SUPPLY CHAIN MANAGEMENT SOFTWARE SOLUTIONS IN THE FOREST PRODUCTS INDUSTRY AND THE POTENTIAL OF AGENT-BASED TECHNOLOGY

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1 ASAC 2004 Québec, Québec, Constance Van Horne Jean-Marc Frayret Alain Rousseau FORAC Research Consortium Department of Mechanical Engineering Université Laval SUPPLY CHAIN MANAGEMENT SOFTWARE SOLUTIONS IN THE FOREST PRODUCTS INDUSTRY AND THE POTENTIAL OF AGENT-BASED TECHNOLOGY This paper gives a review of software companies that have developed best of breed software to manage the supply chain of the forest products industry, their products and strategies. We also explore the possibilities offered by multi-agent technology and provide an example of an experimental platform using such technology. Introduction The challenges facing companies of the forest products industry are diverse and can be approached from many view points. How can these companies adapt to a changing environment and an increased need to improve their bottom line? Can new information technologies (IT) be adapted to this particular industry to allow it to flourish in the future? This paper will first focus on the particularities and trends of the forest products industry and the supply chain planning solutions offered by software companies for use in this industry. We highlight certain tendencies that can be found with these software companies and examine more deeply the different applications offered dedicated to the forest products industry. Next we discuss some of the potential roles of the so called multi-agent technology to complement the traditional optimization supply chain and manufacturing planning tools. Lastly we present a multi-agent experimental planning platform which aims at providing a flexible solution to integrate advanced planning tools throughout the supply chain into a collaborative planning system. A specific industrial context The forest products industry in the province of Quebec, Canada, is different from other resource based industries for several reasons; government ownership of the resource and strict control and changing regulations of procurement rights and practices, uncertainty with regards to quantity and quality of raw material, transformation process is stochastic (due to the very nature of fiber) and both divergent (trees are processed into various wood and fiber products such as lumber, chip, wood panel) and convergent (wood products are then reassembled into finished product such as wood structures, beams, doors, windows, furniture, wood floor, paper) resulting in complicated and specific operations management methods. Certainly because of the level of this specificity compared to the relative size this industry, the major software providers, including SAP, JD Edwards, Oracle Applications and Baan, have never really developed solutions that directly address these specific problems. However, because this traditional industry is known, in North America, as a laggard in terms of IT adoption, (Vlosky, 2001; Kozak, 2002; Karuranga et 1

2 al., submitted), the need for dedicated business approaches and tools that take into account the specificity of this industry to organize information and plan operations is apparent. These needs for computer supported process management are even more appealing in the new social and economic contexts. Four major trends have indeed emerged in the forest products industry as a results of these changes; (1) restructuring, consolidation and search for profitability, (2) cost reduction through production optimization and technological innovation, (3) a customer orientation, centered on differentiation and adding value and (4) the confronting of environmental challenges (Juslin and Hansen, 2002). Consolidation and reduction of production capacity stem from the fragmented nature of the forest products industry which led to overproduction, price wars and the incapacity of the industry to manage its own trends (Juslin and Hansen, 2002). Process optimization and product innovation have resulted from capital investments in research and development, as well as on new equipment and machines built on sophisticated technology. These technologies have allowed the industry to become more flexible and reduce manufacturing delays thus increasing productivity (Juslin and Hansen, 2002). As the industry needs to continually improve service to its customers, companies must consider their entire value creation network, focusing on the whole systems perspective that begins with land use planning, followed by timber harvesting, first and second transformation and ends with the delivery of the final product to the consumer (Innes, 2002). Finally there are three aspects about the environment that directly concern the forest products industry; environmental management, (ISO sets environmental management standards) forestry certification and environmental labeling (Juslin and Hansen, 2002). The particularities and the current challenges faced by the forest products industry require effective use and management of all information available in order for companies to survive and prosper into the future. The choice is not whether to adopt information management systems, but which solutions choose, how to integrate these solutions. Software solutions for the forest products industry During the fall of 2003 a survey of the software companies that provide the forest products industry with supply chain and logistics management solutions, which can include advanced planning and scheduling (APS) systems, was carried out by the authors. The goal of this survey was to better understand the nature and the various solutions currently available to the forest products companies. Given the size of the world-wide industry, with the top 100 companies working in the industry reporting sales of US $300 billion (Price Waterhouse Coopers, 2003), and the special requirements of that industry there were fewer specialized solution providers than expected. While there were several small providers found, with software dedicated to forestry equipment and machines, there were only nine software companies found that had developed supply chain software dedicated to the forest products industry. Once companies were identified, through Internet research and requests for information from managers of software companies specialized in the forest products industry (but not concentrated in the supply chain), an requesting further product information was sent. All nine companies contacted sent additional information and several phoned to give additional information. A preliminary analysis of these nine companies and their systems provided the authors with some interesting revelations, although further study, which would include a survey of customers using the different supply chain solutions, is needed to garner a more complete picture. Six of the studied software companies sell specific solutions that are meant to be somehow integrated with other systems in order to create best-of-breed (BoB) software suites for supply chain and operations management. The three other companies offer enterprise resource planning (ERP) systems, which include 2

