1

Next Wave AgencyOcean Carrier Bid OptimizationFinal Presentation

Senior Design Team:Juan ArayaSteven ButtsOwen CarrollEmily SarverJustin StoweJan Zhang

April 16, 2008

Sponsor:John Trestrail, CEO & Principal Consultantjtrestrail@nextwaveagency.com

Advisor:Dr. Ozlem Ergunozlem.ergun@isye.gatech.edu

*This presentation was created in the framework of a student design project. The Georgia Institute of Technology does not sanction its content in any way.

2

Outline

Company and Problem BackgroundProject DescriptionSolution ApproachDeliverablesValue and Benefits

3

Food for Peace Program

USDA & USAIDInvitation published monthly to procure:

Food commoditiesTransportation

USDA awards contracts to minimize cost

4

Food for Peace Program

5

Food for Peace Program

6

Next Wave Agency Background

Specializes in Food for Peace ProgramOcean carrier consulting agencyRecommends bid prices to carriers

7

Project Introduction

Bids based on market knowledgeWanted to develop an analytical methodCreated a tool to determine bid prices

8

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

9

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

10

Solution Approach: Forecasting

Parameters for optimization modelSupplier bids

Origin portsCommodities

Carrier bids

11

Solution Approach: Forecasting Supplier Bids

Data availableWinning and losing bids for past year

Bids are for a commodity at an origin3,750 pairs10 suppliers per pair

Supplier interaction

14121086420

425

400

375

350

325

300

275

250

Invitation

Bid

Price

SEMO MILLING - CAPE GIRARDEAU, MO

ADM - LINCOLN, NE

AGRICOR - MARION, INBUNGE-ATCHISON KS

BUNGE-CRETE, NEBUNGE-DANVILLE ILDIDION MILLING - CAMBRIA, WI

FAIRVIEW MILLS INC - SENECA, KSLIFELINE FOODS - ST JOSEPH MO

MILLSTONE MILLS - LA PORTE, IN

Supplier

Bid Price Trends for Suppliers over Timefor 'CORNMEAL, 25 KG HP' in 2007

12

Solution Approach: Forecasting Supplier Bids

Minimum bids for origin-commodity pairsUpward trendNon-seasonal

1614121086420

500

450

400

350

300

250

Invitation Sequence

Min

imum

Bid

HOUSJACI

LCLCHINORF

ByVar3

Minimum Supplier Bid by Origin Port for 2007Commodity: CORN-SOY BLEND, 25 KG HP

13

Solution Approach: Forecasting Supplier Bids

Double exponential smoothing Non-seasonal dataTrend in minimum supplier bids over timeLow mean absolute percent error (5.62%)

1413121110987654321

375

350

325

300

275

250

Index

Min

imum

Bid Alpha (level) 0.2

Gamma (trend) 0.2

Smoothing Constants

MAPE 6.871MAD 21.557MSD 707.447

Accuracy Measures

ActualFitsForecasts95.0% PI

Variable

Smoothing Plot for Minimum BidDouble Exponential Method

121110987654321

1500

1400

1300

1200

1100

1000

900

Index

Min

imum

Bid Alpha (level) 0.1

Gamma (trend) 0.1

Smoothing Constants

MAPE 3.72MAD 44.50

MSD 3944.90

Accuracy Measures

ActualFitsForecasts95.0% PI

Variable

Smoothing Plot for Minimum BidDouble Exponential Method

14

Solution Approach: Forecasting Carrier Bids

Data availablePast winning carrier bidsPast market indices for ocean freight costsVoyage lengths

Past voyage factors highly variableAnalyze past voyage data

3 carriers10 vessels

15

Solution Approach: Forecasting Carrier Bids

Estimate costs per tonFuelDaily leasingPort call costs

Method considered: RegressionHigh R-squared (87-99.7%)Not adaptable for unusual voyages

16

Solution Approach: Forecasting Carrier Bids

Use market costs to estimate profit per tonPast profit per ton fits normal distribution

360300240180120600

18

16

14

12

10

8

6

4

2

0

Profit per ton

Frequency

Mean 154.1StDev 71.94N 59

Histogram of Profit Per TonCarrier: Sealift

17

Solution Approach: Forecasting Carrier Bids

Method developedCalculate voyage costs using current indicesAdd random variable for profit per tonCalculate confidence intervals for bid prices

Carrier Expected Profit 80% CI 90% CI 95% CI

Maersk 128.70 ±22.14 ±28.23 ±33.64

Sealift 153.80 ±12.08 ±15.41 ±18.36

Liberty 144.80 ±19.46 ±24.81 ±29.56

18

Solution Approach: Forecasting Carrier Bids

Houston to Djibouti20,000 tonsMaersk vessel

Fuel $29.18Lease $44.12Port call $5.00Total costs per ton

$78.30

Add Expected Profit

$128.70

Expected Bid Price

$207.00

19

Solution Approach: Forecasting Carrier Bids

Confidence intervals for expected bid price

Expected Bid Price $207.00

80% Confidence Interval (195, 219)

90% Confidence Interval (192, 222)

95% Confidence Interval (189, 225)

20

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

21

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

22

Solution Approach: Optimization

Simulates USDA’s process of awarding contractsObjective: Minimize total costConstraints:

Carrier quantity Commodity demand Carrier-supplier pairing US vessel priority

23

Solution Approach: Optimization

Validated using actual data from 3 past invitationsChecked tonnage distribution among carriersAverage accuracy for each carrier: 91.7%

Actual Awarded Tonnage and Simulated Awarded Tonnage

0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Carrier Bid Number

To

nn

age

Aw

ard

ed

Actual Tonnage Awarded Model's Tonnage Awarded

24

Solution Approach: Optimization

Tested with forecasted supplier bidsChange in error: <1%

Revenue Comparisons with Forecasted and Actual Supplier Bids

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

Vessel

Re

ve

nu

e o

f B

id

By USDA By Model with Forecasted Supplier Bids By Model with Actual Minimum Supplier Bids

25

Solution Approach: Optimization

Run model multiple timesIncrement bid prices for Next WaveAnalyze resultsDetermine bid prices

26

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

27

Project Flowchart

OptimizationModel

Carrier BidForecasting

Supplier BidForecasting

Analysis

User Input

BidRecommendations

28

Deliverables

29

Deliverables

Expected Price

30

Deliverables

31

Deliverables

32

Deliverables

33

Deliverables

34

Deliverables

35

Deliverables

36

Value and Benefits

Financial value from increase in bids wonIf 10% increase:

$550,000 for clients$5,500 for Next Wave

Unique advantage over competitorsPotential to attract new clients

37

Summary

Created analytical bidding toolForecasting bidsOptimization modelSoftware

User InterfaceDatabaseReports

38

Questions?