Tag: Manufacturing

Integrated Waste: Lather, Rinse, Repeat

shampoo
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Admittedly, it has been a while since I checked a shampoo bottle for directions, however, I do recall a time in my life reading:  Lather, Rinse, Repeat.  Curiously, they don’t say when or how many times the process needs to be repeated.

Perhaps someone can educate me as to why it is necessary to repeat the process at all – other than “daily”.  I also note that this is the only domestic “washing” process that requires repeating the exact same steps.  Hands, bodies, dishes, cars, laundry, floors, and even pets are typically washed only once per occasion.

The intent of this post is not to debate the effectiveness of shampoo or to determine whether this is just a marketing scheme to sell more product.  The point of the example is this:  simply following the process as defined is, in my opinion, inherently wasteful of product, water, and time – literally, money down the drain.

Some shampoo companies may have changed the final step in the process to “repeat as necessary” but that still presents a degree of uncertainty and assures that exceptions to the new standard process of “Lather, Rinse, and Repeat as Necessary” are likely to occur.

In the spirit of continuous improvement, new 2-in-1 and even 3-in-1 products are available on the market today that serve as the complete “shower solution” in one bottle.  As these are also my products of choice, I can advise that these products do not include directions for use.

Scratching the Surface

As lean practitioners, we need to position ourselves to think outside of the box and challenge the status quo.  This includes the manner in which processes and tasks are executed.  In other words, we not only need to assess what is happening, we also need to understand why and how.

One of the reasons I am concerned with process audits is that conformance to the prescribed systems, procedures, or “Standard Work” somehow suggests that operations are efficient and effective.  In my opinion, nothing could be further from the truth.

To compound matters, in cases where non-conformances are identified, often times the team is too eager to fix (“patch”) the immediate process without considering the implications to the system as a whole.  I present an example of this in the next section.

The only hint of encouragement that satisfactory audits offer is this: “People will perform the tasks as directed by the standard work – whether it is correct or not.”  Of course this assumes that procedures were based on people performing the work as designed or intended as opposed to documenting existing habits and behaviors to assure conformance.

Examining current systems and procedures at the process level only serves to scratch the surface.  First hand process reviews are an absolute necessity to identify opportunities for improvement and must consider the system or process as a whole as you will see in the following example.

Manufacturing – Another Example

On one occasion, I was facilitating a preparatory “process walk” with the management team of a parts manufacturer.  As we visited each step of the process, we observed the team members while they worked and listened intently as they described what they do.

As we were nearing the end of the walk through, I noted that one of the last process steps was “Certification”, where parts are subject to 100% inspection and rework / repair as required.  After being certified, the parts were placed into a container marked “100% Certified” then sent to the warehouse – ready for shipping to the customer.

When I asked about the certification process, I was advised that:  “We’ve always had problems with these parts and, whenever the customer complained, we had to certify them all 100% … ‘technical debate and more process intensive discussions followed here’ … so we moved the inspection into the line to make sure everything was good before it went in the box.”

Sadly, when I asked how long they’ve been running like this, the answer was no different from the ones I’ve heard so many times before:  “Years”.  So, because of past customer problems and the failure to identify true root causes and implement permanent corrective actions to resolve the issues, this manufacturer decided to absorb the “waste” into the “normal” production process and make it an integral part of the “standard operating procedure.”

To be clear, just when you thought I picked any easy one, the real problem is not the certification process.  To the contrary, the real problem is in the “… ‘technical debate and more process intensive discussions followed here’ …” portion of the response.  Simply asking about the certification requirement was scratching the surface.  We need to …

Get Below the Surface

I have always said that the quality of a product is only as good as the process that makes it.  So, as expected, the process is usually where we find the real opportunities to improve.  From the manufacturing example above, we clearly had a bigger problem to contend with than simply “sorting and certifying” parts.  On a broader scale, the problems I personally faced were two-fold:

  1. The actual manufacturing processes with their inherent quality issues and,
  2. The Team’s seemingly firm stance that the processes couldn’t be improved.

After some discussion and more debate, we agreed to develop a process improvement strategy.  Working with the team, we created a detailed process flow and Value Stream Map of the current process.  We then developed a Value Stream Map of the Ideal State process.  Although we did identify other opportunities to improve, it is important to note that the ideal state did not include “certification”.

