BRIDGEi2i has frameworks to establish Analytics CoE for Supply Chain functions within organizations. Procurement solution of BRIDGEi2i aims at increasing the cost savings by appropriate sourcing methodologies of commodities, reduce spend on freights and improve supply base optimization.

1. @ 2013 BRIDGEi2i Analytics Solutions Pvt. Ltd. All rights reserved
Supply & Demand Planning
Analytics for the modern enterprise
Arun Krishnamoorthy
Director - Supply Chain & Pricing Analytics Practice
arun.k@bridgei2i.com

2. Our Core Supply Chain Offerings
2
COMMODITY
INTELLIGENCE
DEMAND FORECASTING QUALITY ANALYTICS
FRIEGHT SPEND
REDUCTION
INVENTORY MODELLING
EXCESS & OBSOLETE
CONTROL
S O U R C I N G &
P R O C U R E M E N T C o E
S U P P LY & D E M A N D
P L A N N I N G C o E
M A N U FA C T U R I N G
O P E R AT I O N S C o E
Understand your commodity landscape and
stay in-the-know of factors that affect prices
Develop better statistical demand forecasting
models to match market dynamics
Improve utilization/ yield and reduce failures
by employing a predictive control process
Analytical control of Freight and other non-
material spend
Continuous tracking & optimization of
inventory to improve SC agility
Control Excess & obsolete costs by bringing
predictability into demand
BRIDGEi2i has frameworks to establish Analytics CoE for Supply Chain functions within organizations
INDIRECT PROCUREMENT
PLAN TRACKING
DASHBOARDS ORDER FULFILLMENT
Identify opportunities to reduce indirect
spend through supply base optimization
Track revenue, bookings and builds along
with backlogs and inventory – Real-time
Build an “analytical control tower” that alerts
delayed orders & bottlenecks before time

3. The Planning Analytics CoE in Action – a Case Study
Client : A global Fortune 100 Networking Equipment company
Timeline  Year 1 of engagement Year 2 of engagement Year 3…
Client Org.
Imperatives
1. Improve forecast accuracy of the demand planning
process by 20%
2. Improve lead-time attainment by 10%
1. Reduce inventory targets by 15%
2. Further improve forecast accuracy by 10%
Build a Plan Tracking
dashboard to determine
revenue planning
performance
First six months M6-M12 M12-M18 M18-M24 M24-M36
What did we
do?
1. Built key product
segments based on
forecast-ability
2. Built new forecast
models for top product
segments
3. Developed a system to
generate monthly
forecasts with minimum
human touch
4. Tracking the segments
for which custom forecast
models were developed
5. Refined the models to
ensure accuracy
improvement
6. Built predictability into
lead-time attainment
1. Analyse inventory
build-up across the
enterprise : by channel,
product, region etc.
2. Calibrate optimal
inventory to
improvements achieved
in forecast accuracy
3. Developed inventory
flexibility models to
allows CMs visibility on
demand-side
4. Developed more
powerful forecast
models for Big Deal
planning and volatile
SKUs
1. Currently focused on
building an enterprise-
wide dashboard
2. Tracks revenue plan vs.
actual realization
3. Dis-aggregates the gap
to Mfg., Demand
Planning and S&OP
4. Tracks Inventory &
Backlogs alongside
How did the
client benefit
from it?
1. An extended team with
very good understanding
of client systems, data
and nuances
2. A demand planning
system where planners
focus more on building
consensus with Mfg rather
than forecasting demand
1. Continuous support in
every planning cycle from
an adept team of analysts
2. Ability to quickly resolve
systemic issues with the
planning platform
3. Fully automated, cross-
enterprise, advanced
forecasting solution
1. An understanding of
pockets of business
where inventory was not
commensurate with
planning accuracy
2. A phased approach to
relieve inventory
clogging downstream
3. Continuous tracking of
inventory and backlogs
1. A sophisticated method
to determine how much
inventory the CM must
hold to address
demand peaks
2. A method to plan for
Large Deals –
something the client
historically did not do
well on
NET IMPACT
1. ~100X ROI on Analytics
Investment
2. A best-in-class Planning
Organization
3. Completely driven by
analytics; ~90% analytics
adoption rate
4. Focus day-to-day on top,
high impact challenges;
analytics COE takes care
of the rest$ Impact or ROI
• ~26% planning accuracy value-add
• Lead-time attainment improved by 12%
• Inventory targets were achieved within an year and is
closely monitored for being too tight

