Design & Optimization in E-Supply Chains

Doctoral Research

Roshan Gaonkar

Supervisor: Prof N. Viswanadham

The Logistics Institute – Asia Pacific

Agenda

• The Internet and E-Supply Chains.• Assumptions, Motivation and Contributions.• Mathematical Models for Planning in E-Supply

Chains– Basic LP model for Private Marketplaces.– Realistic MILP model for Private Marketplaces– QP and MILP model for Supply Chains with Public

Trading Exchange.

• Future Work

Fundamentals of E-Supply Chains

Trends in E-Supply Chains

• Emergence of Electronic Marketplaces– Private Marketplaces.

– Public Trading Exchanges.

• Virtual Organizations and Extended Supply Chains– Information-based Supply Chain Managers.

• Alliances and Partnerships– Outsourced Manufacturing and Logistics.

– Global Supply Chain Networks.

Global Extended SC Networks

Supplier Assembler

Supplier Distributor

Logistics Provider

Customer

Retailer Bank

Internet

IT Network (Extranet)

Logistics Hub

Logistics Network

Material Flow Integration

Source : Analysis of Manufacturing Enterprises by Prof. N. Viswanadham

A Typical Scenario

Global

Partner selection based on customerlocation

Extended Supply Chain Planning

• Global optimum in planning, using global visibility.

Motivation, Assumptions and Contributions

Physical Significance

• Dynamic Manufacturing Networks

– Network of companies sharing same destiny.

– Information visibility between partners.

– Contract Manufacturing in the Electronics

Industry.

Hi-Tech Manufacturing

• Dell private marketplace– Receives orders from customers.– Global Supply Chain.– Manufacturing outsourced to contract

manufacturers and logistics outsourced to 3PLs.– Constant access to supply chain operational

information.– Manages supply chain through superior

planning.

Motivation

• To understand emerging business models in E-Supply Chains.– Channel Masters.

– 4th Party Logistics.

– Contract Manufacturing.

• To develop planning tools for knowledge based businesses Internet-enabled supply chains.

Basic AssumptionsPrivate Marketplace

• Controlled by dominant channel master.• Contract Manufacturers and Logistics Partners.• High-level of trust exists between partners.• Global Visibility in the Extended Supply Chain

– Schedules– Capacities– Costs– Inventories

• Profit sharing between partners.

Basic AssumptionsPublic Trading Exchange

• Market-maker builds environment of trust.

• Supply-demand information– Quantity– Cost– Delivery Date

• Companies participate in multiple marketplaces

Research Contributions

• Defined and formulated specific research problems in Internet-enabled extended supply chain networks.

• Developed optimization models for systematic management of on-line knowledge-based businesses.

Research Contributions

• Develop a common framework to analyze various supply chain strategies.– Make-to-Order, Make-to-Stock, New Product Development etc.

• Models for partner selection in supply chain networks.– Contract Manufacturers.– Strategic and Operational Level.

• Inclusion of logistics in supply chain planning.– Fixed Schedules.– Transshipment Hubs.– Synchronization of Manufacturing and Logistics

Classification of Models

Private Marketplace

Private Marketplace with fixed costs

Public Marketplace with combinatorial auctions

Public Marketplace with dynamic pricing

LP MILP QP

Complexity

Fea

ture

s

Less

More

Mathematical Models for Planning in E-Supply Chains

A Basic LP Planning Model for Private Marketplaces

Models deployed in the SC

MP Model

Basic AssumptionsPrivate Marketplace

• Controlled by dominant channel master.• Contract Manufacturers and Logistics Partners.• High-level of trust exists between partners.• Global Visibility in the Extended Supply Chain

– Schedules– Capacities– Costs– Inventories

• Profit sharing between partners.

Channel Master

S

S

M

M

B

B

•Capacity•Sub-Assy•Cost

Logistics Logistics

•Capacity•Models•Cost

•Costs•Capacity

•Demand•Due Date•Buying Price

•Costs•Capacity

Model Formulation

•Activities

–Sub-Assembly Production

–Transport from Suppliers to Manufacturer

–Manufacturing/Assembly

–Transport from Manufacturer to Buyers

•Inventories

–Sub-Assembly inventory at Supplier

–Sub-Assembly inventory at Manufacturer

–Model inventory at Manufacturer

–Model inventory at Buyer

C

C

•Capacity•Component•Cost

Logistics

•Costs•Capacity

Channel Master

Model Features

Supply Chain Information Shared

Decisions to be Made

1. Available to promise Manufacturing Capacity for each Supplier.

2. Fixed Schedules for Transportation

3. Complex Product structure with multiple components, sub-assemblies, brands

4. Inventory costs at multiple levels

5. Transportation costs

6. Production costs

1. Determination of multiple plant schedules

2. Determination of multi-period schedules

3. Allocation of procurement quantities amongst multiple suppliers

Strategic level Partner selection and Operational level Scheduling

Notation

• i : index used to denote products• j : index used to denote suppliers• k : index used to denote

