A Case Study in
Direct Channel
Demand Planning
A Fortune 100 Technology Company
Quick Context
Objective
• 17% higher
forec...
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Direct Channel Demand Planning (Fortune 100 Technology Company)

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In this case study learn how BRIDGEi2i helped a Fortune 100 Technology company to develop an algorithm that identifies patterns in Direct Customer bookings
and to develop a unique forecasting model for just Direct customers.

Published in: Data & Analytics
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Direct Channel Demand Planning (Fortune 100 Technology Company)

  1. 1. A Case Study in Direct Channel Demand Planning A Fortune 100 Technology Company Quick Context Objective • 17% higher forecast accuracy in the Direct Channel • Insights on how different Direct Customers order products Impact • BRIDGEi2i specializes in a vast array of forecasting applications • Our knowledge of key forecasting aspects enables us to quickly identify the root-cause issues and address them analytically Key Success Elements Our Approach 3 Months 3 Years Client Project length Length of relationship with client • Data was securely accessed and handled within client environment • Order data was accessed for specific Customer attributes and Model-Option information • Historical Bookings data was used to identify Customer-SKU associations • All analysis was done in Client SAS environment • Segmentation based on Coefficient of variation for product ids exhibiting similar volatility structure • Medium and High contributors were treated with ensemble forecasting models • Monthly seasonal profiling were obtained at product family level and was imposed on each product • A rigorously tested code was developed and validated repeatedly on historical Bookings prediction accuracy • The final SAS code would fetch data from Teradata, Order Data and historical Bookings, Identify and flag Direct Bookings in Demantra • Model has yielded great results; ~80% adoption by Demand Planners Data Management Algorithmic Play Operationalization a. ~12,000 SKUs are sold solely through the Direct Channel; very volatile and cyclical demand b. Short product lifecycles and highly competitive landscape a. To develop an algorithm that identifies patterns in Direct Customer bookings b. To develop a unique forecasting model for just Direct customers

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