An Agent-Based Supply Chain Model for Strategic Analysis in Forestry. Saba Vahid

Transcription

1 An Agent-Based Supply Chain Model for Strategic Analysis in Forestry by Saba Vahid M.A.Sc., The University of British Columbia, 2006 B.Sc., Sharif University of Technology, 2002 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2011 Saba Vahid, 2011

2 Abstract An agent-based forest sector model, CAMBIUM 2.0, is developed and applied to case studies of the forest industry in the coastal British Columbia (BC). By combining optimization and simulation, this model allows policy makers and managers to examine the impact of different supply chain (SC) configurations (e.g. establishing new facilities), and changing forest management policies (e.g. harvest restrictions). The forest sector structure and the state of the forest resources that develop over time are a result of autonomous agents interacting with each other while competing for available forest resources needed to manufacture forest products. The thesis is presented in four chapters. Chapter 1 introduces SC modelling concepts and techniques and identifies research objectives and methods. Chapter 2 presents and discusses the structure of the agent-based simulation model and the formulation of the facility location problem, presenting a novel algorithm for integrating the optimization problem with the simulation model. The model is applied to the case of a forest industry SC to establish a new agent. The predictions of the new agent about its profits are not strongly affected by higher levels of information about the cost structure of its competitors, while improving the accuracy of market predictions has a noticeable impact on such predictions. Chapter 3 evaluates the impact of establishing a log sort yard on the profitability of the forest products SC. Considering different market price scenarios, establishing a sort yard does not seem to benefit the forest products SC, mainly because of intense competition for timber. In Chapter 4, CAMBIUM 2.0 is used to investigate the impact of harvest policy changes on the SC performance and the timber supply sustainability. Alternative harvest priorities (e.g. harvesting stands with highest value first) and modifying the harvesting preference of the mills (i.e. harvesting a mix of high and low value stands) improves the timber supply sustainability with less negative economic impacts compared to lowering the harvest limit. The modelling framework developed in this research can be extended to address other research questions such as changing log export policies, setting stumpage prices, or encouraging replanting of economically desirable species. ii

3 Preface This thesis is based on a series of manuscripts that are published or will be submitted for publication in peer-reviewed journals. I developed the model, gathered the required data from various sources, designed and conducted the simulation experiments, and wrote all of the manuscripts. Dr. Thomas Maness, my PhD supervisor, advised me in the process of model development and validation, as well as designing the scenario analyses. He also edited the manuscripts and is the co-author on all articles. The simulation model developed in my dissertation was based on a model created by Dr. Olaf Schwab and Dr. Thomas Maness in the University of British Columbia (Schwab et al., 2008) which was extended and modified significantly to fit the purpose of my research. My dissertation research was conducted in collaboration with FPInnovations (Vancouver, BC). Mr. Joel Mortyn and Mr. Jack MacDonald from FPInnovations helped in estimating and verifying the collected forest resource and industrial data during model development. Versions of the following chapters have been or will be submitted for publication: Chapter 1: Vahid, S. and Maness, T. (2010). Modelling customer demand in forest products industry supply chains: a review of the literature. International Journal of Simulation and Process Modelling. 6(2): Chapter 2. Vahid, S. and Maness, T. (to be submitted). New Facility Location in a Forest Products Supply Chain Model. Chapter 3. Vahid, S. and Maness, T. (to be submitted). Impact of Establishing a Centralized Sort Yard in Coastal British Columbia. Chapter 4. Vahid, S. and Maness, T. (to be submitted). Impact of Harvest Policy Changes on Sustainability. iii

4 Table of Contents Abstract... ii Preface... iii Table of Contents... iv List of Tables... vii List of Figures... viii List of Abbreviations... xii Acknowledgments... xiii Dedication... xiv Chapter 1: Introduction Forest Products Supply Chain Research Objectives Supply Chain Models Forest Products Supply Chain Models Optimization Models Simulation Models Facility Location in Supply Chain Models Policy Analysis Using Supply Chain Models Summary of Relevant Literature Structure of This Thesis Chapter 2: New Facility Location in a Forest Products Supply Chain Model Introduction Facility Location in CAMBIUM CAMBIUM Single Facility Location Optimization Problem Model Assumptions Parameters Variables Objective Function iv

5 Constraints CAMBIUM 2.0 Simulation Flow Case Study: British Columbia s Coastal Primary Forest Products Industry Forest Inventory Data Operational Data for Manufacturing Facilities Scenarios Results & Discussion Predicted Profits Observed Profits Predicted and Observed Log Exports Conclusions Chapter 3: Impact of Establishing a Centralized Sort Yard in Coastal British Columbia Introduction Modelling Sort Yard Operations in CAMBIUM Data and Scenarios Case Study Data Market Price Scenarios Results and Discussion Scenario I Scenario II Conclusions Chapter 4: Impact of Harvest Policy Changes on Sustainability Introduction History of Forest Management in BC Coastal BC Timber Resource Methods Data and Scenarios Data Scenarios Results and Discussion Impact of AAC Reduction Impact of Harvest Priority Change v

