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SUPPLY CHAIN MODELING: THE AGENT BASED APPROACH
INCOM'2006: 12th IFAC/IFIP/IFORS/IEEE/IMS Symposium Information Control Problems in Manufacturing May 17-19 2006, Saint-Etienne, France
SUPPLY CHAIN MODELING: THE AGENT BASED APPROACH Subhash Wadhwa Professor, Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India E-mail: [email protected] Bibhushan Research Scholar, Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India E-mail: [email protected] Abstract: In the era of fierce competition Supply Chain Management (SCM) has gained enormous amount of importance. A major decision that affects the overall performance of a supply chain is the modeling of the supply chain. Some of the widely used supply chain modeling tools are reviewed in this paper. A case for agent-based architecture for modeling of supply chains is proposed. Agents act as autonomous units and are able to take decisions according to the dynamic business environment. It was found that agent based architecture is capable of more realistic and dynamic modeling because of the autonomy of members of a supply chain. Copyright © 2006 IFAC Keywords: Agents, Autonomous Control, Distribution Networks, Simulation, Supply Chain Modeling
focus of this paper if on one such agent based supply chain system.
1. INTRODUCTION Supply Chain Management (SCM) is a rapidly growing area in the research and management domains. The overall performance of the supply chains depends to a large extent on how the supply chain has been modeled. Although large amount of work has been done in SCM there seems to be no unanimity among the practitioners on the best way of modeling a supply chain. One of the major reasons for this fact is that the structure of the supply chain depends to vast array of variables like product life cycle, nature of product, the target customers, competition and so on. Hence the complexity of the supply chains becomes appalling because of its dependence on many variables and interdependence of these variables.
The paper is structured in the following manner. A brief literature review of the supply chain models is presented in the next section. The limitations of existing models and need for an agent based approach is highlighted in the following section. This is followed by some applications of agent based systems in supply chains. The conclusions are presented in the final section. 2. LITERATURE REVIEW For the purpose of literature review, the internet search on www.sciencedirect.com was undertaken taking “Supply Chain Modeling” as the keyword. The relevant results were grouped according to the tool used for modeling the supply chain (the results are shown in Table 1). The brief description of the relevant papers is presented in this section.
Many models for the modeling and analysis of supply chain have been presented by researchers and practitioners. One of the major limitations of these models is that supply chain as whole is considered as a system that could be centrally controlled by a single decision maker. In practice, the different members of a supply chain act as autonomous units. They may be part of more than one supply chain also. This limitation of the model has been solved by modeling the supply chain as an Agent Based system where different nodes of the chain are modeled as autonomous agents. The
The paper by Wang et. al. (2003) analyzed noncooperative behavior in a two-echelon decentralized SC, composed of one supplier and many retailers using a game theoretic approach. One of the serious limitations here is that only two echelons of the chain are studied. In reality only a few chains have only two echelons. Moreover, the behavior of the two members is assumed to be static and completely deterministic.
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S. No. 1.
Method
Models
Standard Mathematical Models
Gunnarsson et. al. (2004), Korpela et. al. (2001), Mokashi and Kokossis (2003), PereaLo´pez et. al. (2003), Syarif et. al. (2002), Talluri and Baker (2002), Wang et. al. (2003), Yan et. al. (2003) Gunnarsson et. al. (2004) Escudero et. al. (1999), Kaihara (2003), Nagatani (2004) Kaihara (2003), Kimbrough et .al. (2002), Minegishi and Thiel (2000), Nagatani (2004), Reiner and Trcka (2004) Syarif et. al. (2002), Vergara et. al. (2002)
2.
Heuristics
3.
Analytical Models
4.
Simulation Models
5.
Evolutionary/ Genetic Algorithms Artificial Agents
6.
7.
8.
Decision Making Techniques (AHP, ANP) Empirical Study
criteria efficiency models based on game theory concepts and linear and integer programming methods. Even with these features, the model lacks dynamism and adaptability which are essential requirements of any such business network. Yan et. al. (2003) proposed a strategic production– distribution model for SC design that considered logical constraints in the form of Bill Of Materials (BOM). They formulated their model using Mixed Integer Programming approach. Perea-Lo´pez et. al. (2003) described a model predictive control strategy to find the optimal decision variables to maximize profit in SCs with multi-product, multiechelon distribution networks with multi-product batch plants using MILP dynamic programming. Gunnarsson et. al. (2004) used MILP for strategic planning of a forest fuel SC. To obtain quicker solutions they also proposed a heuristic algorithm. All these models used integer programming as the solution strategy. But here again the models are assumed to remain constant over whole planning horizon. Further, all the supply chain members are assumed to follow a centralized strategy. This is very difficult to achieve in practice.
