Phd Defence 25 Jan09

Department of Mechanical EngineeringIndian Institute of Technology Delhi

Supply Chain Performance Supply Chain Performance Improvement: The Role of ITImprovement: The Role of IT

Presented By:Bibhushan

Entry No: 2002RME027

Supervisors:

Prof. S. Wadhwa and Prof. Anoop Chawla


22

Presentation OutlinePresentation Outline

Research Context and Motivation Research Objectives An Overview of the Research Work Significant Contributions of Research Work Publications Response to Examiner’s Comments


33

Research Context and MotivationResearch Context and Motivation

Simulation for Supply Chain Modeling and Analysis Used for analysis of complex systems Type of problems modeled range from tactical to strategic

Object-Oriented Simulation Modeling Detailed model of a complex system can be made by combining basic

building blocks Has advantages of inheritance, encapsulation, modularity, etc.

Multiple Entity Flow Perspective Five flows: Material, Information, Money, Resource, Decision

Focus on Inventory Management to improve IT facilitated SC performance


44

Research ObjectivesResearch Objectives

Highlight the research motivation to Develop an object-oriented supply chain

simulation-modeling environment Develop demonstrative models to illustrate the

efficacy of the approach in SC performance Study the inventory management in supply chains

working under stochastic demands


55

Research ObjectivesResearch Objectives Develop an object-oriented supply chain modeling

and simulation environment based on multiple-entity flow perspective which should be capable of: Modeling the flow of multiple entities Stochastic modeling Adding user-defined decision rules in addition to major

control decision rules User-friendly and cost effective Robust modeling by means of effective error handling and

fool-proofing in data input Distributed simulation


66

Research ObjectivesResearch Objectives Analyze inventory management along multiple criteria

(demand variance, inventory, service level etc.) Understand the effect of Expected Service Quality (ESQ) on

different inventory policies Determine optimal ESQ for each node Determine optimal Information sharing level for the ESQ levels found

above Understand the effect of ordering and capacity constraints on

different inventory policies Determine Optimal Ordering and Capacity constraints for each node

Determine the optimal information sharing level for ordering and capacity constraint levels determined above

Determine the effect of change in Coefficient of Variance (COV) on each supply chain node

Determine the optimal Information sharing level for different COV levels


77

Overview of Research WorkOverview of Research Work Organization of Thesis Conceptual Framework Simulation Modeling Environment Performance of Supply Chain under Controlled

Variability Optimizing ESQ for Supply Chain Nodes Optimizing Optimal Ordering and Capacity

Constraint levels for Each Supply Chain Node Understand the Effect of Changing COV on supply

chain


88

Organization Organization of Thesisof Thesis Chapter 5: Effect of Demand

Impulses on the Performance of Supply Chain

Chapter 6: Effect of Expected Service Quality (ESQ) on the Performance of Supply Chain

Chapter 7: Effect of Ordering and Capacity Constraints on the

Performance of Supply Chain

Chapter 8: Supply Chain Performance under Different Levels of Demand Variance

Chapter 9: Conclusions and Scope for Future Work

Start

Stop

Introduction

Experim

ental Setup

Effect of Im

pulse Am

plitude on the

Supply C

hain

Effect of B

alance Gap

on the S

upply Chain

Effect of N

umber of Im

pulses on the S

upply Chain P

erformance

Cha

pter Sum

mary and K

ey C

onclusions

Effect of Im

pulse Width on the

Supply C

hain

Intr

oduc

tion

Exp

erim

enta

l Set

up

RS

Opt

imiz

atio

n of

Sup

ply

Cha

in

with

DF

P

RS

Opt

imiz

atio

n of

Sup

ply

Cha

in

with

OQ

P

RS

Opt

imiz

atio

n of

Sup

ply

Cha

in

with

sS

P

RS

Opt

imiz

atio

n of

Sup

ply

Cha

in

with

sQ

P

Cha

pter

Sum

mar

y an

d K

ey

Col

clus

ion

s

Sel

ectio

n o

f Opt

imal

Info

rmat

ion

Sha

ring

Leve

l fo

r E

ach

Nod

e

Introduction

Experim

ental Setup

MnO

Q S

election for Each

Node in

the Supply C

hain

MxO

Q S

election for Each N

ode in the S

upply Chain

Selection o

f Optim

al Information

Sharing Level fo

r each Node

Cha

pter Sum

mary and K

ey C

onclusions

Intr

oduc

tion

Per

form

ance

of D

FP

-Bas

ed

Sup

ply

Cha

in u

nder

CO

V

Per

form

ance

of s

SP

-Bas

ed

Sup

ply

Cha

in u

nder

CO

V

Per

form

ance

of s

QP

-Bas

ed

Sup

ply

Cha

in u

nder

CO

V

Cha

pter

Sum

mar

y an

d K

ey

Col

clus

ion

s

Thesis S

umm

ary

Key C

onclusions

Industry Implications

Salient C

onstributions

Limitations and F

uture Directions

Chapter 1: Introduction

Research C

ontext and M

otivation

Research O

bjectives

Cha

pter Plan

Chapter 2: Literature Review

Intr

oduc

tion

Sup

ply

Cha

in S

imul

atio

n

Inve

nto

ry M

ana

gem

ent i

n S

uppl

y C

hain

Incr

ease

d V

isib

ility

thro

ugh

In

form

atio

n T

echn

olog

y (I

T)

Res

earc

h G

aps

Cha

pter

Sum

mar

y

Chapter 4: Development of Simulation Modelling

Environment and Simulation Models In

trod

uctio

n

Mod

ellin

g of

Sup

ply

Ch

ain

Bui

ldin

g B

lock

s

Mod

ellin

g S

upp

ly C

hain

Dec

isio

ns

Per

form

ance

Me

tric

s

Mod

ellin

g th

e S

uppl

y C

hain

Mod

el V

erifi

catio

n an

d V

alid

atio

n

Cha

pter

Sum

mar

y

Chapter 3: An Object-Oriented Simulation-Based Framework for Modelling Supply Chains

