1. 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@C Canada Research Chair on planning value creation network

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

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

4. Basys’06, Niagara Falls, Ontario, Canada, 2006 Partners

5. Basys’06, Niagara Falls, Ontario, Canada, 2006 Supply chain

6. Basys’06, Niagara Falls, Ontario, Canada, 2006 Forest products supply chain

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

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

9. 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

10. Basys’06, Niagara Falls, Ontario, Canada, 2006 Fiber flow

11. Basys’06, Niagara Falls, Ontario, Canada, 2006 Fiber transformation

12. Basys’06, Niagara Falls, Ontario, Canada, 2006 customers Forest supply chain

13. Basys’06, Niagara Falls, Ontario, Canada, 2006 Pulp and paper supply chain

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

15. Transportation in the supply chain

16. Basys’06, Niagara Falls, Ontario, Canada, 2006 Supply chain planning challenges in the forest products industry

17. Basys’06, Niagara Falls, Ontario, Canada, 2006  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 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

18. Basys’06, Niagara Falls, Ontario, Canada, 2006 Domtar supply chain Ship to points Distribution Centers Satellite Warehouses Mills Converters Merchants

19. Basys’06, Niagara Falls, Ontario, Canada, 2006 Harvesting/procurement plan 2006 2008 2007 Sustainable development Road construction Mixed of products, uneven aged Plantation

20. Basys’06, Niagara Falls, Ontario, Canada, 2006 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 Productivity not always linear Sequence dependent set-ups Attribute based products

21. Basys’06, Niagara Falls, Ontario, Canada, 2006 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. N. American Consumption/Real GDPGlobal Consumption/Real GDP Source: RISI, CIBC World Markets Source; Roberts, 2005, Vision 2015 FOR@C

22. Basys’06, Niagara Falls, Ontario, Canada, 2006 Demand/supply propagation Mix of spot market and contracts Facilities Markets

23. Basys’06, Niagara Falls, Ontario, Canada, 2006 Advanced Planning System for the Pulp and Paper Industry (APS-PPI)

24. Basys’06, Niagara Falls, Ontario, Canada, 2006 Distributed planning systems Top level planning problem Anticipation model of the base level planning problems Base level planning model Final Set of decisions IN* Reaction RE* Instruction IN* Anticipation functions Instructions Schneeweiss (2003)

25. Basys’06, Niagara Falls, Ontario, Canada, 2006 Supply chain scheduling: application to the lumber industry

26. Basys’06, Niagara Falls, Ontario, Canada, 2006 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)

27. Basys’06, Niagara Falls, Ontario, Canada, 2006 The lumber supply chain Customers

28. Basys’06, Niagara Falls, Ontario, Canada, 2006 Log Requirement

29. Basys’06, Niagara Falls, Ontario, Canada, 2006 Sawing Line Plan Solved using mathematical programming (MIP or LP)

30. Basys’06, Niagara Falls, Ontario, Canada, 2006 Sawing 2x4 1x6 2x3 2x6 Type 1 Type 2 Type 3 Cutting Pattern #12 Cutting Pattern #57 Cutting Pattern #25 Cutting Pattern #9

31. Basys’06, Niagara Falls, Ontario, Canada, 2006 Drying Plan Solved using a constraint programming model

32. Basys’06, Niagara Falls, Ontario, Canada, 2006 Kiln Drying Green Dried Kiln Kiln Drying Drying Kiln Air Drying Kiln Yard Equalizing Different Loading Patterns (products distribution) Different Drying Process

33. Basys’06, Niagara Falls, Ontario, Canada, 2006 Finishing Line Plan Solved using heuristics

34. Basys’06, Niagara Falls, Ontario, Canada, 2006 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 “92 5/8 “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 “92 5/8 “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 “92 5/8 “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 %---

35. Basys’06, Niagara Falls, Ontario, Canada, 2006 Shipment Orders Solved using a linear programming model

36. Basys’06, Niagara Falls, Ontario, Canada, 2006 Integration and system dynamics Supplier Production site WarehouseSales Decentralised order material Simple integration Limited information exchanged Impact of the bullwhip effectbullwhip effect Minimum return – local optimisation Supplier Production Site WarehouseSales Centralised 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

37. Basys’06, Niagara Falls, Ontario, Canada, 2006 Planning challenges 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 decisions models and algorithms Decisions distribution and localization where events must be managed

38. Basys’06, Niagara Falls, Ontario, Canada, 2006 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)

39. Basys’06, Niagara Falls, Ontario, Canada, 2006 FOR@C V-Lumber Experimental Platform

40. Basys’06, Niagara Falls, Ontario, Canada, 2006 Distributed & Specialized Tools

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

42. Supply Chain Planning Make Agent Deliver Agent Source Agent Planning Unit Demand Plan Supply Plan Demand Plan Supply Plan Make Agent Deliver Agent Source Agent Planning Unit Make Agent Deliver Agent Source Agent Planning Unit Tactical planning unit Analysis Tools Agents Data

43. Basys’06, Niagara Falls, Ontario, Canada, 2006 Agent Architecture

44. Basys’06, Niagara Falls, Ontario, Canada, 2006 Customer AgentSupplier Agent Conversation Workflow Planning Event New Customer Demand Event New Supplier Demand Event New Supplier Supply Event New Customer Supply © FOR@C – experimental platform

45. Basys’06, Niagara Falls, Ontario, Canada, 2006 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 perspective all resources can be view as shareable resources

46. Basys’06, Niagara Falls, Ontario, Canada, 2006 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 dimensions of collaboration : Humain Organisationnal (strategy & process) Technology

47. Basys’06, Niagara Falls, Ontario, Canada, 2006 Transactionnal relationship Information exchange relationship Joint planning Collaborative operation planning and execution Intensity of the collaboration Nature of exchanges weakstrong Co-evolution Frayret, D’Amours and D’Amours 2003 simple complex Concepts of collaboration Contracts & mechanisms Collaborative rules Allocation Pricing Incentives… Local & collective goals Information & decision technologies Protocols & workflows

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

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

50. Basys’06, Niagara Falls, Ontario, Canada, 2006 The wood supply game Satisfy demand Minimize inventory FOREST Complex Sawing Saw Mill Wholesaler Paper Wholesaler Wood Retailer Wood Retailer Paper

51. Basys’06, Niagara Falls, Ontario, Canada, 2006 1. Traditional order transmission 2. Decoupled demand/order transmission 3. 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

52. Basys’06, Niagara Falls, Ontario, Canada, 2006 Moving toward collaboration  Order based relationship  Continous replenishment –Transportation based –Capacity based  Vendor managed Inventory  Collaborative planning, forecasting and replenishment

53. Basys’06, Niagara Falls, Ontario, Canada, 2006 Agent-based simulation in supply chain

54. Basys’06, Niagara Falls, Ontario, Canada, 2006 Knowledge-based supply chain planning systems Forget et al. 2006

55. Basys’06, Niagara Falls, Ontario, Canada, 2006 Multi-behavior agent Forget et al. 2006

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

57. 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

58. Basys’06, Niagara Falls, Ontario, Canada, 2006 Thank you www.forac.ulaval.ca