Leveraging Big Data for Supply Chain Benchmarking
Agenda
Who is Chainalytics?
Freight Market Intelligence Consortium
Sales & Operations Variability Consortium
Agenda
Who is Chainalytics?
Freight Market Intelligence Consortium
Sales & Operations Variability Consortium
Who is Chainalytics?
MILESTONES
Founded in 2001
Established Bangalore office in
2005
Acquired Chainnovations and
Adalis’ Packaging Solutions
Group in 2011
Strategic Investment by GEF
Acquired ROCE Partners in 2013
ACCOLADES
“2013 Cool Vendor in Supply
Chain Services” – Gartner
“Great Supply Chain Partner” for
10 Years –
SupplyChainBrain
8 “Pros to Know” –
Supply &
Demand Chain Executive
One of “10 Coolest Supply Chain
Boutiques
” –
ARC Advisory
BY THE NUMBERS
135 FTEs Worldwide
Serve 300+ Unique Clients
–
17 of Gartner’s Top 25 Supply
Chains
–
80 Fortune 500 Companies
Delivered 500+ Engagements
ATLANTA
MINNEAPOLIS
MILAN
STOCKHOLM
HELSINKI
BANGALORE
Years
Quarters
Months
Weeks
Planning
Horizon
Value-Driven Supply Chain Decisions
At what service
level can we
profitably satisfy
demand?
How should
we transport
product through
the supply
chain?
How much and
where should
inventory be
positioned in the
supply chain?
Can we reduce
our transport and
logistics costs by
improving cube
utilization?
Should our
warehousing
and material
operations be
insourced
or outsourced?
When should
we buy or make
product to make
the best use of
our capacity?
What is the
best flowpath?
How well do
our current
operations
mitigate repair
and warranty
costs?
How can we
increase
visibility to
stakeholders?
Sales &
Operations
Planning
Transportation
Logistics
Operations
Service
Supply Chain
Packaging Optimization
Some of Our Clients
LSP
Chemical/Process
Automotive & Industrial
Packaging
Healthcare
HIGH TECH
& TELECOM
FOOD &
BEVERAGE
RETAIL
HOME/OFFICE
DURABLES
HOME/OFFICE
NON-DURABLES
OTHER
INDUSTRIES
SERVED
Widespread
implementation of
ERP and Supply
Chain Planning
Hardware
commoditization
enabled “big data”
era
Analytics
converts
big data into small
data
Content
enables
fact-based
decision-making
Evolution of the “Big Data” Opportunity
Content makes the realization of the full value of big data possible.
Chainalytics empowers fact-based decisions using…
Powerful Technology
:
Advanced tools to assess impacts and predict
outcomes
Specialized Knowledge
:
Superior intellectual capital to bridge the
supply chain “expertise gap”
Proprietary Content
:
Competitive differentiation
Content
Knowledge
Agenda
Who is Chainalytics?
Freight Market Intelligence Consortium
Sales & Operations Variability Consortium
Model-Based Benchmarking Advantage
Traditional Benchmarking
Model-Based Benchmarking
Shipper’s freight characteristics are unique
– Lack of “apples to apples” comparison
– Need to have significant volumes represented
across many shippers for exact lane by lane
match
Proprietary rates restrict direct sharing
– Inability to share rate information due to
contractual obligations
Informal “peer network” not a good basis
for comparison
Only total cost is provided
– Inability to separate line haul and accessorial
costs
– Inability to determine implied cost of business
practices that impact operation
Identify/quantify transportation cost drivers
– Origin, destination, distance, loading
conditions, service requirements, regional
imbalances…
Build econometric model for the market
– Includes broad cross-section of shippers &
locations
– Ensure it is robust and statistically valid
– Develop an reliable estimators to predict the
cost per load for TL freight, given unique
characteristics of the freight
Generate results consistent with our
experience
– Actual and observed results are related
– Test all policies and characteristics for
statistical strength
– Amass significant amounts of information
– Corridor volume
Consortium
What is a “Freight Market Intelligence Consortium”?
Freight
Market
GUIDELINES
No input data shared
Membership remains confidential
Intelligence
MARKET
INTELLIGENCE
External Focus
BUSINESS
INTELLIGENCE
Internal Focus
FMIC Overview
2004
TL Model for
6 Shippers
TODAY
TL & IM Models: 108 Shippers ($18.2B)
LTL Model: 23 Shippers ($482MM)
Ocean: 16 Shippers ($290MM)
What is my overall cost
position to the market?
In which lanes am I over
market?
(ALL MODES)
Performance
Reports
Rate
Estimators
What are estimated
costs for lanes in which I
am not operating today?
Where are some
opportunities to convert
from collect to prepaid
freight?
(TL & LTL)
Members Gain Access to…
Freight Market
Intelligence
In what direction will rates
trend in the future
(according to the views of
the members)?
What does my carrier
profile look like?
How do my policies and
practices affect my rates?
Lane Specific Analysis
Firm
BU
FMIC ID
Origin
City
Origin
State
Origin
ZIP
Origin
Country
Destination
City
Dest
State
Dest
ZIP
Dest
Country
Distance
(miles)
Annual
Volume
Avg.