6 4.4.3 Impact of Removing Quality Requirements Conclusion Chapter 5: Model Validation and Verification Introduction Verification Validation Chapter 6: Conclusions Conclusions Limitations Future Work References Appendices Appendix A. Additional Graphs for Chapter A.1 Average Agents Profit in the Optimal Solution of Facility Location Problem Appendix B. Additional Graphs for Chapter B.1 Average Total Harvest B.2 Average Total Saw Log Volume Purchased by Sawmills from the Sort Yard Appendix C. Additional Graphs for Chapter C.1 Base Case C.2 Scenarios I and II C.3 Scenarios III and IV C.4 Age Distribution of Timber Harvesting Land Base (THLB) for All Scenarios vi

7 List of Tables Table 2.1 Log quality ratio by site index Table 2.2 Cost, recovery, and other operating assumptions for BC Coast sawmills Table 2.3 Harvesting and transportation cost assumptions for the BC Coast Table 2.4 Cost, recovery, and other operating assumptions for BC Coast sort yards Table 2.5 Log and lumber prices for BC Coast at the beginning of the simulation Table 3.1 Average sawmill product recovery factors for scenario I Table 3.2 Average sawmill product recovery factors for scenario II Table 4.1 Log quality ratio by site index and age group Table 4.2 Sawmill costs for all scenarios Table 4.3 Market price for all scenarios Table 4.4 Scenario descriptions Table 5.1 Initial sawmill specifications vii

8 List of Figures Figure 1.1 A typical forest products supply chain... 1 Figure 2.1 Figure 2.2 Value of manufacturing shipments for sawmills and wood preservation sector in BC,Source: Statistics Canada (2011b) (a) BC Coast total sawn lumber production and its share of Canadian sawn lumber production, (b) Wood products manufacturing employment level and its share of total employment in BC, Source: Statistics Canada (2011a, 2011c) Figure 2.3 CAMBIUM 2.0 flow of simulation in every time step Figure 2.4 Flow of Simulation in CAMBIUM Figure 2.5 Strategy selection schematic Figure 2.6 Forest region and sawmills included in the case study of the coastal BC industry Figure 2.7 Percentage of change in market prices for log and lumber products Figure 2.8 Figure 2.9 Figure 2.10 (a) Average profits of existing agents in the optimal solution for the facility location problem, (b) Average profit of new agent in the optimal solution for the facility location problem (a) Average profit ( 1SD) of existing agents based on simulation results in scenario 3, (b) Average profit ( 1SD) of the new agent based on simulation results, scenario Average log export ratio ( 1SD) of the new sort yard based on simulation results Figure 3.1 Ratio of saw and pulp logs delivered from the forest and the sort yard Figure 3.2 (a) Framing lumber composite price, (b) Weighted average log price (over all species) for the BC Coast, Source: Random Lengths (2011), BC Ministry of Forests, Land, and Natural Resource Operations (2011c) Figure 3.3 Market price changes for scenario II Figure 3.4 (a) Average total profits of supply chain members ( 1SD) scenario I without a sort yard, (b) Average total profits of supply chain members ( 1SD) scenario I with a sort yard Figure 3.5 Figure 3.6 (a) Average total profits of supply chain members with and without the sort yard: scenario I, (b) Average total harvest volume with and without the sort yard: scenario I (a) Average total imported logs ( 1SD), scenario I without a sort yard, (b) Average total imported and purchased sort yard logs ( 1SD), scenario I with a sort yard Figure 3.7 Log output and export volume for the sort yard, scenario I viii

9 Figure 3.8 (a) Average lumber production and capacity levels ( 1SD), scenario I without a sort yard, (b) Average lumber production and capacity levels ( 1SD), scenario I with a sort yard Figure 3.9 Log production of the sort yard ( 1SD) for scenario I Figure 3.10 (a) Average total profits of supply chain members ( 1SD): scenario II without a sort yard, (b) Average total profits of supply chain members ( 1SD): scenario II with a sort yard Figure 3.11 Figure 3.12 Average total harvest volumes with the presence of a sort yard for scenario I and II (a) Average total imported logs ( 1SD), scenario II without a sort yard, (b) Average total imported and purchased sort yard logs ( 1SD), scenario II with a sort yard Figure 3.13 Log output and export volume for the sort yard for scenario II Figure 3.14 (a) Average lumber production and capacity levels ( 1SD), scenario II without a sort yard, (b) Average lumber production and capacity levels ( 1SD), scenario II with a sort yard Figure 3.15 Log production of the sort yard ( 1SD) for scenario II Figure 4.1 Figure 4.2 Figure 4.3 Figure 4.4 (a) Average total harvest of sawmills ( 1SD) Base case and scenarios I and II, (b) Average total volume of imported logs Base case and scenarios I and II (a) Average total profits of sawmills ( 1SD) Base case and scenarios I and II, (b) Average total production capacity of sawmills ( 1SD) Base case and scenarios I and II Average number of active agents in scenario II. Error bars show the observed maximum and minimum number of active agents in each time interval (a) Average remaining volume of standing timber - Base case and scenarios I and II, (b) Average remaining value of standing timber - Base case and scenarios I and II Figure 4.5 Average unit value of remaining timber - Base case and scenarios I and II Figure 4.6 Figure 4.7 Figure 4.8 (a) Average total harvest of sawmills Base case and Scenarios III and IV, (b) Average total volume of imported logs Base case and Scenarios III and IV (a) Average total production capacity of sawmills Base case and scenarios III and IV, (b) Average total profits of sawmills Base case and scenarios III and IV Average unit value of remaining timber - Base case and scenarios III and IV ix