3 supply chain and operations management modules specifically built for the forest products industry. These ERP systems are also modular in nature. Table 1 gives a more detailed accounting of the companies working in this industry, the solutions they provide and the alliances that they have formed to attempt to overcome integration problems. Table 1: Synthesis of supply chain solution providers for forest products industry Name and country Exactmodus (Canada): Scoopsoft (Canada): IFS (Sweden): Indus (United States): Savcor Group (Finland): Nistevo (United States): OMPartners (Belgium): TietoEnator (Finland): Linnet (Canada): Alliances BoB or ERP Product description integration with Scoopsoft s ERP system Exactmodus solution No Oracle, Peoplesoft Forest Express No Several, Forest Express. Several including Oracle and SAP SAP No BoB software suite From the forest to the mill ERP created specifically for the forest products industry; Modular BoB component for supply chain management of the pulp and paper industry; integration with their own generic ERP system BoB for the pulp and paper and cardboard industries ERP for supply chain management of the forest products industry BoB transportation management system BoB supply chain planning for corrugated cardboard and solid board industry BoB supply chain solutions ready for integration with SAP ERP BoB supply chain solutions from the forest to the mill Seven FELIX modules: include FELIX Weighing, FELIX Harvesting, FELIX Hauling and FELIX Lumber sales. The Scoopsoft Management System is made up of modules for production, inventory, sales, transportation, accounting, purchasing, procurement and woodlands planning. Their supply chain software is part of their enterprise asset management system They use an open architecture for integration with other systems. EAM software that covers aspects of the supply chain including materials and procurement; specific integration module with ERP systems MEKA ERP for stock management, production planning, e-business, budgeting and statistics. Module based Web-based logistics system is hosted by the company and uses various integration technologies such as EDI, XML and J2EE. Integrated modules for planning and scheduling, shop floor integration, business system integration, management reporting and supply chain visibility, integration modules. Integrated Paper Solution includes sales, collaboration, manufacturing execution and ERP integration solutions. Wood Supply Solution that covers logistics and procurement and the Wood Product Solution that covers production, warehouse and sales management. Fully integrated modular solution or suite that includes woodlands management and inventory and wood flow management. The Bridgefield Group defines BoB as systems or functions that exhibit the highest level of performance in their class. Along this line, Light et al. (2001) refer to BoB as an IT strategy that 3

