Data-Driven Decision Support for SKU Rationalisation in FMCG Portfolio Management
A case study at Unilever
S. Brons (TU Delft - Civil Engineering & Geosciences)
J.M. Vleugel – Graduation committee member (TU Delft - Civil Engineering & Geosciences)
A. Napoleone – Graduation committee member (TU Delft - Mechanical Engineering)
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Abstract
Fast-moving consumer goods (FMCG) firms compete through innovation, but the portfolios that result grow faster than they are pruned.
The literature on SKU rationalisation is extensive, yet it consistently presupposes that the data required to evaluate a delisting candidate is available in analysable form. This paper reports a design science case study in the European personal care division of a large multinational FMCG manufacturer, where that assumption does not hold. The division already received periodic, criteria-based delisting recommendations from a central analytics team, but could not readily act on them: the recommendations contain only a code and its financial figures, and the evidence needed to interpret them was dispersed across more than ten independently maintained brand files and successive, unconnected snapshots of the commercial reporting system.
A decision-support framework was designed, built, and evaluated in response. Two automated pipelines consolidate the fragmented sources, one of which recovers a multi-period view by stacking snapshots that individually contain none, and four dashboards present the resulting evidence for portfolio exploration and for the evaluation of individual candidates. Applied to three recommended products, the framework confirmed two for delisting and surfaced the strategic grounds on which a third, financially indistinguishable, was retained.
The confirmed delistings raise the gross margin of the brands concerned by and percentage points and remove stock-keeping units across country-market combinations, at a cost of € million turnover. The artefacts are in use within the division. The central finding is that the binding constraint on data-driven rationalisation was not the sophistication of the analysis, which already existed, but the accessibility of the evidence required to act on it.