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.

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