Stopoffs
DEMO
WWD1
10417 Anytown Anystate 18953
HUN
Anytown
Anystate 08123
GER
2922
64
3
DEMO
WWD2
11142 Anytown Anystate 50995
FRA
Anytown
Anystate 21999
GER
2414
134
0
DEMO
WWD1
13682 Anytown Anystate 18394
GER
Anytown
Anystate 68960
POL
2771
157
0
DEMO
WWD2
14131 Anytown Anystate 35199
POL
Anytown
Anystate 77899
HUN
546
352
1
DEMO
WWD2
11452 Anytown Anystate 29979
HUN
Anytown
Anystate 27716
POR
2919
23
0
DEMO
WWD1
13132 Anytown Anystate 13752
POL
Anytown
Anystate 58260
GER
2921
19
1
DEMO
WWD1
13467 Anytown Anystate 13149
FRA
Anytown
Anystate 64882
GER
2914
68
1
DEMO
WWD1
10702 Anytown Anystate 17090
ITA
Anytown
Anystate 13249
GER
2886
20
1
DEMO
WWD3
10541 Anytown Anystate 73508
GER
Anytown
Anystate 10568
POL
2440
99
0
DEMO
WWD2
10041 Anytown Anystate 47489
HUN
Anytown
Anystate 00807
FRA
2791
70
0
DEMO
WWD2
14132 Anytown Anystate 52411
ITA
Anytown
Anystate 77914
FRA
2738
69
2
DEMO
WWD3
14098 Anytown Anystate 60540
GER
Anytown
Anystate 77223
POR
2796
67
0
DEMO
WWD2
12340 Anytown Anystate 27880
ITA
Anytown
Anystate 43424
POR
2912
14
2
DEMO
WWD2
12503 Anytown Anystate 45769
ITA
Anytown
Anystate 46709
ITA
471
236
0
Estimated
CPL
(Including Fuel
Surcharge)
Estimated
CPM
(Including Fuel
Surcharge)
Estimated
Annual Cost
(Including Fuel
Surcharge)
Difference
CPL
(Including Fuel
Surcharge)
Annual Cost
Difference
(Including Fuel
Surcharge)
Difference
Percent
(Including Fuel
Surcharge)
Status
(Including Fuel
Surcharge)
EUR 4,276.43
EUR 1.46
EUR 273,691.39
(EUR 2,821)
(EUR 180,513)
-65.96%
BELOW
EUR 4,308.32
EUR 1.78
EUR 577,314.61
(EUR 581)
(EUR 77,873)
-13.49%
BELOW
EUR 4,085.46
EUR 1.47
EUR 641,416.86
(EUR 495)
(EUR 77,683)
2.19% AT
EUR 1,437.86
EUR 2.63
EUR 506,128.30
(EUR 199)
(EUR 70,205)
2.22% AT
EUR 4,271.54
EUR 1.46
EUR 98,245.45
(EUR 2,835)
(EUR 65,203)
-66.37%
BELOW
EUR 4,274.80
EUR 1.46
EUR 81,221.18
(EUR 2,947)
(EUR 56,001)
7.53%
ABOVE
EUR 4,505.28
EUR 1.55
EUR 306,359.18
(EUR 812)
(EUR 55,193)
4.28%
ABOVE
EUR 4,219.29
EUR 1.46
EUR 84,385.81
(EUR 2,643)
(EUR 52,863)
1.77% AT
EUR 3,808.57
EUR 1.56
EUR 377,048.79
(EUR 529)
(EUR 52,393)
7.47%
ABOVE
EUR 5,274.95
EUR 1.89
EUR 369,246.79
(EUR 684)
(EUR 47,885)
-12.97%
BELOW
EUR 4,481.19
EUR 1.64
EUR 309,202.12
(EUR 684)
(EUR 47,180)
-15.26%
BELOW
EUR 4,978.87
EUR 1.78
EUR 333,584.07
(EUR 610)
(EUR 40,871)
-12.25%
BELOW
EUR 4,257.00
EUR 1.46
EUR 59,598.06
(EUR 2,858)
(EUR 40,016)
-2.87% AT
Compan
y
B
Co
mpa
ny
A
Carriers by Spend and Position to Market
Each square represents a carrier in a
shipper’s network
Size is relative to volume with that carrier
Color and percentage represent the
carrier’s relative cost to market across all
lanes they service
The FMIC allows shippers to
see how their carriers are
performing across their total
spend, which prompts such
questions such as “Who
should I grow with?” and
Industry
Benchmark
Lane
Information
JDA has partnered with Chainalytics to provide access to the
largest transportation benchmarking database in North America
directly within its TMS solution.
