Design & Optimization in E-Supply Chains Doctoral Research Roshan Gaonkar Supervisor: Prof N....
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Transcript of Design & Optimization in E-Supply Chains Doctoral Research Roshan Gaonkar Supervisor: Prof N....
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
t L
l
M
mIW
L
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K
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I
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mlmP
MaxPROFIT
lmtlmlktlk
iktikijtij
1
1 11 1
1 11 1
1 1 1 1
1 1 1 1
1 1
Revenue
TransportationProduction
Inventory
Constraints
• Capacity Constraints– Production Capacity– Transportation Capacity
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TtKkJjIiforallijktTijktS &,,
TtKkLlforalllktClktQ &,
TtMmKkLlforalllkmtTlkmtS &,,
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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&,,,
11)1(
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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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Snt
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1
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nt
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Kit
I
&,
1
TtLlforall
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nt
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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
L
l
M
mlmt
Ilm
WCL
l
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klkt
Ilk
WC
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kikt
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WCI
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Irv
WC
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D
d
T
tlkmdt
Slkmd
TClkmdt
Flkmd
TFC
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K
k
D
d
T
tijkdt
Sijkd
TCijkdt
Fijkd
TFC
R
r
V
v
J
j
D
d
T
trvjdt
Srvjd
TCrvjdt
Frvjd
TFC
L
l
K
k
T
tlkt
Qlk
PClkt
Flk
PFC
I
i
J
j
T
tijt
Qij
PCijt
Fij
PFC
R
r
V
v
T
trvt
Qrv
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
Sght
SPgi
XJ
j
D
dijhdt
Stih
I
&,1 1 11 1
')1(
TtHhGgforallght
IK
k
D
dghkdt
Sght
SPtgh
I
&,1 1
)1(
TtHhGgforallght
SPCapght
SP &,
TtDdKkHhIiforallihkdt
Fihkdt
TCapihkdt
S &,,,
TtDdKkHhGgforallghkdt
Fghkdt
TCapghkdt
S &,,,
TtDdHhJjIiforallijhdt
Fijhdt
TCapijhdt
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
TtDdMKLKJIJVRcbaforallabcdt
Fabcdt
S
TtKLJIVRbaforallabt
Fabt
Q
&,,,,,,,,,,,
&,,,,,,
TtDdMKLKJIJVRcbaforall
abcdtF
t
wabw
F
&,,,,,,,,,,,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
K
k
T
tlkt
Ilk
Blkt
Ilk
A
I
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k
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tikt
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Iik
A
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Slkm
A
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Bijkt
Sijk
A
I
i
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PT
tijt
Oij
Bijt
Oij
A
L
l
K
klkmt
Slm
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
TtLlKkJjIiforallL
likt
Ilkt
Mli
RJ
jijkt
Stik
I
&,,,11
')1(
TtKkLlIiforallL
llkt
Mli
Rtik
I
,,,1
)1(
TtKkLlforalllkt
CMlkt
M &,
TtKkLlforallM
mlkt
Ilkmt
Slkt
Mtlk
I
&,1
)1(
Raw MaterialAvailability
ModelInventory
ProductionCapacity
Constraints
• Finished Models MarketplaceModels
Inventory
ServiceLevel
TtMmKkLlforalllmt
IK
klkmt
Stlm
I
&,,1
')1(
lm
DTtMmLlforalllm
Dlm
SLlm
DDtlmI ,1&,
)(
Constraints• Logistics Marketplace
– WarehousingSupplier
Component
MfgComponent
MfgModel
Buyer end model
TtJjIiforallijt
CIijt
I &,
TtKkIiforallikt
CIikt
I &,
TtKkIiforalllkt
CIlkt
I &,
TtMmLlforalllmt
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
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TtMmKkLlforalllkmt
SLtlkm
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(
TtLlforalllt
IN
nlt
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mmn
Smnt
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)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.