Introduction

A G

eneric Model o

f Supply C

hain

Obje

ct Oriented S

imulation

Modelling

Modelling of E

lementary S

upply

Cha

in Constructs

Hierarchy of O

bjects Used in

Supply C

hain Modelling

Cha

pter Sum

mary


99

Conceptual FrameworkConceptual Framework

A Generic Model of Supply Chain Object Oriented Modeling Perspective Modeling of Elementary Supply Chain

Constructs Hierarchy of Object Used in Supply Chain

Modeling Modeling Supply Chain Decisions


1010

Simulation Modeling EnvironmentSimulation Modeling Environment

Modeling the Supply Chain Building Blocks Modeling the manufacturing system Modeling the transports Modeling the Player Role Modeling the Supply Chain Node Modeling the Inter-Node Interactions

Defining Inter-Node Relationships Defining Inter-Node Lead Times Defining Inter-Node Speeds Defining Inter-Node Distances Defining Product Demands


1111

Simulation Modeling EnvironmentSimulation Modeling Environment

Modeling Supply Chain Decisions Source Selection Policies Inventory Control Decisions Transportation Decisions Production Planning Decisions

Performance Metrics Inventory Related Demand Related Service Related

Supply Chain Model for Research Model Verification and Validation


1212

Performance of Supply Chain under Performance of Supply Chain under Controlled VariabilityControlled Variability

Experimental Setup Demand impulses Simulation parameters Balancing the inventory policies Performance metrics considered

Effect of Transformed Relative Impulse Amplitude (TRIA) on the Supply Chain

Effect of TRIA on the Supply Chain using Demand Flow Policy (DFP)

Effect of TRIA on the Supply Chain using Order Q Policy (OQP) Effect of TRIA on the Supply Chain using (s, S) Policy (sSP) Effect of TRIA on the Supply Chain using (s, Q) Policy (sQP)


1313


Effect of Balance Gap (BG) on the Supply Chain Effect of BG on the Supply Chain using DFP

Effect of Negative Impulse BG (NIBG) Effect of Positive Impulse BG (PIBG)

Effect of BG on the Supply Chain using OQP Effect of NIBG Effect of PIBG

Effect of BG on the Supply Chain using sSP Effect of NIBG Effect of PIBG

Effect of BG on the Supply Chain using sQP Effect of NIBG Effect of PIBG


1414


Effect of Number of Impulses (NI) on the Supply Chain Effect of NI on the Supply Chain using DFP

Effect of Number of Negative Impulses (NNI) Effect of Number of Positive Impulses (NPI)

Effect of NI on the Supply Chain using DFP Effect of NNI Effect of NPI




1515


Effect of Impulse Width (IW) on the Supply Chain Effect of IW on the Supply Chain using DFP

Effect of Negative Impulse Width (NIW) Effect of Positive Impulse Width (PIW)

Effect of IW on the Supply Chain using DFP Effect of NIW Effect of PIW




1616


1717

Supply Chain ProcessesSupply Chain Processes Plan

Balances aggregate demand and supply Source

Procures goods and services to meet planned or actual demand

Make Transforms product to a finished state

Deliver Provides finished goods and services

Return Post-delivery customer support


1818

A Generic Model of Supply ChainA Generic Model of Supply Chain


1919

Supply Chain FlowsSupply Chain Flows

Primary Flows (Between Nodes) Material Flow Information Flow Cash Flow

Secondary Flows (Only inside Node) Resource Flow Decision Flow


2020

Object Oriented Object Oriented Supply Chain SimulationSupply Chain Simulation

Simulation is a technique where computers imitate the operations of various kinds of real-world facilities or processes (Law and Kelton 1991)

Discrete-event simulation Object oriented modelling OOPs based simulator for modeling flexible

supply chains


2121

Need for Object Oriented Need for Object Oriented Supply Chain SimulationSupply Chain Simulation

Supply chain flexibility offers many challenges and opportunities

It offers decision choices as the system evolves which is dynamic in nature

There is a need for developing a modeling environment to deal with flexibility and dynamic decision making

A OOPs based simulation system is developed and explored for its efficacy in this research


2222

Advantages of Advantages of Object Oriented ModelingObject Oriented Modeling

Inheritance A class of objects can itself be linked to one or several

super-classes from which it acquires characteristics and behavior

Encapsulation Describes its characteristics along with its relationships to

other components and the functionality of the object Allows structured development of the model Hides unimportant details

Modularity Provides a very high degree of code reusability


2323


Allows the model builder to develop the models with much less effort

Suitable for modeling distributed systems having client-server architecture

Plug-and-play software capability Interoperability across the network Platform independence Allows complex systems to be constructed

with minimum of redundant work


2424


A logical choice for developing custom or dedicated simulation models

Sub-components may be prefabricated by some expert group for a specific need or application

Productivity of software development improves if code is reused, since the specific modules are already extensively tested by their developers


2525


Provides a natural mapping paradigm which allows one-to-one mapping between objects in the system being modeled and their abstractions in the object model Allows the developer to achieve a faster transition

of the conceptual model into the software implementation

Object-oriented models generally have a cleaner structure than the event oriented ones


2626

Overall ArchitectureOverall Architecture Basic building blocks are

used to create some lower level complex objects

Lower level objects are then used to define the higher level objects

Level 1 objects are made up of basic building blocks

Basic building blocks are combined with the object(s) of level 1 to form level 2 objects


2727

Object Oriented Modeling of Object Oriented Modeling of Supply ChainsSupply Chains

Supply chain decision making requires rapid and flexible modeling approach at various levels of detail

Object oriented modeling can be used for Designing and implementing reusable classes for

building models of supply chains Creating a supply chain object library

Facilitates rapid model development Aid in application of the modeling architecture to

specific scenarios at various levels of abstraction


2828

Object Oriented Features in Arena Object Oriented Features in Arena Simulation EnvironmentSimulation Environment

Offers model development in object oriented manner by means of objects called “modules”

Modules are essentially composed of other basic level modules

Once properly developed, these modules can be reused in other simulation models

However…


2929

Limitations of Object Oriented Limitations of Object Oriented Features in ArenaFeatures in Arena

Modules can be run only on systems having ARENA

Version Conflicts Not suitable for distributed computing Cost of buying this simulation package Additional cost of buying the customized

module libraries

What is the solution then?


