Tag: Root Cause

Learning From Mistakes

always make new mistakes
Always make new mistakes (Photo credit: elycefeliz)

An event occurred this afternoon that required an immediate resolution. When asked whether we were going to pursue the root cause, I could only respond with this question:

What’s the point of making mistakes if we’re not going to learn from them?

This is likely the shortest post I ever published here, however, I think the simplicity of the message makes the point very clear.

If you do wish to delve deeper into the topic of mistakes, I encourage you to read some of the related articles featured below.

Until Next Time – STAY lean

[twitter-follow screen_name=’Versalytics’ show_count=’yes’]

Vergence Analytics

Lean – Burnout, Apathy, and Pareto’s Law

Example Pareto chart
Typical Application to Analyze Quality Defects

The Premise:  Pareto’s Law

The late Josheph Juran introduced the world to Pareto’s Law, aptly named after Italian economist Vilfredo Pareto.  Many business and quality professionals alike are familiar with Pareto’s law and often refer to it as the 80 / 20 rule.  In simple terms, Pareto’s Law is based on the premise that 80% of the effects stem from 20% of the causes.

As an example, consider that Pareto’s Law is often used by quality staff to determine the cause(s) responsible for the highest number of defects as depicted in the chart to the right.  From this analysis, teams will focus their efforts on the top 1 or 2 causes and resolve to eliminate or substantially reduce their effect.

In this case, the chart suggests that highest number of defects are due to shrink followed by porosity.  At this point a problem solving strategy is established using one of the many available tools (8 Discipline Report, 5 Why, A3) to resolve the root cause and eliminate the defect.  Over time and continued focus, the result is a robust process that yields 100% quality, defect free, products.

In practice, this approach seems logical and has proven to be effective in many instances.  However, we need to be cognizant of a potential side effect that may be one of the reasons why new initiatives quickly wane to become “the program of the day.”

The Side Effects:  Burnout and Apathy

Winning the team’s confidence is often one of the greatest challenges for any improvement initiative.  A common strategy is to select a project where success can be reasonably assured.  If we apply Pareto’s Law to project selection, we are inclined to select a project that is either relatively easy to solve, offers the greatest savings, or both.

In keeping with the example presented in the graphic, resolving the “shrink” concern presents the greatest opportunity.  However, we can readily see that, once resolved, the next project presents a significantly lower return and the same is true for each subsequent project thereafter.

Clearly, as each problem is resolved, the return is diminished.  To compound matters, problems with lower rates of recurrence are often more difficult to solve and the monies required to resolve them cannot be justified due to the reduced return on investment.  In other words, we approach the point where the solution is as elusive as “the needle in a haystack” and, once found, it simply isn’t feasible to fund it.

The desire to resolve the concern is significantly reduced with each subsequent challenge as the return on investment in time and money diminishes while the team continues to expend more energy.  Over extended periods of time the continued pursuit of excellence leads to apathy and may even lead to burnout.  As alluded to earlier, adding to the frustration is the inability to achieve the same level of success offered by the preceding opportunities.

The Solution

One of the problems with the approach as presented here is the focus on resolving the concern or defect that is associated with the greatest cost savings.  To be clear, Pareto Analysis is a very effective tool to identify improvement opportunities and is not restricted to just quality defects.  A similar Pareto chart could be created just as easily to analyze process down time.

Perhaps the real problem is that we’re sending the wrong message:  Improvements must have an immediate and significant financial return.  In other words, team successes are typically recognized and rewarded in terms of absolute cost savings.  Not all improvements will have a measurable or immediate return on investment.  If a condition can be improved or a problem can be circumvented, employees should be empowered to take the required actions as required regardless of where they fall on the Pareto chart.

To assure sustainability, we need to focus on the improvement opportunities that are before us with a different definition of success, one with less emphasis on cost savings alone.  Is it possible to make improvements for improvements sake?  We need to take care of the “low hanging fruit” and that likely doesn’t require a Pareto analysis  to find it.

