A Case Study in
Demand Planning for
Big Deals
A Fortune 100 Technology Company
Quick Context
Objective
• 1% higher forecas...
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Demand Planning for Big Deals (Fortune 100 Technology Company)

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In this case study learn how BRIDGEi2i helped a Fortune 100 Technology company to recognize anomalies in historical bookings as outliers and to treat them in statistically acceptable ways for better forecasting and demand.

Published in: Data & Analytics
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Demand Planning for Big Deals (Fortune 100 Technology Company)

  1. 1. A Case Study in Demand Planning for Big Deals A Fortune 100 Technology Company Quick Context Objective • 1% higher forecast accuracy; ~$30mn business impact • Better Revenue Planning enabled due to removal of forecasted anomalies Impact • BRIDGEi2i has developed numerous ways of dealing with Outliers - a key element in forecasting • We bring our experience & knowledge of best practices in other industries to our clients Key Success Elements Our Approach 3 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 Anomaly Detection algorithm development and deployment • SFDC data was used to recognize Large Deals based on business rules • When Large Deals happen or fall-off, demand peaks are observed – Order Data was used to recognize outcome • Anomalies happen when Large Deals go through or fall off • An algorithm was used to generate a Risk Score for each Large Deal • The Risk Score – a factor of Moving Average Demand based on product & customer attributes – was used to deflate the Deal Size • High Risk deals were excluded while forecasting demand • A rigorously tested code was developed and validated repeatedly on historical Bookings prediction accuracy • The final SAS code would fetch data from SFDC, Order Data and historical Bookings, Identify and flag outliers in Demantra – the single platform for demand intelligence for the Planners • Model has yielded great results; ~80% adoption by Demand Planners Data Management Algorithmic Play Operationalization a. ~40,000 SKUs and a global dynamic demand scenario; very volatile demand b. Short product lifecycles and highly competitive landscape a. To recognize anomalies in historical bookings as outliers b. To treat them accordingly – in statistically acceptable ways for better forecasting of demand

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