CCFC Transportation Project Maged Dessouky James Moore, II Alejandro Toriello Christine Nguyen

1. CCFC Transportation Project Maged Dessouky James Moore, II Alejandro Toriello Christine Nguyen Qisheng Pan Jiyoung Park Aug 31, 2011

2. Outline • Project Objectives • Data Analysis and Methodology • Results • Economic Analysis • Implementation Plan

3. Project Objectives • To evaluate the current transportation practices within the CCFC membership; • To compare transportation costs under current conditions, in which farmers ship product individually, to projected shipping costs under a consolidation plan; • To determine impact on demand with reduced transportation cost; • To develop a plan for consolidation if desired option.

4. Data Collection • 2010 Data received from 16 farmers (approximately 53%). • LTL rates provided by farmers. • Full truckload rates provided by SCC. • FedEx/UPS rates found on respective websites. • 4 farmers submitted 2008 but not 2010 data.

5. Data Analysis • 2010 Total Production in Dollars (USD) received for 70 farmers. • We performed a 2 Sample t-test using 2010 and 2008 data from farmers who participated in both studies. • Linearly extrapolated 2008 data to 2010 based on the Average Percent Volume difference (approx. 104%). • We wanted to account for farmers that did not submit 2010 data.

6. Data Analysis Extrapolation for Missing Farmers • Using submitted 2010 sales data and the 2010 Total Production, we calculated the Average Volume per Dollar (cubic feet/$). • We calculated the Average Annual Volume per missing farmer (production volume/farmer), to be used for the missing 50 farmers.

7. Methodology Assumptions and parameters • Simulation time: 365 days • LTL and Full truck rates provided by farmers and SCC, respectively. Baseline Model • Simulated each farmer’s operation individually. • Calculated each farmers’ direct transportation cost based on the cheapest option per shipment (LTL, Full, FedEx/UPS).

8. Methodology Consolidation Model • Infinite number of trucks. • Infinite inventory capacity at consolidation point. • No consolidation at farmer’s site. • We used Mixed-Integer Programming to formulate the Consolidation model.

9. Results Simulated Baseline and Consolidation models for 6 scenarios • Scenario 1: Do not extrapolate for the top missing farms • Scenario 2: Extrapolate for 20% of the top missing farms • Scenario 3: Extrapolate for 40% of the top missing farms • Scenario 4: Extrapolate for 60% of the top missing farms • Scenario 5: Extrapolate for 80% of the top missing farms • Scenario 6: Extrapolate for 100% of the missing farms

10. Detailed Results – Scenario 3

11. Results

12. Results

13. Results

14. Economic Analysis: Shipments arriving in Miami are transported to the national market, and the magnitudes and costs of these shipments to various population centers provide the information needed to estimate how these domestic shipments respond to changes in transport costs.  

15. Economic Analysis: Flower shipments arriving in Miami are transported to the national market, and the magnitudes and costs of these shipments to various population centers provide the information needed to estimate how these domestic shipments respond to changes in transport costs. Or, rather, they would if the data published for the associated sector in the FHWA Freight Analysis Framework (FAF3) were trust worthy. Neither USC nor ORNL believes these data are reliable (useful, accurate, etc.).  

16. Economic Analysis: Data We shifted to a combination of alternative data sources. • The National Interstate Economic Model (NIEMO), incorporates state-level commodity flows based on the Commodity Flow Survey (CFS). • A separate data set licensed from WISERTrade fairly precisely identifies the magnitude of cut-flower imports into Miami. • Other reports show that about 90% of these imports leave Florida for other locations in the U.S.

17. Economic Analysis: Methodology We estimate a gravity model formulation using a standard log-log transformation of the data. • This formulation has the advantage of directly estimating demand elasticities as coefficients.. • The formulation estimates the impact of changes in distance, shipping cost, and market size on cut-flower flows between Miami and U.S. market locations. • California farmers and CCFC can expect the same sort of effects.

18. Economic Analysis: Results • All parameter estimates are significant at conventional statistical levels. • The signs for the coefficients associated with the population and full-truck-load shipping cost variables have the expected sign. • The finding relevant to California farmers and CCFC is that on average, for flows originating in Miami, a 1% decrease in full truck load shipping costs is associated with a 2.9% increase in flower trade flows.

19. Implementation Plan • Establish CCFC Infrastructure and Leadership • Hire/appoint a CCFC employee as Director of Supply Chain • Determine Management Structure • Determine how to manage the consolidation load center. Consider 3PL warehouse providers, or consider hiring a staff managed by the CCFC Director of Supply Chain • Operations/System Implementation • Evaluate and acquire Warehouse Management Software and Transportation Management Software

20. Conclusions • Approx. 50% participation shows a 22.8% decrease in annual transportation costs, or $6.7 Million, when shipping is consolidated. • As participation increases, savings increase. • If 100% of California farms participate, the analysis estimates $20 Million in annual savings.

21. Conclusions • Our analysis of estimated cut-flower trade flows originating from Miami shows that the magnitudes of these flows are relatively sensitive to shipping cost, controlling for market size. • In the Miami case, a 1% decrease in the shipping cost is associated with a 2.9% increase in trade-flow volume. • California’s farmers stand to benefit from this sensitivity if they can reduce their own transportation costs.