Pdca Versus Optimizing and Satisficing

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The optimizing strategy for decision making is the most black and white of the models. In this model there is one and only one best solution to a problem, which can be discovered and implemented through a series of specific steps. This model is appropriate only in situations where all, or most of the information is available, and the outcome is limited to complete success or not. This differs significantly from the satisficing model, which is much more appropriate for most problems because the satisficing model accounts for a lot of gray area, which is quite common in most problems and decisions. The satisficing model assumes that all of the information is not available and seeks out the best solution that satisfies the objective, not necessarily the single best solution as in the optimization strategy. Both of these models have a definitive end, where a solution is developed and then implemented. This is significantly different from the PDCA model, which is a more dynamic solution that does not simply deduce based on the information available, but tests the solutions and evolves over time because these tests provide meaningful information that is not available during the initial investigative phases. I believe that I use all three of these models consistently in my professional life, depending on the situation. A common example of when the optimizing model is most appropriate is when we have a design problem or new application for one of our existing pieces of electrical test equipment that we offer. For example, if a customer wants to purchase a particular piece of equipment to perform a specific set of tests, but the available equipment only accepts 220 volt AC input power, and the customer only has 110 volt AC power available, we would use the optimizing strategy to resolve this problem. The solution would be to modify our existing unit to accept 110
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