Distribution Network Optimisation for Domestic Market Obligation (DMO) Cooking Oil: A Satellite Relocation and Expansion Study of PT IBU
DOI:
https://doi.org/10.59141/jrssem.v6i1.1647Keywords:
Facility location optimization, K-Means clustering, AHP-TOPSIS, demand disaggregation, DMO cooking oil, cannibalization riskAbstract
PT Indonesia Berkah Usaha (IBU), a Minyakita cooking oil distributor in Bandung City, suffers from critical spatial misalignment as its satellite network was established through partner availability rather than systematic demand analysis, resulting in suboptimal positioning that raises delivery costs and constrains revenue growth. This study aims to evaluate historical satellite placements, determine optimal reallocation for underperforming satellites, and identify viable expansion areas. Demand was proportionally disaggregated to stall level from monthly sales across 1,136 stalls. The Elbow Method and Silhouette Coefficient identified k=6 optimal clusters, and weighted K-Means identified six demand zone centroids. Haversine displacement ratios classified satellites with ratio >1.0 as suboptimal. AHP pairwise comparisons from three management respondents were followed by TOPSIS to rank six relocation candidates across four criteria: road accessibility (23.93%), zone demand (53.48%), displacement ratio (8.18%), and cannibalization risk (14.41%). TOPSIS ranked relocating Mama Minyak first (CC=0.805), driven by Zone B's high demand (1,874 boxes/month), lowest displacement among Zone B candidates (1.21x), and moderate cannibalization risk (0.46). Planet Sembako ranked last (CC=0.068) despite extreme displacement (4.91x) due to lower demand and high cannibalization risk (0.815). Zone E, the only unserved zone, was excluded as its 596 boxes/month demand falls below the viability threshold, with targeted canvassing recommended before capital commitment. The integrated K-Means, AHP, and TOPSIS framework effectively prioritizes relocation actions based on demand potential, accessibility, and cannibalization risk, providing a data-driven approach for thin-margin distributors to optimize network performance under capital constraints.
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