BRIDGEi2i has frameworks to establish Analytics CoE for Supply Chain functions within organizations. Demand planning solution of BRIDGEi2i aims at using advanced statistical forecasting coupled with real-time decision engines models for demand planning, inventory optimization.

1. @ 2015 BRIDGEi2i Analytics Solutions Pvt. Ltd. All rights reserved
Sourcing & Procurement 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. 3
ValueRealization
Timeframe
Low
High
Commodity
Intelligence
Component & Commodity Cost Forecasting
Forward-Buy & Contract Recommendations
Design Solve Implement Track Value & Learn
• Understand cost drivers of
Memory, Resins and base
metals
• Provide monthly
intelligence to Commodity
managers
Commodity Dashboards Procurement Risk Mgmt
Forward-buy Opportunity Smart Contracting
Build analytics capacity at affordable cost
• Automate commodity
intelligence
• Scale to HDD, Panels,
Rare metals, batteries,
power supplies
• 80% spend had should-
costs
6 Months 12 Months 24 Months 36 Months
> $1bn cost savings by leveraging analytics
• Ability to predict
commodity prices using
drivers understanding
• Predict inflexion points
• Mature process for
intelligence
• Leverage price forecasts
for large forward buys
• Identify opportunities to
bake price intelligence,
rebate mechanisms and
share-of-wallets into a
smart contract offering
for suppliers
+10% Price Forecast
Accuracy
+25% Portfolio Price
Forecast Accuracy
+25% Spend Forecast
Accuracy
Net Impact >100X ROI
The Sourcing Analytics CoE in Action – a Case Study
Client : A global Fortune 500 PC and printing company
Length of Relationship : 3+ years

4. How does it work?
4
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

5. A few case studies on
Commodity Intelligence

6. Commodity Intelligence Solutions
Commodity Profiling
Develop a deep understanding of commodity
landscape by corroborating information in various
intel reports. Develop KPIs for the commodity
specific to business
Commodity Profiles
corroborated & created from
multiple industry reports
Commodity Intelligence for Precious Metals
For a Fortune 100 High-Tech company
Commodity Intelligence for Memory
(DRAM)
For a Fortune 500 Networking and Storage
company
Commodity Intelligence for Plastics
For a buyer of commodity plastics (ABS/ HIPS) in
Singapore
Tracking & Monitoring Commodity KPIs
Detailed profiling of key target accounts to assess
financial performance, future objectives, potential
technology spending to build better customer
understanding
Case Studies
Correlate factors such as demand, supply,
inventory and prices to draw a holistic picture of
the commodity

7. Cost Forecasting Solutions
Fundamental Factors
Demographics, weather, trade flows, production
quotas and export controls influencing the
demand and supply of commodities.
Understand Drivers of
Commodity Costs
Value at Risk Measurement
Providing beforehand visibility into the risk
associated with future prices outlook and specific
purchase commodity price over the time horizon.
Continuous Tracking
Continuously track the performance of the price
forecasting model to ensure the minimal
divergence from the actual commodity prices and
immediate intervention in the forecasting model.
Hope For The Best But Plan For
The Worst
Price Forecasting model
Build a mathematical model to accurately predict
the future commodity prices depending on the
historical data decompositions and driver impact.
Macroeconomic Factors
Demand & supply side economic factors like
investments, savings, labour indices etc.
Other Factors
Substitute material prices, global political
situations, price speculation determines
commodity prices.
Scenario Forecasting
Stochastic forecasts based on low probability and
high impact exceptional scenarios to plan for the
worst situations.
Develop Spectrum Of Mutually
Beneficial Contracting Terms
Supplier Collaboration
Develop different types of contracts (buy-back,
revenue sharing, quantity flexibility) to create
negotiation friendly environment and positively
engage supplier for conversation.
RFQ Design
Historical supplier performance analysis on the
contract attributes to develop preferred list of
potential suppliers to participate in the RFQ
Attribute Selection
Identification of negotiable contract attributes
price, rebates, share-of-wallet, lead times etc. for
the negotiations.
Contract Design
Analysis of contract attributes to lay down terms
of contract make the deals interesting for
suppliers and cost efficient for buyers
Case Studies
BRIDGEi2i’s Bachelier Commodity Price
Prediction Tool
A unified analytics platform for cost management
Advanced Cost Forecasting model developed
For a Fortune 10 high-tech company