Finally, not all improvement strategies require a formal infrastructure to assure improvements occur.  In this regard, the ability to solve problems at the employee level is one of the defining characteristics that distinguishes companies like Toyota from others that are trying to be like them.  Toyota and the principles of lean are not reliant on tools alone to identify opportunities to improve.

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

How Effective is Your Problem Solving?

The re-drawn chart comparing the various gradi...
Image via Wikipedia

Background

Of the many metrics that we use to manage our businesses, one area that is seldom measured is the effectiveness of the problem solving process itself.  We often engage a variety of problem solving tools such as 5-Why, Fishbone Diagrams, Fault Trees, Design of Experiments (DOE), or other forms of Statistical Analysis in our attempts to find an effective solution and implement permanent corrective actions.

Unfortunately, it is not uncommon for problems to persist even after the “fix” has been implemented.  Clearly, if the problem is recurring, either the problem was not adequately defined, the true root cause was not identified and verified correctly, or the corrective action (fix) required to address the root cause is inadequate.  While this seems simple enough, most lean practitioners recognize that solving problems is easier said than done.

Customers demand and expect defect free products and services from their suppliers.  To put it in simple terms, the mission for manufacturing is to:  “Safely produce a quality part at rate, delivered on time, in full.”  Our ability to attain the level of performance demanded by our mission and our customers is dependent on our ability to efficiently and effectively solve problems.

Metrics commonly used to measure supplier performance include Quality Defective Parts Per Million (PPM), Incident Rates, and Delivery Performance.  Persisting negative performance trends and repeat occurrences are indicative of ineffective problem solving strategy.  Our ability to identify and solve problems efficiently and effectively increases customer confidence and minimizes product and business risks.

Predictability

One of the objectives of your problem solving activities should be to predict or quantify the expected level of improvement.   The premise for predictability introduces a nuance of accountability to the problem solving process that may otherwise be non-existent.  In order to predict the outcome, the team must learn and understand the implications of the specific improvements they are proposing and to the same extent what the present process state is lacking.

To effectively solve a problem requires a thorough understanding of the elements that comprise the ideal state required to generate the desired outcome.  From this perspective, it is our ability to discern or identify those items that do not meet the ideal state condition and address them as items for improvement.  If each of these elements could also be quantified in terms of contribution to the ideal state, then a further refinement in predictability can be achieved.

The ability to predict an outcome is predicated on the existence of a certain level of “wisdom”, knowledge, or understanding whereby a conclusion can be formulated.

Plan versus Actual

Measuring the effectiveness of the problem solving process can be achieved by comparing Planned versus Actual results. The ability to predict or plan for a specific result suggests an implicit level of prior knowledge exists to support or substantiate the outcome.

Fundamentally, the benefits of this methodology are three-fold as it measures:

  • How well we understand the process itself,
  • Our ability to adequately define the problem and effectively identify the true root cause, and
  • The effectiveness of solution.

Another benefit of this methodology is the level of inherent accountability.  Specific performance measurements demand a greater degree of integrity in the problem solving process and accountability is a self-induced attribute of most participants.

The ability for a person or team to accurately define, solve, and implement an effective solution with a high degree of success also serves as a measure of the individual’s or team’s level of understanding of that process.  From another perspective, it may serve as a measure of knowledge and learning yet to be acquired.

As you may expect, this strategy is not limited to solving quality problems and can be applied to any system or process.  This type of measurement system is used by most manufacturing facilities to measure planned versus actual parts produced and is directly correlated to overall equipment effectiveness or OEE.

Any company working in the automotive manufacturing sector recognizes that this methodology is an integral part of Toyota’s operating philosophy and for good reason.  As a learning organization, Toyota fully embraces opportunities to learn from variances to plan.

