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Logward created a custom transport optimization model for an FMCG company. The model combines several levers to identify annual savings of up to 〜€720,000 and significantly reduce emissions, without impacting service levels.
An FMCG company selling coffee and related products with branches in several European countries launched a “Digital Logistics” initiative. This defined a strategic direction for its logistics operations and specified key objectives to be achieved. A central part of this strategy is Logward, which is implementing a comprehensive Transport Management System (TMS) EU-wide to optimize processes and improve cooperation with logistics services providers.
Warehouse picking and packing costs, along with transport costs, are increasing exponentially with the rising inflation and energy crises in the European Union. In addition to this, the company has committed to reduce its carbon footprint, with the goal to achieve climate neutrality for all emissions resulting from transport by 2030.
Logward’s team developed a model that combines three levers: order bundling in the warehouse, shipment consolidation based on postal code, and adjusting frequency of regular shipments to uncover consolidation opportunities. Starting by classifying data based on business units and year of execution, the model was fed with more than 400K deliveries and 28 different rate sheets from freight forwarders. Taking into consideration several constraints and hyperparameters that Logward defined together with the customer such as warehouse operation costs, revenues generated from end consumers, and maximum weight/ volume per truck. This model allowed the customer to identify which orders can be bundled and which deliveries can be consolidated, which enabled cost and emissions optimization efforts.
The combination of the three levers allows the company to reach up to 21.5% savings compared to the 2019-2020 operational setup, corresponding to 〜 €720,000 on an annual basis. While the full impact on emissions has not yet been measured, the effect of reducing packaging and truck journeys is expected to be significant.