[PPT] - Leveraging Big Data for Supply Chain Benchmarking JDA FocusConnect

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Leveraging Big Data for Supply Chain Benchmarking

JDA FocusConnect | November 5, 2013

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Agenda

Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions

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Agenda

Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions

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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

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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

ur transport and

logistics costs by improving cube utilization? Should our warehousing and material

perations be

insourced

r outsourced?

When should we buy or make product to make the best use of

ur capacity?

What is the best flowpath? How well do

ur current
perations

mitigate repair and warranty costs? How can we increase visibility to stakeholders?

Sales & Operations Planning Transportation Logistics Operations Service Supply Chain Packaging Optimization Supply Chain Design

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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

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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
utcomes
Specialized Knowledge: Superior intellectual capital to bridge the

supply chain “expertise gap”

Proprietary Content: Competitive differentiation

Content

Knowledge Technology

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Agenda

Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions

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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

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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 DATA MODELS REPORTS

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FMIC Overview

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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
pportunities 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?

(ALL MODES)

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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 DEMO WWD1 13682 Anytown Anystate 18394 GER Anytown Anystate 68960 POL 2771 157 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 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 DEMO WWD2 10041 Anytown Anystate 47489 HUN Anytown Anystate 00807 FRA 2791 70 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 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 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

EUR 963.32 EUR 2.05 EUR 227,343.77 (EUR 161) (EUR 37,894)

16.67% BELOW

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Long Haul Dry Van Comparison to Industry

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Company B Company 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 “Who should I look to divest?”

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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

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Integrated Benchmark Rates

Carrier or Load Comparison

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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
ngoing service

parameters

We will analyze 12 months of data (6 months apart), and produce two sets of deliverables each year.

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Agenda

Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions

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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 Sales & Operations Variability Consortium 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%

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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

n business practices and

forecasting processes Forecast accuracy and bias intelligence

n-demand

ONLINE PORTAL

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62% 76% 90% 89% 87% 83% 55% 53%

30% 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)

Forecast Accuracy

What does this tell you? Are all companies equal?

Item-Network

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Demand Patterns Influence FCA and Bias

81% 61% 54% 2.9% 5.6% 18.7%

% of Units Shipped in Pattern 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 Units Shipped in Pattern

Stable Launch/End Trending Other Seasonal/Uplift Intermittent FCA Bias

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S&OVC Demand Segmentation Enables “Apples-to-Apples” Benchmarking

DEMAND VARIABILITY LAG DEMAND PATTERNS DEMAND VELOCITY

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Company FCA Performance vs. Forecastability

“Apples-to-Apples” Benchmarking

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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

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

s 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

DO FREQUENT PARAMETER UPDATES

POLICY PROFILE FOR MEMBER 1

PRACTICE 2 PRACTICE 3 PRACTICE 4 PRACTICE 5 PRACTICE 6 PRACTICE 7 PRACTICE 8 PRACTICE 9 PRACTICE 10 CHANGE IN LIKELIHOOD OF OCCURRENCE AS POLICY/PRACTICE OVERALL OBSERVATION PERFORMER FREQUENCY OF OCCURRENCE (% OF USABLE SEGMENTS) RESULT LAG INCREASES (0 TO 3) VELOCITY INCREASES (LOW TO HIGH) VARIABILITY INCREASE (LOW TO HIGH)

How frequently do you typically update the algorithm parameters in your demand planning tool?

Top performers tend to update

parameters more frequently

Weekly updates had the highest
ccurrence among top performers
Annual updates in the bottom

performers

Bottom performers never update

monthly Top 54% Weekly/ Monthly Bottom 38% Quarterly / Annually 25

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SOVC Member Benefits

The SOVC provides actionable insights into the areas needing improvement as well as the best practices that can lead to real performance gains.

Compare forecast accuracy and bias and their respective drivers across a relevant peer group and industries.

+

Identify the underlying drivers of forecast accuracy, including product portfolio mix, customer order patterns, seasonality, and new product launches.

+

Proven analytical framework and tool to estimate anticipated forecast accuracy and bias for existing and new products.

+

Target demand planning improvement initiatives that can generate the most business value.

+

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Agenda

Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions

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