4 integrates the standard software of a variety of vendors that provides companies with constant state of the art functionality. According to Montgomery (2003) BoB supplies greater flexibility, faster response to individual needs and specialized vendors familiar with particular industries, which is the case in the forest products industry. Problems with legacy systems and system integration, are often cited as the greatest downfalls of BoB strategies and systems, both in terms of cost and the time and effort needed to ensure seamless integration (Sauer and Wilcocks, 2003). From the survey conducted by the authors a dominant strategy was evident in seven of the nine companies surveyed that appears to overcome this apparent weakness. These companies attempt to ease integration concerns through the establishment of strategic alliances and partnerships with larger ERP providers. In fact these strategic alliances allow solution providers to develop specific supply chain and operation management solutions as a part or a module of a larger and greater whole. The three companies that provide niche ERP systems built for the forest products industry are Scoopsoft (Canada), IFS (Sweden), and the Savcor Group (Finland). They are small and medium sized companies which tend to have their largest client base in small and homogeneous markets. Scoopsoft has the majority of its clients in the province of Quebec, and the Nordic countries in the case of the Savcor Group and INS. Even so Scoopsoft has a strategic alliance with a smaller BoB execution control solution provider, Exactmodus also from Quebec. The systems also have similarities. All make use of web and portal technologies to plan and even optimize supply chain activities. Other technologies such as Electronic Data Exchange (EDI) and Exchange Markup Language (XML) are used to ease integration with other BoB or ERP systems and exchange data within an organization or with supply chain partners. Indeed, intra-organizational integration is now expending through the frontiers of organizations to include inter-organizational forms of integration. According to a survey in 2002 of IT and purchasing professionals from pulp and paper companies in the United States by ForestExpress, a B2B integration services provider, 60% of responding companies were integrated or planned to integrate their back-end systems with their customers and or suppliers (Paperloop, 2003). The survey also found that from those having integration systems, nearly 80% used electronic data interchange (EDI) compared to 29% that used XML and 18% with point-to-point integration. Purchasing and procurement systems were the most often integrated at nearly 30% followed by systems to integrate with SAP Fully Integrated Software (FIS) solutions at 13%, invoicing and billing at 9%, shipping and accounting at 4.4% respectively. As integration seems to pose the greatest concerns for the industry and thus solution providers, it would appear that an ideal solution would be to create a system that integrates the best aspects of BoB with the advantages of FIS systems. The potential of agent-based systems is promising as each enterprise business unit (agent) is a BoB solution in itself, as it is tailored to the unit s functions, yet the agent-based system itself provides integrated planning support. The next section presents agent-based technology and identifies how such technologies could be used to provide an intelligent integration platform that fully takes advantage of specialized planning tools. This is followed by a description of the multi-agent experimental planning platform being developed for the forest products industry by FORAC. Agent based technologies Since the middle of the 1980s, scholars have been developing a family of approaches for the planning and control of manufacturing and supply chain systems, which is an alternative to 4

5 traditional OR tools usually found in APS systems, such as those identified in the survey presented previously. Such alternatives are sometime referred to as agent-based supply chain planning systems. These systems are the Industrial Engineering applications of a technology known as multi-agent systems, from the field of Distributed Artificial Intelligence. An agent-based manufacturing system may be defined as a planning and scheduling system made of software agents responsible for handling a part of the overall control problem (e.g., manufacturing order planning, scheduling of orders, and allocating tasks on resources). Such software agents generally exhibit characteristics that allow them to handle their part of the overall problem. Wooldridge and Jennings (1994) identify four main characteristics: autonomy (capability to operate with some kind of control over its actions and internal state); reactivity (capability to perceive an environment and respond in a timely fashion to changes that occur in it); social (capability to communicate with each other through some forms of explicit negotiations or signal exchanges); pro-activity (capability to exhibit goal-directed behavior by taking initiatives). In many agent-based approaches, software agents are programmed so as to exhibit behaviors with limited decisional scope, which combined altogether allow to completely address a particular manufacturing control problem (Valckenaers et al., 1999; Tharumarajah and Wells, 1997; Tsukada and Shin, 1998; Liu and Sycara, 1997; Sycara et al., 1991; Duffie and Prabhu, 1996; Kouiss et al., 1997). Software agents occasionally have the ability to address a larger part of the overall planning and scheduling problem. In this type of approach, the profile of an agent (i.e. its role in the system) is sometimes customized so as to fit within a particular control framework (Fox et al., 2000; Cloutier et al., 2001; Sadeh et al., 2003). Because of the mentioned characteristics, software agents have been developed for many purposes. For example, because of their ability to run autonomously, agents are used to automate various forms of more or less complex tasks involved in supply chain and operations planning that do not require human intervention (e.g., processing data, managing the use of optimization tools, or negotiating possible commitments with other agents within limited degrees of freedom). Also, because of their ability to be reactive, agents are used to sense their environment (e.g., the state of a set of machines, the inventory levels in an inventory management system, customer orders in a Customer Relationship Management (CRM) system, etc.), concerning new information, parameter changes, or whatever events related to the agents function, and propose alternative plans or whatever adjustments which take into account the new context. Because of this ability, agents can be used as intelligent event management systems which function is to trigger the most appropriate response to particular events. Because of their ability to communicate with each other, agents are used to provide a technological integration layer between information systems. In other words, agents are used as information systems wrappers to provide some kind of standardized interaction interface (Sikora and Shaw, 1998; Kim and Nof, 1998). Along this line, because of their ability to sustain complex interaction protocols and intelligent behaviors, agents are also used to provide an integration layer of the planning and control processes distributed between departments, divisions or supply chain partners, or between planning systems such supply, manufacturing and demand planning systems. In other words, agent are literally implemented in order to integrate and be part of a distributed planning and control process, whether it is within an enterprise (Pan and Tenenbaum, 5

