The views represented in the paper are solely those of the authors and not necessarily those of the Postal Regulatory Commission Lyudmila Y. Bzhilyanskaya, Lyudmila Y. Bzhilyanskaya, J.P. Klingenberg and J.P. Klingenberg and Michael J. Ravnitzky Michael J. Ravnitzky

From 2002 to 2011 for the United States Postal Service Retail Revenue 22% Customer Retail Transactions 28% From 2002 to 2011 for the United States Postal Service Retail Revenue 22% Customer Retail Transactions 28%

MAX(Revenue) / MIN (Costs) for Postal Operator Major Elements of Optimization Strategy Customer Access to Post Offices Factors that complicate Optimization Consider interests of both customers and Postal Operator SpatialOptimizationModels Modeling Analysis Overview Analyze Postal network as a transportation network Driving distance vs. straight-line distance Broader than population access Data collection and database development EconometricRegressionModel 3

Customer and Supplier Sides in Optimization Supply Customer Supplier MIN or MAX Function Set of Constraints Optimization Problem Excess Supply Equilibrium Excess Demand Demand 4

Spatial Approach to the Analysis Source: Denver Council of Government 5

International Access Standards: Would they Work in the U.S? Population Proximity to a Post Office in the U.S. Within 9.3 miles99% Within 3 miles81% Within 1 mile33% Population Proximity to a Post Office Standards In Canada Within 9.3 miles98% Within 3.1 miles88% Within 1.6 miles78% In France: Within 3.1 miles90% 6

Walk-In Revenue as a function of Socio-economic Variables  Hypothesis:  Hypothesis: there is a relationship between postal revenue and socio-economic variables Tools:  Tools: SAS, ArcGIS, MS Access, MapPoint, Excel Study Area:  Study Area: USA at 5-digit ZIP code level Analyzed Data  Analyzed Data [for 2008] United States Postal Service walk-in-revenue Geographic coordinates for post office locations Employment, # of establishments and households Adjusted gross income and # of tax returns 7

Employment by place of work Average Adjusted Income per Tax Return Postal Walk-In Revenue Households

Model Calibration Results Variable Parameter Estimate t ValuePr > |t| Variance Inflation Intercept-25,794 Average Adjusted Income per Tax Return < Households < Employment < Number of observations (5-digit ZIP codes) 18,629 Confidence Limits F ValuePr > FR- Square 99%11,159.7<

Application for Performed Regression Analysis Helps postal operator estimate spatial demand Allows evaluation of actual revenue vs. predicted Supports decision making in postal retail network reorganization Provides a building block for future analysis in other regions or countries Can be transferred into forecasting regression model 10

of Customer Access Under Multiple Constraints Optimization of Customer Access Under Multiple Constraints  Analytical Task:  Analytical Task: optimize customer access under demand and supply constraints Tools:  Tools: IBM LogicNet, SAS, MS Access, Excel Study Area:  Study Area: State of Alaska Analyzed Data:  Analyzed Data: [for 2010] United States Postal Service walk-in-revenue, number of retail windows, geographic coordinates for post offices Population, # of households by Census Block and 5- digit ZIP code, geographic coordinates for the center of each Census Block 11

Multi-criteria Optimization Model Average Weighted Distance ij MIN Max. Possible Distance ij < D Customer Demand i > 99% Walk-In Revenue j > R Capacity i < C i k i= 1,..n j=1,..m k=1,..w n – number of post offices m – number of census blocks w – number of retail windows 12

Alaska Multi-Objective Analysis # of Post Offices Average Distance to Post Offices The trade-off between the # of post offices and the average weighted distance from customers (population center of each Census Block) to the post office. (Distance is measured as driving distance) 14

Future Study Include costs as constraints or optimization function Define more refined market areas Run models for the entire nation or specific regions Model access to post offices for employees of nearby businesses Include data on postal competitors into analysis Use projected data and develop forecasting model 15

Thank You.