Performance expectations are methodically evaluated and calculated before engaging the resources of the company.  It is important to note that exceeding expectations is as much a cause for concern as falling short.  Failing to meet the planned target (high / low or over / under) indicates that a knowledge gap still exists.  The objective is to revisit the assumptions of the planning model and to learn where adjustments are required to generate a predictable outcome.

Steven Spear discusses these key attributes that differentiate industry leaders from the rest of the pack in his book titled The High Velocity Edge.

First Time Through Quality (FTQ)

FTQ can also be applied to problem solving efforts by measuring the number of iterations that were required before the final solution was achieved.  Just as customers have zero tolerance for repeat occurrences, we should come to expect the same level of performance and accountability from our internal resources.

Although the goal may be to achieve a 100% First Time Through Solution rate, be wary of Paralysis by Analysis while attempting to find the perfect solution.  The objective is to enhance the level of understanding of the problem and the intended solution not to bring the flow of ideas to a halt.  Too often, activity is confused with action.  To affect change, actions are required.  The goal is to implement effective, NOT JUST ANY, solutions.

Jishuken

Literally translated, Jishuken means “Self-Study”.  Prior to engaging external company resources, the person requesting a Jishuken event is expected to demonstrate that they have indeed become students of the process by learning and demonstrating their knowledge of the process or problem.  It pertains to the collaborative problem solving strategy after all internal efforts have been exhausted and external resources are deployed with ”fresh eyes” to share knowledge and attempt to achieve resolution.  While the end result does not appear to be “self study”, the prerequisite for Jishuken is “exhausting all internal efforts”.  In other words, the facility requesting outside resources must first strive to become experts themselves.

Summary

Many companies limit their formal problem solving activities to the realm of quality and traditional problem solving tools are only used when non-conforming or defective product has been reported by the customer.  Truly agile / lean companies work ahead of the curve and attempt to find a cure before a problem becomes a reality at the customer level.

With this in mind, it stands to reason that any attempt to improve Overall Equipment Effectiveness or OEE also requires some form of problem solving that, in turn, can affect a positive change to one or all of the components that comprise OEE:  Availability, Performance, and First Time Through Quality.

As a reminder, OEE is the product of Availability (A) x Performance (P) x Quality (Q) and measures how effectively the available (scheduled) time was used to produce a quality product.  To get your free OEE tutorial or any one of our OEE templates, visit our Free Downloads page or pick the files you want from our free downloads box in the side bar.  You can easily customize these templates to suit your specific process or operation.

Many years ago I read a quote that simply stated,

“The proof of wisdom is in the results.”

And so it is.

Until Next Time – STAY lean!

Vergence Analytics

Using TRIZ for Problem Solving – Introduction

Using TRIZ for Problem Solving – Introduction

A famous quote from Albert Einstein, “The problems that exist in the world today cannot be solved by the level of thinking that created them.“, applies to the discussion of problem solving and more so to the topic of TRIZ, The Theory of Inventive Problem Solving, developed by Genrich S. Altshuller.

TRIZ – Theory of Inventive Problem Solving

Genrich S. Altshuller developed TRIZ based on his search for a standard method to solve problems.  At the very basic level, once a problem is identified the objective is to determine whether a similar problem has already existed elsewhere.  If so, study the solution and determine whether it can be incorporated into the current solution being sought.  Taken one step further, consider the possibility that a different perspective of the problem may also present a unique inventive solution.

It does not seem too far fetched that the problem to be solved has occurred elsewhere in a completely different context.  The solution that is found may also be out of the context but the concept may lead to an innovative solution for the current problem at hand where one never before existed.

The application of TRIZ requires an open mind.  We often bring our “tool box” of experience to the table and draw on those tools and our wealth of knowledge to create a solution.  TRIZ is a tool that can be used to create completely new and unique solutions to a given problem.  This doesn’t mean that we need to abandon our current technology and know-how; it simply means that there may be other options where the current know-how and / or technology may not apply or it may be applied in a manner that is quite different than it is today.