3030

Generic Programming LanguagesGeneric Programming Languages

Not as easy as developing models using simulation packages

However, It is more general and the SC flexibility related

issues can be modeled in detail. Availability of customized object libraries for a

variety of applications can significantly reduce the time and effort involved in model building process

It offers platform independence to a large extent


3131

IT tool used: VB.NetIT tool used: VB.Net

Ease of designing the user interface Now fully object-oriented Provides a very high degree of platform

independence only for Windows based platforms however

Supply chain flexibility and dynamic decision making can be developed as a customized option.


3232

Research GapsResearch Gaps

Need to develop simulation tools ideally suited for flexible supply chain simulation Effective modeling of Supply Chain Flexibility Web-based simulation environment

Demonstrate benefits of collaborative decision making Non-deterministic and dynamic modeling Analyzing the impact of different control

decisions in an integrated manner Distributed computing needs to be explored


3333

Research GapsResearch Gaps

Need to study the impact of information sharing under different IT options

Supply Chain performance under different levels of Demand History, Service Level, Demand Variance needs to be studied

There is need for demonstrative models to illustrate the benefits of IT tools focused on modeling of the flexible supply chains.


3434

Overview of the Research WorkOverview of the Research Work Development of the IT tools for modeling Flexibility

and Dynamic decision making Manufacturing systems and supply chains were modelled

in terms of five types of flows: information flow, decision flow, material flow, resource flow and money flow

Extended the Multiple Entity flow perspective proposed by Wadhwa & Rao (2003)

Development of demonstrative simulation models for illustrating supply chain performance improvement by the use of IT


3535

Overview of the Research WorkOverview of the Research Work Supply Chain performance improvement under

flexibility and dynamic decision making. Focus on inventory management.

Comparison of Inventory Control Policies under Deterministic Variability

Effect of Demand History on Supply Chain Performance

Effect of Service Level on Supply Chain Performance

Effect of Demand Variance on Supply Chain Performance


3636

Supply Chain Management DefinedSupply Chain Management Defined

SCM is “the integration of business processes from end-user through original suppliers that provides products, services, and information that add value for customers” (Lambert et. al. (1998)


3737

Modeling ElementaryModeling ElementarySupply Chain ConstructsSupply Chain Constructs

Classification of Objects Multiple Entity Flow Perspective Action Points as Processes in the System


3838

Classification of ObjectsClassification of Objects


3939

Multiple Entity Flow PerspectiveMultiple Entity Flow Perspective


4040

Action Points as Action Points as Processes in the SystemProcesses in the System


4141

Hierarchy of Object Used inHierarchy of Object Used inSupply Chain ModelingSupply Chain Modeling

Modeling of a Supply Chain Network Modeling of Supply Chain Nodes Modeling of Supply Chain Operations Modeling the Manufacturing System


4242

Levels of Abstraction forLevels of Abstraction forSupply Chain ModelingSupply Chain Modeling


4343

Modeling of aModeling of aSupply Chain NetworkSupply Chain Network

As a collection of supply chain nodes Each node is a fully autonomous unit Define relationships between each pair of

nodes Two types of relationships

Buyers (can select Sellers) Sellers (can only be selected)

Constrained relationships By the level of respective nodes


4444

Integration of Supply Chain NodesIntegration of Supply Chain Nodes


4545

Multiple Supply Chains in a Multiple Supply Chains in a Collection of Supply Chain Nodes Collection of Supply Chain Nodes


4646

Modeling of Supply Chain NodesModeling of Supply Chain Nodes Two kinds of Nodes:

Manufacturing (Value-adding) Non-Manufacturing (store the material and supply it to

other nodes) Flows through each node:

Material flow Information flow Money flow

Flows Inside node Resource Flow Decision Flow


4747

Modeling of Supply Chain NodesModeling of Supply Chain Nodes

Five Processes Plan, Source, Make, Deliver and Return

Return Out of scope of this work

Store Additional Process


4848

A Manufacturing NodeA Manufacturing Node


4949

A Non-manufacturing NodeA Non-manufacturing Node


5050

Integration Integration of Majorof MajorSupply Supply Chain Chain OperationsOperations


5151

MakeMake

Manufacturing operations Product quantity is decided by planning Produces the goods according to the control

policies determined by production planning Routing Scheduling


5252

SourceSource

Decides the sellers from whom to procure necessary goods

A sourcing policy is a decision rule that determines the best seller(s) out of a number of available sellers in accordance with some predefined criterion e.g. Maximum Inventory, Minimum Lead Time etc.


