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.

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