assemblers• l : index used to denote models• m : index used to denote the buyers

• Subscripts– I : set of components.– L : set of finished models– J : set of suppliers.– K : set of Manufacturers– M : set of Buyers

• Parameters– D : buyer’s demanded quantity– P : cost of production for

manufacturer/supplier or cost price to buyer

– U : unit transportation cost– C : production/manufacturing capacity– T : Transportation capacity

• Variables– S : supplies transported between two

parties.– I : inventories at each time period– Q : quantity produced in each time

period

Objective• Maximise ProfitProfit = Revenue – (Cost of Production + Cost of

Transportation + Cost of Inventory)

T

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MaxPROFIT

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1 11 1

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1 1

Revenue

TransportationProduction

Inventory

Constraints

• Capacity Constraints– Production Capacity– Transportation Capacity

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TtKkJjIiforallijktTijktS &,,

TtKkLlforalllktClktQ &,

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Production

Transportation

Constraints• Inventory Flow Constraints

– Tracking of inventory level at each time period– Consumption and addition to inventory

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&,,

1)1(

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1)1(

Supplier Component

MfgComponent

MfgModelBuyer end

model

Constraints

• Availability of Raw Materials

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llktlitik

,,,

1)1(

lmlmDtlm DTtMmLlforallQIlm

,1&,)(

• Fulfillment of Order

Experiments

• Dynamic Supply Chain Network Configuration for different orders.

• Quantifying the Impact of Information Sharing.– Make-to-Order– Make-to-Stock (modeled by inventory holding)

Data

Available Manufacturing Capacity per Time Period

0

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12

Time Period

Un

its M1

M2

M3

Manufacturer Brand Production Cost

9

10

11

12

13

M1 M2 M3

ManufacturerPr

ice

($ p

er u

nit)

ManufacturerBrand ProductionCost

Sub-Assembly Manufacturer Production Capacity Availability for Sub-Assembly 1

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

Time Period

Un

its

S1

S2

S3

S4

S5

Sub-Assembly Production Costs

05

10152025

Sub-Assembly 1 Sub-Assembly 2

Sub-Assembly

Pro

du

ctio

n C

os

t ($

p

er

un

it) S1

S2

S3

S4

S5

Sub-Assembly Transportation Costs

05

10152025

Transportation Link

Co

st

($ p

er

un

it)

Sub-Assembly 1

Sub-Assembly 2

Transportation capacity per time period for sub-assembly 1 for each link

0

100

200

300

400

1 3 5 7 9 11

Time Period

Cap

acit

y (U

nit

s)

S1M1

S1M2

S1M3

S2M1

S2M2

S2M3

S3M1

S3M2

S3M3

S4M1

S4M2

S4M3

S5M1

S5M2

S5M3

Sub-Assembly Inventory Holding Cost

02468

10

Inventory Location

Co

st

($ p

er

un

it)

Sub-Assembly 1

Sub-Assembly 2

Dynamic SC ConfigurationPartner Selection

Profit : $85,724

Profit : $87,935

Quantifying the Impact of Information Sharing

• No information sharing– Need to rely on forecasting.– Need to keep safety stock.– Make-to-stock.

• Information sharing– Synchronization of activities.– JIT manufacturing and

delivery. • No inventory.

– Make-to-order.

Constraints modeling MTS• Stock level constraints

– Enough components to meet same production level as last n periods.

– Enough finished goods to meet same demand as last n periods.

Supplier Component

MfgComponent Mfg

Model

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The Value of Sharing InfoComparison of Supply Chain Costs with and without

information sharing

600000

700000

800000

900000

1000000

1100000

1200000

1300000

1400000

50 60 70 80 90 100

Demand Load on the Supply Chain

Co

st

of

me

eti

ng

th

e d

em

an

d

With InformationSharing

Without InformationSharing

Impact on the Capacity of the Network

• Minimal warehousing requirements for make-to-order SC.