5353

Deliver (Transportation)Deliver (Transportation)

Out of a number of transports one or more transports are selected based on some pre-defined transportation policy like Maximum Speed Minimum Cost Maximum Capacity


5454

Inventory ManagementInventory Management

Concerned with maintenance of sufficient amount of inventory to fulfil demands

Whenever the inventory of any item falls below the critical levels, the inventory management sends the order(s) to procure the required material or product to planning operation

Planning subsequently decides either to make or buy the required product


5555

Modeling the Modeling the Manufacturing SystemManufacturing System

Can be modeled by combining two basic building blocks Materials (Transformed or Consumed) Resources (Negligible Transformation/Consumption)

One or more resource is used to perform some operation on the material (called process)

Each product requires some processes to be performed in a specific sequence

Two decisions are taken before each process Resource selection Material selection


5656

Typical Material Processing in a Typical Material Processing in a Manufacturing System Manufacturing System


5757

Modeling Supply Chain DecisionsModeling Supply Chain Decisions

Source Selection Policies Inventory Control Decisions Transportation Decisions Production Planning Decisions


5858

Source Selection PoliciesSource Selection Policies Classification (based on no. of sources)

Single Source Multiple Source Transport Based.

Source Selection Rules Shortest distance Minimum cost Maximum inventory Preference selection Probability based selection (only for multiple source

policies) User defined selection (only for single source policies)


5959

Source Selection Policies Source Selection Policies Start

R = Sourcing RuleN = Number of Sources

No

R = Shortest distance

Arrange sources in ascending order

of their distance

YesR = Minimum

Cost

No


of cost of the required product

YesR = Maximum

Inventory

No


of the available inventory

Yes R = Preference

No

Arrange sources in ascending order of the preference

Yes

N =1Select the first

source from the listYes

R = User Defined

Select the defined Source

Yes

Send Orders According to Probability of each source

No

Stop

Send Multiple Orders

No


6060

Probability Probability Based Source Based Source SelectionSelection

Start

Get Source List

i = Source IndexQ = Order QuantitySi = Quantity requested from source IWi= Probability of source i

i = 1

Place an order of Quantity Si with

source i

i = Number of sources

Si = Q * Wi

i = i + 1

No

Stop


6161

Multiple Multiple Source Source SelectionSelection

Start

Get the pre-ordered source list

i = 1(Select the first source)

Q ≤ Si

i = Source IndexQ = Required QuantitySi= Inventory available at source i

i = i + 1(Select Next Source)

No

Place an order of Si units with source i

Place an order of Q units with source i

Yes

Q = Q - Si

i = Number of Souces

No

Yes

Stop


6262

Inventory Control DecisionsInventory Control Decisions Demand Flow Order Q Order Upto (s, Q) Policy (s, S) Policy Updated (s, S) Policy Days of Supply, Demand Based (DOS

Demand) Days of Supply, Forecast Based (DOS

Forecast)


6363

Inventory Control DecisionsInventory Control DecisionsStart

P = Demand flow

P = Inventory PolicyO = Order QuantityI = Inventory

P = Order Q

P = Order to S

P = s,S

No

O = Demand

YesNo

O = Q

Yes

O = S - I

Yes

No

I < s

O = S - I

Yes

Yes

O = 0

No

P = s,Q

I < s

O = Q

Yes

Yes

O = 0

No

No

P = Updated s

No

O = Updated_s()

P = DOS (Demand)

No

Yes

O = DOS_Demand

Yes

No

O = DOS_Forecast

Stop


6464

Updated (s, S) PolicyUpdated (s, S) Policy

Start

Get demand data

n = Number of Periodsd = Number of data pointsm = Maximum number of data pointss = Reorder levelt = Average lead timeZ = Standard normal variate corresponding to required service level

d > m

n = m

n = dYes

No

Avg = sample average of of previous n periodsσ = sample standard deviation of previous n periods

s = Avg * t + Z * σ

Inventory < s

Order Quantity = s

Yes Order Quantity = 0

No

s < Maximum order quantity

YesOrder Quantity =

Maximum Order Quantity

No

Stop


6565

Updated (s, S) PolicyUpdated (s, S) Policy Reorder Level (s) is calculated as

Where LT is the lead time of the selected source σ is the estimate of standard deviation of the demand in

previous n periods Z is the standard normal variate corresponding to the

desired service level Special Cases

n-period moving average (Z = 0) Demand Flow (n = 1, Z = 0)

ZLTs


6666

DOS DemandDOS Demand

Start

n = Minimum days of supply to keepN = Maximum days of supply to keep

Get Demand Data

MinQ = Moving average of previous n periods

MaxQ = Moving average of previous N periods

Inventory < MinQ

Yes

Order Quantity = MaxQ - Inventory Order Quantity = 0

No

Stop


6767

DOS ForecastDOS Forecast

Start

n = Minimum days of supply to keepN = Maximum days of supply to keep

Get Demand Data

MinQ = Sum of forecasts of future n periods

MaxQ = Sum of forecasts of future N periods

Inventory < MinQ

Yes

Order Quantity = MaxQ - Inventory Order Quantity = 0

No

Stop


6868

Transportation DecisionsTransportation Decisions

Each transport uses some transportation mode e.g. rail, road, air, or water

Depending on the location, not all transports may be possible for a node pair

Alternative transports are selected based on which transport modes are available between a node pair


6969

Transportation DecisionsTransportation Decisions Alternative transports differ according to their

specific characteristics Each transport has some properties like capacity

speed, cost, etc Transport selection rules

Maximum speed Maximum volume capacity Maximum weight capacity Minimum cost User defined transport selection


7070

Transportation Transportation Selection PoliciesSelection Policies

Start

R = Selection Rule

R = Maximum Speed

Find Fastest Transport

YesR = Maximum

Weight Capacity

No

Find transport with Maximum weight

capacity

YesR = Maximum

Volume Capacity

No

Find transport with Maximum volume

capacity

YesR = Minimum Cost

No

Find transport with Minimum Cost

Yes

Find the user defined transport

No

Stop

Return selected Transport


7171

More Classifications in TransportsMore Classifications in Transports

The loading in the selected transport may again be of two types Pooled (all the products shipped between a node-

pair are sent through the same transport) Non-pooled (different products are shipped

through different transports) Based on capacity utilization of transport

FTL (Full Truck Load) LTL (Less than Truck Load)