Identify the Real Problem to be Solved

Any problem solving method can only be successful if the true root cause is identified.  Once found, a clear and concise problem statement must be formulated to assure that the solution developed and implemented indeed addresses the true root cause.

Searching for Solutions:

Once a problem has been identified, the next question is, “How do we solve it?”  There are a number of techniques that can be used such as brain storming and idea mapping, however, one seldomly used technique is TRIZ:  Theory of Inventive Problem Solving.

Every day we are challenged with a diverse range of problems from machine malfunctions to defective parts.  The very nature of any company’s operations requires an immediate fix to restore operations to “normal”.  Recognizing that a problem exists is not the same as understanding what the problem is and effectively solving the problem requires that we have identified the true root cause and not just the symptoms.

Many tools are readily available to even help us address these concerns or identify where opportunities exist to make improvements.  Unfortunately, these tools seldom provide the solution to the problem.  Too often we are trapped inside the box of current thinking, technologies, standards, methodologies, present knowledge, and even company policy.  Our own levels of thinking and plausible solutions are influenced and limited by our current understanding and knowledge of the problem as well as our own experiences.

The Basis for Using TRIZ to Solve Problems:

Technology

In some cases, product or part designs themselves may be constrained as engineers and designers work to generate a design tailored to a specific, known, technology.  Quality Function Deployment is one strategy that provides a platform to explore alternative design and process approaches before committing to a specific technology or process.

It is worth noting that, although product design is critical, processes and technologies used to manufacture the product itself are often overlooked and seldom are the process constraints and their affects ever considered.  There are many examples where numerous hours are wasted attempting to develop tools using traditional technologies to produce parts that conform to the wishes of engineers and designers.

How do we actually go about solving problems where the technology or the design present constraints that prevent success?  This is the basis for TRIZ:  We have clearly identified the problem to be solved, now we need a solution to resolve it.

Problem Classifications

Although problems may have varying degrees of difficulty, the solutions for them can only fall into one of two overly simplified categories:  Known or Unknown.  While this classification may appear simple on the surface, consider the unknown solution.  Is it truly unknown or is it only unknown to you.
  1. Known:  Surrogate process already proven and only requires adaptation for the current situtation.  The “problem solver” has an awareness or experience related to the solution.
  2. Unknown:  Typically, solutions are often limited by the scope of experience of the person or person(s) attempting to solve the problem.
    1. The problem solver is not aware of the solution’s existence (Personal)
    2. The solution is outside the problem solver’s scope of experience, training, or field of expertise, but may exist within the company (Company)
    3. The solution is not known within the company but is known within the industry (Industry)
    4. A solution can be realized although it does not presently exist (Outside Industry).
    5. Requires an inventive solution that goes beyond improving the existing condition and is not known to exist anywhere.
  3. Although a solution may be found or developed internally, it may not necessarily be ideal.  We recommend continual review of trade journals, going to trade shows, and networking not only with industry peers but outside your areas of expertise as well.

We will pursue the TRIZ methodology as both a learning and problem solving method.  Often times the solution to a problem requires a different perspective to achieve an effective resolution.

Applying TRIZ in the real world:

TRIZ can be used to develop solutions in a wide range of applications.  As Contingency Plans are developed, you may determine that a solution is required to address a problem or crisis that company has not yet experienced.  As we have discussed, the information or solution to the pending “crisis” may already exist elsewhere.  Similarly, improvements to Overall Equipment Efficiency may require solutions to be developed to address problems or opportunities that are inhibiting continued improvement. 

We will continue to pursue the application of TRIZ in the real world and present a more detailed case study.  

Note:  We would also recommend and encourage you to visit http://www.mazur.net/triz/ for an indepth presentation and detailed discussion of TRIZ.  This site provides greater detail and background that is presently beyond the application or scope of this series.

Until Next Time – STAY Lean!