Tag: Costing with OEE

OEE Measurement Error

How many times have you, or someone you know, challenged the measurement process or method used to collect the data because the numbers just “don’t make sense” or “can’t be right”?

It is imperative to have integrity in the data collection process to minimize the effect of phantom improvements through measurement method changes.  Switching from a manual recording system to a completely automated system is a simple example of a data collection method change that will most certainly generate “different” results.

Every measurement system is subject to error including those used to measure and monitor OEE.  We briefly discussed the concept of variance with respect to actual process throughput and, as you may expect from this post, variance also applies to the measurement system.

Process and measurement stability are intertwined.  A reliable data collection / measurement system is required to establish an effective baseline from which to base your OEE improvement efforts.  We have observed very unstable processes with extreme throughput rates from one shift to the next.  We learned that the variance in many cases is not always the process but in the measurement system itself.

We decided to comment briefly on this phenomenon of measurement error for several reasons:

  1. The reporting systems will naturally improve as more attention is given to the data they generate.
  2. Manual data collection and reporting systems are prone to errors in both recording and data input.
  3. Automated data collection systems substantially reduce the risk of errors and improve data accuracy.
  4. Changes in OEE trends may be attributed to data collection technology not real process changes.

Consider the following:

  1. A person records the time of the down time and reset / start up events by reading a clock on the wall.
  2. A person records the time of the down time event using a wrist watch and then records the reset /start up time using the clock on the wall.
  3. A person uses a stop watch to track the duration of a down time event.
  4. Down time and up time event data are collected and retrieved from a fully automated system that instantly records events in real time.

Clearly, each of the above data collection methods will present varying degrees of “error” that will influence the accuracy of the resulting OEE.  The potential measurement error should be a consideration when attempting to quantify improvement efforts.

Measurement and Error Resolution

The technology used will certainly drive the degree of error you may expect to see.  A clock on the wall may yield an error of +/- 1 minute per event versus an automated system that may yield an error of +/- 0.01 seconds.

The resolution of the measurement system becomes even more relevant when we consider the duration of the “event”.  Consider the effect of measurement resolution and potential error for a down time event having a duration of 5 minutes versus 60 minutes.

CAUTION!

A classic fallacy is “inferred accuracy” as demonstrated by the stop watch measurement method.  Times may be recorded to 1/100th of a second suggesting a high degree of precision in the measurement.  Meanwhile, it may take the operator 10 seconds to locate the stop watch, 15 seconds to reset a machine fault, and 20 seconds to document the event on a “report” and another 10 seconds to return the stop watch to its proper location. 

What are we missing?  How significant is the event and was it worth even recording?  What if one operator records the “duration” after the machine is reset while another operator records the “duration” after documenting and returning the watch to its proper location?

The above example demands that we also consider the event type:  “high frequency-short duration” versus “low frequency-long duration” events.  Both must be considered when attempting to understand the results.

The EVENT is the Opportunity

As mentioned in previous posts, we need to understand what we are measuring and why.  The “event” and methods to avoid recurrence must be the focus of the improvement effort.  The cumulative duration of an event will help to focus efforts and prioritize the opportunities for improvement.

Additional metrics to help “understand” various process events include Mean Response Time, Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR).  Even 911 calls are monitored from the time the call is received.  The response time is as critical, if not more so, than the actual event, especially when the condition is life-threatening or otherwise self-destructive (fire, meltdown).

An interesting metric is the ratio between Response Time and Mean Time To Repair.  The response time is measured from the time the event occurs to the time “help” arrives.  Our experience suggests that significant improvements can be made simply by reducing the response time.

We recommend training and providing employees with the skills needed to be able to respond to “events” in real time.  Waiting 15 minutes for a technician to arrive to reset a machine fault that required only 10 seconds to resolve is clearly an opportunity.

Many facilities actually hire “semi-skilled” labour or “skilled technicians” to operate machines.  They are typically flexible, adaptable, present a strong aptitude for continual improvement, and readily trained to resolve process events in real time.

Conclusion

Measurement systems of any kind are prone to error.  While it is important to understand the significance of measurement error, it should not be the “primary” focus.  We recommend PREVENTION and ELIMINATION of events that impede the ability to produce a quality product at rate.

Regrettably, some companies are more interested in collecting “accurate” data than making real improvements (measuring for measurements sake). 

WHAT are you measuring and WHY?  Do you measure what you can’t control?  We will leave you with these few points to ponder.

Until next time – STAY Lean!

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How to Reduce Costs with OEE: Cost Control

OEE is a great metric to help identify where you may be incurring losses in your processes or operation.  As one of the goals of implementing a Lean strategy is to reduce costs, it only seems natural that we should be able to determine what processes to focus on that are driving the greatest losses.

From the example developed in our previous posts we determined that the OEE and related factors for our three processes were as follows:

Machine Availability Performance Quality OEE
A 92.97% 88.26% 97.77%  80.22%
B 96.04% 77.23% 94.44% 70.05%
C 95.16% 61.70% 95.20% 55.90%

Based on the OEE results, one would be inclined to take a look at Machine C as it has the lowest OEE.  Is this really the greatest opportunity?  The only way to answer the question is to understand what factors are driving costs and ultimately affecting profitability.