7272

Transport Transport selection with selection with pooled pooled transportstransports

Start

Pooled Transports

P = ProductS = SenderR =ReceiverT = Transport PolicyTs = Transport Selection Rule

Get Transport List for Pooled Transports

Get Transport List for Non-pooled Transports

Yes

No

Select Transport

Q = Quantity to be shippedS = Quantity that could be added to selected transport

S ≥ Q

Add S units to the selected transportQ = Q - S

No

Add Q units to the selected transport

Yes

Shipment Policy = FTL

Release selected shipment

No

Is Selected transport full

Yes

Yes

Stop

No


7373

Transport Transport selection selection with non with non pooled pooled transportstransports

Start

P = ProductS = SenderR = ReceiverL() = Transport listi = Transport IndexTi = ith Transport

i = 1

Sender of Ti = S

Receiver of Ti = R

Yes

i = i +1

No

No

Add Ti to L() Yes

i = Number of transports

No

Stop

Return L()

Yes

Receiver of Ti = R

Yes

No


7474

Production Planning DecisionsProduction Planning Decisions

Routing Concerned with selection of best possible

resources out of a number of available resources Scheduling

Decides the timing of each process or each job in the manufacturing system

Quantity to be produced is determined by the inventory policy


7575

Production Planning DecisionsProduction Planning Decisions Options Available

Produce as directed by inventory policy Produce short batches

Once the product is routed to a resource, it is added to the queue of the corresponding resource

Routing policy is used to select the next resource for the next process when current processing is over

Sequence of resource selection and allocation continues until all the processes on the job are completed


7676

Production Production Planning Planning OperationOperation

Start

Evaluate Make Quantity

Make Quantity =Q

Acquire Raw Materials

Yes

Select First Process

Select Machine based on Routing

Perform Processing

Select Next Process

Is current process the last process

No

Add products to Inventory

Yes

Stop

No

Q = Quantity to be made

Short Batches Possible

No

Yes

Make Quantity >0

Yes

No


7777

Determining the Determining the Make QuantityMake Quantity

Start

Get Quantity required to be

made (Qm)

Get Available Inventory of each

Raw Material

Qm = Quantity to makeRi = ith Raw material of the productQ(Ri) = Available quantity of RiNi = Number off ith raw material required for each product

Q > Q(Ri)*Ni

i = 1Q = Q(Ri) * Ni

i = i + 1

Q = Q(Ri) * Ni Yes

No

i= Number of Raw materials

No

Stop


7878

Resource Selection PoliciesResource Selection PoliciesR = Minimum

Processing Time

R= Routing Selection Rule

Start

Select the Resource with Minimum

Processing Time

Yes R = MinimumSetup Time

Select the Resource with Minimum Loading Time

Yes

No

R = Minimum Cost

Select the Resource with Minimum Cost

Yes R = MinimumQueue

Select the Resource with Minimum Queue

Yes

No

No

R = Minimum Loading

Select the Resource with Minimum Loading

Yes

No

No

Select the User-defined Resource

Allocate the selected resource to the part

Stop


7979

Modeling the Modeling the Supply Chain Building BlocksSupply Chain Building Blocks

Consists of Modeling the Manufacturing System Modeling the Transports Modeling the Player Role Modeling the Supply Chain Node Modeling Node Interactions

Different objects in the SC Network are linked with each other, they can be represented using the concepts of Relational Database Management System (RDBMS)


8080

Modeling the Modeling the Manufacturing Manufacturing SystemSystem


8181

Modeling Modeling the the TransportsTransports


8282

Modeling the Player RoleModeling the Player Role


8383

Modeling Modeling the Supply the Supply Chain Chain NodeNode


8484

Performance MetricsPerformance Metrics

Inventory related Minimum Inventory Maximum Inventory Total Inventory Average Inventory Standard Deviation of Inventory


8585

Performance Metrics UsedPerformance Metrics Used Service related

Backorders Stockouts Fill Rate Service Level

Demand related Minimum Demand Maximum Demand Total Demand Average Demand Standard Deviation of Demand


8686

Supply Chain Performance Under Supply Chain Performance Under Deterministic VariabilityDeterministic Variability

Experimental Setup Demand Impulses Simulation Parameters Balancing the Inventory Policies

Effect of Impulse Amplitude Effect of Impulse Width Effect of Step Width Effect of Number of Impulses


8787

Demand ImpulsesDemand Impulses


8888

Simulation ParametersSimulation ParametersParameter Demand

AmplitudeBalance Gap

Number of Impulses

Step Width

Run Length (Days) 110 110 110 110

Warmup Period (Days)

20 20 20 20

Observation Period (Days)

90 90 90 90

Information Lead Time (Days)

2 2 2 2

Transportation Lead Time (Days)

2 2 2 2

Mean Demand 100 100 100 100

Impulse Amplitude Variable 0.9 and 1.9 0.9 and 1.9 0.9 and 1.9

Balance Gap 0 Variable 0 0

Number of Impulses 1 1 Variable 1

Impulse Width 1 1 1 Variable


8989

Balancing the PoliciesBalancing the Policies Each policy was balanced so that all of them gave same

results for the test demand under steady state condition Demand Flow: The test demand was a constant demand of

100 units per week. To fulfill the current obligations, each node has to keep a minimum of 100 units. Each node has to keep an initial inventory equal to four weeks of demand. As a result, an initial inventory of 400 units was allocated to each node.

Order Q: In this policy, orders are placed even when no there is no demand. Therefore, inventory builds up for each node, until the actual demand is received. As a result, all nodes only need to keep an inventory equal to the value of demand per week (100 units).


9090

Balancing the PoliciesBalancing the Policies

(s, Q) Policy: The initial inventories for each node were same as those for demand flow policy. A reorder point (s) of 400 and order quantity (Q) of 100 was set for this policy.

