Outline

GE Energy Wind Turbine Supply Chain Modeling

Abhinav Agarwal Sanjay G. Charles Sam HolsterRatnakar N. Pawar Sunil Ravichandran

Richa Rastogi Michael Y. Thelen

H. Milton School of Industrial and Systems EngineeringGeorgia Institute of Technology

April 8th, 2008

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

Outline

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

Outline

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

Outline

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

Outline

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Project Scope

GE is one of the world’s leading wind turbine suppliers withover 8,400 worldwide wind turbine installations comprisingmore than 11,300 MW of capacity.

GE assembles 1.5 to 3.6 MW complexes worldwide.

Customers are almost exclusively utility companies.

As of last year, GE Wind Energy is a $2-2.5B enterprise peryear.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Project Scope

GE is one of the world’s leading wind turbine suppliers withover 8,400 worldwide wind turbine installations comprisingmore than 11,300 MW of capacity.

GE assembles 1.5 to 3.6 MW complexes worldwide.

Customers are almost exclusively utility companies.

As of last year, GE Wind Energy is a $2-2.5B enterprise peryear.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Project Scope

GE is one of the world’s leading wind turbine suppliers withover 8,400 worldwide wind turbine installations comprisingmore than 11,300 MW of capacity.

GE assembles 1.5 to 3.6 MW complexes worldwide.

Customers are almost exclusively utility companies.

As of last year, GE Wind Energy is a $2-2.5B enterprise peryear.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Project Scope

GE is one of the world’s leading wind turbine suppliers withover 8,400 worldwide wind turbine installations comprisingmore than 11,300 MW of capacity.

GE assembles 1.5 to 3.6 MW complexes worldwide.

Customers are almost exclusively utility companies.

As of last year, GE Wind Energy is a $2-2.5B enterprise peryear.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Goals

Need a strategic tool to bridge the gap between wind turbinesupply and demand.

Optimization engine to automate assignments.

Implications: database and GUI design to integrate businessintelligence and optimal resource allocation.

Adaptive interface to contend supply chain networkuncertainties.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Goals

Need a strategic tool to bridge the gap between wind turbinesupply and demand.

Optimization engine to automate assignments.

Implications: database and GUI design to integrate businessintelligence and optimal resource allocation.

Adaptive interface to contend supply chain networkuncertainties.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Goals

Need a strategic tool to bridge the gap between wind turbinesupply and demand.

Optimization engine to automate assignments.

Implications: database and GUI design to integrate businessintelligence and optimal resource allocation.

Adaptive interface to contend supply chain networkuncertainties.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Introduction

Goals

Need a strategic tool to bridge the gap between wind turbinesupply and demand.

Optimization engine to automate assignments.

Implications: database and GUI design to integrate businessintelligence and optimal resource allocation.

Adaptive interface to contend supply chain networkuncertainties.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Wind Turbine

Turbine

Hub

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Objective Function

Min: z = TotalTransportationCostss,w ,p + TotalLDCostsp

where

TotalTransportationCostss,w ,p =∑s∈ Supplier

∑w∈ Week

∑p∈ Project

Supplys,w ,p · TransportationCosts,p

andTotalLDCostp =

∑p∈ Project

LDCostp

for supplier s, week w , and project p.Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

Net Inventory Constraint

ExcessNetInventoryp,w ,c − NetInventoryShortagep,w ,c =

ExcessNetInventoryp,w−1,c − NetInventoryShortagep,w−1,c+

ArrivingInventoryp,w ,c − Demandp,w

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Quantity Arriving at Customer Base p at Week w

ArrivingInventoryp,w ,c =∑

s∈ Supplier

τs,p,w

where

τs,p,w =

Supplys,w− LeadTimes,p ,p, if w − LeadTimes,p ≥ 1

0, otherwise

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

For each customer, the total inventory arriving must exceed thetotal demand.∑

w∈Week

ArrivingInventoryp,w ,c ≥∑

w∈Week

Demandp,w

Production must be kept within production capacity specifications.

Productions,w ≤ ProductionCapacitys,w

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

For each customer, the total inventory arriving must exceed thetotal demand.∑

w∈Week

ArrivingInventoryp,w ,c ≥∑

w∈Week

Demandp,w

Production must be kept within production capacity specifications.

Productions,w ≤ ProductionCapacitys,w

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

SupplierInventorys,w = SupplierInventorys,w−1 + Productions,w−∑p∈ Project

Supplys,w ,p

∑p∈Project

Supplys,w ,p ≤ Productions,w + SupplierInventorys,w−1

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

SupplierInventorys,w = SupplierInventorys,w−1 + Productions,w−∑p∈ Project

Supplys,w ,p

∑p∈Project

Supplys,w ,p ≤ Productions,w + SupplierInventorys,w−1

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

Maximum Early Delivery

ExcessNetInventoryp,w ,c ≤Ω∑

w ′=w+1

Demandp,w ′

where Ω = minw + MaximumEarlyDelivery, ω and ω = theweek number of the GE’s horizona.

aFor our project, GE’s horizon is defined as week #104

Supplier Stock Inventory Capacity

SupplierInventorys,w ≤ StorageCapacitys

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

Maximum Early Delivery

ExcessNetInventoryp,w ,c ≤Ω∑

w ′=w+1

Demandp,w ′

where Ω = minw + MaximumEarlyDelivery, ω and ω = theweek number of the GE’s horizona.

aFor our project, GE’s horizon is defined as week #104

Supplier Stock Inventory Capacity

SupplierInventorys,w ≤ StorageCapacitys

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

The Switchp would capture the liquidated damages costs perproject into the portion below the cap and the portion above.