I worked with the team to facilitate a series of problem solving workshops where we identified and confirmed root causes, conducted experiments, performed statistical analyses, developed / verified solutions, implemented permanent corrective actions, completed detailed process reviews and conducted time studies.  Over the course of 6 months, progressive / incremental process improvements were made and ultimately the “certification” step was eliminated from the process.

We continued to review and improve other aspects of the process, supporting systems, and infrastructure as well including, but not limited to:  materials planning and logistics, purchasing, scheduling, inventory controls, part storage, preventive maintenance, redefined and refined process controls, all supported by documented work instructions as required.  We also evaluated key performance indicators.  Some were eliminated while new ones, such as Overall Equipment Effectiveness, were introduced.

Summary

Some of the tooling changes to achieve the planned / desired results were extensive.  One new tool was required while major and minor changes were required on others.  The real tangible cost savings were very significant and offset the investment / expense many times over.  In this case, we were fortunate that new jobs being launched at the plant could absorb the displaced labor resulting from the improvements made.

Every aspect of the process demonstrated improved performance and ultimately increased throughput.  The final proof of success was also reflected on the bottom line.  In time, other key performance indicators reflected major improvements as well, including quality (low single digit defective parts per million, significantly reduced scrap and rework), increased Overall Equipment Effectiveness (Availability, Performance, and Quality), increased inventory turns, improved delivery performance (100% on time – in full), reduced overtime,  and more importantly – improved morale.

Conclusion

I have managed many successful turnarounds in manufacturing over the course of my career and, although the problems we face are often unique, the challenge remains the same:  to continually improve throughput by eliminating non-value added waste.  Of course, none of this is possible without the support of senior management and full cooperation of the team.

While it is great to see plants that are clean and organized, be forewarned that looks can be deceiving.  What we perceive may be far from efficient or effective.  In the end, the proof of wisdom is in the result.

Until Next Time – STAY lean!

Vergence Analytics
Twitter:  @Versalytics
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Lean – A Race Against Time

The printer Benjamin Franklin contributed grea...
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Background

If “Time is Money”, is it reasonable for us to consider that “Wasting Time is Wasting Money?”

Whether we are discussing customer service, health care, government services, or manufacturing – waste is often identified as one of the top concerns that must be addressed and ultimately eliminated.  As is often the case in most organizations, the next step is an attempt to define waste.  Although they are not the focus of our discussion, the commonly known “wastes” from a lean perspective are:

  • Over-Production
  • Inventory
  • Correction (Non-Conformance  – Quality)
  • Transportation
  • Motion
  • Over Processing
  • Waiting

Resourcefulness is another form of waste often added to this list and occurs when resources and talent are not utilized to work at their full potential.

Where did the Time go?

As a lean practitioner, I acknowledge these wastes exist but there must have been an underlying element of concern or thinking process that caused this list to be created.  In other words, lists don’t just appear, they are created for a reason.

As I pondered this list, I realized that the greatest single common denominator of each waste is TIME.  Again, from a lean perspective, TIME is the basis for measuring throughput.  As such, our Lean Journey is ultimately founded on our ability to reduce or eliminate the TIME required to produce a part or deliver a service.

As a non-renewable resource, we must learn to value time and use it effectively.  Again, as we review the list above, we can see that lost time is an inherent trait of each waste.  We can also see how this list extends beyond the realm of manufacturing.  TIME is a constant constraint that is indeed a challenge to manage even in our personal lives.

To efficiently do what is not required is NOT effective.

I consider Overall Equipment Effectiveness (OEE) to be a key metric in manufacturing.  While it is possible to consider the three factors Availability, Performance, and Quality separately, in the context of this discussion, we can see that any impediment to throughput can be directly correlated to lost time.

To extend the concept in a more general sense, our objective is to provide our customers with a quality product or service in the shortest amount of time.  Waste is any impediment or roadblock that prevents us from achieving this objective.

Indirect Waste and Effectiveness

Indirect Waste (time) is best explained by way of example.  How many times have we heard, “I don’t understand this – we just finished training everybody!”  It is common for companies to provide training to teach new skills.  Similarly, when a problem occurs, one of the – too often used – corrective actions is “re-trained employee(s).”  Unfortunately, the results are not always what we expect.

