J. Lee and M. Barley (Eds.): PRIMA 2003, LNAI 2891, pp. 13–24, 2003.
© Springer-Verlag Berlin Heidelberg 2003
Supply Chains with Complexity and Uncertainty
Hyung Jun Ahn and Sung Joo Park
Graduate School Of Management, KAIST, 207-43, Cheongryang-ni, Dongdaemoon-gu, Seoul, Korea
{s_hjahn,sjpark}@kgsm.kaist.ac.kr
Abstract. In supply chain management, improving the efficiency of the overall supply chain is important. It has been found that sharing information between supply chain members can increase the efficiency of the supply chain by varying degrees in different situations. There are, however, many practical difficulties in sharing information with all the companies in a complex supply chain. This paper presents a multi-agent system for supply chain coordination where agents discover the structure of supply chains and share information on inventory levels only by local collaborations. Agents use the gathered information to estimate demands propagated from multiple markets through a complex network of companies with different lead times and market shares.
The performance of the suggested system is investigated with a simulation experiment and the result is compared with an alternative strategy.
1 Introduction
The importance of supply chain is increasing with market globalization and the advancement of electronic commerce [17][19]. A supply chain can be defined as a network consisting of suppliers, warehouses, manufacturers, wholesalers, and retailers through which material and products are acquired, transformed, and delivered to consumers in markets. In today’s supply chains, it is a critical issue to enhance the efficiency of supply chains in the perspective of the whole chains.
One of the issues many companies and researchers are concerned is how information sharing between companies can be facilitated to improve the efficiency of supply chains. For example, there have been studies that showed sharing inventory levels of buyers helps predicting future demands more accurately, resulting in lower average inventory levels and higher service rates [1][12]. But information sharing between companies is not always possible [2][7]. Supply chains can be complex and dynamic, and many companies may not want to share their information for various reasons. Moreover, even for companies who are willing to share information, incompatibility among heterogeneous information systems can hinder the information sharing. In general, it is especially difficult for distantly located companies in supply chains to share information, compared to closely located companies.
This research, reflecting these practical barriers, suggests a multi-agent system for distributed and collaborative supply chain management. Multi-agent technology has many beneficial features for autonomous, collaborative, and intelligent systems in
distributed environments, which makes it one of the best candidates for complex supply chain management [20][22][25]. The suggested system of this paper was designed to improve the efficiency of supply chains by utilizing only local information sharing between neighboring companies. With the suggested architecture, agent systems will discover the structure of supply chains by local information sharing and utilize the structure to accurately predict future demands that will come to their companies in a specific point of time. To evaluate the performance of the suggested architecture, a simulation experiment was performed.
The remainder of this paper is organized as follows: The second section reviews related research. The third section presents the architecture of the suggested system and analyzes the performance of the system with simulation. The fourth session presents a discussion and conclusion.
2 Review of Related Research
2.1 Information Sharing in Supply Chains
There have been many efforts at showing how information sharing in supply chains can increase efficiency and reduce costs. They deal with information sharing strategies mainly involving inventory levels, buyers’ strategy, or market demands;
they show how the strategies affect supply chain performance in different situations using analytic methods or simulation experiments [1][3][12][9].
In contrast, there are some studies that showed sharing information can provide little benefit. The studies of [10][11] claim that investing in physical flows and utilizing history data intelligently can be enough. These contradicting results imply that the benefit of sharing information can vary with circumstances and that it is important to utilize given information carefully and effectively. For example, [3]
showed that the benefit of sharing inventory information is different under diverse demand patterns and companies’ processing capacities, and [5] showed that the role of the participants in a supply chain portal significantly affects the benefits of information sharing.
We also have to consider some practical barriers to information sharing. It is difficult to share information between companies using different information systems;
many companies are not willing to disclose their information, worried with possible strategic losses [7]. In general, information sharing is more difficult between distantly located companies in supply chain topology than between neighboring companies. It has been actually found that some companies can incur losses owing to information sharing under certain conditions [2]. From these results, it becomes obvious that it is impractical to assume global information sharing between companies in supply chains, and also that it is difficult to effectively utilize shared information.