• Bull-whip effect.X

Without InfoSharing

2½ X

With InfoSharing

Profit Increase of

380% at a cost

increase of only 12%

A Realistic MILP Planning Model for Private Marketplaces

Additional Features

• Fixed costs– Production– Transportation– Can be used to model international trade tariffs.

• Transportation Lead-times– Air & Sea

• Transshipment Hubs and Merge-in-Transit• Customer Service Levels

Additional Notation

• d : index to denote transportation mode (1 = Air; 2 = Sea).

• D : Set of Transportation modes.

• h : index to denote transshipment hub.

• H : Set of Transshipment hubs.

• g : index to denote shipment package.

• G : Set of shipment packages.

Parameters

• TFC : Fixed cost of Transportation.

• PFC : Fixed cost of Production.

• TL : Transportation lead-time.

• CSL : Customer Service Level.

• LSC : Cost of Lost Sale.

• BD : Buyer Demand.

Variables

• S’ : Supplies received at the destination.

• BS : Qty sold to Buyer.

L

l

M

m

T

tlmt

LSClmt

BSlmt

BD

T

i

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TCijkdt

Fijkd

TFC

R

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tlkt

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PClkt

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PFC

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PFC

R

r

V

v

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trvt

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PCrvt

Frv

PFC

L

l

M

m

T

tlmt

BSlm

P

MaxPROFIT

1 1 1

1

1 11 1

1 11 1

1 11 1

1 1 1 1 1

1 1 1 1 1

1 1 1 1 1

1 1 1

1 1 1

1 1 1

1 1 1

Objective –Maximize Profit

Revenue Production

Inventory

Transportation

Lost Sales

FixedCosts

Capacity Constraints

• Capacity Constraints with Fixed Costs– Production Capacity– Transportation Capacity

Production

Transportation

TtJjIiforallijt

Fijt

PCapijt

Q &,

TtDdKkJjIiforallijkdt

Fijkdt

TCapijkdt

S &,,,

TtKkLlforalllkt

Flkt

PCaplkt

Q &,

TtDdMmKkLlforalllkmdt

Flkmdt

TCaplkmdt

S &,,,

Transportation Constraints

TtDdKkJjIiforallTLtijkd

Sijkdt

Sjkd

&,,,)(

'

• Qty shipped received at a later stage– Lead-time dependent on origin and destination.– Lead-time dependent on mode of shipment

TtDdMmKkLlforalllkmdt

Flkmdt

TCaplkmdt

S &,,,

• Multi-modal Logistics

Customer Service Level

• Service Level Limitations

TtMmLlforalllmt

BDlmt

BSlmt

BDlm

BSL &,

TtDdKkJjIiforalllmt

BSlmt

IK

k

D

dlkmdt

Stlm

I

&,,,1 1

')1(

• Inventory at Point of Sale

Transshipment Hub

• Model scenario where suppliers may be preferred for procurement, if they are already supplying other components.

• Model merge-in-transit and cross-docking centers.

• In-coming inventory, Packaging and Outgoing inventory

Transshipment Hub Constraints

ComponentsInventory

TtHhIiforallG

giht

IK

k

D

dihkdt

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&,1 1 11 1

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TtHhGgforallght

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Sght

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&,1 1

)1(

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SPCapght

SP &,

TtDdKkHhIiforallihkdt

Fihkdt

TCapihkdt

S &,,,

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Fghkdt

TCapghkdt

S &,,,

TtDdHhJjIiforallijhdt

Fijhdt

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S &,,,

TtDdHhJjIiforallTLtijhd

Sijhdt

Sijh

&,,,)(

'

In-boundComponents

ShipmentPackaging

OutboundShipmentPackages

ShipmentPackage Inv

OutboundComponents

Computational Complexity

• Production planning problems with fixed cost are NP hard.

• Using Branch and Bound– Network flow problems with fixed cost do not

converge fast enough.

• Hence, need to develop tighter formulations.

Tighter Formulation

• Zero-Production Nodes

• Implication Constraint

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Fabcdt

S

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Q

&,,,,,,,,,,,

&,,,,,,

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abcdtF

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&,,,,,,,,,,,1

Experiments

• Dynamic Supply Chain Network Configuration for different orders.

• Effect of Transshipment Hubs.

• Analysis of Supply Chain Costs.

• Managing Multiple Generations of Products.

Dynamic SC Network Configuration

Dynamic SC Network Configuration

Dynamic SC Network Configuration

Dynamic SC Network Configuration

• Selection of partners based on location of buyer.• Total landed cost of fulfilling the order.• Logistics congestion can result in underutilized

manufacturing plants.• Synchronization of manufacturing with the

logistics schedules.• In combined planning manage trade-off

– In savings from joint procurement against the need to procure from more expensive suppliers.

