Manufacturing ERP Software
Manufacturing Processes—Production and Business: Operational Effectiveness, Part 6

Manufacturing Processes—Production and Business: Operational Effectiveness, Part 6

By Bob Sproull

Review of Operational Effectiveness, Part 5

In my last post, I explained how to interpret our calculated operational effectiveness values, prioritize paths to throughput improvement, and understand the limitations of OEE. I discussed how levels for OEE differ depending upon the industry, equipment features, and production systems involved. But as a general rule, world class levels for OEE are in the 80 to 85 percent range or better.

The human side of OEE

In Part 6, I will explore the “human element” of operational effectiveness and demonstrate how different personnel groups can impact our Overall Equipment Effectiveness (OEE) results. As in my last two series, much of what I will be presenting in this series of posts is taken from my second book, [1] The Ultimate Improvement Cycle – Maximizing Profits Through the Integration of Lean, Six Sigma and the Theory of Constraints.

The human side of downtime analysis is often overlooked. How quickly we restore equipment so that throughput isn’t lost is a function of how efficient we are as humans. So, if in our calculation of OEE, we include this human downtime factor, then our OEE calculation becomes a measure of our total effort, linking manpower performance with equipment performance. In essence, OEE conveys how well our employees are making product. Let’s return to our previous OEE example and see what the impact of the human element is on OEE.

Remember from our previous example that our plant’s working hours were 60 min/hr x 8 hours/day or 480 minutes/day. We have lunches and breaks, but we assumed that we relieved workers at this machine during these times so that we would maximize throughput. We did have 30 minutes of scheduled downtime, so we subtracted that amount from our available time, making our loading time equal to 450 minutes. You recall that we experienced only 30 minutes of equipment downtime on this day so our availability was:

Availability = (Loading Time – Downtime) / Loading Time

= (450 minutes – 30 minutes) / 450 minutes

= 0.9333 or 93.33 %

A hypothetical to demonstrate the impact of human involvement

But now suppose that in addition to the 30 minutes of constraint equipment downtime, we also had another 45 minutes of constraint downtime that was not related to the equipment. Suppose that we had 15 minutes of downtime due to late arriving materials, 20 minutes waiting for a quality inspection, and 10 minutes waiting for a relief operator to arrive. What would happen to our availability?

AvailabilityOverall = (Loading Time – Downtime) / Loading Time

AvailabilityOverall = (450 minutes – 75 minutes) / 480 minutes

AO = 0.8333 or 83.33%

As you can see, our availability factor decreases from 93.33 to 83.33 percent. What happens to our OEE?

OEE Overall = AO x P x Q

Our original values for P and Q were 56 percent and 98.1 percent respectively, so our new overall OEE (OEEO) is:

= 0.833 x 0.560 x 0.981

= 0.4576 or 45.76%

OEEO is a superior performance metric

Since our original OEE was 51.3 percent, this represents a difference of 5.5 percent. Although this difference may appear to be subtle, the implications to our improvement effort can be quite profound. It’s the difference between only considering equipment issues versus considering all of the factors that limit throughput within our process.

In fact, if we are to make meaningful gains in throughput, then we need to consider and focus on all of the interruptions and subsequent downtime caused by the equipment, the people operating the equipment, the people maintaining the equipment, and the people supplying the equipment. All of these people have a considerable impact on downtime and throughput losses. This includes all forms of downtime including planned and unplanned maintenance, material and component shortages, quality inspections, and anything that prevents us from achieving maximum throughput. As a constraint performance metric, OEEO is a much better performance metric than OEE and my recommendation is to use it.

Coming in the next post

In Part 7, I will continue this discussion on factors impacting OEE. I will also explore what we might be able to do to improve the individual components of OEE.

Until next time.

Bob Sproull

Post References:

[1] Bob Sproull, The Ultimate Improvement Cycle – Maximizing Profits Through the Integration of Lean, Six Sigma and the Theory of Constraints, CRC Press, Taylor & Francis Group, 2009

[2] Seiichi Nakajima, TPM Development Program – Implementing Total Productive Maintenance, Productivity Press, 1989

Bob Sproull

About the author

Bob Sproull has helped businesses across the manufacturing spectrum improve their operations for more than 40 years.

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