Garcia-Flores et. al. (2000), Julka et. al. (2002), Kaihara (2003), Kimbrough et .al. (2002) Korpela et. al. (2001a), Korpela et. al. (2001b)
Perea et. al. (2000) modeled the flow of information and material within the SC and used them to capture its dynamic behavior. They used the ideas from dynamics and control for the design of systematic decision-making processes. Lin et. al. (2004) developed a discrete time series model of a SC system involving using material balances and information flow. Both these models find the optimal flow of materials in the supply chain. But this has the limitation that only a single variable i.e. material flow has been considered for optimization. The real life supply chains are affected by numerous variables. Further, the relative importance of different variables may change depending on the product to be supplied and the policies of the member companies. For instance, Reiner and Trcka (2004) assert that universally valid statements based on the behavior of specific SCs can be quite doubtful. They point out that the analysis of SCs should be product and company specific.
Alvarado and Kotzab (2001), Chung et. al. (2004), Garcia-Flores et. al. (2000), Hameri and Lehtonen (2001), Yusuf et. al. (2004) 9. Theoretical Al-Mudimigh et. Al. Models (2004), Alvarado and Kotzab (2001), Chung et. al. (2004), Heikkilä (2002), Manthou et. al. (2004) 10. Others Carlsson and Rönnqvist (2005), Dong et. al. (2003), Julka et. al. (2002), Kimbrough et .al. (2002), Lin et. al. (2004), Perea et. al. (2000), Tan and Khoo (2005) Table 1 Supply Chain Models and Tools Used
Minegishi and Thiel (2000) used system dynamics based simulation to improve the knowledge of the complex logistic behavior of an integrated food industry. System dynamic models can represent the dynamic behavior of the very effectively. But too long term modeling may result in faulty planning. In this kind of a scenario, an adaptive system that modifies its behavior in accordance with the changing conditions would be more effective.
Talluri and Baker (2002) presented a multi-phase mathematical programming approach for effective SC design that utilized a combination of multi-
472
(VeC). Alvarado and Kotzab (2001) conceptualized the SCs from a channel governance point of view on the basis of phenomenon of Efficient Consumer Response (ECR). Heikkilä (2002) proposed a framework for prioritizing lead time reduction in a demand chain improvement project. All these models are at present only at the conceptual level. Although they can handle the present problems effectively, they need to be modified in future in view of the changing environment.
Some hybrid models that used a combination of standard mathematical techniques and some other techniques have also been recently proposed. For instance, Korpela et. al. (2001a) used a combination of Analytical Hierarchy Process (AHP) and Mixed-Integer programming to optimize a company's SC based on customer service requirements within the constraints of the SC. The AHP has the limitation that it can help only in strategic decisions in the sense that only these decisions are dependent on many factors. The operational and tactical decisions may be handled more effectively by mathematical models and the Decision Support Systems.
Yusuf et. al. (2004) reviewed the emerging patterns in SC integration and explored the relationship between the emerging patterns and attainment of competitive objectives. Their results showed that only a few companies have adopted agile SC practices but most companies have embraced long term collaboration with supplier as well as customer (which they conceptualized as lean SC practices). Here also, only the agility of the supply chain is of main focus. In the changing business environment, the functions of supply chain management and logistics will also change. For example, Chung et. al. (2004) are of the view that global sourcing will be transformed from one that purely performs the task of sourcing input to one that coordinates and manages the entire SC. They identify four attributes for greater organizational response to changing environment: collaborative advantage, regional advantage, innovation capacity (knowledge resource) and competencies.
Similarly, Syarif et. al. (2002) used a combination of Genetic Algorithms (GA) and 0-1 MILP for the design of a logistics network. Hameri and Lehtonen (2001) found the 30% of inventory could be regarded as slack when production cycles are reduced and logistical alternatives are fully exploited. According to them, this can directly generate cost saving of about 2-5% without additional investment. Here also the supply chain is considered to static, deterministic and non adaptive. Nagatani (2004) presented a stochastic model of the SC composed of series machines and buffers. He considered the adaptation time of each machine to be finite and individual buffers for each machine. Escudero et. al. (1999) described a modeling framework for the optimization of a manufacturing, assembly and distribution (MAD) SC planning problem under uncertainty in product demand and component supplying cost and delivery time. Vergara et. al. (2002) developed an evolutionary algorithm (EA) for optimal synchronization of SCs. These models also suffer from the same limitations as the other models.
3. EVALUATION OF MODELLING TOOLS A review of the existing models highlighted some serious limitations of traditional methods of modeling the supply chains. Some of the more serious limitations are discussed here. Static and Deterministic Nature of the Supply Chain: the mathematical models, heuristics and analytical models consider the supply chain as a static system. Hence it gives only a snapshot view of the problem. This kind of modeling cannot represent the ever changing nature of the supply chains. Moreover, the supply chain members are assumed to remain in the chain for the entire duration considered in the model. These models are more suitable for products with a stable demand particularly the Fast Moving Consumer Goods (FMCG). For products with uncertain or variable demand the structure of the supply chain may change over time.
