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Beth H. Junker is the senior director of fermentation and development operations at Merck Research Laboratories
How to optimize facility utilization and improve plant performance.
Already being actively followed by several industries, the overall equipment effectiveness (OEE) approach quantifies the percentage of time that equipment operates to produce an acceptable product. It measures how effectively machines are being used by examining productivity and performance diagnostics at the equipment level.1,2 It also monitors actual performance relative to performance capabilities under optimal manufacturing conditions.3 OEE breaks down non-operable time into shutdown losses (such as preventative maintenance, personnel breaks, and training), operational downtime, changeover downtime, equipment failure, process failure, and production adjustment losses, which then are measured to calculate availability, performance, and quality losses.1
Overall equipment effectiveness (OEE) helps maximize value-added activities by indicating precisely where potential improvements might be most effective. This makes it an ideal measure for capital equipment-intensive businesses such as biopharmaceutical manufacturing.1 Other typical OEE improvement opportunities include faster changeover, less idle time, optimized equipment maintenance, shorter production cycle times, increased equipment reliability, and optimized equipment purchases.4
Different versions of OEE have been developed and adapted to specific industry problems. Some are oriented to measure overall factory or plant effectiveness instead of equipment (Table 1).5 At a micro level, OEE can focus on a specific piece of equipment; at a macro level it can focus on a processing suite, equipment process train, or even the facility itself. Different OEE versions use similar methodologies.6
Table 1. Versions of overall equipment effectiveness
In many industries, a plant-wide view is needed to optimize factory effectiveness, especially for complex, resource-constrained processes that often have significant human and equipment interactions.7 A composite picture is developed for key attributes such as equipment effectiveness, cycle-time efficiency, on-time delivery, manufacturing costs, process yields, production volumes, inventory turn rates, and ramp up performance.8 The effectiveness of this approach was demonstrated by wafer fabricators, and it is readily applicable to biopharmaceutical manufacturing.4
OEE's goal is to develop systems interacting and interfacing with all process equipment to ensure "the right material is with the right tool at the right time."4 This approach avoids instituting locally beneficial controls and improvements that may unintentionally reduce overall efficiency.4 Activities and relationships among different equipment and processes are combined, integrating information, decisions, and actions across several independent systems.2,6,8,9 Such an approach is explicitly applicable to several aspects of biomanufacturing (such as water for injection and clean steam usage across processing suites, and product stream flow from cultivation to harvest to isolation suites). Computer integrated manufacturing (CIM) through automated manufacturing execution systems (MES) supports these integrated improvement goals by 1) managing complexity, traceability, and genealogy, 2) simplifying quality and yield management, 3) facilitating production planning and scheduling, and 4) managing data to support decisions.4
OEE is calculated based on the product of availability, performance, and quality, each expressed as a time-based ratio:2
OEE = Aeff*Peff*Qeff Eq. 1
in which the availability efficiency, (Aeff = Tu/Tt), is the operating or "up" time divided by the total time; performance efficiency, (Peff = Tth/Tact), is the theoretical processing time to achieve output goals divided by actual processing time, and the quality efficiency (Qeff = Pg/Pa), is the time spent producing good product output divided by total time spent making all product lots.
Simple OEE is calculated as the ratio of good to total (theoretical) output, each expressed as a count:
Simple OEE = Pg/Pth Eq. 2
in which Pg is the number of actual conforming (good) lots and Pth is the number of theoretical possible lots, assuming maximum levels of availability, performance, and quality.2,3,6,10
OEE data permit benchmarking within and across industries that drives targets for continuous improvement initiatives (Table 2).10 Typically, quantitative data is desirable but qualitative data (e.g., estimates from subject matter experts) also can be useful.7 An OEE around 85% is considered world class performance across industries for a batch plant.11–14 If OEE approaches 85% but is still constraining, then likely additional capacity or process redesign is needed. If OEE is <70%, then usually the desired improvement goal is achievable using current equipment and processes.13
Table 2. Overall equipment effectiveness and component values for various industries
OEE associated with continuous manufacturing plants (e.g., chemical, petroleum, or paper industries) typically have high Aeff and Peff with OEE primarily determined by Qeff (i.e., yield).15 OEE associated with batch manufacturing plants have lower Aeff owing to more frequent set up and clean-up steps. Most biomanufacturing processes are batch in nature with a few notable exceptions. OEE data can be used to create either a histogram of OEE level versus its frequency or a progressive run chart showing OEE levels before and after improvement implementation.16
