Case study: Supply Chain Demand Planning for Global Networking Major
About the Client
The client is a global technology major, with a market dominance in networking equipment. The client has multiple partners, distribution channels, and customers at diverse geographic locations all over the world.
The client also had an existing process consisting of ensemble of different forecasting streams. They were looking to improve the SKU forecasting accuracy in order to improve planning efficiency. Additionally the client wanted to add incremental value with the help of SFDC data.
BRIDGEi2i partnered with the client for the development of a new stream to incorporate the SFDC data with an aim to improve the forecasting process. BRIDGEi2i’s supply chain experts and AI Labs helped integrate new external data sources and develop machine learning aided “Best Fit” algorithms leading to an effective and demand planning process and improved Supply Chain Management.
- BRIDGEi2i’s data engineering team created a Data Mart to integrate all the data required for the forecasting
- Data experts carried out Quote age analysis to establish total quote exposure. They also carried out an association analysis to identify opportunities at a product family level.
- Data augmentation was performed with sales pipeline, marketing projections and financial projections.
BRIDGEi2i’s Data Scientists and AI Labs’ machine learning experts built a robust, self-learning, automated regression model determining the relation between quote age and bookings. Conversion Models were then used to establish the association rules to determine the opportunity at product family level.
BRIDGEi2i consulting team and AI Labs created and deployed Demand planning optimizer for accurate forecasting of demand at product family level.
Improved the efficiency of planning process