Tan and Khoo (2005) demonstrated the application of the concept of Life Cycle Assessment (LCA) to the SC producing aluminum billets. The effectiveness of decision support systems in SCM has been showed by Carlsson and Rönnqvist (2005). Korpela et. al. (2001b) applied AHP for SC development. Dong et. al. (2003) develop a SC network model consisting of manufacturers and retailers considering random demands. Mokashi and Kokossis (2003) used dispersion algorithm for optimizing a SC problem consisting of production planning and distribution scheduling in two tiers. Highlighting the limitations of SCM, Al-Mudimigh et. al. (2004) discussed the concept of Value Chain Management (VCM) and propose a model that is driven by a focus on agility and speed. Manthou et. al. (2004) presented a SC collaboration framework in a virtual environment called Virtual e-Chain
Dominance of a Single Member: in most of the mathematical models, it is assumed that all members take their decisions in accordance with the solution provided by the model. Hence this kind of modeling is more suitable to supply chains with
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a single member as the dominant player. This modeling becomes ineffective in the supply chains where there is no clear-cut dominance among the members or where the different members have a high degree of autonomy.
agents where each agent performed one or more SC functions. In their model, the interactions between agents were made through the common agent communication language called Knowledge Query Message Language (KMQL).
Focus on a Single or Limited Number of Objective: most of the models considered only one or a limited number of objectives for modeling the supply chain. But practitioners are of the view that since supply chain decisions are based on multiple variables, the objectives in the supply chain are also more than one. A major reason for this is this fact that supply chain is a very complex system in itself. It cannot be understood by a single model in its entirety.
Julka et. al. (2002) developed an agent based Decision Support System (DSS) called Petroleum Refinery Integrated Supply Chain Modeler and Simulator (PRISMS). They used PRISMS as a central DSS for studying the processes of a refinery. This DSS enabled them to take integrated decisions with respect to the overall refinery business. Kaihara (2003) formulated the SC as a discrete resource allocation problem under dynamic environment using a combination of an analytical model and agent based system. He demonstrated the virtual market concept to their framework by the use of agents. The findings also confirmed that careful construction of the decision process according toe economic principles leads to efficiently distributed resource allocation in SCM.
Limited Decision Alternatives: in most of the models reviewed, only limited options were considered in various decisions. For instance, policies like sS Policy and sQ Policy were used in sourcing decisions. Tools like Genetic Algorithms and Simulated Annealing may provide better policies for a particular duration of time.
Chang and Lee (2004) developed a case-based modification scheme that could formulate optimization models automatically and designed a prototype tool AGENT-OPT that can manage a model warehouse. Through their studies they confirmed that the architecture AGENT-OPT, which uses the representation of UNIK-OPT, could answer the requirements of a model warehouse and interactions with other software agents effectively. They also designed the ontology for delivery scheduling problems, and validated the architecture.
Partial Modeling of the Supply Chain: many of these models consider only a partial portion of the supply chain rather than the entire supply chain. For instance, most of the mathematical models considered only a few echelons of the supply chains close to the manufacturer. 4. AGENT BASED APPROACH IN MODELING SUPPLY CHAINS Although a comparatively new concept in SCM, the agent based systems have found various applications at different levels in the Supply Chain. We will discuss here some of these applications. The discussion is only illustrative and not comprehensive.
An agent-based model for coordinating the management of enterprise resource in SMEs has been developed by Huin (2004). The model tries to organize, interface and manage the various enterprise resources in an SME. This agent based model helps the SMEs in better project management of their ERP systems in usually informal interactions.
Kimbrough et .al. (2002) investigated the impact of artificial agents on the SC with the help of the traditional beer game. They found that agents were capable of playing the Beer Game effectively. According to them, agents were able to track demand, they eliminated the Bullwhip effect, discovered the optimal policies (where they are known), and found good policies under complex scenarios where analytical solutions were not available. Their second important finding was that automated SC controlled by artificial agents was adaptable to an ever-changing business environment.
