Fast-moving consumer goods (FMCG) companies operating multi-echelon distribution networks face a persistent challenge: the time required to collect, clean, and calibrate data constrains their ability to evaluate network performance across dozens of markets. This project develops a replicable Route to Market network optimization framework for a multinational FMCG leader, designed to reduce the data preparation phase from months to weeks and enable the evaluation of more than 10 markets per year, compared to approximately two under the company’s current methodology. The framework combines a standardized data pipeline with automated parameter calibration, and a stochastic model that incorporates demand uncertainty through scenario-based optimization. Validated in an emerging market pilot, the framework demonstrates total daily network cost reductions exceeding 15% across all evaluated scenarios. Results are delivered through an interactive comparison tool that allows leadership to assess the cost implications of alternative network configurations without requiring deep technical expertise. Together, these components equip the sponsor team with a fast, objective, and scalable basis for prioritizing which markets warrant full-scale optimization studies.
