1. Geographical Perspectives on Business Analytics with Applications in Construction Supply and Automotive Colorado State University - Pueblo Justin Holman, PhD 02-05-14 Copyright 2014 TerraSeer, Inc. All rights reserved.

2. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

3. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

4. Background Education Claremont McKenna College B.A., Philosophy and Mathematics, 1990 University of Oregon Geography, GIS, Spatial Statistics, Cartographic Visualization M.S., 1996, Ph.D., 2004 Northwestern University - Kellogg School of Management Certificate, Designing and Managing the Supply Chain, 1998

5. Background Professional Experience Dynamix Inc. 3D Simulation Software Development LogicTools, Inc. Supply Chain, Network Optimization, Map UI (Acquired by IBM) US Geological Survey Data Visualization, Spatial Statistics, Environmental Modeling MapInfo Retail Location Research, Applied Statistical Modeling TerraSeer (dba Aftermarket Analytics) Location Analytics, SaaS Development Automotive Aftermarket, Construction Supply Industry

6. Background: Select Clients

7. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

8. Construction Materials Supply Chain

9. Raw Materials Manufacturing Assembly Engineering Distribution Construction Materials Supply Chain

12. Types of Location Problems Raw materials Production/Plant location Assembly/Kitting Distribution Centers (DCs) Cross docks Branch Retail Location Showroom Construction Site Construction Materials Supply Chain

13. Types of Location Problems Raw materials Production/Plant location Assembly/Kitting Distribution Centers (DCs) Cross docks Branch Retail Location Showroom Construction Site Min Cost Max Revenue Construction Materials Supply Chain

14. Types of Location Problems Raw materials Production/Plant location Assembly/Kitting Distribution Centers (DCs) Cross docks Branch (Counter + Delivery) Retail Location Showroom Construction Site Min Cost Max Revenue Construction Materials Supply Chain

15. Overview Background/Experience/Perspective The Branch Location Problem 2 Competing Objectives - Maximize Revenue - Minimize Delivery Costs Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

16. Sugar Land, TX Example

17. Sugar Land Model Trade Area

18. Disaggregate Trade Area Forecasting Counter$20,009 Delivery$207,653 Total$227,662 Model predicts anticipated counter sales and delivery sales originating within each trade area ZIP Code

19. Branch Sales Forecasting Sugar Land$18,500,000 Model calculates a branch sales forecast by summing ZIP forecasts and making adjustments (see below) Adjustments: Beyond Sales (proportion of sales projected to come from beyond the trade area), Branch Size (model assumed an average branch size of 16,000 gsf), Region/DMA (White Cap achieves stronger performance in some markets than others), Contractor Density (markets with exceptionally high contractor counts achieve stronger sales)

20. Houston Market Results suggest that 3 additional $10M+ branches can be supported in the Houston market

22. WINNERS LOSERS

23. Houston Market Results suggest that 3 additional $10M+ branches can be supported in the Houston market

24. Overview Background/Experience/Perspective The Branch Location Problem 2 Competing Objectives - Maximize Revenue - Minimize Delivery Costs The Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

27. Miami Baseline Demand Met: $29.9 M Delivery Cost: $1.6 M Average Distance: 22 mi

28. Miami Optimized Demand Met: $32.5 M Delivery Cost: $1.5 M Average Distance: 20 mi

29. Miami Optimized – Add 1 Demand Met: $32.5 M Delivery Cost: $0.5 M Average Distance: 7 mi

30. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

31. Inventory Assortment Optimization Process

32. 1. Calculate Repair Rates Repair Rate Modeling Process

33. 2. Build Statistical Models Ball Joint Repair Rates by Vehicle Age and Type Repair Rate Modeling Process

34. 3. Create Adjustment Factors Repair Rate Modeling Process - Make, Model, and Regional Adjustments - Analyze Residuals and Calibrate Model 1.Alabama 2.California 3.Georgia 4.Washington 5.Oregon 1.New Hampshire 2.Vermont 3.Maine 4.Massachusetts 5.Rhode Island Lower than ForecastHigher than Forecast Sample Adjustments for Ball Joints