Symeonidis et al. (2003) developed a multi-agent system that added adaptive intelligence as a powerful add-on for ERP software customization. The system could be considered to be recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system could be periodically retrained as new information is added to the ERP. This paper by Ghiassi (2003) presented a technology-based SCM system that utilized Internet, object-oriented, Java, and mobile
Garcia-Flores et. al. (2000) modeled a SC involving retailers, warehouses, plants and raw material suppliers as a network of co-operative
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Carlsson D., Rönnqvist M.; (2005); Supply chain management in forestry-case studies at Södra Cell AB; European Journal of Operational Research; 163, Issue 3, pp 589-616 Chang Y.S., Lee J.K.; (2004); Case-based modification for optimization agents: AGENTOPT; Decision Support Systems; 36; pp 355– 370 Chung W.W.C., Yam A.Y.K., Chan .F.S.; (2004); Networked enterprise: A new business model for global sourcing; Int. J. Production Economics; 87; pp 267–280 Dong J., Zhang D., Nagurney A.; (2003); A supply chain network equilibrium model with random demands; European Journal of Operational Research; 156; pp 194-212 Escudero L.F., Galindo E., Garcia G., Gomez E., Sabau V.; (1999); Schumann, a modeling framework for supply chain management under uncertainty; European Journal of Operational Research; 119; pp 14-34 Ganeshan R., Tyworth J.E., Guo Y.; (1999); Dual sourced supply chains: the discount supplier option; Transportation Research Part E; 35; pp 11-23 Garcia-Flores R., Wang X.Z., Goltz G.E.; (2000); Agent-based information flow for process industries' supply chain modeling; Computers and Chemical Engineering; 24; pp 1135-1141 Ghiassi M, Spera C.; (2003); Defining the Internetbased supply chain system for mass customized markets; Computers & Industrial Engineering; xx; pp 1–25 Gunnarsson H., Rönnqvist M., Lundgren J.T.; (2004); Supply chain modelling of forest fuel; European Journal of Operational Research; 158, Issue 1, pp 103-123 Hameri A., Lehtonen J.; (2001); Production and supply management strategies in Nordic paper mills; Scand. J. Mgmt.; 17; pp 379-396 Heikkilä J.; (2002); From supply to demand chain management: efficiency and customer satisfaction; Journal of Operations Management; 20; pp 747–767 Huin S.F.; (2004); Managing deployment of ERP systems in SMEs using multi-agents; International Journal of Project Management; Article in press; Available online at www.sciencedirect.com Julka N., Karimi I., Srinivasan R.; (2002); Agentbased supply chain management-/2: a refinery application; Computers and Chemical Engineering; 26; pp 1771-/1781 Kaihara T.; (2003); Multi-agent based supply chain modelling with dynamic environment; Int. J. Production Economics; 85; pp 263–269 Kimbrough S.O., Wu, D.J. Zhong F.; (2002); Computers play the beer game: can artificial agents manage supply chains?; Decision Support Systems; 33; pp 323–333
intelligent technologies. The system used ecommerce technologies such as e-marketplaces, agent based brokers, and online auctioning for advanced production planning to support synchronized supply chain systems. This SCM system can support a mass customized business model and allows the manufacturer and its suppliers and subcontractors to quickly and efficiently produce and deliver products. This system supports dynamic sourcing, in which an exception in a supplier’s order is automatically sent to other participants for immediate resolution. An intelligent agent based trading system is used in the resolution of the orders. All the above applications demonstrate that agent based systems can function effectively in dynamic environments. The agents are able to adapt to the environment in which they work. Agents are also capable of taking better decisions than those provided by heuristic methods. In most of the supply chains, the different members are autonomous units and they take decisions which are of their best interest. All the agents behave as autonomous units and they can represent the supply chain members more accurately. 5. CONCLUSION A brief review of literature on supply chain models with focus on the tools used for modeling was presented in this paper. The review highlighted some of the serious limitations of the existing methods of modeling the supply chains. Some of the applications of agent based systems in supply chain were discussed with the objective of presenting a case of agent based systems in supply chain modeling. It was found out that agent based supply chains are more adaptable to the changing environment. Therefore, dynamic modeling of supply chains is possible. Moreover any number of agents could be added to system at any point of time. Hence, the model could be extended to any level of detail. Most importantly, the agents function as autonomous units and therefore they represent the behavior of supply chains in more realistic terms. REFRENCES Al-Mudimigh A.S., Zairi M., Ahmed A.M.; (2004); Extending the concept of supply chain: The effective management of value chains; Int. J. Production Economics; 87; pp 309–320 Alvarado U.Y., Kotzab H.; (2001); Supply Chain Management: The Integration of Logistics in Marketing; Industrial Marketing Management; 30; pp 183–198
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simulation based analysis; Int. J. Production Economics; 89; pp 217-229 Syarif A., Yun Y., Gen M.; (2002); Study on multistage logistic chain network: a spanning treebased genetic algorithm approach; Computers & Industrial Engineering; 43; pp 299-314 Symeonidis A.L., Kehagias D.D., Mitkas P.A.; (2003); Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques; Expert Systems with Applications; 25; pp 589– 602 Talluri S., Baker R.C.; (2002); A multi-phase mathematical programming approach for effective supply chain design; European Journal of Operational Research; 141; pp 544–558 Tan R.B.H., Khoo H.H.; (2005); An LCA study of a primary aluminum supply chain; Journal of Cleaner Production; 13, Issue 6, pp 607-618 Vergara F.E., Khouja M., Michalewicz Z.; (2002); An evolutionary algorithm for optimizing material flow in supply chains; Computers and Industrial Engineering; 43; pp 407-421 Wang H., Guo M., Efstathiou J.; (2003); A gametheoretical cooperative mechanism design for a two-echelon decentralized supply chain; European Journal of Operational Research; 157; pp 372-388 Yan H., Yu Z., Cheng T.C.E.; (2003); A strategic model for supply chain design with logical constraints: formulation and solution; Computers & Operations Research; 30; pp 2135–2155 Yusuf Y.Y., Gunasekaran A., Adeleye E.O., Sivayoganathan K.; (2004); Agile supply chain capabilities: Determinants of competitive objectives; European Journal of Operational Research; 159, Issue 2, 379-392
