In this case study learn how BRIDGEi2i helped a Fortune 100 Technology company to understand patterns in New Product Introductions and use this understanding for better insights into planning demand for NPIs

1. A Case Study in
Demand Planning for
NPIs
A Fortune 100 Technology Company
Quick Context
Objective
• 5% higher forecast
accuracy for New
Products
• Ability for supply
chain to rely on a
statistical forecast
for NPIs rather than
just marketing
inputs
Impact
• BRIDGEi2i has significant experience
in managing NPI and EOL programs for
large Technology companies
• Predictive analytics must be made
available to SC planners proactively
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
• Engineering data was accessed for NPI
plans and any Model-Option data
• Historical Bookings data was used to
identify dead products and associate
them to NPIs as a predecessor
• All analysis was done in Client SAS
environment
• A known distribution (Weibull) was
fitted to all dead products – products
that have no Bookings in recent periods
• Distributions were clustered based on
their parameters – Scale, Shape and
Slope - ~255 clusters were created
• A 3 or 4 month old NPI was associated
with any one of the clusters based on a
probability score and a 24 month
lifecycle forecast was generated
• A rigorously tested code was developed
and validated repeatedly on historical
Bookings prediction accuracy for
NPIs
• The final SAS code would fetch data
from Engineering and historical
Bookings, Identify NPIs and its closest
Cluster and generate 24-month
forecasts – Every planning cycle
• Model has been deployed in Demantra
Data Management Algorithmic Play Operationalization
a. ~40,000 SKUs; ~2000 NPI SKUs and a 10% monthly product churn due to
short product lifecycles and highly competitive landscape
b. NPIs are difficult to forecast statistically since no history is available
a. To understand patterns in New Product Introductions and their lifecycles
b. To use this understanding for better insights into planning demand for NPIs