LDCapp+ Switchp = LDRatep×∑

w∈ Week

maxcNetInventoryShortagep,w ,c

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

LDCostPerProjectp = BinarySwitch · LDCapp+

(1− BinarySwitch) · LDCostPerProjectp

where BinarySwitchp = ISwitch(p)≥0

LDCostPerProjectp ≤ 4 · LDCapp

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

ObjectiveConstraints

Constraints

LDCostPerProjectp = BinarySwitch · LDCapp+

(1− BinarySwitch) · LDCostPerProjectp

where BinarySwitchp = ISwitch(p)≥0

LDCostPerProjectp ≤ 4 · LDCapp

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

User Interface Demonstration

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Breakdown of Costs

Proportion of Liquidated Damages

297170032, 99%

2140850, 1%

Total Transportation Cost

Total Liquidated Damages

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Breakdown of Costs–After Random Supplier Removed

Proportion of Liquidated Damages

295078678, 98%

5421322, 2%

Total Transportation Cost

Total Liquidated Damages

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Supplier Shortages

Comparison of LD Costs

0

100000

200000

300000

400000

500000

600000

1-HAGHL

1-EN

K06

1-MQ1D

T_Q1

1-9F

U6L

1-P9

MU5-Q2 5

5024

85

5044

27

1-C4Z

S4 (2

)

5036

64

1-CH8X

E

5039

07

5043

82

5031

61

Projects

LD Costs

All Suppliers

Random Sample

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Breakdown of Costs–After Major Suppliers Removed in CA and FL

Proportion of Liquidated Damages

35120000, 8%

3.92E+08, 92%

Total Transportation Cost

Total Liquidated Damages

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Capacity Utilization Adjustments

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Summary of Findings

Optimization module complete. Able to effectively allocateresources to minimize costs.

Developed a GUI to incorporate business strategy to makebetter decisions from optimal results.

Google Earth application to provide spatial visualization to aidin manual assignment of resources.

Application is robust to business growth.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Summary of Findings

Optimization module complete. Able to effectively allocateresources to minimize costs.

Developed a GUI to incorporate business strategy to makebetter decisions from optimal results.

Google Earth application to provide spatial visualization to aidin manual assignment of resources.

Application is robust to business growth.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Summary of Findings

Optimization module complete. Able to effectively allocateresources to minimize costs.

Developed a GUI to incorporate business strategy to makebetter decisions from optimal results.

Google Earth application to provide spatial visualization to aidin manual assignment of resources.

Application is robust to business growth.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Summary of Findings

Optimization module complete. Able to effectively allocateresources to minimize costs.

Developed a GUI to incorporate business strategy to makebetter decisions from optimal results.

Google Earth application to provide spatial visualization to aidin manual assignment of resources.

Application is robust to business growth.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Outline

1 Introduction

2 Optimization ModelObjective FunctionConstraints

3 Results

4 ConclusionsSummary of FindingsFuture Possibilities

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Future of User Interface

Continue to develop Google Earth interface and other spatialvisualization tools.

Tracking function to store manual changes made over time.

Manually override optimization results.

Building function to maintain customer and supplierrelationships, in terms of a long term horizon.

Completely integrate Access VB for mass customization.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Future of User Interface

Continue to develop Google Earth interface and other spatialvisualization tools.

Tracking function to store manual changes made over time.

Manually override optimization results.

Building function to maintain customer and supplierrelationships, in terms of a long term horizon.

Completely integrate Access VB for mass customization.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Future of User Interface

Continue to develop Google Earth interface and other spatialvisualization tools.

Tracking function to store manual changes made over time.

Manually override optimization results.

Building function to maintain customer and supplierrelationships, in terms of a long term horizon.

Completely integrate Access VB for mass customization.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Future of User Interface

Continue to develop Google Earth interface and other spatialvisualization tools.

Tracking function to store manual changes made over time.

Manually override optimization results.

Building function to maintain customer and supplierrelationships, in terms of a long term horizon.

Completely integrate Access VB for mass customization.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Conclusions

Future of User Interface

Continue to develop Google Earth interface and other spatialvisualization tools.

Tracking function to store manual changes made over time.

Manually override optimization results.

Building function to maintain customer and supplierrelationships, in terms of a long term horizon.

Completely integrate Access VB for mass customization.

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Questions?

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling

IntroductionModel

ResultsConclusions

Summary of FindingsFuture Possibilities

Thank You

Agarwal, Charles, Holster, Pawar, Ravichandran, Rastogi, Thelen GE Energy Wind Turbine Supply Chain Modeling