Korpela J., Lehmusvaara A., Tuominen M.; (2001); Customer service based design of the supply chain; Int. J. Production Economics; 69; pp 193-204 Korpela J., Lehmusvaara A., Tuominen M.; (2001); An analytic approach to supply chain development; Int. J. Production Economics; 71; pp 145-155 Lin P., Wong D.S., Jang S., Shieh S., Chu J.; (2004); Controller design and reduction of bullwhip for a model supply chain system using z-transform analysis; Journal of Process Control; 14; pp 487–499 Manthou V., Vlachopoulou M., Folinas D.; (2004); Virtual e-Chain (VeC) model for supply chain collaboration; Int. J. Production Economics; 87; pp 241–250 Minegishi S., Thiel D.; (2000); System dynamics modeling and simulation of a particular food supply chain; Simulation Practice and Theory; 8; pp 321-339 Mokashi S.D., Kokossis A.C.; (2003); Application of dispersion algorithms to supply chain optimisation; Computers and Chemical Engineering; 27; pp 927-/949 Nagatani T.; (2004); Dynamical transition in random supply chain; Physica A; 335; pp 661– 670 Perea E., Grossmann I., Ydstie E., Tahmassebi T.; (2000); Dynamic modeling and classical control theory for supply chain management; Computers and Chemical Engineering; 24; pp 1143-1149 Perea-Lo´pez E., Ydstie B.E., Grossmann I.E.; (2003); A model predictive control strategy for supply chain optimization; Computers and Chemical Engineering; 27; pp 1201-/1218 Reiner G., Trcka M.; (2004); Customized supply chain design: Problems and alternatives for a production company in the food industry. A
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SUPPLY CHAIN MODELING: THE AGENT BASED APPROACH Subhash Wadhwa Professor, Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India E-mail: [email protected] Bibhushan Research Scholar, Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India E-mail: [email protected] Abstract: In the era of fierce competition Supply Chain Management (SCM) has gained enormous amount of importance. A major decision that affects the overall performance of a supply chain is the modeling of the supply chain. Some of the widely used supply chain modeling tools are reviewed in this paper. A case for agent-based architecture for modeling of supply chains is proposed. Agents act as autonomous units and are able to take decisions according to the dynamic business environment. It was found that agent based architecture is capable of more realistic and dynamic modeling because of the autonomy of members of a supply chain. Copyright © 2006 IFAC Keywords: Agents, Autonomous Control, Distribution Networks, Simulation, Supply Chain Modeling
focus of this paper if on one such agent based supply chain system.
1. INTRODUCTION Supply Chain Management (SCM) is a rapidly growing area in the research and management domains. The overall performance of the supply chains depends to a large extent on how the supply chain has been modeled. Although large amount of work has been done in SCM there seems to be no unanimity among the practitioners on the best way of modeling a supply chain. One of the major reasons for this fact is that the structure of the supply chain depends to vast array of variables like product life cycle, nature of product, the target customers, competition and so on. Hence the complexity of the supply chains becomes appalling because of its dependence on many variables and interdependence of these variables.
The paper is structured in the following manner. A brief literature review of the supply chain models is presented in the next section. The limitations of existing models and need for an agent based approach is highlighted in the following section. This is followed by some applications of agent based systems in supply chains. The conclusions are presented in the final section. 2. LITERATURE REVIEW For the purpose of literature review, the internet search on www.sciencedirect.com was undertaken taking “Supply Chain Modeling” as the keyword. The relevant results were grouped according to the tool used for modeling the supply chain (the results are shown in Table 1). The brief description of the relevant papers is presented in this section.
Many models for the modeling and analysis of supply chain have been presented by researchers and practitioners. One of the major limitations of these models is that supply chain as whole is considered as a system that could be centrally controlled by a single decision maker. In practice, the different members of a supply chain act as autonomous units. They may be part of more than one supply chain also. This limitation of the model has been solved by modeling the supply chain as an Agent Based system where different nodes of the chain are modeled as autonomous agents. The
The paper by Wang et. al. (2003) analyzed noncooperative behavior in a two-echelon decentralized SC, composed of one supplier and many retailers using a game theoretic approach. One of the serious limitations here is that only two echelons of the chain are studied. In reality only a few chains have only two echelons. Moreover, the behavior of the two members is assumed to be static and completely deterministic.
471
S. No. 1.
Method
Models
Standard Mathematical Models
Gunnarsson et. al. (2004), Korpela et. al. (2001), Mokashi and Kokossis (2003), PereaLo´pez et. al. (2003), Syarif et. al. (2002), Talluri and Baker (2002), Wang et. al. (2003), Yan et. al. (2003) Gunnarsson et. al. (2004) Escudero et. al. (1999), Kaihara (2003), Nagatani (2004) Kaihara (2003), Kimbrough et .al. (2002), Minegishi and Thiel (2000), Nagatani (2004), Reiner and Trcka (2004) Syarif et. al. (2002), Vergara et. al. (2002)
2.