A major part of OEE is time. Waterfall charts and similar tables are useful ways to visually depict time losses (Table 3). Starting with the total available hours as 100%, the hours that production is not planned to run (such as planned preventative maintenance, shutdowns, and holidays) and the time lost to set up or clean up and breakdowns (availability loss) are subtracted. Next, the time caused by capacity losses caused by slow running speeds is subtracted (performance loss), followed by the time caused by losses from waste and defects such as discarded lots (quality loss). The result is the effective hours of processing for output of acceptable product lots.17
Table 3. Breakdown of overall equipment effectiveness availability time calculations2,6
Availability efficiency is the fraction of time that a piece of equipment, suite, or facility is in a condition to perform its intended function or simply the amount of time processing actually occurs.18,19 There are six main states: 1) the unscheduled state (not planned to be used); 2) the unscheduled down state (not able to perform); 3) the scheduled down state (not planned to be available); 4) the engineering state (used to conduct equipment or process trials); 5) the standby state (not operated because of lack of personnel or support equipment); and 6) the productive state (performing as intended).20
Availability is limited by: 1) equipment, process and facility shutdown, maintenance, or failures, which lead to the unscheduled state and both the scheduled and unscheduled down states, and 2) set up and adjustment which primarily lead to scheduled downtime.3,5,16,18,21 The unscheduled state and scheduled down state includes planned maintenance, operational shutdowns, unworked shifts, and holidays. The unscheduled down state generally includes unplanned shutdowns, lack of demand, breakdowns, and operator unavailability.16,18 Set up or adjustment losses occur when production of one product ends and equipment is adjusted to meet requirements of another product [such as changeovers, preventative maintenance, cleaning and sterilization, equipment or process trials (engineering runs), and qualification]. These changeover times can be greater than production runs, and in multiproduct bioprocessing facilities, they typically are substantially greater.19 Changeover is often a key improvement opportunity, with best-in-class companies achieving significant reductions through cross-training team members, implementing lean principles such as workplace organization, and developing quick changeover techniques.22
Performance efficiency is the fraction of equipment uptime that the equipment is processing actual lots at theoretically efficient rates.18 Consequently, it reflects losses incurred by suboptimal operation, specifically the difference between design and actual speeds.21 Idling or minor stoppage and rate losses prevent achieving maximum speed because production is interrupted or slowed respectively by a temporary malfunction, lack of input because of poor raw material or intermediate stream quality, or insufficient personnel.1,3,10,16 This category can be a catchall for unclassified or immeasurable losses.23 In biopharmaceutical manufacturing, performance efficiency can reflect inefficiency from unexpected campaign starts and stops (e.g., in-process assay delays), or from longer than expected step processing times (such as slower than expected depth filtration).
Another factor is quality, defined as "right the first time it is done."19 Unacceptable quality includes yield losses (leading to scrap) and defects (leading to rework) caused by equipment malfunction and start up losses in which yields are lower during early stages of a new campaign before stabilization.3,16,21 In biopharmaceutical manufacturing, quality losses include atypical events such as contamination, impurities, and incorrect documentation that often occur in initial batches for a new product campaign in a multipurpose facility.
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:
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.
1. Burns A. Choosing the right tool from the toolbox. IEE Seminar Kaizen from Understanding to Action. 2000;IEE 6/1–10.
2. Muthiah KMN, Huang SH. Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection. Int J Prod Res. 2007;45(20):4753–69.
3. Pomorski T. Managing overall equipment effectiveness (OEE) to optimize factory performance. 1997 IEEE Inter Sym on Semicond Manuf Conf Proc. Piscataway, NJ: IEEE; 1997. pp. A33–A36.
4. Scott D. Overall factory effectiveness, the corollary to Moore's law. Machine Design. 1999. 71:22,170.
5. Juric Z, Sanchez AI, Goti A. Money-based overall equipment effectiveness. Hydrocarbon Processing. 2006;85(5):43–5.
6. Oechsner R, Pfeffer M, Pfitzner L, Binder H, Müller E, Vonderstrass T. From overall equipment efficiency (OEE) to overall Fab effectiveness (OFE). Materials Science in Semiconductor Processing. 2003;5:333–9.
7. Tal O. Overall resources effectiveness, the key for cycle time reduction & capacity improvements. GaAs Mantech Conference; 2001; Las Vegas, NV. pp. 255–8.
8. Scott D. Can CIM improve overall factory effectiveness? Proceedings of the Pan Pacific Microelectronics Symposium; 1999. pp. 411–24.
9. Scott D, Pisa R. Can overall factory effectiveness prolong Moore's Law? Solid State Technol. 1998;41:3,75–82.
10. Fogelholm J. Assessment of main stages in productivity steering in mills. Paperi Ja Puu-paper & Timber. 2006;88(6):342–7.
