Implementing model solutions

I just came across this blog post which talks about another layer of doing math modeling. The basis of my blog is that you should try to make sure you solve the right problem, otherwise you can't hope to get a good solution. What the blogger points out is that even if you solve the right problem, that does not guarantee that the solution will be implemented correctly. His example comes from doing retail stock forecasting. He describes several of the ways his customers may use the information gleaned from the software to make inappropriate decisions.

There are similar issues in my research area. I work on "the newsvendor problem" where you sell newspapers during the day. You start by choosing a quantity of newspapers y to buy at the beginning of the day for c dollars each, and sell them for r apiece. If you run out early, you have a lost sales cost of e, and if you have leftovers you can recycle them for v each. Empirical studies find that even when participants are trained about the optimal order quantity, they tend to over-order.

So how to resolve these issues? Effective communication skills are a key starting point. By asking the right questions of practitioners, modelers can ensure both that they solve the right problem and do their best to make sure that the solutions are taken seriously.