Heuristics
3.
Analytical Models
4.
Simulation Models
5.
Evolutionary/ Genetic Algorithms Artificial Agents
6.
7.
8.
Decision Making Techniques (AHP, ANP) Empirical Study
criteria efficiency models based on game theory concepts and linear and integer programming methods. Even with these features, the model lacks dynamism and adaptability which are essential requirements of any such business network. Yan et. al. (2003) proposed a strategic production– distribution model for SC design that considered logical constraints in the form of Bill Of Materials (BOM). They formulated their model using Mixed Integer Programming approach. Perea-Lo´pez et. al. (2003) described a model predictive control strategy to find the optimal decision variables to maximize profit in SCs with multi-product, multiechelon distribution networks with multi-product batch plants using MILP dynamic programming. Gunnarsson et. al. (2004) used MILP for strategic planning of a forest fuel SC. To obtain quicker solutions they also proposed a heuristic algorithm. All these models used integer programming as the solution strategy. But here again the models are assumed to remain constant over whole planning horizon. Further, all the supply chain members are assumed to follow a centralized strategy. This is very difficult to achieve in practice.
Garcia-Flores et. al. (2000), Julka et. al. (2002), Kaihara (2003), Kimbrough et .al. (2002) Korpela et. al. (2001a), Korpela et. al. (2001b)
Perea et. al. (2000) modeled the flow of information and material within the SC and used them to capture its dynamic behavior. They used the ideas from dynamics and control for the design of systematic decision-making processes. Lin et. al. (2004) developed a discrete time series model of a SC system involving using material balances and information flow. Both these models find the optimal flow of materials in the supply chain. But this has the limitation that only a single variable i.e. material flow has been considered for optimization. The real life supply chains are affected by numerous variables. Further, the relative importance of different variables may change depending on the product to be supplied and the policies of the member companies. For instance, Reiner and Trcka (2004) assert that universally valid statements based on the behavior of specific SCs can be quite doubtful. They point out that the analysis of SCs should be product and company specific.
Alvarado and Kotzab (2001), Chung et. al. (2004), Garcia-Flores et. al. (2000), Hameri and Lehtonen (2001), Yusuf et. al. (2004) 9. Theoretical Al-Mudimigh et. Al. Models (2004), Alvarado and Kotzab (2001), Chung et. al. (2004), Heikkilä (2002), Manthou et. al. (2004) 10. Others Carlsson and Rönnqvist (2005), Dong et. al. (2003), Julka et. al. (2002), Kimbrough et .al. (2002), Lin et. al. (2004), Perea et. al. (2000), Tan and Khoo (2005) Table 1 Supply Chain Models and Tools Used
Minegishi and Thiel (2000) used system dynamics based simulation to improve the knowledge of the complex logistic behavior of an integrated food industry. System dynamic models can represent the dynamic behavior of the very effectively. But too long term modeling may result in faulty planning. In this kind of a scenario, an adaptive system that modifies its behavior in accordance with the changing conditions would be more effective.
Talluri and Baker (2002) presented a multi-phase mathematical programming approach for effective SC design that utilized a combination of multi-
472
(VeC). Alvarado and Kotzab (2001) conceptualized the SCs from a channel governance point of view on the basis of phenomenon of Efficient Consumer Response (ECR). Heikkilä (2002) proposed a framework for prioritizing lead time reduction in a demand chain improvement project. All these models are at present only at the conceptual level. Although they can handle the present problems effectively, they need to be modified in future in view of the changing environment.
Some hybrid models that used a combination of standard mathematical techniques and some other techniques have also been recently proposed. For instance, Korpela et. al. (2001a) used a combination of Analytical Hierarchy Process (AHP) and Mixed-Integer programming to optimize a company's SC based on customer service requirements within the constraints of the SC. The AHP has the limitation that it can help only in strategic decisions in the sense that only these decisions are dependent on many factors. The operational and tactical decisions may be handled more effectively by mathematical models and the Decision Support Systems.
Yusuf et. al. (2004) reviewed the emerging patterns in SC integration and explored the relationship between the emerging patterns and attainment of competitive objectives. Their results showed that only a few companies have adopted agile SC practices but most companies have embraced long term collaboration with supplier as well as customer (which they conceptualized as lean SC practices). Here also, only the agility of the supply chain is of main focus. In the changing business environment, the functions of supply chain management and logistics will also change. For example, Chung et. al. (2004) are of the view that global sourcing will be transformed from one that purely performs the task of sourcing input to one that coordinates and manages the entire SC. They identify four attributes for greater organizational response to changing environment: collaborative advantage, regional advantage, innovation capacity (knowledge resource) and competencies.