4. 4
ValueRealization
Timeframe
Low
High
Descriptive Analytics
(Data & Systems)
Predictive & Diagnostic Analytics
(Functional Dashboards)
Drive and Own Key Outcomes
(Lead-time attainment & forecast accuracy)
Design Solve Implement Track Value & Learn
• Forecast accuracy is key
imperative
• Developed forecasting
models to achieve 10%
value-add in planning
accuracy
Forecast Accuracy Inventory Optimization
Accuracy Tracking Streamline Fulfillment
Build analytics capacity at affordable cost
• Developed advanced
forecast models
• +15% enhancement in
accuracy
• Demand peak articulation
3 Months 9 Months 15 Months 24 Months
The Planning Analytics CoE in Action – a Case Study
Client : A global Fortune 100 Networking Equipment company
Length of Relationship : 2+ years
100X ROI in Analytics
• Inventory must be
calibrated to enhanced
accuracy
• Flexibility models to
counter demand peaks
• Automated dashboards
for planning accuracy
• Track and identify
bottlenecks in fulfillment
10% Forecast Accuracy
enhancement
+15% Forecast
Accuracy
7% Inventory
reduction
+25% Forecast
Accuracy
+10% Lead-
time attainment
Net Impact ~100X ROI

5. How does it work?
5
Identify
Imperatives
Accelerate
Solutions
Realize
Impact
• Identify a business challenge
• Employ data analytics to address
the challenge in a smaller set-up
• Scale and Build analytics
solution into systems
• Make it accessible to operations
• Ensure expected impact is realized
• Identify new gaps in process
efficiency
BRIDGEi2i partners with
businesses to form an Analytics
Center of Expertise (A CoE)
Our CoE will
 Learn your business from an
analytical standpoint
 Embed the knowledge within
analytical solutions
 Make analytics accessible,
actionable and operational
 Ensure sustained impact

6. A few case studies
6

7. Case Study : Prediction of new product sales trajectory leveraging social media
buzz and sentiments
77
• Crawling reviews, comments
from various sources like Twitter,
Amazon & Google reviews, CNET
for products launched in last 2
years
• Advanced text mining to identify
key features and sentiments
• Creation of social indices around
mentions, promotion, average
reviews, sentiments across key
features for each product
lifecycle
• Standardization of growth
trajectory for similar products.
• Creation of a advanced panel
regression model to relate the
social indices and trends over
time
• Assessing most predictive factors
for relating with growth
trajectory and build a scoring
model
• Developed set of indices which
are highly predictive about
product performance
• Operationalizing the technology
solution by using automated
crawlers and predictive algorithm
• The solution
provided initial
insights on key social
media indices to
track for assessing
performance of a
product and react
quickly to potential
corrective actions.
• Such solution is
expected to be
technology enabled
and operationalized
across various
products
Data Sources Approach Outcome
The Client is a global marketing organization of a leading manufacturer of personal computers. In a market where product
life-cycles are a few months long and competition is heavy, waiting for and relying solely on point-of-sales data was less
predictive and constraining in terms of quick course corrections. The PC manufacturer wanted to utilize the market buzz and
indications obtained from social media on early days of launch to predict potential growth path of the product
Objective
Sales
Forecast for
the family of
products
Creation of Social Indices Build Predictive Model
Operationalization of
SolutionTwitter
Amazon
Google
CNET

