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Agent-based supply chain planning in the forest products industry. Sophie D’Amours Ph.D. Professor, Université Laval General Director, Research Consortium [email protected] Canada Research Chair on planning value creation network. Agenda. [email protected] Research Consortium Forest products industry

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agent based supply chain planning in the forest products industry

Agent-based supply chain planning in the forest products industry

Sophie D’Amours Ph.D.

Professor, Université Laval

General Director, Research Consortium [email protected]

Canada Research Chair on planning value creation network

agenda
Agenda
  • [email protected] Research Consortium
  • Forest products industry
  • Supply chain planning challenges in the forest products industry
  • Supply chain scheduling: application to the lumber industry
  • [email protected] V-Lumber Experimental Platform
  • Agent-based simulation in supply chain
mission of the consortium

T

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.

Mission of the Consortium
canadian industry snapshot
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

qu bec
Québec

80% is public land

  • 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.
forest supply chain
Forest supply chain

customers

customers

customers

slide17

Top 5

International 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

  • 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.
domtar supply chain

Merchants

Converters

Mills

Satellite Warehouses

Distribution Centers

Ship to points

Domtar supply chain
harvesting procurement plan
Harvesting/procurement plan

2007

2008

2006

Sustainable developmentRoad construction

Mixed of products, uneven aged

Plantation

alternative divergent processes
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 pattern

Attribute basedproducts

Recipe/cutting pattern

Recipe/cutting pattern

Productivity not always linear

Sequence dependent set-ups

commodity price trends
Commodity Price Trends

N. American Consumption/Real GDP

Global Consumption/Real GDP

Source: RISI, CIBC World Markets

Source: RISI, CIBC World Markets

  • 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.

Source; Roberts, 2005, Vision 2015 [email protected]

demand supply propagation

Markets

Facilities

Demand/supply propagation

Mix of spot market and contracts

slide23

Advanced Planning System for the

Pulp and Paper Industry (APS-PPI)

distributed planning systems
Distributed planning systems

Top level planning problem

Final

Set ofdecisions

IN*

Anticipationfunctions

Instructions

Anticipation model ofthe base levelplanning problems

ReactionRE*

InstructionIN*

Base level planning model

Schneeweiss (2003)

scheduling
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)
sawing line plan
Sawing Line Plan

Solved usingmathematicalprogramming

(MIP or LP)

sawing

Type 1

Type 2

Type 3

Sawing

Cutting Pattern #9

2x3

2x4

Cutting Pattern #25

2x6

1x6

Cutting Pattern #12

Cutting Pattern #57

drying plan
Drying Plan

Solved usinga constraint programmingmodel

drying
Drying

Different Loading Patterns

(products distribution)

Kiln

Dried

Green

Kiln

Drying

Different Drying Process

Kiln

Kiln

Kiln

Kiln

Kiln

Kiln

Air Drying

Yard

Equalizing

Kiln Drying

finishing line plan
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)

Finishing
shipment orders
Shipment Orders

Solved usinga linear

programmingmodel

integration and system dynamics

order

Supplier

Production

site

Warehouse

Sales

Decentralised

material

Planning centre

Supplier

Production

Site

Warehouse

Sales

Centralised

material

Integration and system dynamics
  • Simple integration
  • Limited information exchanged
  • Impact of the bullwhip effect
  • Minimum return – local optimisation
  • 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
Planning challenges

Global Performanceof the entire supply chain network (avoid local optimum et information distortion)

Synchronization of decisions

d-APS

Manufacturing and logisticAgility

(ability to re-plan quickly)

Operation plans feasibility

(avoid plans that are not feasible)

Specialization of decisionsmodels and algorithms

Decisions distribution and localizationwhere events must be managed

raise the needs for tools designed
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)
supply chain planning

Planning Unit

Planning Unit

Planning Unit

Supply Chain Planning

Analysis

Tools

Agents

Data

Tactical planning unit

Demand Plan

Supply Plan

Demand Plan

Supply Plan

Source

Agent

Deliver

Agent

Source

Agent

Deliver

Agent

Make

Agent

Make

Agent

Source

Agent

Deliver

Agent

Make

Agent

slide44

Customer Agent

Supplier Agent

Workflow

Workflow

Workflow

Conversation

Conversation

Conversation

Event

Event

Event

Event

New Customer

Supply

New Customer

Demand

New Supplier

Demand

New Supplier

Supply

Planning

© [email protected] – experimental platform

definition of collaboration
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.

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

D’Amours et Frayret (2003)

concepts of collaboration
Concepts of collaboration
  • Main characteristics of inter-organizational collaboration (from literature):
    • Common goals and objectives, shared or jointly decidedJacobs (2002)
    • Implication of decision makersPollard (2002)
    • Mutual trust Jacobs (2002)
    • Through organisational structures Pollard (2002)
    • Shared operation planning and executionSimatupang and Sridharan (2002), Jacobs (2002), Schrage (1990)
    • Sharing of risk, rewards and responsibilitiesLambert and al. (1999)
    • Be more efficient, get a competitive advantageSimatupang and Sridharan (2002), Lambert and al. (1999), Pollard (2002)

Three important dimensions

of collaboration :

Humain

Organisationnal (strategy & process)

Technology

concepts of collaboration47
Concepts of collaboration

Nature of exchanges

Co-evolution

complex

Collaborative operation planning and execution

  • Contracts & mechanisms
  • Collaborative rules
    • Allocation
    • Pricing
    • Incentives…
  • Local & collective goals
  • Information & decisiontechnologies
  • Protocols & workflows

Joint planning

Information exchange relationship

Transactionnal relationship

simple

weak

strong

Intensity of the collaboration

Frayret, D’Amours and D’Amours 2003

value of collaboration
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
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

slide50

Complex

Sawing

Wholesaler

Wood

Retailer

Wood

FOREST

The wood supply game

Retailer

Paper

Wholesaler

Paper

Saw Mill

Satisfy demand

Minimize inventory

slide51

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

1. Traditional order transmission

2. Decoupled demand/order transmission

3. Real-time end customer demand transmission

moving toward collaboration
Moving toward collaboration
  • Order based relationship
  • Continous replenishment
    • Transportation based
    • Capacity based
  • Vendor managed Inventory
  • Collaborative planning, forecasting and replenishment
multi behavior agent
Multi-behavior agent

Forget et al. 2006

concluding remarks
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 challenges

Event management

Decision delay

Execution up-date

Players behaviours

Debugging

thank you

Thank you

www.forac.ulaval.ca

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