Basys’06, Niagara Falls, Ontario, Canada, 2006 Agent-based supply chain planning in the forest...

Basys’06, Niagara Falls, Ontario, Canada, 2006

Agent-based supply chain planning in the forest products industry

Sophie D’Amours Ph.D.Professor, Université Laval

General Director, Research Consortium FOR@CCanada Research Chair on

planning value creation network


Agenda FOR@C Research Consortium Forest products industry Supply chain planning challenges in the

forest products industry Supply chain scheduling: application to

the lumber industry FOR@C V-Lumber Experimental

Platform Agent-based simulation in supply chain


Mission of the Consortium

o become a Canadian and International centre of expertise in the development of the knowledge and skills required to integrate and optimize value creation networks in the forest products industry by taking advantage of the potential of new technologies and electronic business models.

T


Partners


Supply chain


Forest products supply chain


Canadian Industry Snapshot 3% GDP Exports for 45 billion $ of lumber, pulp

and paper every year Contributing 60% to the net export of

Canada 900 000 direct and indirect jobs More than 350 localities depend

economically on the industry Source: FPAC, March 2006


Québec The forests of the province

of Quebec cover 750 000 km², that is the equivalent of Sweden and Norway combined.

It counts for 20 % of forested land in Canada and 2 % of all the world’s forests.

This is why the vast majority of foreigners see Quebec as a huge green carpet.

80% is public land


Fiber flow


Fiber transformation


customers

customers

customers

Forest supply chain


Pulp and paper supply chain


Transportation in the supply chain


Supply chain planning challenges in the forest products industry


In the United States at December 31, 2005, the Company operated 23 pulp, paper and packaging mills, 93 converting and packaging plants, 25 wood products facilities, six speciality chemicals plants and 270 distribution branches.

Top 5International paper (~$26 B)Weyerhaeuser (~ $20 B)Georgia Pacific (~ $20 B)Stora Enso (~ $15 B)Kimberly Clark (~ $15 B)

PWC – Global Forest and Paper Industry Survey 2005


Domtar supply chain

Ship to pointsDistribution CentersSatellite WarehousesMillsConvertersMerchants


Harvesting/procurement plan

2006 20082007

Sustainable developmentRoad constructionMixed of products, uneven agedPlantation


Alternative divergent processes Trees are cut to produce a set of logs Logs are cut to produce a set of lumbers Chips are mixed to produce different grades of

pulp and paper Rolls are cut to produce a set of rolls or sheets

Recipe/cutting patternRecipe/cutting pattern

Recipe/cutting pattern

Productivity not always linearSequence dependent set-ups

Attribute basedproducts


Commodity Price Trends

• In North America, the link between consumption and real GDP is falling for all the major grades of paper, but worst for newsprint.

• Even globally, the link between consumption and real GDP plateaued in the mid-1990s.

40.00

50.00

60.00

70.00

80.00

90.00

100.00

110.00

120.00

130.00

140.00

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Containerboard New sprint UFS

.

N. American Consumption/Real GDP

40.00

50.00

60.00

70.00

80.00

90.00

100.00

110.00

120.00

130.00

140.00

1970

1974

1978

1982

1986

1990

1994

1998

2002

Containerboard New sprint P&W

.

Global Consumption/Real GDP

Source: RISI, CIBC World MarketsSource: RISI, CIBC World Markets

Source; Roberts, 2005, Vision 2015 FOR@C


Demand/supply propagation

Mix of spot market and contracts

Facilities Markets


Advanced Planning System for the Pulp and Paper Industry (APS-PPI)


Distributed planning systems

Top level planning problem

Anticipation model ofthe base level

planning problems

Base level planning model

FinalSet ofdecisionsIN*

ReactionRE*

InstructionIN*

Anticipationfunctions

Instructions

Schneeweiss (2003)


Supply chain scheduling: application to the lumber industry


Scheduling Decide what to do, when to do it

and how to do it Support mixed mode: Pull & Push

– Satisfy demand (committed orders & contracts)– Maximize throughput value

Constraints:– Planned available inventory– Machine capacity (potential bottlenecks)


The lumber supply chainCustomers

Customers

Customers


Log Requirement


Sawing Line Plan

Solved usingmathematicalprogramming(MIP or LP)


Sawing

2x4

1x6

2x3

2x6

Type 1

Type 2

Type 3

Cutting Pattern #12

Cutting Pattern #57

Cutting Pattern #25

Cutting Pattern #9


Drying Plan

Solved usinga constraint programmingmodel


KilnKilnDrying

Green Dried

KilnKiln

Kiln Drying

Drying

KilnKiln

Air Drying

KilnKilnYard

Equalizing

Different Loading Patterns(products distribution)

Different Drying Process


Finishing Line Plan

Solved usingheuristics


Finishing

Co-Products Management:– Finishing 1 product type can results in 11 different product types

simultaneously– All of them can have demand: they are not by-products

Campaign Optimization (Setup management)

96 “ 925/8 “ 88 “ 72 “

Premium 11,71 % 4,93 % - -

Stud 35,70 % 14,15 %

10,68 % 6,81 %

No 3 6,81 % - - -

Economy 4,49 % - 2,68 % 1,61 %

Kiln wet (TH > 19 %) 0,50 % - - -

96 “ 925/8 “ 88 “ 72 “

Premium 11,71 % 4,93 % - -

Stud 35,70 % 14,15 %

10,68 % 6,81 %

No 3 6,81 % - - -

Economy 4,49 % - 2,68 % 1,61 %

Kiln wet (TH > 19 %) 0,50 % - - -

96 “ 925/8 “ 88 “ 72 “

Premium 11,71 % 4,93 % - -

Stud 35,70 % 14,15 % 10,68 % 6,81 %

No 3 6,81 % - - -

Economy 4,49 % - 2,68 % 1,61 %

Kiln wet (TH > 19 %) 0,50 % - - -


Shipment Orders

Solved usinga linear programmingmodel


Integration and system dynamics

SupplierProduction

siteWarehouse SalesDecentralised

order

material

Simple integration Limited information exchanged Impact of the bullwhip effect Minimum return – local optimisation