8. Case Study : Analytical Demand Planning
Cover More SKUs
Segmented view of SKUs allows for focused
planning around a portfolio – helps cover more
SKUs
End-State ProcessCurrent Process
Mitigate Dependence on Single
Forecasting Methodology
Create several forecasts by differentiating each on
techniques and genesis – mitigate the risk of
singular models
Limited SKU Coverage
Focus is on few SKUs that contribute significantly
to revenue. Scenario can change quickly.
Collaboration Is Not Prioritized
Demand Planner collaborates with sales/
marketing on small, repetitive set of critical SKUs –
other SKUs may need more attention
One-Size-Fits-All Forecasting
Most platforms consume only bookings/
shipment data to generate a univariate forecast.
Prioritized Collaboration
Critical SKUs where several forecasts do not
converge imply special focus required for medium
term planning
Limited Accuracy
System generates a 50-60% accuracy across the
board.
Best-In-Class Accuracy
~20-25% improvement in accuracy can be
achieved from a best-in-class process - leads to a
lower inventory requirement
How?
Assess Forecast-ability
Through statistical
segmentation of SKU
portfolio
Build Forecast Repertoire
of Competing Models
For each segment of SKUs - to
mitigate over-dependence on
single model
Enable Recommendation
of Best Forecast for DP
For each SKU – based on
Stream Trust Indices
Track & Repair Models
As non-performance becomes
a consistent trait of model
+7%
**Value-Add
+12%
Value-Add
+6%
Value-Add
** - Potential Accuracy Improvement over current
25%+ value-add in forecast accuracy from a baseline of >50% accuracy.
Enhanced accuracy directly relates to enhanced supply chain resilience.

9. Case Study : Big Deal Demand Planning
Big Deal Exposure index
Segment SKUs based on peakiness of their bookings –
based on moving-window kurtosis
Only a few SKUs are actually exposed to Big Deals
Analytical FrameworkData Sources Used
Attribute demand volatility to a set of customers
Understand which customers are likely to place large one-
time orders vs. linear orders
Correlate order patterns with company performance
Historical Bookings
Primary data sources that captures monthly
bookings at SKU level
Historical Delivery Schedules
Historical delivery schedules requested by
customer – scheduled and unscheduled backlogs
Customer Profiling
Bookings data at a customer X SKU level – derived
from order-line data
Customer quarterly performance data – D&B or
Hoovers
Differentiated Buffering
Buffer build-plan differently for big deal prone SKUs for the
specific customers
Use historical delivery patterns to build linearity into the
buffer
Product Life-cycle
Nature and current life-cycle of the SKU
Continuous Measurement
Measure big-deal planning accuracy separately from
normal DP accuracy
If BD planning accuracy below historical DP accuracy, it is
time for refinement
Technology Stack
• For Big Deal exposure index
and customer profiling
• Can be done in any equivalent
software
Automated ODS publish – every
month
• DP platform
• Demand Planner sees an
additional forecast stream for
Big Deal prone SKUs
• Pre-planning week SKU-level
insights available in tableau
dashboard
• Metrics defined around model
performance - alerts• Only a few SKUs are truly exposed to Big Deals from a few customers (often
Direct customers)
• Resolving the order bundles and customer profiles, can help understand and
predict the nature of demand for such SKUs

10. Case Study : Supply Planning to optimize inventory turns
1010
• Identified factors such as
stock prices of vendors and
aircraft manufacturers, metal
prices etc. that affect
industry demand for parts
• Powerful statistical models
to relate the predictors and
forecast part-level demand
• Each SKU was segmented
based on Gross Margin %, #
of customers, volatility of
part & vendor performance
• Turn targets were
established such that the
blended inventory turns for
the full portfolio was close
to the strategic objective of
the company
• The replenishment solution
incorporating forecasts,
future orders and backlogs
to define where orders must
be pulled in or pushed out
• Developed an Analytical
Hierarchical Process (AHP)
to be able to prioritize and
de-prioritize the pull-in and
push out parts.
A monthly
replenishment
dashboard that
gives a full
portfolio view of
parts, forecasts and
projected end-of-
month inventory
scenario with
recommendations
on corrective
actions
Data Key Features Outcome
Forecasting SKU Segmentation Replenishment ModelHistorical Shipment
Data
Current inventory
status
Future Open orders
and Back-logs
Macro-economical
Data
• A Forecasting engine capable to reading patterns in the aircraft industry and macroeconomic conditions to predict the
demand
• A tool to handle the forecasts and inventory status simultaneously to be able to manage turns targets.
Objective
Replenishment
Dashboard and
Turns
Forecasting
and
Dashboard
Tool

