SFDC in Demand Planning (Fortune 100 Technology Company)

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In this case study learn how BRIDGEi2i helped a Fortune 100 Technology company to use SFDC pipeline data for better bookings prediction and delivered a method that necessarily accommodate for inn-accuracies in SFDC data

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SFDC in Demand Planning (Fortune 100 Technology Company)

  1. 1. A Case Study in SFDC in Demand Planning A Fortune 100 Technology Company Quick Context Objective a. ~40,000 SKUs and a global dynamic demand scenario b. Short product lifecycles and highly competitive landscape • 15% higher forecast accuracy • ~$300mn business impact • NASSCOM “best 50 analytics case studies” award winner Impact • BRIDGEi2i understands SFDC data and its pitfalls – but also knows how to circumvent them with appropriate statistical treatments • Our prowess in identifying the right algorithm for the right problem Key Success Elements Our Approach 5 Months 3 Years Client Project length Length of relationship with client • All data was securely accessed within the client environment • SAS was used for the predictive algorithm development and deployment • SFDC data was accessed through Teradata queries written in SAS • SFDC data has several inaccuracies – regional adoption challenges, last- minute updates, fat-thumbing numbers • All of this was treated appropriately • Expected Bookings from SFDC pipeline with a 3 month offset was created as an explanatory variable for Bookings • A Generalized Additive Regression (GAM) model was used with a self- written Spline Smoothing function • Outliers in the X variable were treated with BRIDGEi2i’s own algorithm LOF** • Model is non-linear and non-parametric – learns very quickly • A rigorously tested code was developed and validated repeatedly on historical Bookings prediction accuracy • The final SAS code would fetch data from SFDC, treat it appropriately, run the GAM model, generate 24 months of predictions and populate the forecasts in Demantra – the single platform for demand intelligence for the Planners • Model has yielded great results Data Management Algorithmic Play Operationalization a. To use SFDC pipeline data for better bookings prediction b. Method should necessarily accommodate for inaccuracies in SFDC data * LOF – Local Outlyingness Factor – our own algorithm developed for Fraud Detection in Credit Cards

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