Warehouse Operations
Using AI to Tighten Supply Chain Efficiency
Business Need
A multibillion food distribution company, operating on a low-margin, high-volume business model, needed to improve its sales forecasts to increase profits. The company's biggest challenge was inaccurate forecasts, which forced it to hold excess inventory of specialty, organic, and fresh food products. Their method involved making simple macro adjustments to recent sales data and qualitative input from sales executives. They generally excluded external factors and historical trends. This situation tied up cash and cut into its bottom line.
Solution
Customer Analytics developed a custom AI/ML model that provides a more rigorous and accurate sales forecasting solution. The model uses a customizable time-series forecasting model that can be tuned to the specific business needs of the distributor, including a 52-week sales forecast. An accurate long-range forecast enables the distributor to tighten supply chain and ordering processes, which enhances cash flow and profitability.
Highlights
Predictive Analytics for Supply ManagementData-driven ValidationDynamic/Adaptive Model
Outcome
To demonstrate the capabilities of the Customer Analytics model, a proof-of-concept was conducted using historical sales data from four of the distributor's top customers, representing different sales profiles, including stable, volatile, and growing accounts. The model produced a 52-week sales forecast, which would enable the distributor to tighten supply chain and ordering processes, which enhances cash flow and profitability.
• 95% accuracy
• 10% projected reduction in needed on-hand inventory annually
• 30% estimated improvement in free cash flow, for $26M annual savings
PREVIOUSEmployee Skills Data Management
NEXTOptimizing Warehouse Management System