Integrated Benchmark Rates
Industry Leading
TMS Solution
Freight Market
Intelligence
Consortium
Integrated Benchmark Rates
Carrier or Load Comparison
FMIC Europe Milestones
Charter Member
Identification
Promote to current
multi-national FMIC
members
Prepare data
collection materials
Completion
November 2013
1
Product Design
& Development
Completion
2013 Q4
2
Define Europe
specific deliverables
with charter
members
Completion
2014Q1+2
Modeling,
Analytics &
Reporting
3
Development of
econometric and
reporting constructs
Modification of
existing capabilities
defined specifically
for Europe
Charter Member
Feedback &
Adoption
Completion
2014 Q2
4
Obtaining feedback
Adapting process,
reports and survey
insights for future
rounds
Determination of
ongoing service
parameters
We will analyze 12 months of data (6 months apart),
and produce two sets of deliverables each year.
Agenda
Who is Chainalytics?
Freight Market Intelligence Consortium
Sales & Operations Variability Consortium
Demand Planning Market Intelligence
Questionnaire-based
Participants self-report forecast
accuracy as they measure it
Forecasting process checklist
Attempt to define best practices
Limited root-cause and comparative
analysis
Model-based using transaction data
Common metrics
Insights into drivers
Supplemental questionnaire
– Business practices driving forecast
accuracy (FCA)
– Demand and supply planning practices
Conventional
Benchmarks
Variability Consortium
Sales & Operations
SOVC Member Demographics
Industry:
Non-Durable Consumer Product Goods and
Food & Beverage
Geography:
U.S. Customer Demand
Members:
40+ Participants
Item-Locations:
More than 300,000
FOOD & BEVERAGE
49
%PERSONAL CARE
33
%HOME CARE
13
%PET CARE
5
%How does Chainalytics’ SOVC work?
Model-Based Analytics
Results for Members
Questionnaire
tabulation &
analysis
Forecast
accuracy
predictive
model
Accuracy
calculations &
benchmarking
Data review,
clean-up and
validation
Member Inputs
Detailed forecast
and actual order/sales
transaction data
Questionnaire responses
on business practices and
forecasting processes
Forecast accuracy
and bias intelligence
on-demand
62%
76%
90%
89%
87%
83%
55%
53%
40%
50%
60%
70%
80%
90%
100%
Lag 0
Lag 1
Lag 2
Lag 3
Item-Network
63%
81%
79%
77%
83%
54%
43%
40%
40%
50%
60%
70%
80%
90%
100%
Item-Location
A Look at Conventional Benchmarks
Item-Network and Item-Location FCA (Monthly Buckets)
Fore
ca
st
Acc
ur
acy
What does this tell you?
Are all companies equal?
Demand Patterns Influence FCA and Bias
81%
61%
54%
2.9%
5.6%
18.7%
%
o
f
U
n
it
s
Sh
ip
p
ed
in
P
at
ter
n
Stable
Trending
Seasonal/Uplift
Intermittent
Launch/End
Other
FCA
Bias
Member 2
Member 3
Member 1
More stable and
less seasonal and
intermittent
demand results in
higher FCA and
lower bias
%
of
U
nit
s
Shipped
in
Pat
tern
Stable
Launch/End
Trending
Other
Seasonal/Uplift
Intermittent
FCA
Bias
S&OVC Demand Segmentation Enables
“Apples-to-Apples” Benchmarking
DEMAND VARIABILITY
LAG
DEMAND PATTERNS
DEMAND VELOCITY
Company FCA Performance vs. Forecastability
“Apples-to-Apples” Benchmarking
SOVC Sample Deliverable
FCA Policy Analysis for Monthly Forecasters
Do Frequent Parameter Updates
Use Top Down Process
Begin Top Down at Product Group Level
Involve Finance/SC in Adjustments
Do Not Set Up Separate Promo DFUs
Employ Inventory Optimization Update Inventory Targets
Weekly Use Customized Forecasting
Tool Use Moving Average
Time-Series
Use Regression Trend Time-Series
Policies of Top Performers
Do Frequent Parameter Updates
Use Top Down Process
Begin Top Down at Product Group Level
Involve Finance/SC in Adjustments
Do Not Set Up Separate Promo DFUs
Employ Inventory Optimization Update Inventory Targets
Weekly Use Customized Forecasting
Tool Use Moving Average
Time-Series
Use Regression Trend Time-Series
Policy Profile for Member 1
PRACTICE 2
PRACTICE 3
PRACTICE 4
PRACTICE 5
PRACTICE 6
PRACTICE 7
PRACTICE 8
PRACTICE 9
PRACTICE 10
DO FREQUENT
PARAMETER UPDATES
POLICIES OF TOP PERFORMERS
Do Frequent Parameter Updates
Use Top Down Process
Begin Top Down at Product Group Level
Involve Finance/SC in Adjustments
Do Not Set Up Separate Promo DFUs
Employ Inventory Optimization Update Inventory Targets
Weekly Use Customized Forecasting
Tool Use Moving Average
Time-Series
Use Regression Trend Time-Series
Policies of Top Performers
Do Frequent Parameter Updates
Use Top Down Process
Begin Top Down at Product Group Level
Involve Finance/SC in Adjustments
Do Not Set Up Separate Promo DFUs
Employ Inventory Optimization Update Inventory Targets
Weekly Use Customized Forecasting
Tool Use Moving Average
Time-Series
Use Regression Trend Time-Series
