PrintLogin to download pdfChoosing an OEE system [KI]

“A good data capture system is simply a robust resource allocation tool”

It should lead directly to people doing something differently as a result of using the data.

When choosing a system, look at the following areas:

  • What is the reason for capturing data?
  • Identify the measure that not only tells you the extent of the constraint, but what the contributing factors to loss are
  • Understand the metric, automate it and train people to use it thoroughly.
  • Establish a robust management review methodology based on the metric – hence the need for automation; an automated process frees up your management team to fix the losses not spend all their time calculating them.

1.Identify the constraint:

What is your objective for recording the data? Whatever you choose to record, it should measure the constraint in your manufacturing process. If your constraint is related to the performance of a machine then OEE is a great measure. If however, your constraint is related to labour, a man hour / part produced type metric may be more appropriate. At LineView solutions, we can measure both very easily –have a look at the different types of systems.

2. The correct measure:

The value to any metric is not that you get a number. You’ve made what you’ve made – there’s little point in reviewing it. A lot of sites can tell their OEE of amount produced but can’t tell where their losses are. The value to measuring OEE is in the type of loss. If you know the loss you can apply the right countermeasure to fix.

E.g. for OEE, the losses are divided into: Planned downtime (changeover), breakdowns, minor stops, speed loss, quality in process, quality on startup.

If you have a large changeover loss, use the SMED techniques. If you have many breakdowns use the root cause analysis (RCA) techniques. The value is not the OEE number – it’s the amount and categorisation of the loss that matters

3. Understand and train:

There are 2 main opinions on the collection of data. One opinion is the manual method that says it’s better for operators to collect so that they are aware and provide you with an accurate description of the loss. The other is automated that says it’s better to get the correct data.

An automated system only ever tells you symptoms for your downtime – its diagnostics are only so good as the signals you give it. That said, how often has an operator identified the real root cause for a stop – 9.9 times out of 10 they’ll note down a symptom.

It is better to use an automated system that captures your losses accurately…and then use management process and review to drill down. Manual data systems usually take a lot of work to maintain and usually have a very low level of accuracy simply due to the nature of human data capture.

4. Robust process:
We often support people to establish robust internal processes for using the data, documenting the actions that arise from interrogating the data, and then a management review process for driving change. Typically this looks as follows:

  • Daily reviews – Reviewing 24hr data. Objective is to identify what actions are still open, see if we have any reoccurring issues, assign resource where needed.
  • Short Interval Control – a regular review (typically every 4 hours) with front line management and engineers. The objectives are to; review the greatest losses from the last review and ensure they are closed off and to identify what needs to be done differently on the current data available.
  • Strategic reviews (typically Weekly) – Reviewing trended data for purposes of maintenance, engineering, planning, forecasting etc.

Remember – your level of payback is directly related to how you use the data.

So what OEE system works? Whichever one you commit to using fully! Look beyond a software download as you may struggle to use it fully simply as it’s reliant on manual data collection.

Also if you’re running at <50% you don’t need to spend a fortune collecting data because your equipment is down for 4 hours in every 8. People know where the issues are because they’re in them! But when you head up into the 65%+ territory you’ll struggle to continue improving without robust automated data capture.

 

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