productive maintenance (TPM) aims to maximize equipment productivity during its service lifecycle and extend its length of useful service. OEE is a basic building block for TPM because it quantifies both loss types and their durations to appropriately focus on improvement activities.24 TPM analyzes reasons for equipment waste (such as repeated similar repairs and inadequate training) to improve OEE, thus reducing equipment duplication and freeing-up valuable processing space. Its goals are zero breakdowns, speed losses, defects, and accidents.17 It focuses on autonomous maintenance by operators through small group activities to develop ownership, and improving relationships among people, processes, and equipment.19,21,24 Equipment reliability, not only its downtime (availability) but also underuse relative to installed capabilities (performance), minimizes compensating purchases of duplicate units of expensive or large footprint equipment.24 Without OEE, organizations unknowingly invest capital, and thus OEE data can support or challenge capital proposals.24

Related to total productive maintenance, total preventative maintenance focuses on preventing breakdowns rather than fixing broken equipment, also including operators in maintenance and monitoring activities.25 Strategies exist to reduce losses caused by inadequate spare part stocks, attempting to achieve zero losses with the lowest spare part inventory possibly by eliminating waiting time for critical spares.26

Set Up Reduction

Single minute exchange of dies (SMED) aims to reduce equipment downtime consumed by changeovers and set ups associated with switching products.27 It is particularly applicable to biomanufacturing facilities that have multiproduct suites. Downtime is measured as the time from the last good product of type A to first good product of type B.1 Set up reduction helps meet increased customer multiproduct demand because some changeover times can be longer than product cycle times, dramatically reducing suite availability.1 Changeover activities are classified as internal or external based on their ability to be executed during processing of the previous product.17 The next step is to reduce, eliminate, or convert as many internal activities to external, and then progressively shorten remaining internal activity times.1,17

Theory of Constraints

The theory of constraints is a five-step cycle to identify constraints that restrain production systems from achieving high overall operational excellence.28 The five steps include:

  • Step 1—Preparation: a system flowchart is developed. Productivity parameters are defined along with metrics to guide data collection.
  • Step 2—Metrics: metrics are calculated at the equipment, subsystem, and system levels.
  • Step 3—Root cause analysis: bottleneck productivity is evaluated (using Aeff, Peff, and Qeff) and losses identified at upstream and downstream of the bottleneck.
  • Step 4—Simulation: the limiting constraint is identified, then simulation is used to assess various scenarios for its removal.
  • Step 5—Repeat: the entire cycle is repeated to identify and eliminate new constraints.

This strategy can be applied to develop alternatives to alleviate the current purification bottleneck in biomanufacturing because of the rise of bioreactor product titers.29

Generic Problem-Solving Process

A generic problem-solving process can be used in biomanufacturing to efficiently remove OEE limitations.30 The simple steps for this process are: (1) recognition that a problem exists that should be solved; (2) allocation of appropriate priority to develop the solution (i.e., identify possible causes and alternative solutions, and then selecting a solution); and (3) solution monitoring to determine its effectiveness.31

Armed with flow charts, assembled cross-functional teams review the step and its support functions to identify failures, calculate failure values (i.e., failure type x frequency x loss per failure x values per loss), prioritize losses with the greatest improvement opportunity, and then select appropriate solutions to minimize future losses.32

Key to OEE Success

Key factors to using OEE to drive biomanufacturing improvement initiatives are consistent data capture and regular OEE calculations.1 Although OEE jointly measures the efficiency and effectiveness of operations and maintenance, it is not always sufficiently detailed to indicate precisely how and where to focus improvement resources.31,33 Used alone, OEE is a high-level operational measure that might mask counteracting variations in availability, performance, and quality in some biomanufacturing applications.31 Consequently, estimation of OEE's component terms along with the calculated OEE value, can elucidate improvement opportunities, particularly in biopharmaceutical manufacturing.


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Beth H. Junker is senior director of fermentation and development operations at Merck Research Laboratories Rahway, NJ, 732.594.7010,

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