Similarly, Syarif et. al. (2002) used a combination of Genetic Algorithms (GA) and 0-1 MILP for the design of a logistics network. Hameri and Lehtonen (2001) found the 30% of inventory could be regarded as slack when production cycles are reduced and logistical alternatives are fully exploited. According to them, this can directly generate cost saving of about 2-5% without additional investment. Here also the supply chain is considered to static, deterministic and non adaptive. Nagatani (2004) presented a stochastic model of the SC composed of series machines and buffers. He considered the adaptation time of each machine to be finite and individual buffers for each machine. Escudero et. al. (1999) described a modeling framework for the optimization of a manufacturing, assembly and distribution (MAD) SC planning problem under uncertainty in product demand and component supplying cost and delivery time. Vergara et. al. (2002) developed an evolutionary algorithm (EA) for optimal synchronization of SCs. These models also suffer from the same limitations as the other models.
3. EVALUATION OF MODELLING TOOLS A review of the existing models highlighted some serious limitations of traditional methods of modeling the supply chains. Some of the more serious limitations are discussed here. Static and Deterministic Nature of the Supply Chain: the mathematical models, heuristics and analytical models consider the supply chain as a static system. Hence it gives only a snapshot view of the problem. This kind of modeling cannot represent the ever changing nature of the supply chains. Moreover, the supply chain members are assumed to remain in the chain for the entire duration considered in the model. These models are more suitable for products with a stable demand particularly the Fast Moving Consumer Goods (FMCG). For products with uncertain or variable demand the structure of the supply chain may change over time.
Tan and Khoo (2005) demonstrated the application of the concept of Life Cycle Assessment (LCA) to the SC producing aluminum billets. The effectiveness of decision support systems in SCM has been showed by Carlsson and Rönnqvist (2005). Korpela et. al. (2001b) applied AHP for SC development. Dong et. al. (2003) develop a SC network model consisting of manufacturers and retailers considering random demands. Mokashi and Kokossis (2003) used dispersion algorithm for optimizing a SC problem consisting of production planning and distribution scheduling in two tiers. Highlighting the limitations of SCM, Al-Mudimigh et. al. (2004) discussed the concept of Value Chain Management (VCM) and propose a model that is driven by a focus on agility and speed. Manthou et. al. (2004) presented a SC collaboration framework in a virtual environment called Virtual e-Chain
Dominance of a Single Member: in most of the mathematical models, it is assumed that all members take their decisions in accordance with the solution provided by the model. Hence this kind of modeling is more suitable to supply chains with
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a single member as the dominant player. This modeling becomes ineffective in the supply chains where there is no clear-cut dominance among the members or where the different members have a high degree of autonomy.
agents where each agent performed one or more SC functions. In their model, the interactions between agents were made through the common agent communication language called Knowledge Query Message Language (KMQL).
Focus on a Single or Limited Number of Objective: most of the models considered only one or a limited number of objectives for modeling the supply chain. But practitioners are of the view that since supply chain decisions are based on multiple variables, the objectives in the supply chain are also more than one. A major reason for this is this fact that supply chain is a very complex system in itself. It cannot be understood by a single model in its entirety.
Julka et. al. (2002) developed an agent based Decision Support System (DSS) called Petroleum Refinery Integrated Supply Chain Modeler and Simulator (PRISMS). They used PRISMS as a central DSS for studying the processes of a refinery. This DSS enabled them to take integrated decisions with respect to the overall refinery business. Kaihara (2003) formulated the SC as a discrete resource allocation problem under dynamic environment using a combination of an analytical model and agent based system. He demonstrated the virtual market concept to their framework by the use of agents. The findings also confirmed that careful construction of the decision process according toe economic principles leads to efficiently distributed resource allocation in SCM.
Limited Decision Alternatives: in most of the models reviewed, only limited options were considered in various decisions. For instance, policies like sS Policy and sQ Policy were used in sourcing decisions. Tools like Genetic Algorithms and Simulated Annealing may provide better policies for a particular duration of time.
Chang and Lee (2004) developed a case-based modification scheme that could formulate optimization models automatically and designed a prototype tool AGENT-OPT that can manage a model warehouse. Through their studies they confirmed that the architecture AGENT-OPT, which uses the representation of UNIK-OPT, could answer the requirements of a model warehouse and interactions with other software agents effectively. They also designed the ontology for delivery scheduling problems, and validated the architecture.
Partial Modeling of the Supply Chain: many of these models consider only a partial portion of the supply chain rather than the entire supply chain. For instance, most of the mathematical models considered only a few echelons of the supply chains close to the manufacturer. 4. AGENT BASED APPROACH IN MODELING SUPPLY CHAINS Although a comparatively new concept in SCM, the agent based systems have found various applications at different levels in the Supply Chain. We will discuss here some of these applications. The discussion is only illustrative and not comprehensive.
An agent-based model for coordinating the management of enterprise resource in SMEs has been developed by Huin (2004). The model tries to organize, interface and manage the various enterprise resources in an SME. This agent based model helps the SMEs in better project management of their ERP systems in usually informal interactions.
