1. Advanced Spatial Analysis October 7, 2002 Dr. Charles Noon Department of Management The University of Tennessee

2. Agenda Beyond mapping, Geographic Information Systems (GIS) with examples –Buffer and Overlay –Transportation Analysis Advanced Descriptive Modeling example Advanced Prescriptive Modeling example

3. Competitor A Sales Dollars (000s) by State

4. Geographic Information Systems (GIS): A tool for spatial analysis GIS: a computer platform which allows chain-wide data to be easily integrated for display and analysis. GIS is not map making software Data stored and handled in Layers Data is attached to each Feature Display can be a function of the data Query and Analysis within or between layers

5. Buffer and Overlay Buffer around points, lines or polygons Overlay can, for example, merge maps together, clip one map based on another map, find the intersection of two maps, or find the union of two maps that combine both spatial and attribute data.

8. 500-mile buffer zones around the DCs.

9. Clip customer sites that fall within 500-mile buffer zones around the DCs.

11. Select all counties that intersect with the 500-mile buffer zones around the DCs.

13. Transportation Analysis With the addition of a network layer, a number of analyses can be performed, such as –Shortest Path –Service areas –Vehicle Routing

24. Thickness is rate

26. Transportation Analysis With the addition of a network layer, a number of analyses can be performed, such as –Shortest Path –Service areas –Vehicle Routing

34. Large growing industry with much of the consumption in the Southeast Majority of current flights are into Northeast Most DCs within 2 day delivery US Xpress and Averitt Express both currently carry pharmaceuticals An Example of Overlay: Recommended Market Niche Pharmaceuticals

35. One Day Delivery 70.6% 23.7% Two Day Delivery Chattanooga Catchment Area

36. One Day Delivery Two Day Delivery 24.8% 64.5% Charlotte Catchment Area

37. Population served by Chattanooga Population served by Charlotte

38. Population served by Chattanooga Population served by Charlotte Population served by both 2-Day Delivery Coverage

39. Population within two-day catchment –Chattanooga – 70.6% –Charlotte – 64.5% Approximately $8 billion in additional pharmaceutical potential in Chattanooga catchment Results of Analysis:

40. Transportation Analysis With the addition of a network layer, a number of analyses can be performed, such as –Shortest Path –Service areas –Vehicle Routing

43. An Example A distributor to fast-food restaurants with 12 DC’s serving 3922 restaurants with 80 vehicles. Currently, DC’s serve from 209 to 644 restaurants. TransCad was first used to determine optimal weekly delivery routes under the current restaurant-to-DC assignments.

44. CURRENT STORE-TO-DC ASSIGNMENTS Total Mileage Per Week = 192,998

45. An Example A distributor to fast-food restaurants with 12 DC’s serving 3922 restaurants with 80 vehicles. Currently, DC’s serve from 209 to 644 restaurants. TransCad was first used to determine optimal weekly delivery routes under the current restaurant-to-DC assignments. TransCad was then used to re-assign restaurants-to- DC’s and determine approximately 400 vehicle routes that must be run each week.

46. OPTIMIZED STORE-TO-DC ASSIGNMENTS Total Mileage Per Week = 173,702

47. STORES WITH CHANGED ASSIGNMENTS Note: a total of 381 stores had changed DC assignments. Each dot may represent more than one store (in the same zipcode)

48. STORES WITH CHANGED ASSIGNMENTS Currently Assigned DC Optimally Assigned DC Cluster Net savings of 19,296 miles per week (10% reduction)

49. Agenda Beyond mapping, Geographic Information Systems (GIS) with examples –Buffer and Overlay –Transportation Analysis Advanced Descriptive Modeling example Advanced Prescriptive Modeling example