Many companies seem content to use class test scores and instructor feedback to determine whether the training was effective while little consideration is given to developing skill competency.  If an employee cannot execute or demonstrate the skill successfully or competently, how effective was the training?  Recognizing that a learning curve may exist, some companies are inclined to dismiss incompetence but only for a limited time.

The company must discern between employee capability and quality of training.  In other words, the company must ensure that the quality of training provided will adequately prepare the employee to successfully perform the required tasks.  Either the training and / or method of delivery are not effective or the employee may simply lack the capability.  Let me qualify this last statement by saying that “playing the piano is not for everyone.”

Training effectiveness can only be measured by an employee’s demonstrated ability to apply their new knowledge or skill.

Time – Friend or Foe?

Lean tools are without doubt very useful and play a significant role in helping to carve out a lean strategy.  However, I am concerned that the tendency of many lean initiatives is to follow a prescribed strategy or formula.  This approach essentially creates a new box that in time will not be much different from the one we are trying to break out of.

An extension of this is the classification of wastes.  As identified here, the true waste is time.  Efforts to reduce or eliminate the time element from any process will undoubtedly result in cost savings.  However, the immediate focus of lean is not on cost reduction alone.

Global sourcing has assured that “TIME” can be purchased at reduced rates from low-cost labour countries.  While this practice may result in a “cost savings”, it does nothing to promote the cause of lean – we have simply outsourced our inefficiencies at reduced prices.  Numerous Canadian and US facilities continue to be closed as workers witness the exodus of jobs to foreign countries due to lower labor and operating costs. Electrolux closes facility in Webster City, Iowa.

I don’t know the origins of multi-tasking, but the very mention of it suggests that someone had “time on their hands.”  So remember, when you’re put on hold, driving to work, stuck in traffic, stopped at a light, sorting parts, waiting in line, sitting in the doctors office, watching commercials, or just looking for lost or misplaced items – your time is running out.

Is time a friend or foe?  I suggest the answer is both, as long as we spend it wisely (spelled effectively).  Be effective, be Lean, and stop wasting time.

Let the race begin:  Ready … Set … Go …

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

Critical Process Triggers

Critical Triggers

It is inevitable that failures will occur and it is only a matter of time before we are confronted with their effects.  Our concern regards our ability to anticipate and respond to failures when they occur.  How soon is too soon to respond to a change or shift in the process?  Do we shut down the process at the very instant a defect is discovered?  How do we know what conditions warrant an immediate response?

The quality of a product is directly dependent on the manufacturing process used to produce it and, as we know all too well, tooling, equipment, and machines are subject to wear, tear, and infinitely variable operating parameters.  As a result, it is imperative to understand those process parameters and conditions that must be monitored and to develop effective responses or corrective actions to mitigate any negative direct or indirect effects.

Statistical process control techniques have been used by many companies to monitor and manage product quality for years.  Average-Range and Individual-Moving Range charts, to name a few, have been used to identify trends that are indicative of process changes.  When certain control limits or conditions are exceeded, production is stopped and appropriate corrective actions are taken to resolve the concern.  Typically the corrective actions are recorded directly on the control chart.

Process parameters and product characteristics may be closely correlated, however, few companies make the transition to solely relying on process parameters alone.  One reason for this is the lack of available data, more specifically at launch, to establish effective operating ranges for process parameters.  While techniques such as Design of Experiments can be used, the limited data set rarely provides an adequate sample size for conclusive or definitive parameter ranges to be determined for long-term use.

Learning In Real-Time

It is always in our best interest to use the limited data that is available to establish a measurement baseline.  The absence of extensive history does not exempt us from making “calculated” adjustments to our process parameters.  The objective of measuring and monitoring our processes  and product characteristics is to learn how our processes are behaving in real-time.  In too many cases, however, operating ranges have not evolved with the product development cycle.

Although we may not have established the full operating range, any changes outside of historically observed settings should be cause for review and possibly cause for concern.  Again, the objective is to learn from any changes or deviations that are not within the scope of the current operating condition.