Another limitation in the existing researches on information sharing is that most of them used simple supply chain models, consisting of only two or three serial layers.
The simplicity in the models makes it difficult to generalize their implications to practical and complex supply chain environments. This paper will try to overcome this limitation, while considering the practical difficulties of information sharing.
2.2 Supply Chain Management and Multi-agent Technology
There are unique characteristics required for information systems that support supply chain management. First, they should be able to support distributed collaboration among companies. Second, collaborations in supply chains cannot be governed by a single company in a one-directional way, but needs to be coordinated by autonomous participation of companies. Third, they need a high level of intelligence for planning, scheduling, and change adaptation. For these reasons, agent technology is regarded as one of the best candidates for supply chain management [20][22][14][25].
Studies on agent-based supply chain management can be classified into three categories. The first type of research is concerned with the coordination aspect. In this type of research, various types of companies and their capabilities are modeled into individual agents and their interactions are designed for efficient collaboration [21][16][23]. The second type of research focuses on simulation of supply chains using agent-based models. This type of research tries to discover the performance of agent-based supply chain architectures under various strategies and constraints [16][18][22]. The third type of research studies how virtual supply chains can be organized flexibly by multi-agent systems [15][24]. For example, [15] showed how virtual supply chains can be formed by solving distributed constraint satisfaction problems by agents.
The studies listed above provide good basis for modeling various aspects of supply chains. They are, however, deficient in showing how complex supply chains can be coordinated under the practical difficulties of information sharing discussed earlier.
Many of them assume that global information sharing is possible and/or use simple supply chain models. In order to derive more practical implications for complex supply chains, this paper presents a multi-agent system architecture incorporating more realistic assumptions regarding the practical difficulties.
3 MADC: A Multi-agent System for Distributed Coordination of Supply Chains
3.1 Summary of the Problems
Figure 1 illustrates the problems that this paper focuses on. A supply chain can produce products for multiple markets. Also, an individual company is likely to have only limited visibility of the supply chain structure, which makes it difficult to make future demands estimations, because the pattern of demand propagation through the supply chain depends on the capabilities and strategies of companies along the path from the markets to the company. These problems are further amplified if the supply chain can change over time dynamically. As a result of these problems, individual companies are likely to make inaccurate demand estimations and the supply chain can suffer from the well-known Bullwhip effect [6][13]. The Bullwhip effect refers to the problem where the fluctuations of productions and inventory levels are amplified in the upstream parts of supply chains than in the downstream parts. The Bullwhip effect significantly increases operation and production costs and drops service levels.
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Fig. 2. An example supply chain model: a TFT-LCD supply chain of a Korean company
Table 1. Notations in the example supply chain Notation Description
LPi Lead time for production or processing at node i LDij Lead time for delivery from node i to node j Sp Market share of path p
3.2 Overview of MADC Architecture
Figure 2 shows an example supply chain model of TFT-LCD products of a Korean company. In this supply chain, there exist two distinct markets and many types of companies: suppliers of material and components, LCD cell manufacturing plants, LCD module manufacturing plants, and wholesalers. This supply chain model will be used for explaining and evaluating MADC.
Figure 2 also depicts the key approaches of MADC to solve the problems introduced in 3.1. Followings are the three key ideas of MADC:
Market Estimation Agent (MEA)
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Fig. 3. The architecture of MADC
A. Sharing ‘paths to markets’ by local information sharing: In a complex supply chain, there are usually multiple paths between a company and consumer markets (for example, see node 9 which has three paths to markets) Considering the practical difficulties of global information sharing, MADC will share paths to markets by only local information sharing, that is, information sharing by only agents of neighboring companies. Each company will use the paths to estimate the propagation of market demands.
