Leveraging Big Data for Supply Chain Benchmarking JDA FocusConnect November 5, 2013

Transcription

1 Leveraging Big Data for Supply Chain Benchmarking JDA FocusConnect November 5, 2013

2 Agenda Who is Chainalytics? Freight Market Intelligence Consortium Sales & Operations Variability Consortium Questions 2

4 Who is Chainalytics? STOCKHOLM HELSINKI MINNEAPOLIS MILAN ATLANTA BANGALORE 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 4

5 Value-Driven Supply Chain Decisions Years Supply Chain Design Quarters Packaging Optimization Months Logistics Operations Transportation Weeks Planning Horizon Sales & Operations Planning Service Supply Chain When should we buy or make product to make the best use of our capacity? What is the best flowpath? How much and where should inventory be positioned in the supply chain? At what service level can we profitably satisfy demand? Should our warehousing and material operations be insourced or outsourced? How should we transport product through the supply chain? Can we reduce our transport and logistics costs by improving cube utilization? How well do our current operations mitigate repair and warranty costs? How can we increase visibility to stakeholders? 5

6 Some of Our Clients RETAIL FOOD & BEVERAGE HOME/OFFICE NON-DURABLES HOME/OFFICE DURABLES HIGH TECH & TELECOM Packaging Healthcare Automotive & Industrial Chemical/Process LSP OTHER INDUSTRIES SERVED 6

7 Evolution of the Big Data Opportunity Content makes the realization of the full value of big data possible. 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 Technology 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 Knowledge Content 7

9 Model-Based Benchmarking Advantage Traditional 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 Model-Based Benchmarking 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 9

10 What is a Freight Market Intelligence Consortium? DATA MODELS REPORTS Freight Market Intelligence BUSINESS INTELLIGENCE Internal Focus MARKET INTELLIGENCE External Focus Consortium GUIDELINES No input data shared Membership remains confidential 10

11 FMIC Overview 2004 TL Model for 6 Shippers TL & IM Models: 108 Shippers ($18.2B) LTL Model: 23 Shippers ($482MM) Ocean: 16 Shippers ($290MM) TODAY Members Gain Access to Performance Reports (ALL MODES) What is my overall cost position to the market? In which lanes am I over market? Rate Estimators (TL & LTL) What are estimated costs for lanes in which I am not operating today? Where are some opportunities to convert from collect to prepaid freight? Freight Market Intelligence (ALL MODES) 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? 11

12 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 WWD Anytown Anystate HUN Anytown Anystate GER DEMO WWD Anytown Anystate FRA Anytown Anystate GER DEMO WWD Anytown Anystate GER Anytown Anystate POL DEMO WWD Anytown Anystate POL Anytown Anystate HUN DEMO WWD Anytown Anystate HUN Anytown Anystate POR DEMO WWD Anytown Anystate Estimated 13752Estimated POL Anytown Estimated Anystate Difference 58260Annual GER Cost Difference Status DEMO WWD Anytown Anystate CPL CPM FRA Annual Anytown Cost Anystate CPL 64882Difference GER Percent 2914 (Including 68 Fuel 1 DEMO WWD Anytown (Including Anystate Fuel (Including ITAFuel (Including Anytown Fuel Anystate (Including Fuel (Including GERFuel (Including 2886 Fuel Surcharge) 20 1 DEMO WWD Anytown Anystate Surcharge) Surcharge) GER Surcharge) Anytown Anystate Surcharge) 10568Surcharge) POL Surcharge) DEMO WWD Anytown Anystate EUR 4, EUR HUN 1.46 EUR Anytown 273, Anystate (EUR 2,821) 00807(EUR FRA 180,513) % 2791 BELOW70 0 DEMO WWD Anytown Anystate EUR 4, EUR ITA1.78 EUR Anytown 577, Anystate (EUR 581) (EUR FRA 77,873) % 2738 BELOW69 2 DEMO WWD Anytown Anystate EUR 4, EUR GER 1.47 EUR Anytown 641, Anystate (EUR 495) (EUR POR 77,683) % AT 67 0 DEMO WWD Anytown Anystate EUR 1, EUR ITA 2.63 EUR Anytown 506, Anystate (EUR 199) (EUR POR 70,205) % AT 14 2 DEMO WWD Anytown Anystate EUR 4, EUR ITA 1.46 Anytown EUR 98, Anystate (EUR 2,835) (EUR ITA 65,203) % 471 BELOW EUR 4, EUR 1.46 EUR 81, (EUR 2,947) (EUR 56,001) 7.53% ABOVE EUR 4, EUR 1.55 EUR 306, (EUR 812) (EUR 55,193) 4.28% ABOVE EUR 4, EUR 1.46 EUR 84, (EUR 2,643) (EUR 52,863) 1.77% AT EUR 3, EUR 1.56 EUR 377, (EUR 529) (EUR 52,393) 7.47% ABOVE EUR 5, EUR 1.89 EUR 369, (EUR 684) (EUR 47,885) % BELOW EUR 4, EUR 1.64 EUR 309, (EUR 684) (EUR 47,180) % BELOW EUR 4, EUR 1.78 EUR 333, (EUR 610) (EUR 40,871) % BELOW EUR 4, EUR 1.46 EUR 59, (EUR 2,858) (EUR 40,016) -2.87% AT EUR EUR 2.05 EUR 227, (EUR 161) (EUR 37,894) % BELOW 12

