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.
It also monitors actual performance relative to performance capabilities under optimal manufacturing conditions.
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.
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
OEE VERSIONS, CALCULATIONS AND QUANTIFICATION
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
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