geographical perspectives on business analytics with applications in construction supply and...

Geographical Perspectives on Business Analytics with Applications in

Construction Supply and Automotive

Colorado State University - PuebloJustin Holman, PhD

02-05-14

Copyright 2014 TerraSeer, Inc. All rights reserved.

Overview

Background/Experience/Perspective

Branch Location Problem

Retail Inventory Assortment Problem

Consumer Segmentation

Geographical Visualization

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

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

Background: Select Clients

Overview






Construction Materials Supply Chain

Raw Materials

Manufacturing

Assembly

Engineering

Distribution


Types of Location Problems

Raw materialsProduction/Plant locationAssembly/KittingDistribution Centers (DCs)Cross docks

Branch

Retail LocationShowroomConstruction Site




Branch


Min Cost

Max Revenue




Branch (Counter + Delivery)


Min Cost

Max Revenue


Overview


The Branch Location Problem2 Competing Objectives- Maximize Revenue- Minimize Delivery Costs




Sugar Land, TX Example

Sugar Land Model Trade Area

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

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)

Houston Market

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

WINNERS

LOSERS

Houston Market

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

Overview


The Branch Location Problem2 Competing Objectives- Maximize Revenue- Minimize Delivery Costs

The Retail Inventory Assortment Problem



Miami Baseline

Demand Met: $29.9 M

Delivery Cost: $1.6 M

Average Distance: 22 mi

Miami Optimized

Demand Met: $32.5 M



Miami Optimized – Add 1

Demand Met: $32.5 M



Overview






Inventory Assortment Optimization Process

1. Calculate Repair Rates

Repair Rate Modeling Process

2. Build Statistical Models

Ball Joint Repair Rates by Vehicle Age and Type


3. Create Adjustment Factors


- Make, Model, and Regional Adjustments- Analyze Residuals and Calibrate Model

1. Alabama2. California3. Georgia4. Washington5. Oregon

1. New Hampshire2. Vermont3. Maine4. Massachusetts5. Rhode Island

Lower than Forecast Higher than Forecast

Sample Adjustments for Ball Joints

4. Validate Predictive Accuracy


Initial model: R2 = 0.787 Adjusted Model R2 = 0.978

Total Demand ForecastZip Code VIO x Repair Rates

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

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

Sales Forecast > Inventory Recommendation

Estimated Forecast Error

Target Service Level

Replenishment Lead Time

Order Frequency

Holding Cost

Optimization Engine

Inventory Recommendation

SALES FORECASTSKU Level Sales Estimates by Store

Optimize Inventories For Efficiency

Optimize Inventories For Efficiency

Optimal Working Capital Utilization

Recommendations via Web Portal


Select A Store


Click To See Inventory


Search Function


Multiple Sorts


Save To Excel


Go Back To Pick Another Store

Inventory Optimization Process

Overview





Why Spatial is Special

2013 Q2 Starter Demand By CountyU.S. Total Units: 2.85M

2013 Q2 Starter Demand By CountyU.S. Total Units: 2.85M

3000+ Counties or Aggregate to Fewer Units?

Most use State Borders

But why not use Watersheds

Or Topography

Or Solar Radiation

Or Precipitation

Or Earthquake Zones

Or Population Density

Or Racial Density

Or Sports Affiliation

Or Language Preferences: Pop vs Soda vs Coke

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

Melanoma Risk

Or Solar Radiation

3. Create Adjustment Factors


- Make, Model, and Regional Adjustments- Analyze Residuals and Calibrate Model

1. Alabama2. California3. Georgia4. Washington5. Oregon

1. New Hampshire2. Vermont3. Maine4. Massachusetts5. Rhode Island

Lower than Forecast Higher than Forecast

Sample Adjustments for Ball Joints

Overview






Power of Geographical Visualization

Maps vs Spreadsheets

• Pattern Detection

• Collaboration

Power of Geographical VisualizationPattern Detection

Power of Geographic VisualizationPattern Detection

Would you discover this problem with a spreadsheet?

Power of Geographic VisualizationCollaboration

Power of Geographic VisualizationCollaboration

Wait….wouldn’t it be better to plan this war with spreadsheets?

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

1. Model Repair Rates2. Generate Demand Forecasts3. Trade Area Sales Forecasts4. Optimize Inventory5. Develop Communication Portal6. Maintain/Refine Models7. Maintain/Refine/Customize Portal8. Start Over, Improve, Rinse and Repeat

Continuous Improvement

Iterative Analytical Approach

Sources of Error in Spatial AnalysisGeocoding

Sources of Error in Spatial AnalysisDistance Measurement

Method of measurement:Straight line distance vs “Great Circle” distance

Scale of Measurement:

Sources of Error in Spatial AnalysisSpatial Autocorrelation

geographical perspectives on business analytics with applications in construction supply and...

Documents

mi slide

stronger sales slide

houston market slide

trade area zip code

delivery sales

branch size model

average branch size

sales proportion of