Case study: Increased sell-through accuracy using Forecasting Engine

Business Context

The client, a Fortune 500 global enterprise technology hardware manufacturer, was generating about 50,000 different forecasts at a monthly level from its distributor network spread across 120 countries. Using sophisticated Demand Planning software could only provide a 3-month accuracy of about 45% however, the client needed improved accuracy and a more powerful demand forecasting application.

How BRIDGEi2i Delivered Value?

BRIDGEi2i developed and built a customized Forecasting Engine with cutting-edge models and methods. This was deployed on the client’s existing system and integrated with the running planning applications. Our Forecasting Engine uses a repertoire of 32 multi-variate and causal forecasting models to identify the best set of models for every SKU x Distributor x region.

Machine Learning algorithms were leveraged to help discern relationships with key leading indicators of demand to provide better forecasts, and with every iteration cycle, the system became more thorough and yielded better results. To improve upon the accuracy, BRIDGEi2i also configured the Forecasting Engine to generate a predictable Accuracy Value-Add over existing Forecasting Accuracy.

We improved FA for a Fortune 500 Company

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