Theory of Constraints Handbook - James Cox Iii [170]
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Frequently inertia prevents one from breaking a paradigm. It was a startling experience to realize that such a common sense paradigm that a complex situation requires sophisticated planning has to be reversed!
Two meaningful citations of Goldratt, said at different times, have contributed to the change of paradigm:
1. In reality, we have both complexity and uncertainty and we are fortunate for having them both together.
2. The more complex the problem is, the simpler the solution should be.
The first saying means that when uncertainty is added upon complexity, then it is not possible to come up with an optimal solution that is also practical. When too many variables impact the output, then an “optimal solution” is usually a “sensitive solution,” meaning even a small deviation from the precise optimal solution would lead to a significant drop in the output. In an uncertain environment, there is no way to implement a multivariable solution without any deviation.
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The following example should demonstrate the flaw in looking for the “optimal solution” to a complex and uncertain situation.
Suppose you live in Tel Aviv, Israel, and you need to arrive in Phoenix, Arizona, for a meeting with the board of directors of an important potential client. You would like to leave home as late as possible and spend the minimum time waiting for your connecting flights.
As the story goes, your agent has found exactly what you have asked for. You are booked on three legs of flights with 30 minutes between landing and takeoff. This time should be just enough to walk to the gate where the next flight is taking off. The agent included detailed calculations of the distance between the gates and the security and passport control in-between to show that it is an exact match to your capability to walk with your carry-on luggage. Eventually you are going to land at Phoenix Airport, 72 minutes before the meeting. The planning takes into account the traffic on the hour following the landing, showing that you will arrive at the meeting on the minute.
What do you think of such optimal planning? Isn’t it ideal? It is great to be just on time without wasting precious time.
Even if we ignore most of the uncertainty and assume all flight schedules are truly precise, we need a lot of luck to arrive on such a nice optimal plan. Usually, the airlines and the availability of seats are not so generous and thus you’d need to waste some time waiting for flights or arrive significantly earlier than in the imaginary ideal solution. Yet, there are many alternatives to connect between Tel Aviv and Phoenix, so your agent should look for all of them until the best one is chosen.
Now, let’s acknowledge the uncertainty. Flights are not always on time, queues for security and immigration fluctuate widely, and you can never rely on the traffic flow. At the end of the day, you realize not only do you need to consider buffering your plan, but also that there is no sense in checking too many options because once buffers are included in the planning the difference between alternative routings is negligible.
This is the insight emerging from the first citation of Goldratt: The inclusion of uncertainty within the complexity of the environment makes the sensible plan to be fairly simple. Focus only on the minimal requirements that are clearly critical and make sure those key requirements are properly protected.
This understanding leads to the second citation: solutions for complex situations must be simple; otherwise, they do not stand a chance in reality. Any small deviation in one of the many inputs (the number of inputs is what makes the environment complex) would cause too large a deviation in the output.
What does this have to do with S-DBR in a complex environment? The current understanding is that in the vast majority of the complicated cases, the use of S-DBR is even more sensible than the use of DBR. In the cases where there is a problem in using S-DBR, the use of straightforward