11. Pauwels J. Closing the manufacturing productivity gap through service solutions. J ABB Automation Technol. 2004;34–5.
12. Allcock A. Understanding OEE. Machinery. 2004;162(4102):16–7.
13. Gregory A. Number cruncher. Works Management. 2006;58(7):18
14. Johnson M. Manufacturing improvement in the paper industry. Paper Technol. 2005;46(7):33–6.
15. Dismukes JP, Vonderembse MA, Chandrasekaran S, Hudspeth L, Caldwell WP. Opportunities for radical innovation in flat glass production operations. Ceramic Engineering and Science Proceedings. 2000;21:1,9–29.
16. Sarkar BN. Capability enhancement of a metal casting process in a small steel foundry through Six Sigma; a case study. Int J Six Sigma and Competitive Advantage. 2007;3(1):56–71.
17. Hide L. 2003. Using overall productivity efficiency (OPE) as a catch-all metric for package printers. Improving Package Printing. Pira Int. Surrey, UK; 2003. pp. 1–9.
18. Jacobs JH, Etman LFP, van Campen EJJ, Rooda JE. Characterization of operational time variability using effective process times. IEEE Transactions on semiconductor manufacturing. 2003;16(3):511–20.
19. Pendrous R. Effective choices. Food Manuf. 2002;77(1):30–2.
20. De Ron AJ, Rooda JE. Equipment effectiveness: OEE revisited. IEEE Transactions on Semiconductor Manuf. 2005;18(1):190–6.
21. Gouvea da Costa SE, Pinheiro de Lima E. Uses and misuses of the overall equipment effectiveness for production management. IEEE International Engineering Management Conf, Cambridge, UK; 2002. pp. 816–20.
22. Bharadwaj S. Pharmaceutical manufacturers set sights on best-in-class operations performance. Pharmaceutical Processing. 2008;24(1):14–20.
23. Brochu DL. Using metrics to manage improve performance. Plant Eng. 2007;61:6,37–41.
24. Godfrey P. Overall equipment effectiveness. Manuf Eng. 2002;81(3):109–12.
25. Abdulmalek FA, Rajgopal J. Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study. Int J Prod Econ. 2007;107:223–36.
26. Owens S, Miller S, Deans D. Availability optimization using spares modeling and the six sigma process. Annual reliability and maintainability symposium, Jan 2006. Newport Beach, CA.
27. Rivera L, Chen F. Measuring the impact of Lean tools on the cost-time investment of a product using cost-time profiles. Robotics and Computer Integrated Manuf. 2007;23:684–9.
28. Huang SH, Dismukes JP, Shi J, Su Q, Wang G, Razzak MA, Robinson DE. Manufacturing system modeling for productivity improvement. J Manuf Systems. 2002;21(4):249–59.
29. Thiel KA. Biomanufacturing from bust to boom...to bubble? Nature Biotechnol. 2004;22:1365–72.
30. Gottschalk U. New and unknown challenges facing biomanufacturing: An editorial. Biopharm Int. 2005;18(3):24–8.
30. Francis R. Providing productive capacity. 59th Appita Annual Conference & Exhibition. Vol. 1, Paper 1B33, 2005;67–74.
31. Moore R. Improvement projects and tools: Where to start and what to use. Plant Engineering. 2003;57(3):30–2.
32. Jonsson P, Lesshammar M. Measuring manufacturing performance: Dimensions and OEE. In: D.F. Kocaoglu, T.R. Anderson, D. Milosevic, K. Niwa, M.J. Gregory editors. Innovation in technology management—The key to global leadership. PICMET. Portland, OR; 1997. pp. 799.
33. Clark R. Concise OEE made E-A-S-Y. Tooling and Prod. 2005;71(12):13.
34. Hutchins D. Introduction TPM. Manufacturing Engineer. 1998;77(1):34–6.
35. Kotze D. Consistency, accuracy lead to maximum OEE benefits, TPM Newsletter, AITPM, Productivity, Inc. Norwalk. 1993;4(2):1–4
36. Herron C, Braiden PM. Defining the foundation of lean manufacturing in the context of its origins (Japan). Agile Manufacturing (ICAM), Institution of Engineering and Technology Manufacturing Enterprise, Page Bros (Norwich) Ltd, Great Britain; 2007. pp. 148–57.
37. Dance DL, Jimenez DW, Levine AL. Understanding equipment cost-of-ownership. Semiconductor Int. 1998;21(8):117–122.
Beth H. Junker is senior director of fermentation and development operations at Merck Research Laboratories Rahway, NJ, 732.594.7010, firstname.lastname@example.org