Trigger Events

A trigger event occurs whenever a condition exceeds established process parameters or operating conditions.  This includes failure to follow prescribed or standardized work instructions.  Failing to understand why the “new” condition developed, is needed, or must be accepted jeopardizes process integrity and the opportunity for learning may be lost.

Our ability to detect or sense “abnormal” process conditions is critical to maintain effective process controls.  A disciplined approach is required to ensure that any deviations from normal operating conditions are thoroughly reviewed and understood with applicable levels of accountability.

An immediate response is required whenever a Trigger Event occurs to facilitate the greatest opportunity for learning.  “Cold Case” investigations based on speculation tend to align facts with a given theory rather than determining a theory based solely on the facts themselves.

Recurring variances or previously observed deviations within the normal process may be cause for further investigation and review.  As mentioned in previous posts, “Variance – OEE’s Silent Partner” and “OEE in an Imperfect World“, one of our objectives is to reduce or eliminate variance in our processes.

Interactions and Coupling

When we consider the definition of normal operating conditions, we must be cognizant of possible interactions.  Two conditions observed during separate events may actually create chaos if the events actually occurred at the same time.  I have observed multiple equipment failures where we subsequently learned that two machines on the same electrical grid cycled at the exact same time.  One machine continued to cycle without incident while a catastrophic failure occurred on the other.

Although the chance of cycling the machines at the exact same moment was slim and deemed not to be a concern, reality proved otherwise.  Note that monitoring each machine separately showed no signs of abnormal operation or excessive power spikes.  One of the machines (a welder) was moved to a different location in the plant operating on a separate power grid.  No failures were observed following the separation.

Another situation occurred where multiple machines were attached to a common hydraulic system.  Under normal circumstances up to 70% of the machines were operating at any given time.  On some occasions it was noted that an increase in quality defects occurred with a corresponding decrease in throughput although no changes were made to the machines.  In retrospect, the team learned that almost all of the machines (90%) were running.  Later investigation showed that the hydraulic system could not maintain a consistent system pressure when all machines were in operation.  To overcome this condition, boosters were added to each of the hydraulic drops to stabilize the local pressure at the machine.

To summarize our findings here, we need to make sure we understand the system as a whole as well as the isolated machine specific parameters.  Any potential interactions or affects of process coupling must be considered in the overall analysis.

Reporting

I recommend using a simple reporting system to gather the facts and relevant data.  The objective is to gain sufficient data to allow for an effective review and assessment of the trigger condition and to better understand why it occurred.

It is important to note that a trigger event does not automatically imply that product is non-conforming.  It is very possible, especially during new product launches, that the full range of operating parameters has not yet been realized.  As such, we simply want to ensure that we are not changing parameters arbitrarily without exercising due diligence to ensure that all effects of the change are understood.

Toyota Update

After a 10 month investigation into the cause of “Sudden Unintended Acceleration”, the results of the Federal Investigation were finally released on February 8, 2011, stating that no electronic source was found to cause the problem.  According to a statement released by Toyota,  “Toyota welcomes the findings of NASA and NHTSA regarding our Electronic Throttle Control System with intelligence (ETCS-i) and we appreciate the thoroughness of their review.”

The findings do,however, implicate some form of mechanical failure and do not necessarily rule out driver error.  It is foreseeable that a mechanical failure could be cause for concern and was seriously considered as part of Toyota’s initial investigation and findings that also included a concern with floor mats.  While the problem is very real, the root cause may still remain to be a mystery and although the timeline for this problem has extended for more than a year, it demonstrates the importance of gathering as much vital evidence as possible as events are unfolding.

A Follow Up to Sustainability

When a product has reached maximum market penetration it becomes vulnerable.  According to USA Today, “Activision announced it was cancelling a 2011 release of its massive music series Guitar Hero and breaking up the franchise’s business unit citing profitability as a concern.”

I find it hard to imagine all of the Guitar Hero games now becoming obsolete and eventual trash.  The life span of the product has exceeded the company’s ability to support it.  This is a sad state of affairs.

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

Lean Is …

A scrapyard.
Image via Wikipedia

What is lean?  The following definition is from the Oregon Manufacturing Extension Partnership website, http://www.omep.org:

Lean Is

A systematic approach for delivering the highest quality, lowest cost products with the shortest lead-times through the relentless elimination of waste.

