Dr. Jorge Hernández, Dr. Andrew Lyons - · PDF fileDr. Jorge Hernández, Dr. Andrew Lyons A multi-agent decision support system for the collaborative decision-making in multi-level

Dr. Jorge Hernández, Dr. Andrew Lyons

A multi-agent decision support system for the collaborative decision-making in multi-

level supply chains

University of Liverpool Management School, UK

Prof. Raúl Poler, Dra. Josefa Mula

Research Centre on Production Management and Engineering

Universitat Politècnica de València, Spain

Structure

1. Introduction

2.Background

3.Collaborative decision-making model in multi-

level supply chains supported by MADSS

4.Case Study

April 12th-13th, 2012

4.Case Study

5.Conclusion and further research

Introduction

• A set of approaches used to efficiently integrate

• Suppliers

• Manufacturers

• Warehouses

• Distribution centers

What is supply chain management (SCM)?


• Distribution centers

• Product is produced and distributed

• In the right quantities

• To the right locations

• And at the right time

• System-wide costs are minimized and

• Service level requirements are satisfied

Source: STEVENS, Institute of Technology

Introduction

Why Is SCM Difficult?

• Uncertainty is inherent to every supply chain

• Travel times

• Breakdowns of machines and vehicles

• Weather, natural catastrophe, war

• Local politics, labor conditions, border issues


• Local politics, labor conditions, border issues

• The complexity of the problem to globally optimize a supply chain is significant

• Minimize internal costs

• Minimize uncertainty

• Deal with remaining uncertainty


Introduction

What Is supply chain collaboration?

• Many different definitions depending on perspective

• The means by which companies within the supplychain work together towards mutual goals by sharing

• Ideas

• Information


• Information

• Processes

• Knowledge

• Information

• Risks

• Rewards


Introduction

Collaborative solutions for the SCM

Supply chain levels Supply chain

example


Level O Level 1 Level 2 ………. Level n

Endcustomer

Lastlevel

supplier

Introduction

Collaborative solutions for the SCM

Information technologiesSupply chain

example


Supply chain node

Frameworks/Architectures/Model

ling languages

Mechanisms

Methodology

Informationrepositories

Endcustomer

Lastlevel

supplier

Introduction

Collaborative solutions for the SCMSupply chain

example


Interoperability

Endcustomer

Lastlevel

supplier

Information technologies'solutions

Interactions

Introduction

Collaborative Decision Making in Supply Chains

ISSUE CONSIDERATIONS

Network Planning • Warehouse locations and capacities

• Plant locations and production levels

• Transportation flows between facilities to minimize cost and time

Inventory Control • How should inventory be managed?

• Why does inventory fluctuate and what strategies minimize this?



Supply Contracts • Impact of volume discount and revenue sharing

• Pricing strategies to reduce order-shipment variability

Distribution Strategies • Selection of distribution strategies (e.g., direct ship vs. cross-docking)

• How many cross-dock points are needed?

• Cost/Benefits of different strategies

Integration and Strategic Partnering

• How can integration with partners be achieved?

• What level of integration is best?

• What information and processes can be shared?

• What partnerships should be implemented and in which situations?

Outsourcing & Procurement Strategies

• What are our core supply chain capabilities and which are not?

• Does our product design mandate different outsourcing approaches?

• Risk management

Product Design • How are inventory holding and transportation costs affected by product design?

• How does product design enable mass customization?

Statements and motivations

It might appear obvious that establishing collaborative

processes in a supply chain must enhance efficiency when

facing non-collaborative situations.

Theoretical issue


Practical issues

•Demonstrating the goodness of collaborative solutions in

practice is not altogether evident.

•Demonstrating the feasibility of implementing systems that can

favour collaborative mechanisms in real and complex settings

as opposed to traditional ones is even less evident.

Background


•FUZZYMRP (PPI-06-05-5703). 2005-2007

•EVOLUTION (DPI2007-65501). 2007-2010

•REMPLANET (NMP2-SL-2009-229333). 2009-2012•REMPLANET (NMP2-SL-2009-229333). 2009-2012

• ERDF – C2I. 2012-2015

Collaborative decision-making

in supply chains within multi-agents systems


Source: Adapted from Zachman (1997)




Source: Adapted from Zachman (1997)

Supply chain information system motivations (Why)

•Design. Generate an architecture capable to support

the dynamics of the environment.

•Implementation. Define the main architecture•Implementation. Define the main architecture

elements specifications to support its implementation.

•Collaborative processes support. Generate

models capable to support the collaborative and non-

collaborative perspective of the supply chain domain.

