Breaking News: ToolsGroup acquires Mi9 Retail Demand Management Business creating a prowerful production-to-purchase planning solution.
Predict Future Demand for New Products With the Power of Artificial Intelligence
Continually introducing new items to market helps you keep up your competitive advantage. Mi9 Retail makes it easy to predict the future demand for new products accurately by using demand profiles. With Mi9 Retail clustering and profiling, you can create, manage, and evaluate demand profiles for historical products and then apply the profiles to new products to get an accurate picture of future demand.
Watch the Clustering and Profiling Video
Watch this short video to learn about the key features and benefits of Mi9 Clustering and Profiling, and see it in action.
Store Clustering
Clustering has become increasingly important in the planning process as the number of customers and locations grow. The Mi9 Retail clustering engine uses machine learning to quickly cluster customers or locations based on multiple performance, demographic, and space metrics. Clustering can be used to identify exceptions throughout the planning process to more accurately target assortments and promotions to the right consumers.
Creating Profiles & Curves
Creating profile demand curves for items based on comparable products or product categories is a powerful way to set the level of expected future sales and minimize the risk involved in launching a new product. Mi9 Retail uses machine learning and advanced pattern matching to allow you to create profiles either manually or automatically, at an item or class/collection level. You can then apply them to any set of items for a known magnitude over a flexible time horizon.
Profile Management & Review
Mi9 Retail provides a tool that allows users to create product forecasts by applying a profile curve to a time and magnitude to create a sales plan that can be based on an initial expectation or on a total volume expected. The resultant profiled sales plan forms the demand forecast through to the effective end date after which a statistical forecast can take over.
