THE THEORY OF CONSTRAINTS IS ITSELF UNDER A CONSTRAINT, SO HOW DO WE RESOLVE IT?

THE THEORY OF CONSTRAINTS IS ITSELF UNDER A CONSTRAINT, SO HOW DO WE RESOLVE IT?

The?Theory of Constraints (ToC)?as a management paradigm has been evolving since 1984, when Eliyahu M. Goldratt proposed it in his book, “The Goal”. A fundamental tenet of the ToC states that any manageable system is being limited by a small number of constraints. Therefore the focus should be on identifying those constraints and restructuring the process to “break” them, i.e., elevating the throughput through the constraint until it is no longer the limiting factor.

This methodology has been successfully applied in manufacturing, supply chain optimization, and project management, among other fields. Although the method’s ability to find optimal solutions has often been questioned, it has nevertheless proven over time to yield practical and repeatable improvements in the systems it has been applied to.

There is, however, a fundamental assumption made in the ToC approach. The ToC framework proposes some methods (or “Thinking Processes”) to resolve the constraints, including Current and Future Reality Trees (representing current and target states), Evaporating Cloud (to find win-win solutions), etc. They all assume detailed knowledge of the process or system under consideration to work as intended. In other words, finding the weakest link in the chain requires all the links to be visible and clearly distinct from one another.

Through experience with many clients at Cognition Shared Solutions LLC, we have found that this assumption is not acknowledged enough in practice and is often taken for granted. The Theory of Constraints does cover the system analysis as the first step of the process, but it does not delve deeper into the issue. After all, as long as the process works, it is known, right? Well, this may be the case for fully automatic production lines, but it is far less likely for any process involving humans. Detailed analysis of any non-trivial process will likely show the presence of one or more of the following:

  • implicit (undocumented) expert knowledge being applied;
  • decisions made based on gut feeling rather than strict criteria;
  • data being gathered only to remain unused; or
  • decisions being made without explicit acknowledgment of their impact on process steps, aside from the directly adjacent steps (either previous or following).

In the case of ToC analysis, such a lack of clarity may lead to an incomplete or erroneous diagnosis of the constraints, including complete misidentification of the actual limiting constraint of the system.

To improve the ToC optimization and deal with the abovementioned complications, our team at Cognition Shared Solutions LLC has successfully applied our Trilayer Business Process Analysis?. Our Trilayer framework should be a step taken before the ToC optimization to ensure complete and detailed mapping of the system or process under consideration. Such a mapping would consider three distinct, but interconnected layers, namely:

  • process (actions and decisions);
  • data and knowledge; and
  • risk.

When the underlying system is modeled this way, the Thinking Processes of ToC can be applied much more effectively, as each step within the process can be analyzed in terms of throughput, the most fundamental performance metric in ToC. In addition, with the help of the Trilayer framework, each step in the process can also be viewed in terms of:

  • the inputs (materials and data going in);
  • outputs (products and data going out);
  • feedback loops (critical for optimization);
  • specific risk (probability of the step going wrong); and
  • cumulative risk (impact on the rest of the system).

All this information is critical to the proper design of the buffers, which the ToC uses to optimize the system's flow and allow additional resilience to be built into the system.

In conclusion, the Trilayer Business Process Analysis? can be utilized to prepare the groundwork for successful implementation of ToC, allowing the latter to be applied with greater precision and lower cost, resulting in more meaningful improvements.

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