Supply chain data flow (What)

Node Attribute Activity Data/Information

Current

•C: Customer

•C/S: Customer/Supplier

•S: Supplier

C C/S S

Supply chain

COLL N-COLL Evaluate Receive Generate Get

Currentstate

information

Answer Requirement

C X X X X X X X X

C X X X X X

C/S X X X X X X X

C/S X X X X X

S X X X X

Supply chain data flow (What)

Customer Customer/

supplier

get

evaluate

Receive generate

C

NC

Receive

Generate

Current state

information

C NC

EvaluateGenerate

Receive Answers

1

1 1

11

1

1

1

1

N

N

N

N

Customer/

supplier

Supplier

Answers Requirements

Long term

Short temr

Requirements

Generate

ReceiveC NC

Long term

Short term

Answers ReceiveGenerate

GenerateReceive Requiements

Largo plazo

Corto plazo

Evaluate

1

1

1 1

11

1

NN

NN

N

N

N

N

N

N

N

Supply chain information relationships (How)

Customer

Customer/Supplier

Generate/send demand plan

Agreement

Receiveproposal/answere

X

Negotiatedagreement

Check availabilityDemand

forecasting

Generate demand plan solutions

Receive demand

plan

Checkcapacity

availability

X

Confirmdemand

plan

Generate demand

XReceive/check

proposalNegotiated agreement

Acceptproposal

Check

18

Customer/Supplier

Supplier

Receive demand

plan

Checkcapacity

availability

X

Confirmdemand

plan

Receive demand

plann

Generate demand plan

solutions/new proposal

Checkavailability

Acceptdemand

plan

X

X

Demand plan unfeasible/generate proposals

Receive/checkproposal

Negotiated agreement

Reject

Acceptproposal

Generate/send demand plan

Receive proposal

Checkavailability

X

Acceptdemand

plan proposal

Demand plan unfeasible/generate proposals

Checkreplenishment

availability

Generate demand plan

solutions/new proposal

RejectGenerate/send demand plan

Checkreplenishment

availability

Supply chain information relationships (How)

Answer

reception

Answer

evaluation

Send

answer

Accept Reject Propose

<<Extend>>

<<Extend>> <<Extend>>

<<Include>>

<<Include>>

Node

S

Order

generation

Short term Long term

<<Extend>> <<Extend>>Send order

<<Extend>>

Answer

generation

Respuestas

Accept Reject ProposeRequest

<<Extend>>

<<Extend>>

<<Include>>

<<Extend>>

<<Include>>

Requirement

generation

Necesities

calculus

<<Include>>

Order

reception

Order

process

Answer

generation

Node

C-C

Node

NC-C

Node

C-C/S

Node

NC-C/S

Answer

reception

reception

Supply chain collaborative process logic (Where)

Behaviourlayers

Executionlayers

Messageslayers

Supply chain entities management (Who)

C

C/S

C

Supply

chain

nodes

NC

S

Supply

chain

Typologies

Supply chain processes schedule (When)


Node State Index

C Needs definitions from the

customer �0�

C Customer wait for answer �2�

C Customer process answer �5�

C Customer evaluates answer �9�

C Customer accepts �11�

C Customer evaluates if generate

�12�

Origin node Transition Index

C Generate order �1�

C Generate plan �5�

C Receive answer �9�

C Evaluate answer �10�

C Accept �11�

C Reject �12�

C Propose �13�

C/S Receive orders �3�

States and transitions

C Customer evaluates if generate

another proposal �12�

C/S Wait for requirement �3�

C/S Wait for request �8�

C/S Define need �0�

C/S Wait for answer �2�

C/S Answer processing �5�

C/S Evaluate answer �9�

C/S Generate answer �13�

C/S Customer/Supplier evaluates if

generate another proposal �12�

S Supplier wait for requirements �3

S Supplier wait for request �8

S Supplier wait for proposal �6

S Supplier generates answer �13

C/S Receive orders �3�

C/S Receive plan �7�

C/S Process orders �4�

C/S Generate order �1�

C/S Generate plan �5�

C/S Receive answer �9�

C/S Evaluate answer �10�

C/S Accept �11�

C/S Reject �12�

C/S Answer �8�

C/S Propose �13�

S Receibe orders �3

S Receive plan �7

S Answer �8

S Receive proposal �15






The image cannot be dis

DistributedData Server

MySQL MsAccess

Agent

Decision makingalgorithm

Applicationserver

Decisionmaker

Demand

Nodo B

Nodo A

Nodo C

Nodo D Nodo E

Nodo F




Automotive Supply Chain Case Study


Source: Hernández et al. (2011)