The eights wastes that accompanied this definition include:

  1. Overproduction
  2. Waiting
  3. Transportation
  4. Non-Value-Added Processing
  5. Excess Inventory
  6. Defects
  7. Excess Motion
  8. Underutilized People

It is very easy to become overwhelmed by the incredible amount of information on the subject of Lean.  I always like to refer back to the basic tenets of lean to keep things in perspective.

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

Variance – OEE’s Silent Partner (Killer)

Example of two sample populations with the sam...
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I was recently involved in a discussion regarding the value of Overall Equipment Effectiveness (OEE).  Of course, I fully supported OEE and confirmed that it can bring tremendous value to any organization that is prepared to embrace it as a key metric.  I also qualified my response by stating that OEE cannot be managed in isolation:

OEE and it’s intrinsic factors, Availability, Performance, and Quality are summary level indices and do not measure or provide any indication of process stability or capability

As a top level metric, OEE does not describe or provide a sense of actual run-time performance.  For example, when reviewing Availability, we have no sense of duration or frequency of down time events, only the net result.  In other words we can’t discern whether downtime was the result of a single event or the cumulative result of more frequent down time events over the course of the run.  Similarly, when reviewing Performance, we cannot accurately determine the actual cycle time or run rate, only the net result.

As shown in the accompanying graphic, two data sets (represented by Red and Blue) having the same average can present very different distributions as depicted by the range of data, height of the curve (kurtosis), width or spread of the curve (skewness), and significantly different standard deviations.

Clearly, any conclusions regarding the process simply based on averages would be very misleading.  In this same context, it is also clear that we must exercise caution when attempting to compare or analyse OEE results without first considering a statistical analysis or representation of the raw process data itself.

The Missing Metrics

Fortunately, we can use statistical tools to analyse run-time performance to determine whether our process is capable of consistently producing parts just as Quality Assurance personnel use statistical analysis tools to determine whether a process is capable of producing parts consistently.

One of the greatest opportunities for improving OEE is to use statistical tools to identify opportunities to reduce throughput variance during the production run.

Run-Time or throughput variance is OEE’s silent partner as it is an often overlooked aspect of production data analysis.  Striving to achieve consistent part to part cycle times and consistent hour to hour throughput rates is the most fundamental strategy to successfully improve OEE.  You will note that increasing throughput requires a focus on the same factors as OEE: Availability, Performance, and Quality.  In essence, efforts to improve throughput will yield corresponding improvements in OEE.

Simple throughput variance can readily be measured using Planned versus Actual Quantities produced – either over fixed periods of time and is preferred or cumulatively.  Some of the benefits of using quantity based measurement are as follows:

  1. Everyone on the shop floor understands quantity or units produced,
  2. This information is usually readily available at the work station,
  3. Everyone can understand or appreciate it’s value in tangible terms,
  4. Quantity measurements are less prone to error, and
  5. Quantities can be verified (Inventory) after the fact.

For the sake of simplicity, consider measuring hourly process throughput and calculating the average, range, and standard deviation of this hourly data.  With reference to the graphic above, even this fundamental data can provide a much more comprehensive and improved perspective of process stability or capability than would otherwise be afforded by a simple OEE index.

Using this data, our objective is to identify those times where the greatest throughput changes occurred and to determine what improvements or changes can be implemented to achieve consistent throughput.  We can then focus our efforts on improvements to achieve a more predictable and stable process, in turn improving our capability.

In OEE terms, we are focusing our efforts to eliminate or reduce variation in throughput by improving:

  1. Availability by eliminating or minimizing equipment downtime,
  2. Performance through consistent cycle to cycle task execution, and
  3. Quality by eliminating the potential for defects at the source.