10 Figure 4.9 Figure 4.10 Figure 4.11 (a) Average total production capacity ( 1SD) of sawmills Base case and scenario V, (b) Average total profits ( 1SD) of sawmills - Base case and scenario V (a) Average total harvest ( 1SD) of sawmills - Base case and scenario V, (b) Average total volume ( 1SD) of imported logs - Base case and scenario V. 94 (a) Average total production ( 1SD) of sawmills Base case, (b) Average total production ( 1SD) of sawmills Scenario V Figure 4.12 Average unit value of remaining timber Base case and scenario V Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 (a) Average number of active agents, base case, (b) Average number of active agents, increased production costs. Error bars show minimum and maximum observed number of agents (a) Average profits of supply chain members ( 1SD), base case, (b) Average profits of supply chain members ( 1SD), increased production costs (a) Average lumber production of sawmills ( 1SD), base case, (b) Average lumber production of sawmills ( 1SD), increased production costs (a) Average number of active agents, increased prices. Error bars show minimum and maximum observed number of agents, (b) Average lumber production of sawmills ( 1SD), increased prices Figure 5.5 Average profits of supply chain members ( 1SD), increased prices Figure 5.6 Figure 5.7 Figure 5.8 Figure A.1 Figure A.2 Figure A.33 Figure B.14 Figure B.25 (a) Average log export ratio ( 1SD) of the sort yard for the base case and the case with lower log prices, (b) Average production capacity ( 1SD) of the sort yard for the base case and the case with lower log prices Average production volume ( 1SD) of high value lumber for the base case and the case with reduced import limit (a) Average sort yard profits ( 1SD) for the base case and the case with limited exports, (b) Average log output ( 1SD) of the sort yard for the base case and the case with limited exports (a) Average profits ( 1SD)of existing agents - scenario 1, (b) Average profit ( 1SD)of new agent, scenario (a) Average profits ( 1SD) of existing agents - scenario 2, (b) Average profit ( 1SD) of new agent, scenario (a) Average profits ( 1SD) of existing agents - scenario 3, (b) Average profit ( 1SD) of new agent, scenario (a) Average Total harvest volume ( 1SD): scenario I - without a sort yard, (b) Average Total harvest volume ( 1SD): scenario I - with a sort yard (a) Average Total harvest volume ( 1SD): scenario II - without a sort yard, (b) Average Total harvest volume ( 1SD): scenario II - with a sort yard Figure B.36 (a) Average Total purchased logs from sort yard ( 1SD): scenario I, (b) Average Total purchased logs from sort yard ( 1SD): scenario II x

11 Figure C.17 (a) Average total harvest volume of sawmills (±1SD) Base case, (b) Average total log import volume of sawmills (±1SD) Base case Figure C.28 (a) Average total profit of sawmills (±1SD) Base case, (b) Average total production capacity of sawmills (±1SD) Base case Figure C.39 (a) Average total harvest volume of sawmills (±1SD) - Scenario I, (b) Average total harvest volume of sawmills (±1SD) Scenario II Figure C.410 (a) Average total harvest of sawmills (±1SD) - Scenario III, (b) Average total harvest of sawmills (±1SD), scenario IV Figure C. 511 (a) Average total log import volume of sawmills (±1SD), scenario III, (b) Average total log import volume of sawmills (±1SD), scenario IV Figure C.612 (a) Average total production capacity of sawmills (±1SD), scenario three, (b) Average total production capacity of sawmills (±1SD), scenario four Figure C.713 (a) Average total profits of sawmills (±1SD) - scenario III, (b) Average total profits of sawmills (±1SD) - scenario IV Figure C.814 Average THLB area (±1SD) by age group for the base case Figure C.915 (a) Average THLB area (±1SD) by age group for scenario I, (b) Average THLB area (±1SD) by age group for scenario II Figure C.1016 (a) Average THLB area (±1SD) by age group for scenario III, (b) Average THLB area (±1SD) by age group for scenario IV Figure C.1117 Average THLB area (±1SD) by age group for scenario V xi

12 List of Abbreviations AAC ABM BC bft C & I DES FL LP m 3 mbf MIP OR SCM SD SFM TFL TSA Annual Allowable Cut Agent-based Modelling British Columbia Board Feet Criteria and Indicators Discrete Event Simulation Forest License Linear Programming Cubic Meter Thousand Board Feet Mixed Integer Program Operations Research Supply Chain Management System Dynamics Sustainable Forest Management Tree Farm License Timber Supply Area xii