11. Case Study : Inventory flexibility models to counter demand peaks
1111
• Forecast Bias removal
• Outlier Treatment
• Inventory & holding cost
• Estimate lost sales cost
• Calculating demand error
• Grouping product using
clustering technique.
• At a group level, use all data
points to find out a error
distribution
• Random samples were drawn
from the best fitting distribution.
• Lost sales cost, inventory cost
and service levels were found out
based on the samples drawn
• Non linear programing used to
optimize the flexible inventory
for all products.
• Total operation cost minimized
such that certain service level are
met.
• For each product, 21 scenarios
from 0% FLEX to 100% FLEX
requirement was simulated
• Front end tool developed using
Excel-VBA
• A front end tool help the
DP to see the optimum
Flex %.
• DP can change the Flex
% to see the change in
total cost and service
level
• DPs can generate report
• This gives the DP
analytics edge to take
better decision
Data Key Features Outcome
Data processing Demand error distribution fitting
and Simulation
Optimization and
Scenario Generation
24 months historical
forecasts & 3
months future
forecast
24 months historical
bookings
Standard cost of
product
Selling Price
• To develop a method for calculating inventory flexibility of 1500 products. Flexible inventory is excess inventory on the top of forecasted
inventory to counter high demand volatility
• Develop a tool / front-end for deploymentObjective
Bias clustering Distribution fitting

12. Case Study : Memory Procurement Risk Management
1212
• Corroborate and validate
info from multiple market
reports
• Metricize market demand
sufficiency
• Understand impact of macro
variables – PC demand,
DDR2-DDR3 transition,
confidence indices etc.
• Set-up the multi-variate
forecasting models for buy-
price with identified drivers
• Add an innovation effect
due to spot market
speculations
• Develop price forecasting
models using VAR, VECM
and Bayesian models
(available in SAS)
• Automate the modeling
process
• Profile price forecasting
accuracy and track based on
REACT (recursive accuracy
testing) framework
• Track the drivers’ influence
regularly to estimate model
maintenance schedules
 An accurate
memory price
forecasting
model –
especially to
predict inflexion
points in prices
 ~93% accuracy 3
months out and
>85% 6 months
out
 Low-touch, self-
learning models
Data Key Features Outcome
Driver Identification
Multi-variate forecasting
models
Profiling & Automation
•Historical buy-price
data for commodity
•Spot market prices
from DRAM
Exchange
•Market reports from
multiple industry
watchers –
inSpectrum, Market
View, Gartner etc.
•Planned demand
volumes
• To accurately forecast prices of memory (1gb equivalents) based on true drivers of prices
• To create a repeatable process to give strategic sourcing and commodity managers proactive insights on the
commodity
Objective
 BRIDGEi2i’s Bachelier Tool has
a suite of forecasting models
configured for commodity
price forecasting
 Ability to run what-if
forecasts
designed for self-driven insights designed for commodities designed for actionability

13. Case Study : Improving Line Audit process to decrease market failures
1313
• Determine a Complexity Index
for each SKU based on product
design features
• Merge the installed base data
with WMS data to determine
SKU level failure rates (ASER)
• Determine production and audit
test volumes in all corresponding
months of failure
• Correlate product life cycle with
failure life cycle
• Determine drivers of the Failure
life cycle – product complexity,
pallet size, production volumes,
line utilization etc.
• Build Accelerated Life Time
Models to determine failure
probabilities for a new product
• Determine the relationship
between Line Audit % and the
modeled failure rates
• The % of pallet to be audited in
the line is derived as a function
of parameters that determine
failure rates
• These parameters are known or
assumed values for a new
product
• The Algorithm is plugged into
the manufacturing partners’ line
management system to provide
monthly Line Audit % numbers
by SKU
• The solution was
tested for a set of
Laser printers
• While
operationalization of
the solution was
challenging, monthly
Audit % reports were
configured which
showed marked drop
in ASER for a few
SKUs with no
additional effort
from manufacturing
partner
Data Sources Approach Outcome
The Client is the Brazilian manufacturing operations organization of a global manufacturer of Printing Solutions. In a growth
economy, the manufacturing partner was highly operations focused and looked to the Client to provide guidance on
controlling market failures due to poor quality. Objective was to define a Line Audit process that analytically differentiated
the % of pallet to audit depending on failures in the market.
Objective
SKU level
failure
analysis
Determine SKU failure rates Build Predictive Model
Operationalization of
Solution
Warranty
Management System
data
Line Operations data
(RFID data)
Current Line Audit
results
SKU level service
event rate data