Overall Equipment Effectiveness. Robert Hansen C.
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Название: Overall Equipment Effectiveness
Автор: Robert Hansen C.
Издательство: Ingram
Жанр: Здоровье
isbn: 9780831191153
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A Case Study Using OEE Metrics
In 1996,I was assigned to a highly automated film finishing work center staffed with about 140 people. It was organized into high performance work teams manning four similar equipment flowlines, 7 days a week, 24 hours a day. The area was lead by a cross-functional business team and was data driven. This area finished many different sizes and formats of product and was challenged with many ‘new’ formats and products, as well as methods of operation to improve inventories. Daily meetings were held to review ongoing performance of the “factory”, which included the output quantity, flow line availability, and equipment reliability expressed as an index of the four lines. Meeting the projected production schedules was critical for just-in-time delivery and avoiding overtime in related work centers.
Figure 1-1 Equipment Index versus Output Index
One advantage for the area was an automated Equipment Performance System (EPS) to gather information. The EPS system provided all of the information suggested for categories to compute a detailed OEE, including frequencies of events. However, in early 1996, OEE was computed only monthly and submitted to plant management. It was not being actively used as an online guiding metric.
New levels of output had been achieved at the end of 1995 and carried into week one of 1996. See week one of figure 1-1. Output projections for 1996 were even higher. This was because prototype equipment improvement projects appeared to be successful. Early in 1996, the equipment improvement upgrades were migrated over all four flowlines.
Although the impact of shutdowns on operating schedules was minimal, the equipment changes required procedure changes and retraining for operators and mechanics.
Figure 1-2 Operational MTTR
Almost from week one, 1996, the output began to decline from expected levels. The second quarter results were very serious with an under production of 10 percent. By week sixteen of 1996 the investigating team had reached a conclusion.
In general, the feeling was that equipment reliability was not good. It was reported that the modifications were causing more problems, and couldn’t reliably handle new products.
With this information, the technical community worked hard to make sure processes and systems were working properly. With intense focus, the equipment reliability index improved over the second quarter by approximately 10 percent. See the increase in the equipment index following week sixteen in figure 1-1 Equipment Index vs. Output per Day Index.
Yet, factory output was still going down.
A more thorough investigation followed.
In review of all parameters, the root cause was found to be operational downtime. However, a study of this category did not reveal any unique or significant single items of downtime, by machine section, crew or product.
Only after plotting Operational Mean Time To Restore (MTTR) did the understanding that the many little events, which used to take 0.8 minutes to restore, were now taking 1.1 minutes.
Over time, poor habits and interruption of concentration had diverted the attention from “making product”. See figure 1-2 Operational MTTR. This underscores the importance of being able to collect time and frequencies of category events.
Week twenty eight of 1996 was the specific “intervention” date when the results of the detailed investigation were shared with each crew. That date is noted in figures 1-1 and 1-2.
Once the community was presented with the information and convinced that they really could influence the outcome by re-focusing their attention to detail, the output per day began to recover. In fact, output did reach the higher levels as predicted with the equipment modification project.
This understanding lead to a review of the OEE metric and revealed that existing online measures could be used to compute OEE. Online OEE does correlate very well with actual pack output for this work center. See figure 1-3 Case Study showing Actual Output and OEE Computed Output in 1997. By instituting online OEE measurement, this area has a powerful tool to monitor the on-going health of their production process.
The work center now makes OEE available each shift and plots the metric weekly. The metric is being used for a portion of everyone’s annual performance rating.
1.6 Total Effectiveness Equipment Performance (TEEP)
Whereas OEE measures the effectiveness of planned production schedules, Total Effectiveness Equipment Performance (TEEP) measures the overall equipment effectiveness relative to every minute of the clock, or calendar time. In many settings, management is especially interested in how well a factory’s key assets are used relative to total calendar time. TEEP is the metric that indicates opportunities that might exist between current operations and world-class levels. It reveals the hidden factory that can and should be leveraged to make the company more competitive. Like OEE, TEEP must be used in combination with financial information.
Figure 1-3 Case Study showing Actual Output and OEE Computed Output in 1997 Correlation is >.9
TEEP numbers can be used to speculate on the potential capacity of an existing plant. The last increments of reaching total capacity usually have higher unit manufacturing costs especially if labor overtime is involved. The final increments need to be evaluated from business and OEE perspectives. With focused improvement projects for OEE and TEEP which makes every hour of operation more effective, it is quite possible that future capacities with overtime will be manufactured at less than current (before OEE) standard unit costs without overtime.
The strategy at many companies is to run their factories 24/7 – 24 hours a day, 7 days a week – and to produce the maximum amount of product possible. These companies often can sell everything they can make; they may also be the lowest cost producer. In some cases, the capital investment in equipment and facilities is quite large; and using the asset around the clock maximizes return on investment. In other cases, the process is continuous and, therefore, expensive or difficult to shut down and start up. A 24/7 strategy may also be appropriate for a portion of the year to meet seasonal demands. Understanding the total size of the hidden factory becomes important. For factories that are already running 24/7, the hidden factory represents an opportunity for increased capacity.
Because TEEP categorizes all events around the clock, it is the metric that should be used when you develop a business case for more capacity or capital expansion. TEEP can be a good indicator of the capacity that is still available within an existing asset. Developing this hidden factory is beneficial because it is cost effective. Other advantages would be the hidden factory could be developed sooner. It СКАЧАТЬ