13 Acknowledgments I would like to thank my research advisor, Dr. Thomas Maness, for guiding me throughout my program and for his intellectual and moral support when I faced challenges. This work would not have been possible without him. I would also like to express my gratitude to my supervisory committee, Dr. John Nelson and Dr. Farrokh Sassani, for their extremely helpful ideas and suggestions for improving the work presented in this dissertation. Additionally, I would like to thank the members of my examining committee - Dr. Robert Kozak, Dr. Gary Schajer, and Dr. Woody Chung - for critically reviewing and commenting on my thesis. I am thankful to other graduate students and post doctoral fellows in our research group for their support and encouragements throughout my program. I would like to especially thank Dr. Olaf Schwab who helped me immensely in understanding the fundamentals of the CAMBIUM model and Dr. Cristian Palma who motivated and inspired me when I most needed it. I am also extremely grateful to Mr. Catalin Ristea who always took the time to listen to my problems and offered his assistance when possible. I would also like to express my highest appreciation to the researchers at FPInnovations in Vancouver - Dr. Darrell Wong, Mr. Joel Mortyn, and Mr. Jack MacDonald for kindly sharing their knowledge and views with me and for answering my endless questions. I could not have endured the pressure of the past few years, if it was not for the unconditional love and support of my family. My lovely parents, Sima and Abolghassem, believed in me when I lost faith and cheered me on when I needed motivation. I am forever indebted to them for all they gave me. My dearest siblings, Hamid and Sepideh, have always inspired me with their love and courage and I am eternally grateful to them for all they have done for me. I went through a long and difficult journey to complete my work and I cannot imagine having done it without the support of my loving partner and companion, Ario. He has given me love, peace, and comfort and I thank him for that. I am enormously thankful to my dearest friends, Nazly and Shora, who have always rooted for me. Thank you Nazly, for being my family away from home and thank you Shora, for being a source of hope and optimism for me. xiii

14 Dedication تقديم به مادر و پدرم که ھميشه مرا باور داشته اند. To my parents, who have always believed in me. xiv

15 Chapter 1: Introduction Chapter 1. Introduction Forest Products Supply Chain The supply chain in the forest products sector is exceedingly complex. Although it includes private companies, it is impacted by a collection of entities that are outside of the supply chain of a typical business. These entities can include government agencies, nongovernmental organizations, environmental groups, and community organizations. Outside influences are constantly changing the dynamics of the system. Additionally, the business cycle impacts the forest products sector in a profound way and creates a ripple effect through regional economies. Furthermore, forest policy and international trade agreements are constantly changing and creating uncertainty for investors. Pulp Based Manufacturing Local Market Export Market Pulp Mill Sawmill or Panel Mill Secondary Wood Products Manufacturing Forest Local Market Export Market Log Sort Yard Flow of Material/Products Figure 1.1 A typical forest products supply chain 1 A modified version of this chapter has been previously published: Vahid, S. and Maness, T. (2010). Modelling customer demand in forest products industry supply chains: a review of the literature. International Journal of Simulation and Process Modelling. 6(2):

16 Chapter 1: Introduction A typical forest industry supply chain is shown in Figure 1.1. The flow starts in the forest where trees are harvested and the branches are removed. Usually, the next step is bucking: cutting trees into transportable length logs based on diameter, length, and quality. The logs are then transported to sawmills, pulp mills, or sort yards, presumably based on the highest valued application. Sort yards are intermediate storage places where long logs from the forest are further sorted based on their grade and sent to appropriate manufacturing processes. Transportation of logs can be by truck, ship, or by log boom (water), depending on the terrain condition and the proximity of roads and water. When logs are received by sawmills, they are sawn into final products (in a multitude of manufacturing steps). If necessary they are also kiln-dried to produce specific products. The market for sawmill products includes construction industries, secondary wood processing plants, and remanufacturing plants among others. Chip residues are produced as a by-product of sawmilling activities and are sold to pulp mills, composite panel plants, or bio-energy plants (Ronnqvist, 2003). Each of these stages requires decisions that affect the outcome of future steps. For example, the bucking decisions made in the forest will affect the type of products that can be produced from the log. Therefore, it is important that all activities be planned and coordinated jointly (Haartveit & Fjeld, 2002). However, because of the nature of this industry, decisions of the upstream and downstream supply chain members are rarely integrated. Supply Chain Management (SCM) is a concept that can assist companies in achieving integrated planning and operations. SCM is the integrated planning of all business activities including purchasing, manufacturing, warehousing and transportation of raw materials and finished products. Effectively linking these activities can greatly decrease the overall cost of the supply chain by eliminating redundant inventories, increasing throughput and reducing waste within the supply chain (Moyaux et al., 2004; Singer & Donoso, 2007). The forest industry is a big part of the BC economy and a major source of employment in rural communities. The industry accounted for approximately 30% of the provincial exports in 2009 and provided direct and indirect jobs equal to 7% of total employment in 2008 (BC Ministry of Forests, 2010). However, the industry is facing challenges; wood products manufacturing shipments of BC has decreased sharply during the past two decades, dropping by more than 60% since 1995 (Statistics Canada, 2011b). Employment has also been decreasing, with approximately 10,000 jobs lost since 1995 (Statistics Canada, 2011a). BC 2

