Network optimization reduces supply chain costs by 10%

Designed a data-driven network optimization algorithm to bring down cost-to-serve across the supply chain.

Business Scenario

The client is one of the largest tobacco product manufacturers globally. As the competition within the industry intensified, the need to evolve from legacy practices and adopt new operating models became apparent. Declining sales volumes and shrinking markets over the past few years necessitated optimization of distribution costs in the EU to lower it by 20% to match market benchmarks. Customer expectations of high service level often led to conservative approaches to find cost-saving pockets by tweaking SLAs wherever feasible. The supply chain teams were unable make informed decisions about process improvements and expanding business capabilities.

Sigmoid Solution

Sigmoid conducted an initial exploratory data analysis on the inbound and outbound data to understand the market situation and track the declining volume/demand trends. The insights were used to determine the need for optimizing the network and associated costs. Hypotheses for cost analysis and business rules were defined as per discussions with the global logistics team to evaluate different cost-saving opportunities. Historical data was explored to perform Greenfield analysis to identify optimal warehouse locations to minimize overall distribution costs. A scenario simulation engine was designed to help logistics and supply chain teams forecast network design scenarios that would guide product placement and movement efforts.

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Business Impact

Sigmoid’s network optimization framework helped in reducing CTS with a zero-based distribution network design. The supply chain teams were better positioned to negotiate and create joint value with suppliers scalably.