Custom top-down probabilistic finished goods forecasting reconciled with AI driven bottom-up material estimates

An industrial products manufacturer migrated from selling a standard set of SKUs to allowing mass customization of the product portfolio by the customers. The requirement made it very difficult to forecast requirements at the finished goods level. A probability based configurator input at the options level was leveraged to estimate WIP and raw material inventory at a product family level. A corresponding custom bottom-up forecasting at a component level across families was leveraged to reconcile the inventory requirement using machine learning predictive techniques. Rapid customization, implementation and integration with the Client’s ERP system ensured seamless absorption and effectiveness from Day 1.

Benefits include

Real time demand sensing and responding to material consumption patterns
Decreased material stockouts by more than 30%
Compliance to customer quoted leadtimes increased from the low 70s to the low 90s
Substantial reduction/elimination of manual imports and exports of data across legacy planning systems, ERP and CRMs

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Enthusiastic Adoption

From the functionalities to the user interface – everything is designed to suit the customer based on input from the potential users. The adoption of the system is immediate and natural with little training or change management

Universal Data

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Finger-tip Analytics

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Seamless unification of processes across different functional areas provides real-time visibility into cross-functional decisions as they happen and promotes enhanced collaboration.

Exception Based Management

Important exceptions are identified and presented to the stakeholders in an actionable way. Inadequate stock, order end-dates, delivery delays etc. are promptly flagged. Exceptions warrant human intervention. The system takes care of the mundane.

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