(s, S) Policy: Initial inventories were kept same as the demand flow policy. Both reorder point (s) and reorder level (S) were set to be 100 units.


9191

Effect of Impulse AmplitudeEffect of Impulse Amplitude

Effect on Individual Supply Chain Nodes Effect on Retailer Effect on Wholesaler Effect on Distributor Effect on Manufacturer

Effect of each policy on the Supply Chain Effect along the Supply Chain


9292

Effect of Amplitude on Effect of Amplitude on Individual Supply Chain NodesIndividual Supply Chain Nodes

Performance Metrics Used Total Inventory Std. Dev. of Inventory Backorders Stockouts Std. Dev. of Demands


9393

Retailer’s Total InventoryRetailer’s Total Inventory

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory

Demand Flow Order Q s, S Policy s, Q Policy


9494

Retailer’s Std. Dev. of InventoryRetailer’s Std. Dev. of Inventory

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



9595

Retailer’s BackordersRetailer’s Backorders

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



9696

Retailer’s StockoutsRetailer’s Stockouts

0

2

4

6

8

10

12

14

16

18

20

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



9797

Wholesaler’s Total InventoryWholesaler’s Total Inventory

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



9898

Wholesaler’s Std. Dev. of InventoryWholesaler’s Std. Dev. of Inventory

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



9999

Wholesaler’s BackordersWholesaler’s Backorders

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



100100

Wholesaler’s StockoutsWholesaler’s Stockouts

0

5

10

15

20

25

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



101101

Wholesaler’s Std. Dev. of DemandWholesaler’s Std. Dev. of Demand

0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. o

f D

em

an

d



102102

Distributor’s Total InventoryDistributor’s Total Inventory

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



103103

Distributor’s Std. Dev. of InventoryDistributor’s Std. Dev. of Inventory

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



104104

Distributor’s BackordersDistributor’s Backorders

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



105105

Distributor’s StockoutsDistributor’s Stockouts

0

2

4

6

8

10

12

14

16

18

20

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



106106

Distributor’s Std. Dev. of DemandDistributor’s Std. Dev. of Demand

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. o

f D

em

an

d



107107

Manufacturer’s Total InventoryManufacturer’s Total Inventory

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



108108

Manufacturer’s Manufacturer’s Std. Dev. of InventoryStd. Dev. of Inventory

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



109109

Manufacturer’s BackordersManufacturer’s Backorders

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



110110

Manufacturer’s StockoutsManufacturer’s Stockouts

0

2

4

6

8

10

12

14

16

18

20

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



111111

Manufacturer’s Manufacturer’s Std. Dev. of DemandStd. Dev. of Demand

0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. o

f D

em

an

d



Effect of Amplitude on Effect of Amplitude on Supply Chain as a WholeSupply Chain as a Whole

Demand Flow Policy

Order Q Policy

(s, S) Policy

(s, Q) Policy


Demand Flow PolicyDemand Flow Policy


114114

Total InventoryTotal Inventory

0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory

Retailer Wholesaler Distributor Manufacturer


115115

Std. Dev. of InventoryStd. Dev. of Inventory

0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



116116

BackordersBackorders

0

100

200

300

400

500

600

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



117117

StockoutsStockouts

0

1

2

3

4

5

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



118118

Std. Dev. of DemandStd. Dev. of Demand

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f D

em

an

d



119119


0

2000

4000

6000

8000

10000

12000

0 5 10 15 20 25 30 35

Impulse Amplitude

To

tal I

nv

en

tory



120120


0

100

200

300

400

500

600

700

800

900

1000

0 5 10 15 20 25 30 35

Impulse Amplitude

Std

. De

v. o

f In

ve

nto

ry



121121


0

500

1000

1500

2000

2500

3000

3500

0 5 10 15 20 25 30 35

Impulse Amplitude

Ba

ck

ord

ers



122122

StockoutsStockouts

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15 20 25 30 35

Impulse Amplitude

Ba

ck

ord

ers



123123


0

50

100

150

200

250

300

350

0 5 10 15 20 25 30 35

Impulse Amplitude

Std

. De

v. o

f D

em

an

d



Order Q PolicyOrder Q Policy


125125


0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



126126


0

10

20

30

40

50

60

70

80

90

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



127127


0

100

200

300

400

500

600

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



128128

StockoutsStockouts

0

1

2

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



129129


0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f D

em

an

d



(s, S) Policy(s, S) Policy


131131


0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



132132


0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



133133


0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



134134

StockoutsStockouts

0

5

10

15

20

25

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



135135


0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f D

em

an

d



(s, Q) Policy(s, Q) Policy


137137


0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7

Impulse Amplitude

To

tal I

nv

en

tory



138138


0

20

40

60

80

100

120

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f In

ve

nto

ry



139139


0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7

Impulse Amplitude

Ba

ck

ord

ers



140140

StockoutsStockouts

0

5

10

15

20

25

0 1 2 3 4 5 6 7

Impulse Amplitude

Sto

ck

ou

ts



141141


0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7

Impulse Amplitude

Std

. De

v. O

f D

em

an

d



142142

Effect of Amplitude on the Effect of Amplitude on the Supply Chain as a WholeSupply Chain as a Whole

Type of Impulse Positive Impulse (0.9) Negative Impulse (-0.9)

Performance Metrics Used Total Inventory Std. Dev. of Inventory Backorders Stockouts Std. Dev. of Demands