17 Chapter 1: Introduction forest companies have low average Return on Capital Employed (ROCE), with an average ROCE of 3.8% in 2010, when the cost of capital is approximately 10% to 12% (Hamilton, 2011). As a result, the industry has been having trouble attracting new capital. This is problematic, since capital investments are one of the main drivers of technological progress. Lack of capital investments, particularly in the coastal BC forest sector, has caused the industry to lag behind other regions in Canada and elsewhere. Outdated manufacturing equipment results in high production costs, and since BC Coast already has higher log and labor costs compared to its competitors, the profitability of the forest industry in this region is further decreased. To remain competitive the BC forest sector needs to find new operating strategies, employing new management techniques in areas such as harvest scheduling, transportation, manufacturing and production planning that will improve the sector s performance. Establishing new facilities such as log sort yards is one potential strategy that may be worth investigating. Log sort yards have been recommended for improving the utilization of smaller logs (Venn et al., 2009) and benefiting small wood manufacturing businesses (Sunderman, 2003). As previously mentioned, SCM concepts and techniques can be of benefit in this context by providing analytical tools and frameworks to investigate and compare different strategies and investment decisions. Decision support tools can be used to investigate how SCM could be applied to the BC Coast to make the process more efficient. Another source of problems for the forest industry in BC in recent years has been the decreasing availability of old-growth timber which has historically been abundant, especially on the coast (Pearse, 2001). Harvesting practices have favored old-growth timber in the past, resulting in aggressive harvesting of the most accessible high quality old-growth stands (Prudham, 2007; Wilson, 1998). Although this trend originally contributed to the growth of the forest industry, it has also caused a decline in availability of the most desired timber resources, which has resulted in higher timber harvesting costs. Although the harvesting policies on BC have been changed over the years to manage the forest resources in a more sustainable manner (BC Ministry of Forests, 2010), some argue more policy reforms are required to benefit the society as well as the industry in the long run (Burda et al., 1998; Wilson, 1998). Similar to production strategies of the industry, the impact of forest policy changes such as harvest restrictions or export regulations can also be investigated using SCM decision support tools that have been customized for use on the BC Coast. 3

18 Chapter 1: Introduction 1.2 Research Objectives The objective of this thesis is to provide a decision support tool that can be used to aid policy research regarding the supply chain of the forest products industry. Specifically, this decision support tool will be used to answer the following questions: 1. What would be the economic impact of establishing a new log sorting facility within the existing supply chain of the coastal BC forest industry? a. How does the sort yard impact the profits and operations of the existing facilities? b. How does the sort yard perform through time as the surrounding environment changes? c. If a sort yard is economically efficient, where should the new facility be best located from a set of potential locations? 2. How would a change in harvest policies impact the performance of the supply chain members? a. How would a change in allowable harvest levels impact the profits and operations of the supply chain members? b. How would a change in harvest priority (the order of harvesting forest stands) impact the profits and operations of the supply chain members? 3. How would a change in harvest policies impact the state of forest resources in the region? a. How would a change in allowable harvest levels impact the sustainability of the forest enterprise? b. How would a change in harvest priority (the order of harvesting forest stands) impact the sustainability of the forest enterprise? The remainder of this chapter provides a review of relevant supply chain literature with a focus on forest products industry supply chains. A review of literature on forest products supply chains is presented first, followed by research on facility location within supply chains, and finally previous policy analysis performed using supply chain models. Finally, the thesis contributions and structure are presented. 1.3 Supply Chain Models Supply chains are networks that connect the raw material sources to finished products consumers through manufacturing activities and distribution channels (Santoso et al., 2005; 4

19 Chapter 1: Introduction Vila et al., 2006). There are many decisions to be made in a supply chain and modelling techniques can, directly or indirectly, help the decision makers by revealing the consequences of the proposed actions and strategies. The research literature on SCM is rapidly growing, offering different classifications of supply chain models. Depending on the operational level of the questions to be answered, the supply chain models are broken down into strategic, tactical or operational hierarchies. Strategic planning is at the highest level and is concerned with broad-scale decisions over long periods of time. Strategic planning is a process designed to give a firm a competitive advantage over competitors (Gunn, 2007). The strategic plan identifies the types of actions that need to be taken, but does not plan the implementation steps for those actions (Church, 2007). An example of a strategic analysis is deciding on which manufacturing facilities to establish in a production-distribution network. On the other end of the spectrum is operational planning, concerned with the day to day operations of the firm or supply chain, with time spans ranging from a day to a few weeks. For example, scheduling truck routes for transporting logs from specific harvest sites to specific destinations is an example of operational planning. Tactical models can provide a link between the two ends of the decision level spectrum. Tactical models translate the strategies into appropriate operational level targets (Church, 2007). These models ensure that the strategic goals are feasible at operational level. For example, harvest scheduling at the strategic level may identify a certain number of hectares from a certain age class that needs to be harvested on a land base. A tactical model is then used to provide more spatial detail about the specific stands that need to be harvested in a specific order. SCM models are also classified into centralized and decentralized models based on how decisions are made. In centralized supply chain models, all procurement, production, and distribution decisions are made by a central unit, considering the state of the entire system. This ensures a higher level of control and collaboration among all supply chain members and a globally optimum decision. Traditionally, many of the models in SCM literature have utilized centralized decision making. However, sometimes it is not realistic to assume that all decisions can be controlled centrally, especially if the supply chain members do not belong to the same organization. Each firm may aim to maximize its benefits without considering the impact on the whole system. Additionally, different firms may not be willing to share their 5

