If your company has millions of dollars in inventory, then we can help you improve your profitability. Our solutions have extensive capabilities for optimizing stocking levels and efficiently replenishing inventories.
The main goal of inventory optimization is to choose the best level of service each item and location, thereby optimizing inventory investments. Smoothie? has extensive capabilities for segmenting inventories by forecastability, ABC, source of supply, etc., and then “what-iffing” various supply policy assumptions. It performs simulations based on real demand and supply data to choose the best strategies.
Smoothie has advanced methods for calculating safety stocks, using lead times, service levels, and a variety of different measures of variability. Unlike other solutions that only consider “within sample” statistics like Standard Deviation or Root Mean Squared Error, Smoothie performs out of sample testing, optionally determining safety stocks according to actual historic forecast errors (and not just theoretical ones).
Not only will Smoothie help you to establish optimal replenishment policies, but our advanced forecasting and collaboration capabilities will shift the whole curve favorably, enabling simultaneous improvements in cost and service.
Replenishment Planning & Execution
It’s important to balance and optimize safety stock and strategic inventory investments, but you also need to execute efficiently and in a way that aligns with your plans. Smoothie plans inventories through multi-tiered distribution channels, with formulations or bills of material, and it complies with ordering constraints such as minimum order quantities, vehicle loading and lot sizes. It identifies exceptions such as shortages and excess inventories, suggests transfers, and communicates order quantities directly to your ERP system.
World Class Technology
All Demand Works server solutions are 100 percent browser-based, making them the most deployable solutions of their kind. The user interface is highly interactive and graphical, yet it performs well over challenging, long-distance networks. It is also extremely scalable, easily supporting scenarios with hundreds of thousands of item-location combinations.