143143

Negative Impulse Total InventoryNegative Impulse Total Inventory

0

500

1000

1500

2000

2500


To

tal I

nv

en

tory



144144


0

10

20

30

40

50

60

70


Std

. De

v. O

f In

ve

nto

ry



145145


0

500

1000

1500

2000

2500


Ba

ck

ord

ers



146146

StockoutsStockouts

0

5

10

15

20

25


Sto

ck

ou

ts



147147


0

20

40

60

80

100

120

140


Std

. De

v o

f D

em

an

d



148148

Positive Impulse Total InventoryPositive Impulse Total Inventory

0

20

40

60

80

100

120


To

tal I

nv

en

tory



149149


0

2

4

6

8

10

12

14

16


Std

. De

v. O

f In

ve

nto

ry



150150


0

20

40

60

80

100

120


Ba

ck

ord

ers



151151

StockoutsStockouts

0

0.2

0.4

0.6

0.8

1

1.2


Sto

ck

ou

ts



152152


0

2

4

6

8

10

12

14

16


Std

. De

v o

f D

em

an

d



153153

Effect of Demand History on Effect of Demand History on Supply Chain PerformanceSupply Chain Performance

Experimental Setup No Information Sharing

Effect on Individual Nodes Effect on Whole Supply Chain and Effect along

the Supply Chain Partial Information Sharing Full Information Sharing Comparison of Information Sharing Levels


No Information SharingNo Information Sharing


Effect on IndividualEffect on Individual Supply Chain Nodes Supply Chain Nodes

Retailer

Wholesaler

Distributor

Manufacturer


156156

Retailer’s Total InventoryRetailer’s Total Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

5000

10000

Past Demands

Tota

l Inv

ento

ry


157157

Retailer’s Maximum InventoryRetailer’s Maximum Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100

200

300

Past Demands

Max

imum

Inv

ento

ry


158158

Retailer’s Std. Dev. of InventoryRetailer’s Std. Dev. of Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10

20

30

40

50

60

70

80

Past Demands

Std

. D

ev.

of I

nven

tory


159159

Retailer’s BackordersRetailer’s Backorders

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

1000

2000

Past Demands

Bac

kord

ers


160160

Retailer’s StockoutsRetailer’s Stockouts

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10

20

30

40

50

Past Demands

Sto

ckou

ts


161161

Wholesaler’s Total InventoryWholesaler’s Total Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10000

20000

30000

40000

Past Demands

Tot

Inv


162162

Wholesaler’s Std. Dev. of InventoryWholesaler’s Std. Dev. of Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100

200

300

400

Past Demands

Std

Dev

Inv


163163

Wholesaler’s BackordersWholesaler’s Backorders

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100

200

300

400

500

600

700

800

900

Past Demands

Bac

kord

ers


164164

Wholesaler’s StockoutsWholesaler’s Stockouts

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10

20

Past Demands

Sto

ckou

ts


165165

Wholesaler’s Std. Dev. of DemandWholesaler’s Std. Dev. of Demand

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

50

100

Past Demands

Std

Dev

Dem


166166

Distributor’s Total InventoryDistributor’s Total Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100000

200000

Past Demands

Tot

Inv


167167

Distributor’s Std. Dev. of InventoryDistributor’s Std. Dev. of Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

1000

2000

Past Demands

Std

Dev

Inv


168168

Distributor’s BackordersDistributor’s Backorders

4942363025201612964210

1000

500

0

Past Demands

Bac

kord

ers


169169

Distributor’s StockoutsDistributor’s Stockouts

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10

20

Past Demands

Sto

ckou

ts


170170

Distributor’s Std. Dev. of DemandDistributor’s Std. Dev. of Demand

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100

200

Past Demands

Std

Dev

Dem


171171

Manufacturer’s Total InventoryManufacturer’s Total Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

500000

1000000

Past Demands

Tot

Inv


172172

Manufacturer’s Manufacturer’s Std. Dev. of InventoryStd. Dev. of Inventory

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

1000

2000

3000

4000

5000

Past Demands

Std

Dev

Inv


173173

Manufacturer’s BackordersManufacturer’s Backorders

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

1000

2000

Past Demands

Bac

kord

ers


174174

Manufacturer’s StockoutsManufacturer’s Stockouts

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

10

20

Past Demands

Sto

ckou

ts


175175

Manufacturer’s Manufacturer’s Std. Dev. of DemandStd. Dev. of Demand

0 1 2 4 6 9 12 16 20 25 30 36 42 49

0

100

200

300

400

500

600

Past Demands

Std

Dev

Dem


Effect on Supply Chain and Effect on Supply Chain and Effect Along the Supply ChainEffect Along the Supply Chain


177177


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3

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178178


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179179


4

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180180

StockoutsStockouts

4

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0

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02

2

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181181


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182182

Organization Organization of Thesisof Thesis


183183

Organization of ThesisOrganization of Thesis


184184



185185



186186



187187

Significant Contributions of the Significant Contributions of the Research Work Research Work

Development of an object-oriented supply chain simulation environment

Role of IT based tools are developed and used to study IT facilitated information and decision flows in flexible supply chains is studied

Development of a framework that incorporates different IT facilitated control policies in SCs

Comparison for inventory policies under deterministic variability and information sharing

Analysis of Supply chain performance under different levels of demand information (IT focus)


188188

Significant Contributions of the Significant Contributions of the Research Work Research Work

Analysis of Supply chain performance under different of Service levels

Analysis of Supply chain performance under different levels of demand variance

Analysis of supply chain performance under different level of information sharing with Different levels of demand history Different service levels Different demand variances


189189

Limitations and Limitations and Scope for Future ResearchScope for Future Research

The modeling environment can be extended in more directions like Closed loop supply chains by adding return

process Manufacturing operations can be extended to

include different kinds of production facilities ….

Focus on Inventory management only…


190190

List of PublicationsList of Publications Published/Accepted for Publication

Postponement strategies for re-engineering of automotive manufacturing: knowledge-management implications, International Journal of Advanced Manufacturing Technology, Article in Press, doi 10.1007/s00170-006-0679-z.