B. Direct observation of markets: Market demands are likely to be distorted as they are propagated to upstream parts of a supply chain. Thus, it often results in erroneous estimation to observe the demands of companies in the middle of the paths to markets. Also, there are many practical barriers to full information sharing with the companies. Thus, it can be often economic and beneficial to observe market demands directly. The observation is used together with the paths for accurate estimation.
C. Sharing inventory information of neighboring buyers: As shown in the literature review, sharing inventory information can be beneficial. In MADC, companies will have visibility of neighboring buyers’ inventory levels, again with local information sharing. When inventory levels of buyers are high, suppliers can reduce production, and when the levels are low, they can increase production.
Figure 3 shows the architecture of MADC. There are four different agents in each company of a supply chain. The agents will collaborate with the agents of neighboring companies in upstream and downstream sides, while observing market demands directly.
3.3 Coordination of Agents in MADC
In MADC, agents collaborate with both internal and external agents. Thus, we need to clearly specify how the activities of the four types of agent should be coordinated in
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Fig. 4. Coordination of the four agents in MADC Table 2. The conversations among agents in MADC Name of Conversation Protocol Participants
(Initiator → Counterpart) Exchanged information
Query Order Plan Query OPA → PSA orderPlan
Query Production Plan Query OPA → PSA productionPlan Share Inventory Level Query [U] PSA → [D] OPA inventoryLevel Issue Order Request Request [D] OPA → [U] OPA order
Query Market Demands Query PSA → MEA marketDemands
Query Paths to Markets Query MEA→ SSA marketPath Share Supply Chain
Structure
Query [U] SSA → [D] SSA marketPath
harmony. This includes the identification of points where agents should share information and synchronize their executions. Figure 4 shows the coordination model of MADC with a Petri-Net that is widely used for specifying coordination of multiple independent systems when they are concurrently executed and synchronization should be considered. The numbered arrows (c ~ h) represent inter-company collaborations.
Communication among agents is performed by a set of messages that follow predefined protocols. In MADC, FIPA (The Foundation of Intelligent Physical
Agents)’s two protocols, Query and Request, were used to model the conversations among agents [26]. Table 2 shows the list of conversations in MADC, protocol types of each conversation, participants of conversations, and the type of information exchanged by each conversation.
3.4 Sharing Paths to Markets
Paths to markets are determined by the structure of supply chains. MEA uses the paths to estimate future demands incoming to its company. Demand in a market associated with a path is realized in each company after the total lead time of the path.
Thus, it is sufficient to know paths to markets to estimate future demands if we assume that market demands and market share of each path can be observed directly.
Moreover, paths to markets are aggregated information that can be propagated along the paths without the risk of disclosing sensitive information and the burden of communication between distantly located companies.
Figure 5 illustrates the procedure of propagating paths to markets by the collaboration of agents in neighboring companies. In node j, there are two paths to markets; in node k, there is one path. As a consequence, node i will have three paths to markets. The paths from node j will be updated by adding the production (or processing) lead time of node j and the delivery lead time between i and j. The same procedure will be applied to the path going through node k. This procedure is presented with a pseudo-code in the right side of Fig. 5. Finally, node i will use the three paths and the observed market demands to estimate future demands. Figure 6 shows an example message that is exchanged by SSAs of neighboring companies for propagating paths. In the message, two paths of node 5 in the LCD supply chain are propagated to node 8, a cell plant.
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Table 3. Notations for demand estimation Notation Description
Spi Market share of the retailer (final supplier) in path p of node i DEijt Estimate of demands coming from node j to node i at time t D’pt Estimated demand at time t in the consumer market of path p
based on direct observation
PMij Set of paths to markets at node i that includes a direct buyer j Equation (1) is used to estimate demands coming from node j node to i at time t, utilizing the paths to markets and direct observation. The estimation is the sum of the product of market share associated with path p and the market demand directly observed by MEAs after the total lead time of each path.