Measuring Capability

To make sure we’re on the same page, let’s take a look at the basic formulas that may be used to calculate Process Capability.  In the automotive industry, suppliers may be required to demonstrate process capability for certain customer designated product characteristics or features.  When analyzing this data, two sets of capability formulas are commonly used:

  1. Preliminary (Pp) or Long Term (Cp) Capability:  Determines whether the product can be produced within the required tolerance range,
    • Pp or Cp = (Upper Specification Limit – Lower Specification Limit) / (6 x Standard Deviation)
  2. Preliminary (Ppk) or Long Term (Cpk) Capability:  Determines whether product can be produced at the target dimension and within the required tolerance range:
    • Capability = Minimum of Either:
      • Capability Upper = (Average + Upper Specification Limit) / (3 x Standard Deviation)
      • Capability Lower = (Lower Specification Limit – Average) / 3 x Standard Deviation)

When Pp = Ppk or Cp = Cpk, we can conclude that the process is centered on the target or nominal dimension.  Typically, the minimum acceptable Capability Index (Cpk) is 1.67 and implies that the process is capable of producing parts that conform to customer requirements.

In our case we are measuring quantities or throughput data, not physical part dimensions, so we can calculate the standard deviation of the collected data to determine our own “natural” limits (6 x Standard Deviation). Regardless of how we choose to present the data, our primary concern is to improve or reduce the standard deviation over time and from run to run.

Once we have a statistical model of our process, control charts can be created that in turn are used to monitor future production runs.  This provides the shop floor with a visual base line using historical data (average / limits) on which improvement targets can be made and measured in real-time.

Run-Time Variance Review

I recall using this strategy to achieve literally monumental gains – a three shift operation with considerable instability became an extremely capable and stable two shift production operation coupled with a one shift preventive maintenance / change over team.  Month over month improvements were noted by significantly improved capability data (substantially reduced Standard Deviation) and marked increases in OEE.

Process run-time charts with statistical controls were implemented for quantities produced just as the Quality department maintains SPC charts on the floor for product data.  The shop floor personnel understood the relationship between quantity of good parts produced and how this would ultimately affect the department OEE as well.

Monitoring quantities of good parts produced over shorter fixed time intervals is more effective than a cumulative counter that tracks performance over the course of the shift.  In this specific case, the quantity was “reset” for each hour of production essentially creating hourly in lieu of shift targets or goals.

Recording / plotting production quantities at fixed time intervals combined with notes to document specific process events creates a running production story board that can be used to identify patterns and other process anomalies that would otherwise be obscured.

Conclusion

I am hopeful that this post has heightened your awareness regarding the data that is represented by our chosen metrics.  In the boardroom, metrics are often viewed as absolute values coupled with a definitive sense of sterility.

Run-Time Variance also introduces a new perspective when attempting to compare OEE between shifts, departments, and factories.  From the context of this post, having OEE indices of the same value does not imply equality.  As we can see, metrics are not pure and perhaps even less so when managed in isolation.

Variance is indeed OEE’s Silent Partner but left unattended, Variance is also OEE’s Silent Killer.

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

The Face of Manufacturing in 2011

Lotus 60th Celebration
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Happy New Year! Another year begins with the confidence that the manufacturing sector will continue to recover from the crash of 2008-2009 that continued its unrelenting grip well into 2010.  There is little doubt that the face of industry in North America and around the world has changed but is it really on the rebound?

I am of the opinion that the manufacturing sector will continue to be redefined by companies that are capable of extending and adapting their technologies to a more diverse range of product applications.  In other words, the industry will shift from traditional product specific expertise to those companies that offer technological expertise to multiple market segments.

An example of this diversification shift is already evident as companies pursue products in new markets such as construction, appliances, wind and solar energy.  Automotive companies here in Ontario (Canada) have certainly learned that technologies such as stamping presses, plastic injection molding machines, and various joining technologies (welding, brazing) can be used to make products that are in demand by other market segments.  Our ability to seek out new industries to complement our existing technologies is perhaps just one of the strategies worthy of consideration to ensure a business remains sustainable well into the future.

The sharp decline in the automotive industry resulted in a significant loss of real manufacturing jobs here in Ontario and certainly extended well beyond our borders.  The strength of the banking industry and business in general does little to appease the unemployed, however, I am encouraged if this cascades into more jobs.

I remain hopeful that 2011 will be the year of transition into prosperity for all – business included.

Until Next Time – STAY lean!