50. Locating a proposed NICU market analysis and visualization with “perfect” data

51. Study Area

52. 20 Hospitals of Study Area Methodist West_TN Baptist Unaffiliated

53. 15 Hospitals with Maternity Service Methodist West_TN Baptist Unaffiliated

54. Sized according to ‘97 deliveries

55. Labeled with number of ‘97 deliveries

56. Methodist West_TN Baptist Unaffiliated Colored according to system

57. Methodist West_TN Baptist Unaffiliated Population density by zipcode (est. 2000)

58. Methodist West_TN Baptist Unaffiliated ‘97 births (mother’s zip) per square mile

59. Baptist Market Share (of births at 15 facilities)

60. Methodist Market Share (of births at 15 facilities)

61. West_TN Market Share (of births at 15 facilities)

62. Meth-LB Patient Density (maternal zipcode)

63. Methodist System Patient Density (maternal zipcode)

64. West_TN System Patient Density (maternal zipcode)

65. Baptist System Patient Density (maternal zipcode)

66. County Baby Exports (only the top 7 “import” counties are color coded, the black includes all others as well as out-of-region counties)

67. Delivering mother imports/own-residents occurring at a county. The black number represents total births from residents of the 20 study counties that occurred in that county (note, the hospital numbers may include KY babies or other TN counties and hence the difference in some situations).

69. Twenty minute drive bands around Dyersburg overlaid on the methodist babies per square mile

70. Twenty minute drive bands around Jackson overlaid on the methodist babies per square mile

72. Agenda Beyond mapping, Geographic Information Systems (GIS) with examples –Buffer and Overlay –Transportation Analysis Advanced Descriptive Modeling example Advanced Prescriptive Modeling example

73. Facility Location Example A global consumer electronics manufacturer. GIS and CAPS Logistics Supply Chain Designer were used in conjunction to perform a network optimization in order to recommend DC locations and shipping zone assignments.

74. 74 uGrowth in offshore production. uEver changing business environment. uPro-active approach to network design. Why the need for a distribution network analysis?

75. The System as Focused... PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA

76. Direct Transfers PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA $60/FTL $0

77. Full Container Loads PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA

78. Full Truck Loads PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA + $60/FTL

79. FTL, LTL, UPS PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA

80. All Movements PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA

81. 81 DC Costs DCs EP GV AN HA There is one base cost of location assigned to each DC. It reflects the cost of the facility being open with no through movements. There is a variable cost assigned for each DC/product combination, which is based on per unit handling cost (handling + cubic capacity cost)

82. 82 DC Costs There is one fixed cost of location assigned to each DC. It reflects the cost of the facility being open with no through movements. Minimum Size Cube Volume Facility Cost

83. 83 DC Costs We determined a variable fixed cost for each DC. It represents the the change in fixed cost as a function of volume through the facility. Minimum Size Cube Volume Facility Cost

84. 84 DC Costs There is a variable cost assigned for each DC/product combination, which is based on per unit handling cost Minimum Size Cube Volume Facility Cost

85. Fixed & per product variable DC costs Candidate DC Data Requirements PLANTSDCsCustomers GV JZ FE Lower 48 Markets (3-digit Zips) EP GV AN HA ? Inbound FTL rates Inbound container rates Outbound FTL rates

92. 92 Inbound Container Rates

93. 93 Example Outbound FTL Rates

95. 95 Shipped by: EL PASO ANAHEIM HANAHAN GREENVILLE Current Supply by Origin DC

96. 96 Levels of Model Decision Making u Open/Close DC Facilities u Determining facility sizes u Flowing product from Plants to DCs to Markets

97. 97 Levels of Model Decision Making u Open/Close DC Facilities u Determining facility sizes u Flowing product from Plants to DCs to Markets Optimally Flowing Fully Optimizing

98. Projection TV’s

99. 13 - 20 inch CTV’s

100. Shelf Systems

101. 101 Shipped by: EL PASO ANAHEIM HANAHAN GREENVILLE Optimized Supply by Origin DC

102. Shipped by: Greeneville Hanahan El Paso Anaheim Current Shipping Pattern Optimal Shipping Pattern

103. 103 Observations uAn understanding of the drivers towards an optimal network solution is often of more organizational value than the actual “solution”.

104. 104 Observations uExecutives at the very highest level of the organization often have good insight on model correctness (or lack thereof) and intuitively understand the value of sensitivity analysis … u… if such information is presented in an efficient and effective manner.