Case study: Demand & Inventory Planning Transformation using Machine Learning

Business Context

Our Client, an American multinational corporation that develops and markets networking products such as routers, switches, and network security products, operates in 100+ countries. With over 4000 products, the Client’s supply chain is riddled with complexities such as sole sourcing, long lead times, multi-regional procurement, vendor-managed inventory, contract manufacturing, and outsourced logistics. This posed a challenge to their Demand Planning group, which has the task of sending an accurate demand signal to the rest of the supply chain.

How BRIDGEi2i Delivered Value?

We built the Forecasting Engine on top of our Client’s Supply Chain system by first engaging in deep consulting with the Client and carried out due diligence in terms of filtering out causal factors and other input data for feeding the algorithms. BRIDGEi2i’s IP algorithm, called Trust Index helped the Planners trust the Machine Learning forecasts and build familiarity.

The Application requires zero human touch, and the Planning Accuracy improves with every cycle.

ML helped improve accuracy value add by 10%

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