Hybrid Tabu-Sample Sort Simulated Annealing (SSA) with Fuzzy Logic Controller: CIM System Context, Studies in Informatics and Control, June 2006, Volume 15, Number 2.

Flexible Supply Chains: A Context for Decision Knowledge Sharing and Decision Delays, Global Journal of Flexible Systems Management, Volume 7 Numbers 3 & 4, July -Dec 2006 (Accepted for Publication).

Impact of Supply Chain Collaboration on Customer Service Level and Working Capital, Global Journal of Flexible Systems Management (Accepted for Publication).


191191

List of PublicationsList of Publications Under Review

A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach, International Journal of Computers Communication and Control.

Inventory performance of some supply chain inventory policies under impulse demands, International Journal of Production Research, Manuscript ID: TPRS-2007-IJPR-0111.

Communicated An Object Oriented Framework for Modeling Control

Policies in a Supply Chain, International Journal of Value Chain Management


192192

List of PublicationsList of PublicationsNational / International ConferencesNational / International Conferences

Web Based Virtual Supply Chain Modeling to Enhance Learning, The International Conference on e-Learning (ICEL 2006), University of Quebec in Montreal, Canada, June 22-23.

Supply Chain Modeling: The agent based Approach, 12th IFAC Symposium on Information Control Problems in Manufacturing (INCOM-2006), Saint-Etienne, France, May 17-19.

Object-Oriented Approach for Simulation of Supply Chain, International Congress on Logistics and SCM Systems (ICLS-2006), Kaohsiung, Taiwan, May 1-2.

Comparison of some Supply Chain Management Software Applications, National Conference on Advances in Mechanical Engineering (AIME-2006), January 20-21.


Response to Response to Examiner’s CommentsExaminer’s Comments


194194

Comments of Examiner 1Comments of Examiner 1

1. No Information Sharing: Is it best for individual wholesalers and retailers?

As we move higher in the supply chain, the demand variability increases because of the inventory policy used

It is not that no information sharing is good for individual wholesalers and retailers, information sharing is just less important for them.

2. Full Information Sharing: Is it best for the overall system? Whether full information sharing is best for the system or not is

dependent on the inventory policy used The thesis aims to demonstrate that after some particular level of

information sharing, the investment in IT may not be economically justified


195195


3. If answers to (1) and (2) above is yes, then explain why optimization on IS level (information sharing) is necessary? Why would an intermediate value (of IS) would be optimal? Whose objective have you considered? Individual wholesalers/retailers or the whole system? Or a combination of the two?

It is important to find the level and type of information sharing

4. On page xxvii: IT should be information technology The required change has been made.

5. Uncertainty in supply chain is demand side and the lead time size. When you consider disturbances: you could have considered lead time disturbances.

We consider this as a future area of research


196196

Comments of Examiner 1Comments of Examiner 16. Advanced IT means: continuous review policies (for

inventory). What implications does it have for your thesis? In a continuous review policy, the inventory position is

continuously monitored Review period is one day; all the policies in our research are the

continuous review policies

7. For single node (such as wholesaler or retailer): given demand and lead time uncertainty: optimal policy for lot sizing can be devised. Then it could be used in your simulation.

Decentralized decision making is found to deteriorate the supply chain performance

The decisions of one node may indirectly affect the performance of other (interaction effects)


197197

Comments of Examiner 1Comments of Examiner 18. A schematic diagram of supply chain (number of plants),

distributor (numbers) and the wholesaler/retailers (numbers) considered in the thesis can be given.

A schematic diagram of the supply chain considered in this thesis is given on page 136. Description of the same is given in section 4.5

9. Main focus of thesis is determination of optimal levels of controllable factors such as … modifying the thesis title.

Motivation of this research is to bring the information technology (IT) as a performance improvement solution in the supply chain

Information sharing and IT are mutually complimentary The research highlights where and how much information needs to

be shared for the optimal performance

10. Factors beyond the control of decision makers (uncertainty) and factors under decision-makers’ control … readability of the thesis.

The required tables have been added in the Appendix A


198198

Comments of Examiner 2Comments of Examiner 21. The current developed framework is limited to only two

players, i.e. manufacturing and inventory … more than three players?

There are four players in the supply chain considered in this research: Retailer, Wholesaler, Distributor and Manufacturer

In addition to the inbuilt player roles like supplier, manufacturer, distributor, wholesaler and retailer, users can also define their own Player Roles.

2. Network manufacturing is a new arena for modern manufacturing environment. How could … contribution in this field?

For network manufacturing also, this framework can still handle the execution side

In network manufacturing, the manufacturing of the finished product takes place through a coordination of multiple autonomous players. Such a network will have most of the players as manufacturing type players.

This framework can be used where higher level modeling of the manufacturing system is sufficient


199199

Comments of Examiner 2Comments of Examiner 23. In this research, “overall supply chain cost” has been used as the major

criterion for the supply chain performance. In fact, there are many Key Performance Indicators (KPI) reported in the supply chain management research work, such as agilability, lead time, flexibility, expandability, trust, etc. How could you consider these issues into your research framework?

The research framework, in its present form, has only the KPIs which were required for this research work, i.e. those related to inventory management

Since the framework is based on object oriented methodology, multiple KPI libraries can be added to it as and when need arises

4. What are the major bottlenecks in the implementation of the developed framework in real-life industrial case?

The framework has been developed considering a very generic nature of the supply chain


200200


5. What are the major limitations of the developed framework in this thesis?

The return operation of a supply chain is not available in the framework.

In the future, some other major supply chain operations may be added in the framework.

The effectiveness of the simulation environment can be immensely improved by incorporating some optimization algorithms for simpler supply chain decisions and some meta-heuristics for complex problems.

Another important direction for future work is to provide animated simulation similar to that available in other simulation languages.


201201


202202

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Phd Defence 25 Jan09