20 Chapter 1: Introduction cost and price information with others. In such cases, decentralized models are more appropriate (Stadtler, 2005). Decentralized supply chain models allow individual supply chain members to make decisions based on their own goals, while still operating in the same environment that inevitably affects all members. This reflects the decision making process in many real world systems and simultaneously decreases the model complexity, particularly in the case of larger supply chains that may be very difficult to model with centralized modelling techniques. Finally, another approach to classifying supply chain models is based on the modelling approach and solution method. Under this classification scheme, supply chain models can be broadly categorized into optimization and simulation models. Optimization models use mathematical programming approaches to find a feasible and optimal solution to a supply chain problem such as designing a transportation network, or locating a new plant. Alternatively, simulation models allow the decision makers to see the performance of the supply chain over time under various scenarios and help them understand the interrelationships between different model components (Shapiro, 2001). Optimization models are mostly centralized, while simulation models can more easily represent decentralized decision making. Simulation and optimization have also been combined for supply chain management in manufacturing industries. In fact, simulation based optimization has become a popular approach, mainly because of its ability to incorporate uncertainty into optimization problems (Fu, 2002; Jung et al., 2004; Mele et al., 2006). In this chapter, the latter classification scheme is used to review the relevant supply chain models. Considering the vast volume of available literature on supply chain models, only the studies focusing on forest industries are presented here. For information on supply chain modelling research in other contexts, the readers may refer to available reviews on the subject (Beamon, 1998; Chan & Chan, 2005; Min & Zhou, 2002; Stadtler, 2005; Thomas & Griffin, 1996). 1.4 Forest Products Supply Chain Models There have been studies that look at each of the operational areas in the forest industry separately, examining the effect of different management scenarios on the performance of individual companies as well as the entire sector in different regions and countries. In recent 6

21 Chapter 1: Introduction years great emphasis has been placed on supply chain management as a result of consolidation of upstream and downstream companies in the forest industry. It is a common opinion in today s forest industry that the supply chain can be improved as a whole if an analysis integrated all the different steps of the wood flow from the forest to the customer (Bredstrom et al., 2004; Ronnqvist, 2003). Although such an analysis would be extremely complicated, even a small improvement in efficiency could result in large financial gains, considering the large volume of wood in a supply chain. In a study on Quebec mills, for example, Moyaux et al. (2004) showed that by effectively managing all nodes in a supply chain, the overall cost can greatly decrease. Another study on the Chilean sawmill industry found that internal supply chain management would increase the profitability of the sawmills by approximately 15% (Singer & Donoso, 2007) Optimization Models Optimization models identify potential improvements that can be made in a supply chain with regards to a certain performance measure (objective function), such as order fulfillment rate or total profits. Supply chain optimization models prescribe a plan for production and distribution activities of supply chain members that is optimal, meaning that no alternative plan can further improve the value of the objective function. In this category of supply chain models, one optimization problem (either deterministic or stochastic) is constructed based on all the constraints and variables of the problem. Interactions among different supply chain members must be translated into constraints in the model. As the size of this optimization problem grows, finding an exact optimal solution becomes a more difficult task and in many cases, approximation techniques and heuristics are needed to find a near-optimal solution. Optimization studies in forestry have mainly focused on individual areas such as harvest scheduling and forest planning (Borges et al., 1999; McDill et al., 2002; Weintraub et al., 1994), sawmill operations (Maness & Adams, 1991; Todoroki & Ronnqvist, 2002), and transportation (Ronnqvist & Ryan, 1995; Weintraub et al., 1995). In recent years however, modelling the entire supply chain has been receiving more attention. Some recent literature reviews in the field have been published (D'Amours et al., 2008; Ronnqvist, 2003; Weintraub & Romero, 2006). The different types of decisions that need to be made in a wood products supply chain have been discussed in length by Ronnqvist (2003). 7

22 Chapter 1: Introduction Presenting an extended review of literature, he focused on how Operations Research (OR) and especially optimization can be used in decision support tools in forestry. The author claimed that developing a robust optimization tool is an important part of improving control over the wood-flow. In addition, Ronnqvist emphasized the importance of incorporating uncertainty and environmental issues in forest supply chain optimization tools. A recent review on the use of OR models in forestry and agriculture by Weintraub and Romero (2006) compared models based on problem areas, data, environmental issues, and the impact of OR models. They also recommended accounting for environmental concerns in forestry OR models. More recently, D Amours et al. (2008) provided a review of strategic, tactical and operational problems in forest industry supply chains and how OR has been used to address such problems. They argued that there was a need for more research on integrating the forest management activities with the forest products supply chains. Combining Linear Programming (LP) and economic equilibrium theory, Gautier et al. (2000) built a model to foresee and explain economic trends facing lumber and paper products in Quebec. Their model consisted of individual LP sub-problems solved for each actor (seller or buyer), and a global master LP problem to find the equilibrium prices and quantities for wood chips. The model was later used by the Quebec ministry of natural resources as a guide in negotiations with the industry. Bredstrom et al. (2004) utilized Mixed-Integer Programming (MIP) to model the supply chain of a pulp manufacturing company in order to facilitate short term decision making. An efficient heuristic technique was proposed for solving the MIP problem and the results showed a lower total cost for the supply chain compared to using manually generated plans. Singer and Donoso (2007) modeled the internal process of sawmills in Chile using Linear Programming (LP). They also modeled the collaborations among sawmills and showed increased profits as a result of internal supply chain management. One limitation of their work however was that they assumed raw material (timber) supply was unlimited which is not realistic. Chauhan et al. (2009) modelled a wood products supply chain with multiple harvest sites (material sources) and multiple mills (demand points). Logs of different length and species were modelled as multiple products, and the resulting MIP problem aimed to minimize the 8