13 Long Haul Dry Van Comparison to Industry 13

14 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? 14

15 Integrated Benchmark Rates JDA has partnered with Chainalytics to provide access to the largest transportation benchmarking database in North America directly within its TMS solution. Lane Information Industry Leading TMS Solution Industry Benchmark Freight Market Intelligence Consortium 15

16 Integrated Benchmark Rates Carrier or Load Comparison 16

17 FMIC Europe Milestones Charter Member Identification Product Design & Development Modeling, Analytics & Reporting Charter Member Feedback & Adoption Promote to current multi-national FMIC members Prepare data collection materials Define Europe specific deliverables with charter members Development of econometric and reporting constructs Modification of existing capabilities defined specifically for Europe Obtaining feedback Adapting process, reports and survey insights for future rounds Determination of ongoing service parameters Completion November 2013 Completion 2013 Q4 Completion 2014Q1+2 Completion 2014 Q2 We will analyze 12 months of data (6 months apart), and produce two sets of deliverables each year. 17

19 Demand Planning Market Intelligence Conventional Benchmarks 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 Sales & Operations Variability Consortium Model-based using transaction data Common metrics Insights into drivers Supplemental questionnaire Business practices driving forecast accuracy (FCA) Demand and supply planning practices 49% 33% 13% 5% FOOD & BEVERAGE PERSONAL CARE HOME CARE PET CARE 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 19

20 How does Chainalytics SOVC work? Member Inputs Model-Based Analytics Results for Members Detailed forecast and actual order/sales transaction data Data review, clean-up and validation Accuracy calculations & benchmarking Forecast accuracy and bias intelligence on-demand Questionnaire responses on business practices and forecasting processes Questionnaire tabulation & analysis Forecast accuracy predictive model ONLINE PORTAL 20

21 Forecast Accuracy A Look at Conventional Benchmarks Item-Network and Item-Location FCA (Monthly Buckets) 100% Lag 0 Lag 1 Lag 2 Lag 3 90% 90% 80% 80% 90% 83% 89% 81% 87% 79% 83% 77% 70% 70% Item- Network Item-Network 76% 60% 60% 63% 62% 50% 50% Item-Location 54% 55% 53% What 40% does this tell you? 40% Are all companies equal? 30% % % 40%

22 % of Units Shipped in Pattern % of Units Shipped in Pattern Demand Patterns Influence FCA and Bias More stable and less seasonal and intermittent demand results in higher FCA and lower bias Stable Trending Seasonal/Uplift Intermittent Launch/End Other FCA Bias 18.7% 81% 61% 54% 5.6% 2.9% Member 1 Member 2 Member 3 22

23 S&OVC Demand Segmentation Enables Apples-to-Apples Benchmarking DEMAND PATTERNS LAG DEMAND VARIABILITY DEMAND VELOCITY 23

24 Company FCA Performance vs. Forecastability Apples-to-Apples Benchmarking 24

25 SOVC Sample Deliverable FCA Policy Analysis for Monthly Forecasters 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 occurrence among top performers Annual updates in the bottom performers Bottom performers never update monthly Top 54% Weekly/ Monthly Bottom 38% Quarterly / Annually Policies Policies of Top of Top Performers POLICIES OF TOP PERFORMERS Do DO Frequent Do FREQUENT Frequent Parameter Parameter PARAMETER Updates Updates UPDATES Policy Policy Profile Profile for for Member 1 1 POLICY PROFILE FOR MEMBER 1 Do Frequent Do DO Frequent FREQUENT Parameter Parameter PARAMETER Updates Updates UPDATES Use Regression Use Regression Trend Trend Time- Time- PRACTICE Series Series10 Use Top Use PRACTICE Down Top Down Process Process 2 Use Regression Use Regression Trend Trend Time- Time- Series PRACTICE Series 10 Use Top Use PRACTICE Down Top Down Process Process 2 Use Moving Use Moving PRACTICE Average Average Time- Time- 9 Series Series Begin Begin Top Down Top Down PRACTICE at 3 Product at Product Group Group Level Level Use Moving Use Moving Average PRACTICE Average Time- Time- 9 Series Series Begin Begin Top PRACTICE Down Top Down at Product at 3 Product Group Group Level Level Use Customized Use Customized PRACTICE Forecasting Forecasting 8 Tool Tool Involve PRACTICE Involve Finance/SC Finance/SC 4 in Adjustments Adjustments Use Customized Use Customized PRACTICE Forecasting Forecasting 8 Tool Tool Involve Involve PRACTICE Finance/SC Finance/SC 4 in Adjustments Adjustments Update Update Inventory Inventory Targets Targets PRACTICE 7 Weekly Weekly Do Not Do Set Not Up Set Separate Up Separate PRACTICE 5 Promo Promo DFUs DFUs Update Update Inventory Inventory Targets Targets PRACTICE 7 Weekly Weekly Do Not Do Set Not Up Set Separate Up Separate PRACTICE 5 Promo Promo DFUs DFUs Employ Employ Inventory Inventory PRACTICE 6 Optimization Optimization Employ Employ Inventory Inventory PRACTICE 6 Optimization Optimization 25

26 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. Target demand planning improvement initiatives that can generate the most business value. + Proven analytical framework and tool to estimate anticipated forecast accuracy and bias for existing and new products. 26

28 Gary Girotti Vice President, Transportation Practice +1 (678) Janne Salmi Managing Director, Chainalytics Europe