Industrial manufacturers with large product portfolios or aftermarket part mixes face unique supply chain planning challenges:
- Lots of slow moving products mean “long tail” demand is variable and hard to predict
- Short response times and geographically distributed customer bases require complex distribution networks
- Considerable numbers of SKU-Locations and large inventories tie up working capital
- Ongoing new product introductions and new models make forecasting more difficult
ToolsGroup solves the industrial supply chain planning problem with an exceptional ability to forecast intermittent demand and optimize multi-echelon inventory.
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.
- Demand modeling incorporates internal and external elements such as market trends, returns and substitutions. These demand models have an exceptional ability to handle slow moving inventory.
- Machine learning reliably models even extremely seasonal demand profiles and new product forecasting.
- Demand collaboration brings together demand and forecast data from multiple sources such as salespeople and distributors in a web-based consensus forecasting platform.
- Rough cut capacity planning covers the entire replenishment planning process, and includes fair allocation logic.
- S&OP bridges a critical modeling gap to reliably connect tactical planning to operational execution.
Our Manufacturing Customers Typically Achieve:
ToolsGroup Solutions for Manufacturing include:
Demand Planning & Sensing
CUSTOMER CASE STUDY
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.