One of the major differences between Theory of Constraints (TOC) thinking and traditional approaches to manufacturing or any industry for that matter is the use of performance metrics. Many companies still hold onto metrics like efficiency and utilization in nonconstraints and probably will continue to do so. Deborah Smith has written an excellent chapter in the TOC Handbook (i.e. Chapter 14) and I encourage everyone to read it. Whether you know it or not, each of the chapters in the handbook can be purchased individually and many, if not all of them, are downloadable electronically. Deborah’s chapter is entitled as Resolving Measurement/Performance Dilemmas.
Deborah tells us that metrics need to encourage the right behavior, but when you’re dealing with organizations of significant size and complexity, it’s always a challenge to construct a system of local metrics that:
1. Encourage the local parts to do what is in the interest of the global objective. I just discussed this in a couple of my most recent postings.
2. Provides relatively clear conflict resolution between and within the local parts.
3. Provides clear and visible signals to management about local progress and status relative to the organizational objectives.
Deborah presents a “simple set of six general measurements” that all assume that a valid TOC model has been implemented. These six measurements are:
1. Reliability
2. Stability
3. Speed/Velocity
4. Strategic Contribution
5. Local OE (i.e. Operating Expense)
6. Local Improvements/Waste
What I want to do in this posting is focus on the metric Stability. Not that the others aren’t important, but to me getting control of the stability metric presents a huge opportunity for improvement. I may touch on a couple others to help make a point, but the focus will be on stability.
The objective of the stability metric is to measure or at least get an idea of the amount of variation that is being passed throughout the system in question. We all agree that having variation and volatility in the system is not conducive to stability. This is especially true when we’re talking about the system constraint, otherwise known as the drum, simply because the drum is the anchor point of our scheduling system or at least it should be. Any disruption of the drum schedule creates a lack of synchronization in the rest of the system as well as reducing the capacity of the constraint and the revenue stream.
One measure that is important is drum utilization which is simply a measure of how well the constraint is being used to produce throughput compared to how well it should be doing. Utilization, which is usually expressed as a percentage, compares the actual time the constraint is used to produce throughput to the total time available. In other words, utilization is 100% minus the time lost due to starvation, blockage and downtime due to breakdowns. Keep in mind that every time the utilization of the constraint falls below 100%, we are losing potential revenue so it’s very important to track this metric and to record the causes of the reduction. Let’s look at some of the causes that we might experience.
· Starvation of the constraint occurs when it runs out of material being fed to it by an upstream process. The cause and the length of time the starvation lasted are very important, so record them.
· Unnecessary/Over-Production is simply a waste of the constraint’s capacity on things that aren’t required.
· Unplanned and Planned Downtime in the constraint takes away the opportunity to produce throughput. The cause and length of the downtime should be recorded.
· Blockages of the constraint occur when the constraint is prevented from running because the operation feeding it experiences downtime. This is somewhat different than starvation in that any upstream location could be the cause of starvation. Once again, record the reason and the length of time the constraint was blocked.
There are other reasons or factors that affect the stability of the constraint such as late releases, absenteeism, etc. but the four I listed are the most important. So now that you’ve collected the causes and times associated with this stability metric, it should be easy for you to develop an action plan to improve the stability of the constraint. Simply create a Pareto chart of the causes and times and attack the top 20% that account for 80% of the stability problem. Pretty simple as long as you put the tracking mechanism in place.
In my next few postings, I’ve ask the co-author of our new book Epiphanized: Integrating Theory of Constraints, Lean and Six Sigma, Bruce Nelson to continue on the theme of performance metrics.
Bob Sproull
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