Kimbrough et .al. (2002) investigated the impact of artificial agents on the SC with the help of the traditional beer game. They found that agents were capable of playing the Beer Game effectively. According to them, agents were able to track demand, they eliminated the Bullwhip effect, discovered the optimal policies (where they are known), and found good policies under complex scenarios where analytical solutions were not available. Their second important finding was that automated SC controlled by artificial agents was adaptable to an ever-changing business environment.
Symeonidis et al. (2003) developed a multi-agent system that added adaptive intelligence as a powerful add-on for ERP software customization. The system could be considered to be recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system could be periodically retrained as new information is added to the ERP. This paper by Ghiassi (2003) presented a technology-based SCM system that utilized Internet, object-oriented, Java, and mobile
Garcia-Flores et. al. (2000) modeled a SC involving retailers, warehouses, plants and raw material suppliers as a network of co-operative
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Carlsson D., Rönnqvist M.; (2005); Supply chain management in forestry-case studies at Södra Cell AB; European Journal of Operational Research; 163, Issue 3, pp 589-616 Chang Y.S., Lee J.K.; (2004); Case-based modification for optimization agents: AGENTOPT; Decision Support Systems; 36; pp 355– 370 Chung W.W.C., Yam A.Y.K., Chan .F.S.; (2004); Networked enterprise: A new business model for global sourcing; Int. J. Production Economics; 87; pp 267–280 Dong J., Zhang D., Nagurney A.; (2003); A supply chain network equilibrium model with random demands; European Journal of Operational Research; 156; pp 194-212 Escudero L.F., Galindo E., Garcia G., Gomez E., Sabau V.; (1999); Schumann, a modeling framework for supply chain management under uncertainty; European Journal of Operational Research; 119; pp 14-34 Ganeshan R., Tyworth J.E., Guo Y.; (1999); Dual sourced supply chains: the discount supplier option; Transportation Research Part E; 35; pp 11-23 Garcia-Flores R., Wang X.Z., Goltz G.E.; (2000); Agent-based information flow for process industries' supply chain modeling; Computers and Chemical Engineering; 24; pp 1135-1141 Ghiassi M, Spera C.; (2003); Defining the Internetbased supply chain system for mass customized markets; Computers & Industrial Engineering; xx; pp 1–25 Gunnarsson H., Rönnqvist M., Lundgren J.T.; (2004); Supply chain modelling of forest fuel; European Journal of Operational Research; 158, Issue 1, pp 103-123 Hameri A., Lehtonen J.; (2001); Production and supply management strategies in Nordic paper mills; Scand. J. Mgmt.; 17; pp 379-396 Heikkilä J.; (2002); From supply to demand chain management: efficiency and customer satisfaction; Journal of Operations Management; 20; pp 747–767 Huin S.F.; (2004); Managing deployment of ERP systems in SMEs using multi-agents; International Journal of Project Management; Article in press; Available online at www.sciencedirect.com Julka N., Karimi I., Srinivasan R.; (2002); Agentbased supply chain management-/2: a refinery application; Computers and Chemical Engineering; 26; pp 1771-/1781 Kaihara T.; (2003); Multi-agent based supply chain modelling with dynamic environment; Int. J. Production Economics; 85; pp 263–269 Kimbrough S.O., Wu, D.J. Zhong F.; (2002); Computers play the beer game: can artificial agents manage supply chains?; Decision Support Systems; 33; pp 323–333
intelligent technologies. The system used ecommerce technologies such as e-marketplaces, agent based brokers, and online auctioning for advanced production planning to support synchronized supply chain systems. This SCM system can support a mass customized business model and allows the manufacturer and its suppliers and subcontractors to quickly and efficiently produce and deliver products. This system supports dynamic sourcing, in which an exception in a supplier’s order is automatically sent to other participants for immediate resolution. An intelligent agent based trading system is used in the resolution of the orders. All the above applications demonstrate that agent based systems can function effectively in dynamic environments. The agents are able to adapt to the environment in which they work. Agents are also capable of taking better decisions than those provided by heuristic methods. In most of the supply chains, the different members are autonomous units and they take decisions which are of their best interest. All the agents behave as autonomous units and they can represent the supply chain members more accurately. 5. CONCLUSION A brief review of literature on supply chain models with focus on the tools used for modeling was presented in this paper. The review highlighted some of the serious limitations of the existing methods of modeling the supply chains. Some of the applications of agent based systems in supply chain were discussed with the objective of presenting a case of agent based systems in supply chain modeling. It was found out that agent based supply chains are more adaptable to the changing environment. Therefore, dynamic modeling of supply chains is possible. Moreover any number of agents could be added to system at any point of time. Hence, the model could be extended to any level of detail. Most importantly, the agents function as autonomous units and therefore they represent the behavior of supply chains in more realistic terms. REFRENCES Al-Mudimigh A.S., Zairi M., Ahmed A.M.; (2004); Extending the concept of supply chain: The effective management of value chains; Int. J. Production Economics; 87; pp 309–320 Alvarado U.Y., Kotzab H.; (2001); Supply Chain Management: The Integration of Logistics in Marketing; Industrial Marketing Management; 30; pp 183–198
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