OAL Insight: Uncovering the Myths of OEE in Food Manufacturing

OEE-Factory-Perfomance-SQ.jpg

OEE measurement can be a powerful catalyst for change in food manufacturing but there can be misconceptions about how best to measure and apply OEE. Alan France, one of OAL Group's OEE Specialists, has coached our OAL Factory Performance customers to use OEE as a measure to significantly improve business performance. With over 3 decades of experience in the manufacturing improvement industry, Alan has a unique view on best practice deployment of the OEE metric.

Alan believes the key value of OEE "is to generate an accurate baseline score so that a team can measure their progress. In practical terms it’s a powerful catalyst for change. To measure the value you have to discover the underlying unplanned losses. Improve those and OEE improves."

Although it’s a simple calculation it’s often misunderstood and sometimes the differences have quite an impact on the integrity of the measure.  This can be due to a difference of opinion on how each element should be applied; for example what is the difference between a breakdown and a minor stop? However, sometimes the calculation itself is questioned.

These are Alan’s top myths that surround the OEE measurement:

Myth 1 - I plan my changeover for 15:00 so it should be a planned event.

OAL Factory Performance CountdownWhilst the changeover is planned to occur at a set time it’s still an opportunity for improvement; for many companies the biggest opportunity.  Let’s say the average lost time due to changeovers is 1 hour per day.  Now imagine a competitor starts up next door with equipment that will automatically changeover at the push of a button.  The competitor can produce for an extra 7 hours each week.  Changeovers should always be unplanned events.

OAL Factory Performance shows the operator every changeover target in real-time using a count-down timer. The display turns red if the target time is exceeded, and make the display visible across mobile devices.

 

Myth 2 - We have a different in-house measure.

One GM told me that their internally developed measure did ignore some of the losses but also triggered his bonus, as long as it remained over 85%.  Surprisingly it always did - so why would he want to measure OEE?

Myth 3 - ‘No ingredients’ should be considered a performance loss.

If we go back in time and separate production and engineering, rather than having cross-functional ‘manufacturing teams’ - we get into arguments.  Speaking as an engineer, the plant is technically available for production, but Operations has let the side down by not having the correct ingredients available.  Measuring engineering KPI’s is always possible by deselecting those losses outside the control of engineering.  However, in terms of ‘lost plant time’, the production process is not available because there are no ingredients.

Myth 4 - Breakdowns and minor stops are the same.

A breakdown usually requires a careful study of circumstances and involvement from several disciplines to identify the root cause and plan resolution.  For instance, a food hoist failure because we ran out of oil.  Minor stops are usually those annoying conveyor jams which occur 100 times a shift and lose a total of 20 minutes.  They still need to be studied and a correction plan identified, but the techniques are different.

Myth 5 - I have a softer ‘planning department’ target speed.

That’s fine as it’s a target speed used by planning to let the customer know when to expect delivery.  But it’s not OEE and won’t help to discover that hidden factory – and all the potential savings.

Myth 6 - I have lots of planned events.

Be cautious, planned events will be excluded from the OEE calculation, so keep them to an absolute minimum - zero is a good start.  Any activity classed as ‘planned’ is unlikely to change as there is no pressure to do so.  The only planned events to consider should be ‘Plant not crewed for production’ or ‘no sales.’

Myth 7 - World class is 85%

This is correct for a machining centre, but if you’re running something like a flour mill or indeed, most continuous processes, world class performance is above 90% OEE.

Myth 8 - We do not need any more output, so why raise the OEE?

If you have a low OEE value you’re wasting energy, labour and materials.

Myth 9 - OEE is a management tool used to benchmark and compare.

It’s mainly a problem solving tool and it’s not appropriate to compare the scores of different operations, many of our customers use different ingredients with high variance in ability to process. Imagine trying to cut a crumbly cheese versus a hard cheese. Its best practice to use OEE to benchmark the improvement trend over time for different operations. Myth 10 – OEE data should always be captured manually.

It’s always a good start, and gets everyone into the improvement mind-set.  However, as you improve there comes a point when most, if not all, of the availability issues and the big breakdowns have been resolved.  What’s left are minor stops and slow running which are very difficult to capture manually, but still have a big impact on performance.  At that stage it’s good to consider some level of automatic data capture, at least a product count and a running/not running signal.

Myth 10 - OEE data should always be captured manually.

It’s always a good start, and gets everyone into the improvement mind-set.  However, as you improve there comes a point when most, if not all, of the availability issues and the big breakdowns have been resolved.  What’s left are minor stops and slow running which are very difficult to capture manually, but still have a big impact on performance.  At that stage it’s good to consider some level of automatic data capture, at least a product count and a running/not running signal.

Myth 11 - With automatic data capture I don’t need the operators involved.

The operator is a critical element in the continuous improvement process.  If they are ignored it will be difficult, if not impossible, to make real improvements.

Operators using OAL Factory Performance select reasons codes on a touchscreen on the line, ensuring buy in to the improvement philosophy.

Myth 12 - We measure OEE, but ignore the quality element.

Really, why?  The quality element has a significant impact on the OEE score and if you don’t measure quality losses how do you know they are not considerably worse than you think?

Myth 13 - We have an Excel wizard on-site and our spreadsheet system is fit for purpose

Spreadsheets are a very good start, but the single user nature of Excel and the large amount of data soon means that people spend more time manipulating data than working on making improvements.  It’s proven that you’ll save time and money with a powerful database based solution and you can use that time to make production improvements.

Myth 14 - Productivity is a more relevant measure in our environment.

Productivity is a good measure.  If you flood the place with extra crew and complete a changeover in double quick time it’s possible to generate a high OEE, but the employee costs will be pretty high.  As such, on its own productivity cannot discover those hidden losses, a better approach is to include a crew size for each product run and then calculate output per employee or OLE (Overall Labour Effectiveness), based on the number of people and OEE score.

Myth 15 - Utilisation is a better measure than OEE.

It’s quite a different measure.  Plant employees do not have much influence on sales.  If the plant is designed for 24/7 operation but sales can only find orders for 24/5, then asset utilisation will be low, even if plant performance when running is good and generating a high OEE.  If utilisation is 20% and OEE 70% blame sales, not the plant operators.

Myth 16 - Performance is really bad and we have to improve before we can measure OEE.

How will you know what to improve if you don’t measure production performance?  Use OEE as the catalyst for change.  If the first OEE value is very low, say less than 10%, it’s actually good news as there are lots of improvement opportunities easily available, and look how people will feel if you move the value to 20% within a few weeks – not to mention how much money you will have saved.

Myth 17 - A high OEE score is just that, but where is the cash value?

The cash value is in reduced energy, materials and labour.  If the order is 60,000 units and the target is 1,000 units per hour (OEE score of 40%) - this means an extra 34 hours are required to meet the order.  If the score can be improved to 60% the extra hours required drops to just 15.  Improve performance, shut down the plant 19 hours earlier and save money.

Myth 18 - We’re not ready to measure OEE or improve?

Well if not now, then when?

If you want to find out more about how OAL can help achieve big reductions in unplanned losses and improve OEE scores please contact us here.