I am involved in a policy evaluation project where lots of effort has been spent on collecting data. Now that we are actually starting to run optimization models using this data, the solutions give rise to other questions. That happens a lot: the solutions of first versions of a model, do not really answer questions. They more often give rise to new questions, and show that some questions are not really well-posed or even relevant. As a result some of the data suddenly seems less important, and at the same time, some new data would be really useful to solve models that help answering new questions. The lesson is really, that it is often a good idea not to delay building the model until you have all the data. Instead try to build early versions of the model as soon as possible, in conjunction with the data collection effort. This can mean we need to “invent” data. This is a useful effort in itself: it requires to form some detailed understanding of a problem that really helps down the road. Building models can help focus the mind on what is important, what we don’t know yet, and what are relevant questions to ask. It helps to pose these issues more sharply than is otherwise possible. It is very difficult to think about all these things in an abstract way: the model helps to identify problems succinctly.
Great post. This is one of the things about Operations Research that often gets left out of discussion. Too often we focus on the results of the model i.e. did it reduce costs, improve throughput, elevate productivity. Yet the process of Operations Research modelling has its benefits of understanding the macro "picture" of the operation being studied. Just the process of sitting down to write a model requires the analyst to understand the underlying process, assumptions, and constraints of the operation. This can be very useful to any manager of an organization. The understanding of the essential elements of a model can be very helpful in finding possible solutions to any problem.
ReplyDeleteIn the case I described the 'macro "picture"' was well defined. In a large organization, project proposals are written and approved, (research) budget is allocated and project teams assembled before starting a project. This effort ensures some good understanding of what the goal is of the project study. (Yes, sometimes these bureaucratic exercises are actually helping). The real problem here occurred when they needed to fill-in the details. Here the model could have helped as analytical framework early on in the project.
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