8. Case Study : Memory Procurement Risk Management
88
• 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

9. Embedding Analytics back in Client Systems
BACHELIER – Commodity Price Forecasting Engine
DATA MANAGEMENT & TREATMENT FORECAST
FORECAST EVALUATION DECISION ENGINE & WHAT-IF FORECASTS
Easy & intuitive
interface for data
management and
treatment
Ensemble forecasts
made from strong &
advanced forecasting
models
Make “What-if”
forecasts and
forward-buy decisions
while understanding
risk involved
Rigorously tested
forecasts to ensure
maximum confidence
in numbers

10. A few case studies on
Indirect Procurement

11. Our Procurement Analytics Solution
11
Data
Enrichment
Define matching
attributes
Integrate data
across sources
Augment data from
other sources like
contracts text, websites
Business
Objectives
Outcome Process and frameworks to proactively
identify and minimize cost
Data driven frameworks to establish
contract terms and ensure compliance
Improve ability to capture information, analyze for insights and enable informed decision making
Identify opportunities to minimize
cost in each category
Category Supplier
Identify supplier consolidation, rate
rationalisation opportunities In and
Across categories
Identify opportunities to optimize
contract terms
Leverage transaction data to segment
spend into categories, analyze supplier
distribution, spend coverage etc.
Within category & sub categories analyse
spend type, supplier performance, rate
variation, dependency and presence
across categories
Analysis of rebates and payment terms
to lay down terms of contract that
make the deals interesting for suppliers
and cost efficient for buyers
Contract
Check completeness of
key fields
Cleanse data – consistent
names, abbreviations, units etc.
Scorecard metric or KPI to measure
progress toward a goal
Analytics Segmentation Text Analytics Variance Drivers
Behavior
Analysis
Forecasting
Dashboards and
Alters

12. Approach to Identify Opportunities of Cost Minimization
1212
• Develop process for
accurate mapping product
and item to defined spend
categories
• Segment each spend
categories based on
recency, frequency and
value of transactions
• Identify similar categories
using attribute analysis
• Concentration of buyers by
category
• Understand buyer behavior
and opportunities to
aggregate spend across
buyers
• Analyze transaction
channels and associated
cost
• Identify top suppliers in
each category
• Understanding commodity-
business-supplier mapping
to reveal overlaps
Prioritize
categories with
opportunities to
minimize cost
Develop data enrichment to
identification of cost
minimization opportunities
Destination-> AdelaideBrisbane Bulwer Darwin Kurnell Kwinana Lytton Perth Sydney
Adelaide 0% 1% 2% 6% 2% 3% 5% 4% 7%
Brisbane 1% 0% 0% 0% 1% 0% 0% 2% 1%
Darwin 5% 0% 2% 0% 0% 0% 5% 4% 1%
Geelong 0% 0% 0% 0% 0% 0% 0% 0% 1%
Kurnell 1% 0% 0% 0% 0% 0% 4% 0% 0%
Lytton 2% 0% 0% 0% 2% 0% 0% 0% 2%
Melbourne 2% 0% 0% 0% 1% 0% 2% 0% 1%
Perth 2% 0% 0% 8% 0% 0% 0% 0% 2%
Sydney 7% 1% 1% 1% 1% 0% 4% 3% 0%
Adelaide 0% 1% 0% 2% 1% 2% 0% 4% 10%
Brisbane 0% 0% 0% 1% 1% 0% 0% 0% 1%
Darwin 1% 0% 0% 0% 0% 0% 0% 1% 0%
Geelong 0% 0% 0% 0% 0% 0% 0% 0% 0%
Kurnell 1% 0% 0% 0% 0% 0% 0% 0% 0%
Lytton 2% 0% 0% 0% 1% 0% 0% 0% 3%
Melbourne 0% 1% 0% 0% 0% 0% 0% 0% 4%
Perth 2% 1% 0% 5% 1% 0% 0% 0% 7%
Sydney 4% 2% 6% 1% 2% 11% 4% 16% 0%
CY 2010
CY 2011
Leveraged BRIDGEi2i text mining solution to
appropriately augment missing data
Segmentation of categories using RFM technique
to identify top spend segments
Data Approach Outcome
CATEGORY
ANALYSIS
BUYER
BEHAVIOR
SUPPLIER
CONCENTRATION
Categories details
(UNSPSC)
Supplier
Firmographics
Buyers details
Transaction details
Our EXPERIENCE

