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.
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.
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.
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!