Current state

Collaborative solution



Front-end MADSS



Demand variability

0% 25% 75% 100% 200%

Level 90 E01 11 21 31 41E E E E

E E E EE

Scenarios definitions

Level 90 E01 11 21 31 41

Level 100 02 12 22 32 42

Level 110 03 13 23 33 43

E E E E

E E E E

E E E E

E

E



NCOL COL DIF NCOL COL DIF NCOL COL DIF NCOL COL DIF NCOL COL DIF

1 4847,73 4847,73 0% 4837,84 4843,04 0,11% 4585,64 4779,37 4,05% 4344,93 4711,04 7,77% 4088,53 4660,67 12,28%

2 4821,43 4821,43 0% 4820,37 4822,13 0,04% 4788,97 4866,97 1,60% 4754,07 4898,99 2,96% 4726,47 4966,38 4,83%

5 4590,00 4590,00 0% 4589,41 4589,43 0,00% 4542,11 4544,40 0,05% 4467,05 4473,77 0,15% 4352,04 4363,27 0,26%

6 4590,00 4590,00 0% 4589,41 4589,43 0,00% 4542,11 4544,40 0,05% 4467,05 4473,77 0,15% 4352,04 4363,27 0,26%

CS 149580,00 149580,00 0% 149353,95 149480,61 0,08% 143491,03 148303,70 3,25% 137800,99 146883,45 6,18% 131737,04 146025,85 9,79%

1 5400,00 5400,00 0% 5191,68 5358,34 3,11% 4880,68 5292,50 7,78% 4780,01 5235,46 8,70% 4433,31 5181,37 14,44%

100% 200%

90

0% 25% 75%

Simulation results in terms of the profit evolutions

1 5400,00 5400,00 0% 5191,68 5358,34 3,11% 4880,68 5292,50 7,78% 4780,01 5235,46 8,70% 4433,31 5181,37 14,44%

2 5400,00 5400,00 0% 5367,44 5436,41 1,27% 5313,99 5480,31 3,03% 5291,34 5477,62 3,40% 5228,97 5527,00 5,39%

5 5400,00 5400,00 0% 5322,68 5325,71 0,06% 5161,92 5168,31 0,12% 5079,33 5087,44 0,16% 4869,21 4885,61 0,34%

6 5400,00 5400,00 0% 5322,68 5325,71 0,06% 5161,92 5168,31 0,12% 5079,33 5087,44 0,16% 4869,21 4885,61 0,34%

CS 167400,00 167400,00 0% 162434,43 166589,86 2,49% 154896,66 165133,89 6,20% 152358,23 163698,22 6,93% 143873,99 162450,32 11,44%

1 5916,68 5916,68 0% 5888,99 5904,63 0,26% 5500,55 5790,33 5,00% 5309,35 5774,01 8,05% 4880,82 5658,78 13,75%

2 5866,71 5866,71 0% 5863,59 5869,33 0,10% 5815,29 5936,54 2,04% 5784,07 5974,71 3,19% 5719,74 6034,13 5,21%

5 5427,00 5427,00 0% 5424,38 5424,57 0,003% 5356,54 5360,98 0,08% 5296,68 5305,97 0,18% 5116,56 5129,94 0,26%

6 5427,00 5427,00 0% 5424,38 5424,57 0,003% 5356,54 5360,98 0,08% 5296,68 5305,97 0,18% 5116,56 5129,94 0,26%

CS 182088,00 182088,00 0% 181451,69 181836,19 0,21% 172432,28 179665,11 4,03% 167887,53 179463,11 6,45% 157649,29 176991,92 10,93%

Level 100

110

Conclusions


Within the MADSS it has been possible:

• Meet, in an efficiently manner, the customers demand in

terms of its required quantities and lead-times.

• Consider common standards to support the collaborative

processes.

• Design and implement the right information flow among• Design and implement the right information flow among

the supply chain companies or nodes to support their

decision-making process collaboratively.

• It has been proved the robustness' of multiagenttechnology to model and implement complex

environments such as multi-level supply chains.

• The generic architecture can be applicable to differenttypes of supply chain, which means that is versatile

architecture

Further Research

• Apply MADSS to different supply chain types

and configurations.

• Use standard ontology's to support more

process in the supply chain management.

• Define different algorithms to support


• Define different algorithms to support

conditional behaviour of every agent

communication process.

• And more...

Dr. Jorge Hernández, Dr. Andrew Lyons

A multi-agent decision support system for the collaborative decision-making in multi-

level supply chains

University of Liverpool Management School, UK

Prof. Raúl Poler, Dra. Josefa Mula

Research Centre on Production Management and Engineering

Universitat Politècnica de València, Spain

Thanks!

Documents

Dr. Jorge Hernández, Dr. Andrew Lyons - · PDF fileDr. Jorge Hernández, Dr. Andrew Lyons A multi-agent decision support system for the collaborative decision-making in multi-level