Part of an O.R practitioners job involves selling O.R to non-believers in the organization. Yet many of us in the O.R comfort-zone are firm non-believers that there even exist such non-believers. After all, isn't 'science of better' or its applied counterpart 'decisioneering' self-explanatory? It isn't. The 'analytics' bandwagon is going to ensure that. Last time we looked at the identity crisis facing the poor OR guy. Today, we'll examine more related aspects.
When we say a product has got 'O.R inside', what do we really mean? Is it because it's been autographed by that lost O.R scientist whose owlish ^oo^ spectacles always makes u think 'infinite loop', or, is it the bullet-proof C++ codes of O.R algorithms, the fiendishly reformulated optimization model, or the brand-new, low-latency, 16M$, 32-node, 64-bit, 128-GB SMP RAM parallel machine (yummy!) that smashes thru all your Lagrangian subproblems in a jiffy? or perhaps it's all in the GUROBI or CPLEX solvers that implements the fundamental algorithms?
The old bilateral debate of man v machine, in this context, starts with 'Math v Programming', and in true O.R fashion, cascades into some NP-complete combinatorial debate. heh. The obvious answer to many may be 'all the above', but called me biased - I feel that its the well-trained O.R grad, her/his model and solution approach that seals the deal here. Everything else is essentially a commodity, and can be quickly purchased, and therefore form the supporting cast (The real answer of course is 'none of the above'. It's the power point decks that made it all happen).
Seriously, a practitioner has to have all the soft skills to ensure that O.R gets some small share of credit in such projects, especially when things go right. After all, when its fails, its because of the O.R inside. It's because of you. Everything else was purchased and they work just fine! Suddenly, you alone know which constraint is hurting profits the most, or why a few more discrete variables kill run-times, or if the exponential service time assumption holds. Which brings me to probabilistic 'OR inside' models in practice (more on that another day). By design, its going to give you 'wrong' answers some of the time - unlike deterministic models that provide the illusion of correctness all the time. Good luck selling that!