Typically OR-projects steps are: observing a situation, modeling it, solving the model, implementing the solution, and evaluating the situation.
In the area of production and logistics, the models are typically NP-hard combinatorial optimization problems. Typically, the contribution of these papers is to determine the complexity status of the problem and give a solution method with a small optimality gap. Some give some managerial insights based on the model. The focus is on the optimization and the steps that come after modeling and before the implementation of the solution.
But any optimization model also has a predictive element to it. (The prediction is if you take this solution, the objective value will be X and the solution will be implementable.)
Are there any studies on the predictive element of OR models? I would be especially interested in studies of the "implementation gap", i.e. the difference in cost predicted by the model and the real cost (or travel distance or ...).
I could so far not find any such studies, but I think such a study would be highly relevant:
The question of the gap between practice and theory is not new: e.g. Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling ask it in 1993.
There still seems to be a large gap with many models: e.g. for cross-docking, this study Cross-docking operations: Current research versus industry practice finds (in 2015):
But the cross-docking optimization literature evoked earlier seems somewhat disconnected from the actual industrial implementations of cross-docking".
There would probably be a considerable implementation gap if the model is "disconnected from the actual industrial implementations of cross-docking".
Assuming that literature reviews are somewhat representative of literature, we can look at: A ‘‘Meta survey’’ analysis in Operations Research and Management Science: A survey of literature reviews and find that "there is a fair concentration of topical coverage in the broad field of OR/MS, focusing on supply chain and logistics, sustainability, and scheduling". This therefore doesn't seem to be a small niche.
Obviously there is research in the more mathematical sense on strongly abstracted problems like the TSP, CVRP or the RCPSP. I also understand, that studying special cases, can lead to insights that might later be abstracted. However, most extensions of these models typically come with the claim, that it is "relevant for practice", yet I've never seen any inclusion of an "implementation gap".
(I'm aware of the informs journal of applied analytics, which does publish case studies, but even there I couldn't find anything on an "implementation gap").
My question: Is there any research on this "implementation gap". Is there maybe a different name/search term for it?
And why is it not always included anyway; some information on the quality of the model seem almost indispensable to me?