35. 4. Validate Predictive Accuracy Repair Rate Modeling Process Initial model: R 2 = 0.787 Adjusted Model R 2 = 0.978

36. Total Demand Forecast Zip Code VIO x Repair Rates

37. Total Demand > Sales Forecast Create Store Trade Areas (ZIP codes) Aftermarket Adjustment Channel Market Share Part Attributes (good, better, best) Sales Forecast TOTAL DEMAND SKU Level Demand by ZIP

38. Trade Area Sales Forecast ZIP Code Total Demand x Market Share = ZIP Forecast Store Location Sum of ZIP Forecasts = Store Sales Forecast Key Factors: Market Share, Aftermarket Adjustment, Proximity to Store Location, Competition, Part Attributes

39. Sales Forecast > Inventory Recommendation Estimated Forecast Error Target Service Level Replenishment Lead Time Order Frequency Holding Cost Optimization Engine Inventory Recommendation SALES FORECAST SKU Level Sales Estimates by Store

40. Optimize Inventories For Efficiency

41. Optimal Working Capital Utilization

42. Recommendations via Web Portal

43. Select A Store

44. Recommendations via Web Portal Click To See Inventory

45. Recommendations via Web Portal

46. Search Function

47. Recommendations via Web Portal Multiple Sorts

48. Recommendations via Web Portal Save To Excel

49. Recommendations via Web Portal Go Back To Pick Another Store

50. Inventory Optimization Process

51. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Why Spatial is Special

52. 2013 Q2 Starter Demand By County U.S. Total Units: 2.85M

53. 2013 Q2 Starter Demand By County U.S. Total Units: 2.85M 3000+ Counties or Aggregate to Fewer Units?

54. Most use State Borders

55. But why not use Watersheds

56. Or Topography

57. Or Solar Radiation

58. Or Precipitation

59. Or Earthquake Zones

60. Or Population Density

61. Or Racial Density

62. Or Sports Affiliation

63. Or Language Preferences: Pop vs Soda vs Coke

64. But Since Government Data is Typically Provided by State, Most use State Political Borders

66. Melanoma Risk

67. Or Solar Radiation

68. 3. Create Adjustment Factors Repair Rate Modeling Process - Make, Model, and Regional Adjustments - Analyze Residuals and Calibrate Model 1.Alabama 2.California 3.Georgia 4.Washington 5.Oregon 1.New Hampshire 2.Vermont 3.Maine 4.Massachusetts 5.Rhode Island Lower than ForecastHigher than Forecast Sample Adjustments for Ball Joints

70. Overview Background/Experience/Perspective Branch Location Problem Retail Inventory Assortment Problem Consumer Segmentation Geographical Visualization

71. Power of Geographical Visualization Maps vs Spreadsheets Pattern Detection Collaboration

72. Power of Geographical Visualization Pattern Detection

73. Power of Geographic Visualization Pattern Detection

74. Would you discover this problem with a spreadsheet?

75. Power of Geographic Visualization Collaboration

76. Power of Geographic Visualization Collaboration Wait….wouldnt it be better to plan this war with spreadsheets?

77. Copyright 2014 TerraSeer, Inc. All rights reserved. blog: justinholman.com twitter: @justinholman

78. Independent Data: Vehicle Registration Repair Survey Channel Data Store Locations and Attributes Current Inventory By SKU/Store/DC Sales History By SKU/Store/DC Delivery and Service Level Requirements Nice to Have Customer (end-user) Locations Estimated Market Share Demand Forecast Competition Inventory Assortment Data Inputs

79. 1. Model Repair Rates 2. Generate Demand Forecasts 3. Trade Area Sales Forecasts 4. Optimize Inventory 5. Develop Communication Portal 6. Maintain/Refine Models 7. Maintain/Refine/Customize Portal 8. Start Over, Improve, Rinse and Repeat Continuous Improvement Iterative Analytical Approach

80. Sources of Error in Spatial Analysis Geocoding

81. Sources of Error in Spatial Analysis Distance Measurement Method of measurement: Straight line distance vs Great Circle distance Scale of Measurement:

82. Sources of Error in Spatial Analysis Spatial Autocorrelation

83. Sources of Error in Spatial Analysis Spatial Autocorrelation