Industrial manufacturers with large product portfolios or aftermarket part mixes face unique supply chain planning challenges:
ToolsGroup’s solutions feature extremely accurate forecasting and service level optimization. Rather than working only with aggregated time series, our demand analytics analyze demand history down to specific channel and individual order-line. This level of detail expertly handles “long tail” items and life cycle planning (new product introductions, substitutions and end-of-life). And it does so automatically, using readily available data.
Lennox Residential Heating and Cooling faced the challenge of transitioning to a hub-and-spoke model with 55 shipping and 161 selling locations. Their goal was straightforward enough: improve service levels and optimize inventories to reallocate working capital and balance inventory allocation in the changing network. But the supply chain environment was daunting.
Lennox implemented ToolsGroup’s SO99+ solution to dynamically rationalize the inventory mix and create an operational plan that sets inventory stocking targets and balances service levels with inventory cost. They also deployed machine learning to reliably model highly variable seasonal demand patterns. It sifts through hundreds of thousands of SKU-Locations to identify “clusters” of those with similar seasonality profiles that substantially increase peak period forecast accuracy.
Service levels were improved by 16% while simultaneously increasing inventory turns by 25%. The implementation also supported significant increases in sales and market share growth.
Polaris Case Study
Inventory Optimization Brief