Operations research (OR) has long supported supply chain decisions for fast moving consumer goods (FMCG), a sector marked by thin margins, short shelf lives, demand that swings with promotions and highly fragmented retail. This review examines whether OR models capture these features and whether richer models reach practice. We analyse 22 peer reviewed applications published between 1994 and 2024, coding each model against seven defining characteristics of FMCG to form an FMCG Specificity Index, and against a four level scale of adoption evidence. Nineteen of the 22 models extend the classical transportation problem. Mean specificity is low, at 2.09 out of 7: promotions are modelled in one study and demand uncertainty in four. Adoption evidence is not significantly related to specificity (Spearman ρ = −0.21, p = 0.36) but tends to decline as solution methods grow more complex (ρ = −0.37, p = 0.12). All five implemented models rely on exact methods or constructive heuristics, whereas none of the six metaheuristic models reports implementation. We therefore propose simplicity preserving specificity as a design principle and demonstrate it with real promotion data from a US grocery retailer. The resulting FMCG Fractile Method can be applied in a spreadsheet, matches the optimal stochastic plan to within 0.005 percent of expected cost, and reduces expected cost by 13 to 57 percent relative to planning that ignores promotions. The review closes with a research agenda and a reporting checklist for FMCG optimisation studies.

Keywords: fast moving consumer goods; supply chain optimisation; operations research; transportation problem; vehicle routing; promotions; newsvendor; model adoption