Vergence Analytics

OEE: Planned Downtime and Availability

Injection Molding Press
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As a core metric, Overall Equipment Effectiveness or OEE has been adopted by many companies to improve operations and optimize the capacity of existing equipment.  Having completed several on site assessments over the past few months we have learned that almost all organizations are measuring performance and quality in real-time, however, the availability component of OEE is still a mystery and often misunderstood – specifically with regard to Set Up or Tool Changes.

We encourage you to review the detailed discussion of down time in our original posts “Calculating OEE – The Real OEE Formula With Examples” and “OEE, Down time, and TEEP” where we also present methods to calculate both OEE and TEEP.  The formula for Overall Equipment Effectiveness is simply stated as the product of three (3) elements:  Availability, Performance, and Quality.  Of these elements, availability presents the greatest opportunity for improvement.  This is certainly true for processes such as metal stamping, tube forming, and injection molding, to name a few, where tool changes are required to switch from one product or process to another.

Switch Time

Set up or change over time is defined as the amount of time required to change over the process from the last part produced to the first good part off the next process.  We have learned that confusion exists as to whether this is actually planned down time as it is an event that is known to occur and is absolutely required if we are going to make more than one product in a given machine.

Planned down time is not included in the Availability calculation.  As such, if change over time is considered as a planned event, the perceived availability would inherently improve as it would be excluded from the calculation.  Of course, the higher availability is just an illusion as the lost time was still incurred and the machine was not available to run production.

If we could change a process at the flip of a switch, set up time would be a non-issue and we could spend our time focusing on other improvement initiatives.  While some processes do require extensive change over time, there is always room for improvements.  This is best exemplified by the metal stamping industry where die changes literally went from Hours to Minutes.

To remain competitive and to increase the available capacity, many companies quickly adopted SMED (Single Minute Exchange of Dies) initiatives after recognizing that significant production capacity is being lost due to extensive change over times.  Overtime through extended shifts and capital for new equipment is also reduced as capacity utilization improves.

Significantly reduced inventories can also be realized as product change overs become less of a concern and also provide greater flexibility to accommodate changes in customer demand in real-time.  Significantly increased Inventory Turns will also be realized in conjunction with net available cash from operations.

Redefining Down Time

The return on investment for Quick Tool Change technologies is relatively short and the benefits are real and tangible as demonstrated through the metrics mentioned above.  Rather than attempt to categorize down time as either planned or unplanned, consider whether the activity being performed is impeding the normal production process or can be considered as an activity required for continuing production.

We prefer to classify down time as either direct or indirect.  Any down time such as Set Up, Material Changes, Equipment Breakdowns, Tooling Adjustments, or other activity that impedes production is considered DIRECT down time.  Indirect down time applies to events such as Preventive Maintenance, Company Meetings, or Scheduled IDLE Time.  These events are indeed PLANNED events where the machine or process is NOT scheduled to run.

Redefine the Objective

Set up or change over time is often the subject of much heated debate and tends to create more discussion than is necessary.  The reason for this is simple.  Corporate objectives are driven by metrics that measure performance to achieve a specific goal.

Unfortunately, in the latter case, the objectives are translated into personal performance concerns for those involved in the improvement process.  Rather than making real improvements, the tendency is to rationalize the current performance levels and to look for ways to revise the definition that creates the perception of poor performance. Since availability does not include planned down time, many attempts are made to exclude certain down time events, such as set up time, to create a better OEE result than was actually achieved.

Attempts to rationalize poor performance inhibits our ability to identify opportunities for improvement.  From a similar perspective, we should also be prudent with. and cognizant of, the time allotted for “planned” events.

It is for this reason that some companies have resorted to measuring TEEP based on a 24 hour day.  In many respects, TEEP eliminates all uncertainty with regard to availability since you are measured on the ability to produce a quality part at rate.  As such, our mission is simple – “To Safely Produce a Quality Part At Rate, Delivered On Time and In Full”.  Any activity that detracts from achieving or exceeding this mission is waste.

Remember to get your OEE spreadsheets at no charge from our Free Downloads Page or Free Downloads Box in the sidebar.  They can be easily and readily customized for your specific process or application.

Please feel free to send your comments, suggestions, or questions to Support@VergenceAnalytics.com

Until Next Time – STAY lean!

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