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(1)3.5 Evaluation of MADC by Simulation
The performance of MADC was analyzed by a simulation experiment by comparing it with an alternative strategy using the example supply chain. In the alternative strategy (NC: No Coordination), each company estimates demands independently using past order history from buyers. For the demand pattern in the markets, we used a monotonically increasing pattern with fixed variance. Accordingly, for the alternative strategy, the double exponential smoothing (DES) technique was used for independent estimation of demands [27]. The parameters of DES were chosen from values of .05n (n = 0, 1, 2, … 20) that showed the best result. Followings are the assumptions for the simulation experiment:
– The cost of ordering and delivery are not considered.
– The performance of the supply chain is analyzed by two measures: inventory levels and service rate. Service rate is defined as the proportion of buyers’
orders that are fulfilled by suppliers. For example, if 100 products are ordered and 90 products are actually delivered, service rate is 0.9.
– The production or processing capacity of each node is infinite.
– If shortage occurs at a supplier, it is not handled as back orders, but it is reflected in service rates.
Performance of MADC. Figure 7 shows a graph that compares the service rate of MADC with that of NC at different levels of estimation errors for market demands:
10%, 20%, and 30%. The horizontal axis represents each node of the LCD supply chain. The graph suggests that the two approaches show approximately equivalent service rates when the estimation error is around 30%. This indicates that if the estimation error is larger than 30%, using the approach of MADC can be irrational.
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Fig. 7. Comparison of service rates between MADC and NCF
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Fig. 8. Comparison of normalized inventory levels between MADC and NC
Figure 8 shows the comparison of normalized inventory levels between MADC and NC. This graph also shows inventory levels of MADC at different levels of estimation errors for market demands. The graph implies that the average inventory levels of MADC approximates that of NC when the estimation error is 15%; at larger levels of estimation errors, MADC shows higher inventory levels.
From these results, it can be concluded that the approach of MADC outperforms the independent estimation strategy only when the estimation of market demands is better than a certain point of accuracy. We can conjecture that this result comes from the strong dependency of MADC on the estimation of market demands by MEAs.
Bullwhip effects in MADC. Figure 9 shows that the Bullwhip effect is decreased as the estimation of market demands gets more accurate in MADC. For example, when the estimation error is about 30 %, there exist big difference between the inventory levels of downstream nodes and upstream nodes (e.g., compare node 2 with node 9). When the estimation error is very low, however, the bullwhip effect is significantly reduced. For example, when the estimation error is about 5%, we can notice little difference between the inventory levels of node 2 and node 9.
Consequently, MADC reduces Bullwhip effects significantly when the estimation of market demands is more accurate.
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Fig. 9. Reduction of Bullwhip effects in MADC (Y axis = normalized inventory levels)
4 Discussions and Conclusion
The importance of supply chain management is increasing with globalization and the widespread adoption of electronic commerce. However, supply chains can change over time and companies in supply chains can have only limited visibility of the supply chains. This paper suggested a multi-agent system named MADC where companies can increase the efficiency of a supply chain by only local information sharing. The ideas behind the suggested system are: observing market demands directly, sharing paths to markets, and sharing inventory information among neighboring companies. The performance of the suggested system was analyzed by a simulation experiment. The result of the simulation revealed that the suggested system can show better performance when the estimation of market demands by direct observation is more accurate than a certain point. If the estimation error is large, the suggested approach can show inferior results compared to the independent estimation strategy that utilizes past order history.
There are some limitations in this research. First, if we cannot observe market demands directly, the approach cannot be used. In practice, however, the demands of consumer markets are often available, while the information of other downstream companies may not. Second, if a target supply chain is simple and global information sharing is possible, alternative strategies such as global planning can be used. But today, many supply chains have complex structures and they can be dynamically organized in the form of the virtual supply chain. Thus, the approach of MADC is more meaningful in supply chains with complexity and uncertainty.
Several further research issues remain. First, we plan to apply MADC to different models of supply chains with different assumptions and alternative measures of performance. Second, it is needed to analyze the performance of MADC with various demand patterns in markets. Third, it will be interesting to experiment with dynamic supply chain structure where the topology and parameters of a supply chain can change over time.
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