6 1991; Roboam and Fox, 1992; Peng et al., 1998), or throughout a supply chain (Fox et al., (2000), Sadeh et al., (2003), Shen et al., (2003)), or both (Frayret et al., (2001)). This latter ability is usually exploited in order to create an intelligent integration framework for the planning at various decision centers, including the various dedicated planning tools developed by software providers. Such an integration of these tools can be used to provide human decision makers with more clever decision alternatives that take into account constraints, needs and capabilities of external decision centers. Because of this ability, these decision alternatives provided by agents to human decision makers may be elaborated through complex information exchange with some forms of negotiation between decision centers within which human decision makers may have an important role for setting the negotiation and interaction parameters. At an advanced level, because of their ability to behave pro-actively, agents can be used to implement goal-driven and learning decision process. In other words, agents can be used for their ability to assess particular contexts (e.g., production and transportation plans, demand plans, order plans) according to particular objectives and performance criteria (e.g., productivity, cost, filled rate, customer satisfaction, staff utilization rate, etc.) and to propose decisional adjustments that could improve the current situation. For instance, in a context where response time is important, agents could be programmed so as to assess the level of emergency of a situation (e.g., an incoming order, a delayed delivery, a machine breakdown) and adapt their decision support processes according to it and the associated objectives in order to cope with the situation within a specified acceptable time range. Similarly, agents could be programmed so as to assess the performance of forecasting tools in order to propose the most adequate forecasting methods (available in commercial software such as Forecast Pro ). This ability to choose the most appropriate decision support processes according to particular objectives enables the modeling and the use of many decision process and their associated utilities in various context, in order to implement more flexible decision support systems. Such an approach is proposed in Fox et al. (2000) in which the framework of Markov decision process is used to model the probabilistic impacts of a discrete set of actions to the state of a system (i.e., probability that an action leads to a transition from one state to another) in order to identify the best behavior to adopt in various situations. Finally, multi-agent systems can be used as alternative approaches (i.e., approaches not based on OR optimization techniques) for the planning and control of particular logistics systems. This is particularly the case when a multi-agent system is designed in order to provide a solution to particular manufacturing problems, such as the manufacturing control of a flow shop (Valckenaers et al., 1999), the manufacturing control of a job shop (Tharumarajah and Wells, 1997; Liu and Sycara, 1997; Sycara et al., 1991; Duffie and Prabhu, 1996), tool management (Tsukada and Shin, 1998) or the scheduling of a flexible manufacturing system (Kouiss et al., 1997). A supply chain experimental planning platform Taking advantage of the mentioned characteristics of agent-based technology, the experimental planning platform discussed in this section is a project being carried out by the FORAC Research Consortium. The objective of this project is to provide an intelligent platform for the distributed planning of the supply chain of the forest products industry. This platform, along with the development of an experimental environment, will allow us to simulate different supply chain configurations in the forest product industry and experiment different planning approaches for each of the business units that are present in the supply chain. Whereas the 6

7 specific nature of the forest products industry and its supply chain requires BoB solutions built for the specificities of the industry, integration problems presented by these solutions are a concern. The use of an agent-based framework to overcome these integration concerns all the while exploiting the benefits of BoB appears to provide an ideal solution for supply chain planning and management in the forest products industry. In the proposed platform, we consider planning distribution at two levels. First, at the business level we distribute the planning by Planning Unit. We define a planning unit as a mission oriented business unit which autonomously manages its business agreements and commitments with other planning units. Among others, this means that there are no financial transactions inside a planning unit. A planning unit can therefore be seen as a black box from the outside; it offers a single face to its customer and to its suppliers. At this level, we are currently developing different planning tools based on OR technology for all types of planning units that can be found in the forest products industry, including forest operations, sawmill, paper mill, and distribution network. This very generic modeling allows us to develop highly specialized solutions that meet the requirements of the different types of planning units. At the second level, planning responsibilities are distributed inside every planning unit based on functional specialization. In our approach, a planning unit is built up as a community of specialized software agents. This distribution is based on the SCOR model defined by Supply Chain Council (Stephens, 2000). SCOR is used to describe the functional specializations of those agents. First, a Deliver agent is specialized in customer relationship management (CRM). On the other side, a Source agent is specialized in supplier relationship management (SRM). In between it is possible to implement many Make agents, each one being specialized for a specific type of production and set of production resources. For example, in a Sawmill Unit (a planning unit specialized for a sawmill), we have different agents for sawing, drying and finishing operational planning. These planning functions have been distributed like this because they have very different requirements. Hence, we have developed specific model and algorithm for each of them instead of trying to fit their different problems inside a single and more generic planning tool, this allows us to use existing solutions provided by the market. This strategy of specific solution development is made easier with technologies such as OPL Studio from ILOG that enables fast prototyping and implementation of optimization tools. This approach pushes the BoB strategy to its extreme. These specialized agents have the same kind of responsibilities and they all require support for the same kinds of interactions. They can receive requests from other agents for products (or services) they can provide and are able to make commitments on how they can satisfy those requests. In order to make those commitments; agents need to plan their operations in order to make requests from other agents for the products (or services) they will need to realize their operational plan. In brief, we model such a community of specialized agents as an internal supply chain where one of the agents plays the role of the end customer (Deliver Agent) and another the role of the raw material supplier (Source Agent). (See Figure 1) 7