13. Approach to Drive Cost Efficiency In & Across Categories
1313
• Identify aberrations in
pricing across region/period
in same category
• Identify large variances in
rates across suppliers for the
same category & similar
supplier performance score
• Identify perceptible fee
deviations from agreed rate
card
• Scorecards to rank suppliers
based on predefined metrics
• Business inputs to validate
preference of suppliers
• Develop list of preferred
suppliers with presence in
multiple or across
categories
• Build aggregate demand
forecast across buyer
groups
• Develop standardized
discounted rate cards in
exchange for volume
commitment & a larger
share-of-wallet
Consolidated list
of suppliers and
contract terms to
enable YoY
deflation of
spend
Enabled Implementation of processes and
frameworks to minimize procurement
• Developed and list of preferred suppliers
taking consideration buyer preferences
• Standardized rate cards to minimize rate
aberrations
Identification of aberrations in pricing
across region for the same category
Overall and Category wise preferred list of suppliers
and suggested rate cards to minimize spend
Data Approach Outcome
CATEGORY SPEND
PATTERNS
SUPPLIER
CONSOLIDATION
DEMAND FORECASTPricing details
Business
inputs/needs
Transaction details
Supplier
Firmographics
Contract terms
details
Our EXPERIENCE

14. Approach To Drive Cost Efficiency In Tier 3 Supply Base
14
Isolate Tier 3 vendors
• Identify and separate Tier 3 vendors
• By non-ASL
• By absence of contracts, catalogues
etc.
Statistical indexing
• Identify category-wise spend
concentration
• Gini or HH index
• Relative importance of vendor in
category based on statistics
Textual analysis
• Identify what is actually purchased
• Extraction of object in text of line item
description
• Extraction of context of purchase
from text
Product mapping
• Identify similar products/ services in
Tier 1 & 2 universe
• Mapping of object to contracted
purchase list
• Identify better suppliers of same
product/service
Supplier dependency
• Identify how and why the Tier 3
supplier is used
• Price competitiveness
• Region/ business/ unique product
dependencies
Initiate consolidation
• Confirm analysis with buyers and
initiate consolidation
• Conversation with Tier 3 supplier for
potential contracting
• Conversation with best alternate
supplier for rate card discussions
1 2
3 4
5 6
The Long Tail Problem in Indirect Sourcing
Tier1~X
suppliers;Yspend
Tier2~5Xsuppliers;
Y*0.2spend
Tier 3 ~ 35X suppliers;
Y*0.2 spend30%
60%
90%
%CumulativeSpend
# of Vendors ---->
Challenge is with Tier 3 suppliers where
1. Supplier spread is high – hard to identify the big ones
2. Supplier products or service are misclassified – hard to identify
what is purchased
3. Supplier mapping is unknown – hard to map their products/
services to capabilities of Tier 1 & 2
4. Supplier dependency in unknown – low spend concentration
implies less insight into why the supplier exists
15% IP
Spend
0
2
4
6
8
1
22
43
64
85
106
127
148
169
190
211
232
253
274
295
316
337
358
379
400
421
442
463
484
505
526

15. Thank You