Transshipment Hubs

Transshipment Hubs

• Existing suppliers are preferred for procurement of other sub-assemblies.

• Sub-assembly suppliers down to 3 from 4, Contract manufacturers down to 2 from 3.

• Results in supplier rationalization.

Analysis of Supply Chain Costs

Cost Distribution for Various Demand Patterns

38.53108 39.0121638.16864 38.19784 38.76235

3.3216323.702548

3.34898 3.4454183.726445

0.874109

0.867158

1.03196 0.9557950.8812475

35363738394041424344

Steady Des Asc Sea-D Sea-U

Demand Patterns

Co

st (

Mil

lio

ns)

InventoryHolding Cost

TransportationCost

ProductionCost

Analysis of Supply Chain Costs

• Decreasing demand and Seasonal-up – More expensive suppliers and transportation to

meet large demands early on.

• Ascending demand and Seasonal-down– Inventory costs are higher because of need to

store goods to meet late demand.

Managing Multiple Generations of Products

Product Demand Over its LifeCycle

0

5

10

15

20

25

30

Time Period

Units

of D

eman

d M1B1

M1B2

M2B1

M2B2

Managing Multiple Generations of Products

Managing Multiple Generations of Products

• Time-to-market vs. Product Introduction cost.

• Trade-off between savings from joint procurement for two different generations and expenses for procurement from expensive suppliers.

QP and MILP model for SC with Public Trading Exchange

Models deployed in the SC

MP Model

Models for PTX

• Quadratic Programming– Dynamic Pricing based on Supply & Demand.– Chooses qty and price in both marketplaces.

• Mixed Integer Linear Programming– Combinatorial auction.– Chooses winning bids in both marketplaces.

Basic AssumptionsPublic Trading Exchange

• Manufacturers participate in Multiple PTX• Participants share supply and demand

information during negotiations.• More information ascertained with each

round of negotiations.• Information

– Supply-Demand Curves or Qty-Price Bids– Delivery Date

Quadratic Programming Model

Features of the Model

• Dynamic Pricing – responsive to market

• Selection of Partners

• Selection of Optimal Price

• Selection of Optimal Quantity

• Synchronization of Manufacturing and Logistics Schedules.

Supply-Demand Curves

Notation• i : index used to denote comp.• j : index used to denote suppliers• k : index used to denote

assemblers• l : index used to denote models• m : index used to denote the buyers

• Subscripts– I : set of components.– L : set of finished models– J : set of suppliers.– K : set of Manufacturers– M : set of Buyers

• Parameters– A : Slope of supply/demand curve– B : Intercept of supply/demand curve– C : Maximum availability of components– CM: Production capacity.– T : Transportation capacity– CI: Inventory capacity– SL : Service Level– B : Buyer’s demanded quantity.– P : cost of production– LT : Transportation lead-time

• Variables– S : supplies transported between two parties.– I : inventories at each time period– M : Qty produced by manufacturer– O : Qty of components procured

L

l

M

m

DD

tlmt

Ilm

Blmt

Ilm

A

L

l

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k

T

tlkt

Ilk

Blkt

Ilk

A

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A

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PT

tijt

Oij

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A

L

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BM

m

T

tlkmt

Slm

A

MaxPROFIT

lm

1 1 1

2

1 1 1

2

1 1 1

2

1 1 1 1

2

1 1 1 1

2

1 1 1 1 11

2

1 1 1 1

2

Objective• Maximize Profit

Profit = Revenue – (Cost of Procurement + Cost of Production + Cost of Transportation + Cost of Inventory)