8 Figure 1: Example of planning unit - Lumber production unit Planning unit: Lumber production unit Source Aggregate coordinator Mid-term objectives Mid-term needs expression Deliver Saw Dry/finish Need and supply plans continuous exchange However, one recognized and important problem with this kind of distributed systems is the lack of a global view of the system and global synchronization and integration. In the proposed solution, this is overcome with another kind of specialized agent, called a coordinator agent. This coordinator agent ensures global synchronization of the planning unit by doing tactical planning: i.e. by defining the responsibilities of each specialized agent (for example, it decides which agent will supply which other for a specified product or period inside the planning unit). This specific architecture, although it is inspired by the approach proposed in Fox et al. (2000), delves further both in terms of supply chain modeling and logistic coordination mechanisms. Another software component has been developed in order to tackle the problem of global visibility: the Supply Chain Cockpit. Indeed, this component is used to provide an integrated view of the different agents concerning, for example their inventory levels. This allows the user to have an overview of the inventory for the entire planning unit. (See Figure 2) These two specialized component (coordinator agent and the supply chain cockpit) can also be used at the company level to ensure global synchronization and visibility over multiple planning units. Figure 2: Network of planning units 8

9 Finally, in order to address both technological and decision process integration challenges, we rely on the concepts of conversation protocols that are commonly used in multi-agent systems. These protocols are used to define a workflow of potential states in a conversation such as using a specialized planning tool to build an operations plan in response to a demand for quote. At each of these states, each agent participating in the conversation has specific tasks to perform to continue or stop the conversation. The different messages resulting from these tasks condition the workflow of a conversation. This type of interaction mechanism is far more flexible than most of the integration techniques currently used by software applications which are based on rigid software APIs. In fact, an agent requires only one API, that is the ability to receive or send a message, and it will dynamically be able to handle any conversation protocol as long as it knows how to internally perform the tasks that are associated with it. In our experimental planning platform, all communication between agents uses this kind of conversation protocol. Conclusion From the preliminary analysis of the nine companies that provide supply chain solutions for the forest products industry, the authors discovered that integration problems were a major issue for solution providers. An interesting challenge that we attempt to address with the development of our platform concerns the integration of multi-agent technology and operation research tools in order to take advantage of the most interesting features of both; respectively the ability to add intelligence to complex integration of systems and decision processes, and the ability to model and accurately optimize constrained planning problems. From a technological point of view, we also address the integration of agent-based technology with legacy and BoB systems already available in the industry, in order to take advantage of specific tools already developed, as it was shown in our survey. From a planning process point of view, our experimental platform enables us to develop specific planning and optimization tools which are meant to be integrated within a broader supply chain management framework. Finally, concerning the survey, further studies of the customers of the nine companies and their experiences with the software solutions are needed, and are planned for the fall of 2004, to provide a more complete picture of the needs and expectations of forestry companies in terms of supply chain planning tools. References Cloutier, L., Frayret, J.M., D'Amours, S., Espinasse, B. and Montreuil, B., A Commitment- Oriented Framework for Networked Manufacturing Co-ordination. International Journal of Computer Integrated Manufacturing, 14(6), Duffie, N.A. and Prabhu, V.V., Heterarchical control of highly distributed manufacturing systems. International Journal of Computer Integrated Manufacturing, 9(4), Fox, M.S., Barbuceanu, M. and Teigen, R., Agent-oriented supply-chain management. International Journal of Flexible Manufacturing Systems, 12(2/3), Frayret, J.-M., D'Amours, S., Montreuil, B. and Cloutier, L., A Network Approach to Operate Agile Manufacturing Systems. International Journal of Production Economics, 74(1/3),

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