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Some Mistakes You Could Be Making With Your Performance Metrics

What you don't see may be the exact information your shop needs to implement lean manufacturing practices.

Overall Equipment Effectiveness (OEE) analytics, included with Manufacturing Execution Software (MES) and MES systems, are the latest metrics in shop floor data collection systems that can bring a great deal of visibility and control to a factory floor. In their simplest form, OEE metrics and Key Performance Metrics (KPI's) are a way of measuring the effective use of a shop's machines.

There are several approaches to finding a "useful" shop floor data collection system. For example, some ERP vendors like SAP and QAD offer a variety of factory floor automation solutions. However, many are more focused on accounting data as a means to update inventory systems and are not necessarily a true MES solution.

Generally speaking, regardless of your ERP implementation, the most cost effective method of obtaining visibility and control of your factory floor is via a robust MES system from a solution provider that has a focused factory floor automation system and provides OEE metrics.

Many simple OEE solutions can provide real-time production information but their system might not have enough information to improve the factory floor performance on a continuous and permanent basis. Some OEE providers do not offer a full function MES solution which can too often mean that your OEE choice may not provide enough information.

OEE is one measurement of operational performance. Included in this KPI Metric is the individual measurements of:

In many ways the combination of these three measurements is a simple and more modern way to focus supervisors and operators on a single measure of production effectiveness.

For example, in the old days, floor management used "efficiency" as the main method of evaluation production performance. If an operation was rated at 60 pieces per hour, then an efficiency of 50 percent would mean the shop floor was only producing pieces at half the expected rate -- 30 pieces per hour.

However, the operational rate in this example may actually have been 60 pieces per hour (100 percent) but during that hour half of the pieces were scrapped. So only half the production was reported as good. Therefore, this efficiency calculation does not clearly identify the problem area -- not slow production, but in reality too much scrap. This older measurement of efficiency, because it is not as granular as OEE, tends to focus management on poor rate of production instead of quality improvement programs.

Using the example above, the same situation occurs if the operation is down for 30 minutes. If during the hour of production, the operation is not working half the time for some unknown reason, the efficiency is measured at 50 percent. But again, the rate of production is actually at the expected rate of 60 pieces per hour. In the 30 minutes the machine was running, 30 parts were produced -- the expected rate.

Once again, the efficiency measure tends to focus management on a lack of speed. In reality, there are no parts being made half the time. The real problem is not poor performance, it is downtime issues.

This is where OEE KPI's, if implemented to their full potential, can be a better analytic with more granular information in order to improve the process. But it requires the OEE vendor provides an easy-to-use touch screen method with an intuitive way to collect scrap reason codes as well as down time reason codes. If you do not collect this type of information at the granular level you are missing out on valuable process improvement opportunities.

Remember, you cannot improve what you cannot see. If you can't see this data because your OEE system is not designed to easily collect these kind of metrics, you could be missing the necessary information to implement lean manufacturing practices.

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