Revenue

Transportation

Production

Inventory

Procurement

Constraints

• Procurement Marketplace

TtJjIiforallijt

Cijt

O &,

MarketplaceCapacity

TtKkJjIiforallK

kijt

Iijkt

Sijt

Otij

I

&,,1

)1(

Component SupplierInventory

Constraints

• Manufacturing FacilitiesComponentInventory

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likt

Ilkt

Mli

RJ

jijkt

Stik

I

&,,,11

')1(

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llkt

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,,,1

)1(

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M &,

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mlkt

Ilkmt

Slkt

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I

&,1

)1(

Raw MaterialAvailability

ModelInventory

ProductionCapacity

Constraints

• Finished Models MarketplaceModels

Inventory

ServiceLevel

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IK

klkmt

Stlm

I

&,,1

')1(

lm

DTtMmLlforalllm

Dlm

SLlm

DDtlmI ,1&,

)(

Constraints• Logistics Marketplace

– WarehousingSupplier

Component

MfgComponent

MfgModel

Buyer end model

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CIijt

I &,

TtKkIiforallikt

CIikt

I &,

TtKkIiforalllkt

CIlkt

I &,

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CIlmt

I &,

Constraints• Logistics Marketplace

– Transportation

TransportCapacity

DeliveryLead-time

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Tijkt

S &,,

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Sijk

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)(

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Tlkmt

S &,,

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Slkm

&,,')(

Experiment

Solution

• Determines optimal quantities and corresponding prices.

• The solution of the model also provides schedules for manufacturing and logistics.

• QP provides integrated strategic-level dynamic pricing and partner selection tool and low level operational scheduling tool.

MILP Model

Combinatorial Auctions

• Sellers quote prices for bundles of components.• Buyers place bids on bundles of finished models.• All bids provide

– Qty - [q1,q2,q3,q4,q5]

– Due Date - [0,0,0,0,1,0,0,0,0]

– Price - $ 123.

• Manufacturer needs to choose optimal seller bids and accept optimal buyer bids.

Features of the Model

• Combinatorial Auctions in Multiple PTX.

• Selection of Partners.

• Selection of Optimal Bids.

• Production Scheduling.

Notation• i : index used to denote comp.• j : index used to denote

suppliers• l : index used to denote models• m : index used to denote buyers• n : index used to denote bids

• Subscripts– I : set of components.– L : set of finished models– J : set of suppliers.– N : set of bids– M : set of buyers

• Parameters– SQ : Qty being sold of components– SD : Date on which bid will deliver– SP : Quoted selling price of component– BQ : Qty demanded of models– BD : Date on which bid needs to be fulfilled– BP : Quoted buying price of models– R : Units of components required for 1 unit of the model– T : Production lead-time– P : Production cost– W: Inventory holding cost

• Variables– S : Accept bid– I : inventories at each time period– M : Qty produced by manufacturer

Objective

I

i

T

t

L

l

T

tlt

Ilt

Wit

Iit

W

L

l

T

tlt

Plt

MJ

j

N

nnj

SPnj

S

M

m

N

nnm

BPnm

S

MaxPROFIT

1 1 1 1

1 11 1

1 1

• Maximize ProfitProfit = Revenue – (Cost of Procurement + Cost of Production + Cost of

Inventory)

RevenueProduction

Inventory

Procurement

ConstraintsComponents

Inventory

TtIiforallit

IN

nlt

Mli

RJ

jnj

Sjnt

SQjnt

SDti

IL

l

&1 1

)1(1

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llt

Mli

Rti

I

&1

)1(

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IN

nlt

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mmn

Smnt

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&1 1

)1(

ModelInventory

Raw MaterialAvailability

• Manufacturer Constraints

Future Experiments

• To study impact of dumping on supply chain.

• To study impact of sudden shortages on the supply chain.

Future Work

Future work

• To develop a multi-layer adaptive control for supply chain planning– Based on SC performance can plan to buy or

sell additional capacity

• To develop risk management models for SC

Academic Papers

Journal Papers

• Journal Paper– N. Viswanadham and Roshan Gaonkar, Internet-

based Collaborative Scheduling in Global Contract Manufacturing Networks, Submitted to the IEEE Transactions on Mechatronics.

• Journal Paper in Revision– N. Viswanadham and Roshan Gaonkar, Partner

Selection and Synchronized Planning in Dynamic Manufacturing Networks, Submitted to the IEEE Transactions on Robotics and Automation.

Conference Papers

• Conference Papers– N. Viswanadham, Roshan S. Gaonkar and V.Subramanian,

Optimal configuration and partner selection in dynamic manufacturing networks, Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, May 2001, pp 854-859.

– Roshan S. Gaonkar and N. Viswanadham, Collaborative scheduling model for supply hub management, Third AEGEAN International conference on Analysis and Modelling of Manufacturing Systems, Tinos Island, Greece, May 16-20, 2001.

– Roshan S. Gaonkar and N. Viswanadham, Systematic Design of Electronic Marketplaces, Proceedings of the Total Enterprise Solutions Conference, Singapore, June 2001.

– N. Viswanadham and Roshan S. Gaonkar, Foundations of E-supply chains, Int. Conf. on Port and Maritime R & D and Technology, Singapore, Oct 29-31, 2001.