Minimal Order Quantities (MOQs) are ubiquitous in supply chain. At a fundamental level, MOQs represent a simple way for the supplier to indicate that there are savings to be made when products are ordered in batches rather than being ordered unit by unit. From the buyer's perspective, however, dealing with MOQs is far from being a trivial matter. The goal is not merely to satisfy the MOQs - which is easy, just order more - but to satisfy the MOQs while maximizing the ROI.
Lokad has been dealing with MOQs for years already. Yet, so far, we were using numerical heuristics implemented through Envision whenever MOQs were involved. Unfortunately, those heuristics were somewhat tedious to implement repeatedly, and the results we were obtaining were not always as good as we wanted them to be - albeit already a lot better than their "manual" counterparts.
Thus, we finally decided to roll our own non-linear solver for the general MOQ problem. This solver can be accessed through a function named
moqsolv in Envision. Solving the general MOQ problem is hard - really hard, and under the hood, it's a fairly complex piece of software that operates. However, through this solver, Lokad now offers a simple and uniform way to deal with all types of MOQs commonly found in commerce or manufacturing.