The performance factor for machine C is definitely pulling down the OEE for this process.  What would you think if the machine is 100% automated (no labour) and the cycle time, although it may be less than standard, is still meeting the takt time to meet customer demand?  Is there really a cost?  Of course there is, but the impact to your business may be minimal in terms of cost when compared to the other machines.

It is clear that we need to develop a model to understand what losses and ultimately costs are associated with each of the factors.  In turn, we will be able to better understand the overall OEE.

What costs do we consider?  We recommend keeping the model simple.  There are typically three cost components associated with any given process or product:  Material, Labour, and Overhead.  Burden is another term used for Overhead and we will use these terms interchangeably.

Our goal over the next few posts will be to develop a simple cost model for each process and, in turn, determine which one may be the process of choice for improvement.  For now, we will provide a general discussion of some of the potential cost considerations.

Improving quality typically yields the greatest return on investment because all of the cost elements stated above are impacted by the Quality factor.  Raw material, Labour, and Burden are all expended to produce a part scrap part.

The costs associated with Quality losses are further challenged when considering the number of parts that would have to be produced in order to recover these lost costs.  If you are lucky enough to enjoy a 10% profit margin (clear), then, at a minimum, 10 parts would have to be produced for every part scrapped.  Of course, more parts would have to be produced to recover other infrastructure costs incurred including documentation, record keeping, and scrapping of the actual parts.

Performance losses typically affect labour and overhead.  Labour losses are easy enough to understand.  If a machine is operator dependent, then we will have to pay a person to stand at the machine to run it.  If it is running slowly, more costs are incurred to cover the additional labour time.

In many cases, direct losses related to overhead are sometimes difficult to assess unless a truly activity based costing system is in place.  The reason for the complexity arises because some of the costs are “fixed”.  Because the equipment exists, expenses such as depreciation or property taxes are incurred whether or not the equipment or, for that matter, the plant is running.  The performance of the machine or any of the other factors for that matter won’t change this fact.

Availability then becomes somewhat more obscure when it comes to calculating hard costs.  If the labour can be redeployed to another process when a machine goes down, perhaps some of the labour losses can be avoided.  If not, then waiting for a machine to be repaired or material to be delivered is a real loss that should be addressed.

Intangible costs are also difficult to quantify but we should be aware of their existence.  The costs associated or related to poor OEE may include overtime, expedited freight, and infrastructure costs related to extra handling of material or management of non-conforming material (containment, extra inspection, rework, and scrap).  Although this is a relatively short list, it addresses the most obvious potential losses.  With a little more thought, the list could easily grow longer.

Other key metrics in your facility such as customer delivery or quality performance indicators may also point to problems that can be traced directly to poor OEE performance.  Although difficult to measure, a company’s competitive position is compromised when efficiencies are low and eventually the costs of poor performance make their way into the “burden” costs required to manage the operation.

While OEE is an effective metric for operations, on its own, it does not provide a direct indicator of real financial losses.  As Lean Practitioners we are challenged to provide an analysis that not only improves the metrics of the business but also translate into real financial improvements on the balance sheet and ultimately – the bottom line.  We would suggest that OEE is a time driven metric (asset time management strategy) versus our proposed COEE which is Finance or “Value” driven (cost management strategy).   We are presently developing a model that will allow your OEE data to be sensitized with cost data as demonstrated by the table below.

We have coined the term COEE or Cost of Overall Equipment Effectiveness.  Consider the following OEE results converted to Cost based drivers using standard costs as our baseline.  The sample data and spreadsheet used to calculate this data will be available as a download soon.  The overall spreadsheet is quite large and based on a fully detailed three shift operation.

Cost driven OEE model - Summary
Cost driven OEE model - Summary

Our OEE cost model clearly presents the real costs or “losses” incurred per part.  Our Weighted OEE Cost Model will change the way you view OEE data, enabling you to set priorities and identify real, quantifiable, opportunities for improvement.  The above snapshot represents the goal of our COEE project – a clean, clear, summary of the losses incurred correlated directly to your OEE index.  Another advantage is that the Availability, Performance, and Quality factors are recalculated based on cost and presents a realistic breakdown of losses for each of these factors from a financial perspective.  Our spreadsheet presents an advanced OEE example that will bring real value to your OEE implementation strategy.

NOTE:  The fully developed spreadsheet is available from our FREE Downloads page or from the FREE Downloads box on the sidebar.

A well implemented OEE strategy should become evident on the balance sheet through improved material utilization, reduced labour variance (straight and overtime reductions), reduced scrap costs, reduced rework costs, and other burden account reductions.

Take quick, effective, and efficient action to solve the problems having the greatest financial impact to your business.  Last but not least, don’t confuse activity with action.  Decisions are not actions and talking about a problem or even writing about it could be construed as activity.  Real actions produce real, measurable, results.

Change requires Change.  Profit is to business as oxygen is to humans – you need it to survive. 

We have created a number of Excel spreadsheets that are immediately available for download from our FREE Downloads page or from the Free Downloads widget on the side bar.  These spreadsheets can be modified as required for your application.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

If you have any questions or comments, feel free to send an email to LeanExecution@gmail.com

Until Next Time – STAY Lean!

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