# Hey bud, want a free manufacturing line?

We used to call running out of manufacturing capacity a “good problem to have,” but it’s still a problem. If your customers want more product than you can build at the moment, you need to work out a way to increase production levels. In the short term, you can get your current staff to work overtime. In the medium term, you can add another shift and increase capacity that way (once you’ve hired and trained people). If you’re working three shifts, five days a week,  you can work out a way to schedule weekends as well. You’ll start looking ways to minimize downtime for preventive maintenance, and even find a way to keep lines running during lunch breaks.

What if all those actions aren’t enough? Is it time to drop \$5 or \$10M to set up a new manufacturing line? Does your factory have the open space available? If not, will you need to build, buy or lease additional space – and staff positions like HR and security for that space?

Or… is it time to work on your OEE?

You probably already know that OEE stands for “Overall Equipment Effectiveness” – essentially, the percentage of time that your manufacturing lines (or your manufacturing operation overall) is actually running, at its ideal cycle time, and making only good parts. OEE is a way to assess the overall performance of a manufacturing operation. The calculation is simple. OEE is the product of three ratios:

OEE = (Availability) x (Performance) x (Quality), where:

• Availability = Actual Run Time/ Planned Production Time
or = (Planned Production Time – Stop Time)/ Planned Production Time
• Performance = Net Run Time/ Actual Run Time
or = (Ideal Cycle Time x Total Count) / Actual Run Time
• Quality = Good Count/ Total Count

If you remember your math, you know that some of these numbers cancel each other out, so OEE can be simplified to:

OEE = (Good Count x Ideal Cycle Time) / Planned Production Time.

For instance, if we plan to run a factory 20 hours/ day, and we expect to build a product every five minutes (12/hour), if we were running at 100% OEE we’d get a count of 240 good products every day, or 1200 in a five-day week. If we actually get:

 Day Monday Tuesday Wednesday Thursday Friday Number good products made 120 180 160 200 140

for a total of 700 good products made in that week, we can calculate an OEE of

700 x (1/12th hour) / 100 hours = 700/1200 = 58.33%.

Understanding which factor – availability, performance, or quality – caused the shortfall is the key to improving OEE:

• Perhaps the factory built 1200 units, but 500 of them failed quality checks. That would indicate quality needs to be improved.
• Maybe quality was ok – 95% – but the line shut down repeatedly for lack of materials (poor availability – in this case, about 61%).
• Possibly, both availability and quality ran at 95% all week, but the line ran too slowly (slow cycle time/ poor performance) – instead of one unit every 5 minutes, it took (on average) 7 minutes 44 seconds (about 65% performance) to complete a unit.

The bad news is that 100% OEE is an unrealistic expectation in the real world. The good news is that OEE can be improved. World-class OEE – around 85% – is achievable. (60% is fairly typical, and 40% isn’t all that unusual. But those numbers represent a real opportunity to obtain additional capacity without investing in new production equipment, buildings, or personnel.) The first step is to measure OEE and begin understanding what the causes of poor performance are, week after week, so you can start to eliminate them.

The better news is that improving OEE more than pays for itself. For example, let’s say you have 10 manufacturing lines, all running between 50% and 60% OEE (average of 55%). You don’t even have to achieve world-class performance to effectively find a new manufacturing line (or two, or three!) hidden inside those numbers. 10 lines running at, say 75% OEE can build more than a third more product than those same 10 lines running at 55% OEE.

The best news is that tools exist today that can help solve two of the most intractable problems that operations people often face: preventing issues in the first place, so they don’t have a chance to generate down time, slow down cycle time, or create quality issues; and detecting and immediately alerting people to problems when they do occur, to minimize reaction time. More on those tools later; first (in my next post), we’ll talk about some ways to isolate the specific issues that are causing poor OEE performance.

Sr. Managing Consultant, Electronics Center of Competence at IBM

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