23 Chapter 1: Introduction total cost of harvesting and transporting logs while meeting the demand of all mills. The authors compared the performance of branch-and-bound technique with their developed heuristic approach for solving the MIP problem and reported that their heuristic performed well for small scale problems. Although this model is useful in linking the resource characteristics to the production activities of the supply chain members (which is relevant to the purpose of this research), the resulting model is very large and difficult to solve even for a single period. Therefore, it would not be computationally feasible to use such a model formulation for optimizing the supply chain activities over multiple periods. Considering the relatively short planning horizon and the level of model details in the studies discussed above, they are suited for tactical and operational planning, as opposed to strategic analysis. Alternatively, the models presented in the following articles have a strategic focus. Troncoso and Garrido (2005) developed an integrated supply chain model using MIP to analyze the strategic issues of forest industry in Chile. Their objective function minimized the net present value of the total cost, including transportation costs, operation costs, and investment costs. The authors used the special structure of the MIP problem to decompose the model into three individual problems and reduced the solution time significantly. Gunnarsson, Ronnqvist and Carlsson (2006) developed an MIP model that combined facility location and ship routing problem and applied it to the case of a pulp mill in Sweden. The purpose of the model was to meet annual demand for pulp products while minimizing the distribution costs. A heuristic was developed to solve the resulting MIP problem and was shown to perform well within practical time limits. Vila et al. (2006), developed a mixed-integer programming model to study the logistics of divergent process 2 supply chains in the lumber industry. They developed a generic model that took into account facility location, technology and capacity selection, upgrading or changing the technologies, temporary shutdowns of facilities, and international markets. They demonstrated the performance of the model by applying it to a lumber company in Quebec. Daugherty and Fried (2007) used mixed-integer programming to model a network of biomass energy production facilities. Their model jointly optimized the prescription of fuel 2 Divergent processes are those that convert one type of raw material into several products. 9

24 Chapter 1: Introduction treatment for acres of land and determined the location and capacity of energy production facilities. These strategic models are useful for the case of vertically integrated companies that manage all supply chain members in a centralized manner. However, if the objective is to model independent firms that belong to the same supply chain, as is the case for this thesis, these centralized model structures are not sufficient. Additionally, sometimes it is desirable to include some uncertainty in the model to represent the supply chain more realistically. When uncertainty is included in supply chain models, deterministic optimization is no longer helpful. Stochastic programming is one way to address this challenge. Probability distributions are identified for unknown parameters and expected values are used in formulations instead of known values. Stochastic network design becomes more complicated and computationally cumbersome as the number of uncertain parameters grows. Modifications of Benders decomposition 3 (Benders, 1962) have been previously used in a variety of contexts to solve large MIP problems that include stochastic parameters (Gutierrez et al., 1996; Mirhassani et al., 2000; Santoso et al., 2005). Hultqvist and Olsson (2004) included uncertainty of weather conditions in their model of roundwood procurement in a Swedish pulp and paper supply chain. They showed that the inclusion of uncertainty was beneficial for the supply chain, however the resulting stochastic program was too large to solve to optimality and the conclusions were based on near-optimal solutions. Vila et al. (2007) developed a framework for designing production-distribution networks using a two-stage stochastic programming model, with a case study on lumber industry in Eastern Canada. Their objective was to include the market forces into the design of the network and improve the competitive position of the company involved. Their results showed that the stochastic program could be solved efficiently for moderate size problems, but was much more difficult to solve to optimality when applied to large problems. Vila et al. (2009) combined the production-distribution network design approach of Vila et al. (2006) with the market framework of Vila et al. (2007) and applied to the case of the Eastern Canadian lumber industry. Instead of considering expected future demands with a low 3 Benders decomposition is a method of solving large optimization problems by separating variables into a master problem and a sub-problem. 10