SupplierProduction

SiteWarehouse SalesCentralised

Planning centre

material

Multi-site integration Standardisation of exchanges and management objectives Global optimisation Large quantity of information (collect and maintain) Transactional technologies available Great potential return – but little success


Planning challenges

Global Performance of the entire supply chain network

(avoid local optimum et information distortion)

Global Performance of the entire supply chain network

(avoid local optimum et information distortion)

Operation plans feasibility(avoid plans that are not feasible)

Operation plans feasibility(avoid plans that are not feasible)

Manufacturing and logistic Agility(ability to re-plan quickly)

Manufacturing and logistic Agility(ability to re-plan quickly)

Synchronization of decisions

Specialization of decisionsmodels and algorithms

Decisions distribution and localizationwhere events must be managed


Raise the needs for tools designed

To evolve in a decentralized, dynamic and specialized environment

To support demand and supply propagation with optimization (e.g. revenue management)

To integrate real-time execution information (e.g. event management systems, contingency planning)

To support collaboration (e.g. collaborative workflows)


FOR@C V-Lumber Experimental Platform


Distributed & Specialized Tools


Supply Chain Planning

MakeAgent

DeliverAgent

SourceAgent

Planning Unit

Demand Plan

Supply Plan

Demand PlanSupply Plan

MakeAgent

DeliverAgent

SourceAgent

Planning Unit

MakeAgent

DeliverAgent

SourceAgent

Planning Unit

Tactical planning unit

Analysis Tools

AgentsData


Agent Architecture


Customer AgentSupplier Agent

Offer

Offer Accepted

Offer Refused

Need

Conversation

Offer

Offer Accepted

Offer Refused

Need

Conversation

Offer

Offer Accepted

Offer Refused

Need

Conversation

Workflow

Engins en approvisionnement infini

Allocations

Engins en approvisionnement fini

Allocations

Workflow


Allocations


Allocations

Workflow


Allocations


Allocations

Planning

Event

New CustomerDemand

Event

New SupplierDemand

Event

New SupplierSupply

Event

New CustomerSupply

© FOR@C – experimental platform


Definition of collaboration An intended cooperative action

between two or more entities that exchange or share resources in order to take decisions or pursue an activity that will generate benefits or loss that are to be shared.

D’Amours et Frayret (2003)

From an intra-organizational perspectiveall resources can be view as shareable resources


Concepts of collaboration Main characteristics of inter-organizational

collaboration (from literature):– Common goals and objectives, shared or jointly decided

Jacobs (2002)– Implication of decision makers

Pollard (2002)– Mutual trust

Jacobs (2002)– Through organisational structures

Pollard (2002)– Shared operation planning and execution

Simatupang and Sridharan (2002), Jacobs (2002), Schrage (1990)– Sharing of risk, rewards and responsibilities

Lambert and al. (1999)– Be more efficient, get a competitive advantage

Simatupang and Sridharan (2002), Lambert and al. (1999), Pollard (2002)

Three important dimensionsof collaboration :

HumainOrganisationnal

(strategy & process)Technology


Transactionnal relationshipTransactionnal relationship

Information exchange Information exchange relationshiprelationship

Joint planningJoint planning

Collaborative operation Collaborative operation planning and executionplanning and execution

Intensity of the collaboration

Nature of exchanges

weak strong

Co-evolutionCo-evolution

Frayret, D’Amours and D’Amours 2003

simple

complex

Concepts of collaboration

•Contracts & mechanisms•Collaborative rules

•Allocation•Pricing•Incentives…

•Local & collective goals•Information & decisiontechnologies •Protocols & workflows


Value of collaboration

What to share? Information sharing– Information– Product – Antitrust law

How to share? Collaboration mechanism– Minimum cost solution– Equal % of benefit (e.g. Shapley value, Nucleus, externalities, etc.)– Equilibrium in between?

How to motivate? Contract and incentive designs– Premium– Volume guarantee


Strategic game Precisely, a strategic game

consists of – a set of players – for each player, a set of actions (sometimes

called strategies) – for each player, a payoff function that gives the

player's payoff to each list of the players' actions.

http://www.chass.utoronto.ca/~osborne/2x3/tutorial/SGAME.HTM


The wood supply game

Satisfy demandMinimize inventory

FOREST

ComplexSawing

Saw MillWholesalerPaper

WholesalerWood

RetailerWood

RetailerPaper


1. Traditional order transmission2. Decoupled demand/order transmission3. Real-time end customer demand transmission

There is always an equilibrium where players demonstrate collaborative behavior. This equilibrium is almost always as good as the minimum cost solution.

Moyaux et al. 2004


Moving toward collaboration

Order based relationship Continous replenishment

– Transportation based– Capacity based

Vendor managed Inventory Collaborative planning, forecasting

and replenishment


Agent-based simulation in supply chain


Knowledge-based supply chain planning systems

Forget et al. 2006


Multi-behavior agent

Forget et al. 2006


Concluding remarks

Building the agent-based simulation ability will permit to model and test emerging supply chain planning approaches in a dynamic, distributed, specialized and stochastic environment.

Technical challengesEvent managementDecision delayExecution up-datePlayers behavioursDebugging


Thank you

www.forac.ulaval.ca

Basys’06, Niagara Falls, Ontario, Canada, 2006 Agent-based supply chain planning in the forest...

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