Most of our Pennsylvania wood products companies are small to mid-size and are susceptible to the dangers of double jeopardy as described in our last TechNote. The process for breaking the double jeopardy stranglehold is a three-step process as described above: 1) analyze your constraints, 3) re-prioritize your goals, and 3) re-allocate your resources to best meet those goals.
In the science of Operations Research (OR), there are several analytical tools that are available to deal with these requirements. There are many different variants of the tools, but for simplicity I'll summarize them generally as follows:
- Process mapping
- Capability analysis
- Simulation modeling
- Employee assessment
Reprioritization of goals
- Decision making under uncertainty
- Accurate product costing
- Mathematical optimization
- Employee development
Reallocation of resources
- Process metrics
- Process sampling
- Project management techniques
- Employee involvement
I'll discuss the first stage: analyzing the constraints of your organization (and processes, and people).
The best book I've ever read that explains constraint analysis is The Goal by Elihayu M. Goldratt. This entertaining and easy-to-read novel is written as a story most of you could relate to…a manufacturing business under pressure from competition in the marketplace; a manager under pressure to turn the operation around, or else; and employees who have tried just about every new management fad and technology thrown at them without success. In it, author Goldratt drives home the point, and in fact builds a science around, the fact that any work performed on non-constraints of the system is wasted work. In his subsequent books, Goldratt develops The Theory of Constraints and details how the analysis of constraints should proceed.
Basically, constraint analysis starts with Process Mapping. There are many different ways to perform this exercise, but the most successful always involve many of the employees involved in the process under scrutiny. In Process Mapping, the process (or business, or market flows, etc.) are sketched out graphically on a board or flip chart, and subjected to detailed review by the analytical team, until they believe the process is correctly represented by the map that has evolved from the exercise.
A next general stage of constraint analysis could be described as capability analysis; that is, determining how much each component of the process as mapped can receive and produce. Again, there are many forms of capability analysis. One of the most popular in recent years has been statistical capability analysis, where data are collected from the process and plotted as a statistical distribution to determine the range of the possibilities that can be produced from the component under question. For example, dried lumber moisture content sample data could be plotted to determine that the lumber produced from a certain dry kiln under a certain kiln schedule could range from 4% to 12% with the most common samples coming in at 9%. Or, as another example, a cabinet clamping operation could be sampled to determine the average and range of times that specific cabinets take to clamp together. As a final example, a production line could be sampled to determine average throughput rates and how much those rates might vary under random situations.
Determining the capability of each of the various components of a process could supply you with enough data to simulate the operation of the process in a computer simulation model. Computer simulations provide you with the opportunity to view graphically and statistically the bottlenecks (constraints) in your process. A good simulation could also provide you with an interface to manipulate the existing process, trying out new configurations, or capital investments, without actually expending the resources to do so. I like to promote the value of simulation models as "Try before you buy!"
Perhaps a last area of constraint analysis, and one that is usually not given enough attention, is thorough assessment of your employees' abilities to perform the jobs, control the process, or respond to changing conditions as they occur. One of the most common things I've observed in my career in manufacturing science is an interesting paradox: that employees are often a constraint in a system, but they are frequently also the best solution to a constraint. Many times high-technology solutions are implemented in mills only to see that technology go unutilized or improperly applied to the point of constraining the process; and in most cases, this is due to the technology not being adopted by the employees. Analysis of employee skills, backgrounds, and concerns for adopting any new technological solutions should be undertaken if you really want to understand the constraints you face in implementing a new business or manufacturing strategy.
Next week, we'll look at the OR tools that deal with the re-prioritization of goals once you understand your system's constraints and are ready to do something about them.