25 Chapter 1: Introduction probability of occurrence, the proposed stochastic programming model considered several future market environments and deployed the production-distribution network to capture profitable opportunities. The discussed stochastic optimization models may be more realistic compared to their deterministic counterparts, but they are significantly more difficult to solve and still have the assumption of centralized decision making which makes them unsuitable for the purpose of the research in this thesis Simulation Models Simulation models do not prescribe an optimal design for the supply chain; their utility is in understanding the dynamics of the supply chain and in determining the outcome of different scenarios. In an optimization model, all interdependencies of the supply chain members should be translated into a mathematical program and the resulting model may be very large and complex, especially when there are uncertainties present. Simulation models on the other hand can accommodate the variability in input data more readily (e.g. different log diameters in a sawmill) and are usually easier to comprehend by end-users compared to large optimization problems. Discrete Event Simulation In Discrete-Event Simulation (DES) models, the activities within the supply chain are represented through individual events that are carried out at separate points in time according to a schedule (Kleijnen, 2005; Lee et al., 2002). This type of simulation has traditionally been used for modelling supply chains (Terzi & Cavalieri, 2004). With regards to forest products industry, discrete-event simulation has most often been used to model sawmill operations. A review of early sawmill simulation models is presented in Randhawa et al. (1994). While many of these studies focused on individual stages of production and distribution (Mendoza et al., 1991; Randhawa et al., 1994), some included the entire supply chain (Beaudoin et al., 2007; Lonnstedt, 1986). Most of the recent work on supply chain simulation, however, has been done in the area of multi-agent simulation as discussed later in this chapter - because of their inherent capacity to easily reflect the complexities of supply chain interactions. 11

26 Chapter 1: Introduction Lonnstedt (1986), simulated the forest sector in Sweden to study the dynamics of cost competitiveness in the long term. Based on the results, he suggested policy changes to increase the investment in the industry, for instance lowering taxes or interest rate. Mendoza et al. (1991) combined optimization and simulation to model a hardwood mill. Their main goal was to develop optimal yet feasible production schedules. First a Linear Programming (LP) problem was solved with the objective of maximizing profits. The resulting optimal log input mix was then fed into a process simulation model to calculate production times and to measure the performance of different machines. Randhawa et al. (1994) developed a discrete-event object-oriented simulation environment that could be used to model sawmills with various configurations. Lin et al. (1995) studied the benefits of producing green dimension parts directly from hardwood logs by comparing four mill designs using simulation. Baesler et al. (2004) used simulation along with experimental design to identify bottlenecks and factors that affect productivity (number of logs per day) in a Chilean sawmill. Their results pointed to the potential for a 25% increase in production. Beaudoin et al. (2007) combined a deterministic MIP and Monte Carlo sampling methods to support tactical wood procurement decisions in a multi facility company. Their test case results showed that their proposed planning process achieved an average profitability increase of 8.8% compared with an approach based on a deterministic model using average parameter values. Similar to some of the studies presented here, this thesis combines simulation and optimization for the purpose of modelling the supply chain of the forest industry in BC. This approach allows for incorporating the uncertainties, without causing modelling complexities and computational problems. However, although the developed simulation model uses a discrete event simulation engine, it is different from the traditional DES models because it uses individual agents to represent the members of the supply chain. A review of agent-based models follows shortly. System Dynamics System Dynamics (SD) modelling is mainly used for simulating continuous systems (as opposed to discrete event simulation). An SD model is characterized by feedback mechanisms and information delays to help explain the behavior of complex systems. In SD, real-world systems are represented in terms of stock variables (e.g. profit, knowledge, 12

27 Chapter 1: Introduction number of people), flow between these stock variables, and the information that determines the flow values. Interacting feedback loops link the stock and flow variables. The resulting model is a system of differential equations and its dynamic behavior is the result of the structure of these feedback loops and delays (Borshchev & Filippov, 2004). It can also be combined with OR techniques to model supply chains. SD approach has been refined and expanded to study supply chain dynamics (Sterman, 2000; Towill et al., 1992). Angerhofer and Angelides (2000) have reviewed the research on SD modelling in supply chain management. Based on their review, System Dynamics can be used in combination with different techniques to be applied in areas such as inventory management, demand amplification, and international supply chain design. SD has rarely been used in modelling forest industry supply chains. Schwarzbauer and Rametsteiner (2001) used SD to analyze the potential impact of Sustainable Forest Management (SFM) certification on forest products in the Western European forest sector. Their results showed only modest changes from SFM-certification in forest products markets. Haartveit and Fjeld (2002) developed the wood supply game based on the Sterman Beer Game 4 (Sterman, 1984, 1989) as educational material for students in forest logistics courses. The game included four stages in the supply chain from the forest to the lumber or paper retailer. Demand of the end customer was decided based on a random draw and the game demonstrated the distortion of demand as it moved upstream through the supply chain (the bullwhip effect). Jones, Seville, and Meadows (2002) modelled the supply chain of the Northeastern US forest industry. Their main goal was to answer policy question on the economic and environmental sustainability of the lumber industry in that region. Their results showed the capacity of the lumber mills could potentially exceed the available timber resources and feedback mechanisms are required to ensure the sustainability of lumber mill operations. What is important to remember is that SD models are better suited for aggregate views of the system and policy questions at a strategic level. The modelled system is evolved as a result of equations that link stock and flow variables together and it is not always possible to identify 4 In a beer distribution game players are trying to minimize their total costs by managing inventories in the face of uncertain demand (a draw from a card deck). The results of the Sterman beer game showed oscillation and amplification of demands for upstream members (Sterman, 2000). 13