In: Proceedings of the IFIP WG 5.7 conference - International Conference on Integrated Production Management - Topic: Information and Communication Technology (ICT) in Logistics and Production Management held in Tromso, Norway from June 28-30, pp. 285-292
Demand Chain Optimization by Using Agent Technology a
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Jens Eschenbächer , Peter Knirsch , Ingo J. Timm Bremen Institute of Industrial Technology and Applied Work Science at the University of Bremen (BIBA), Hochschulring 20, 28359 Bremen, Germany b Research Group on Theoretical Computer Science, University of Bremen, P.O.Box 330 440, 28334 Bremen, Germany c Artificial Intelligence Research Group, University of Bremen, P.O.Box 330 440, 28334 Bremen, Germany Abstract Modern multiagent technology can be applied fruitfully to supply chain or - to be more precise - to demand chain management. In current research there is not much discussed in this direction because today’s business processes are often characterized more by small solutions than by integrated ones. This paper will prove that this approach offers very new and challenging possibilities to manage demand chain networks keeping in mind future developments that are hard to predict. We claim that concerning every facet of logistics efficient information flows are essential. A short case study of an industrial setting with new high technology support will outline the concrete business approach and its benefits. Keywords: demand chain management, multiagent system, information management
1 INTRODUCTION In the early 90ies the significance of customer’ requirements increase in a dizziness causing pace. Nowadays, binding a customer to a company makes it necessary to offer him a huge variety of products. These products must be highly customized to let him feel purchasing a unique product. Within this context one of the greatest challenges companies have to face is the change towards demand driven production. Producing standard products for an unknown market makes no longer sense. The main starting point of these considerations has been the discussion about how supply chain management should or can respond to the customers’ requirements (Eloranta 1999). It turned out that not management of supplies but demands of customer should trigger and influence the production processes. Therefore, logistics gets a new focus. The customer integration into the supply chain changes it to a demand chain. New and more information have to be handled and somehow the information flow is reversed. The group of customer is the main source of information, which has to be integrated and utilized within the production process to stay or become competitive, companies on the one hand will be forced to offer a wider range of products or lager number of variations and must be willing to satisfy the customers’ needs to keep the customer. On the other hand they have to keep their restricted
resources and capabilities in mind, of course. A feasible schedule and efficient resource management is highly required. But they do not only have to cope with these new developments, they furthermore have to continuously optimize their business processes - triggered substantially by the customer - for strategic economic reasons. The information transfer within all kinds of networks, especially demand chain networks, is crucial because integrated IT-solutions, i.e. application software systems, using standardized information processes do not exist, yet. The chance that those processes can be deployed in this scenario is very likely because the problem domain concerning the manufacture of certain products is bounded. This means that there is only a finite and in fact small number of components having a finite number of attributes, and a finite number of processes that have to be considered. Considering the changes in the significance of customers’ requirements the information flows must be reorganized. In former information systems customer were the last chains and were only supported with information in a passive way. Modern information systems must meet customers’ requests and in consequence provide a redirected information flow – directed complementary to the production flow, i.e. a pull-chain must be created. This paper deals with a modern approach to information systems managing demand chains.
• 2 SUPPLYCHAINS AND THEIR LIMITATIONS Producing in supply chains, which are coordinated and controlled centrally causes a lot of different problems: •
Centers of control are bottlenecks
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Centers must have complete knowledge for decision making
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Confidential internal information must provided to the center
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Difficult reorganization of the chains
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Planning in the complete supply chain can be very complex Without sophisticated communication a high coordination expenditure of all partners within the supply chain causes additional transaction costs, which are hard to predict. Therefore, Sydow (1995: 34) argues that transaction costs play the essential role in supply chain networks. The following transaction costs can be identified: •
Organizing the transportation
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Data exchange (e.g. via Fax)
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Failure costs
• Quality problems etc. The objective is to minimize these transaction costs by using (intelligent) electronic data interchange, partially automated. The main problem is that as well the semantics of data as the data transport media are not standardized and different media are often used in a single interchange step, e.g. a product description from an information system of company A via fax to company B and afterwards into the information system of company B. A modern approach in the field of heterogeneous information interchange is based on the use of mediators. For this purpose a global view (ontology) is created for a specific domain, information of different sources are mapped into or translated (Wiederhold, 1992). A natural demand of the customer is to determine in which stage the respective product is. Because of insufficient information systems’ functionalities this information is in most factories not available. Unfortunately distributed resources and production places prevent any easy aggregation of such information. Summarizing the limitations of classical supply chain management and information management in supply chains we can identify two major problem groups: coordinating information between companies for production process optimization and providing information and control features for the customer. Further we can identify the following crucial problems: •
Resource management often does not support real-time communication
Planning in distributed networks is difficult especially when language problems occur
• Estimation of cost is very difficult In our opinion, to solve these problems modern agent technology can be used to automate and therefore facilitate the information transfer between the different supply chain partners. Intelligent Agents provide functionalities as delegation of standard information procedures, e.g. acknowledgements, and highly standardized communication protocols. As a matter of fact the maturity of agent technology is high so that it is predictable that it can be used efficiently in business applications (Jennings, Wooldridge, 1998). The paper will demonstrate this by conceptually applying these technologies to demand chain networks.
3 DEMAND CHAIN MANAGEMENT The current intensive discussion about supply chain management is sometimes underestimating the fact that the customer is the main driver for all sales activities (Whitney, 1999). Therefore, companies like Dell computers have created demand chains instead of supply chains. Customer have the freedom to configure their products individually taking given selections into account. The most important underlying principle is the change from the push towards pull driven activities (Eloranta, 1999). In other words companies should think of applying more consequently the principles of demand chain management instead of supply chain management. In the following sections we will therefore focus on demand chain management. Of course there is not much of a difference but especially practitioners as Anderson (1997) emphasize the demand aspect in their articles. In principle it strongly depends on the product they assemble so that it is necessary to conduct an ABC-analysis about the importance of the product within the production process. The respective information of these products should and can be processed in a multiagent system. In order to face the change companies are no longer free to produce for an unknown unpredictable market demand because the increasing global competition and the availability of products in transparent markets force them to strongly consider their customer’ wishes. Ebusiness or electronic commerce is a major driver for reengineering supply chain networks and makes it even easier to integrate multiagent-based software solutions because of the existing underlying physical networks. The effectiveness of supply chains will be a key issue for the competitiveness of European industry as described in Eloranta et al (1995). As a consequence, starting from the customer’s demand, a flood of information spread through the
network of different production places yielding the launching of the distributed production process in extended or virtual enterprises (Jagdev and Brown, 1998).
3.2 Demand chain integration in the automotive industry In the automotive industry many investigations about complexity have been conducted. Dreher (1997) has developed a complexity index for VOLKSWAGEN showing that an automobile is manufactured from between 3.000 up to more then 20.000 parts. For this reason the assembling process of an automobile is substantially more complex as for a computer shown in the example of Dell. This complexity is shown exemplarily in Figure 3.
figure 1: Flows in a modern supply chain. The prerequisite for such specific information and financial flows (cf. Figure 1) is the predefined either complete or partial specification of a product. This specification is a very complex task, which is still very problematic in modern ERPsystems like SAP or BAAN. 3.1 Advanced management of demand chains – the Dell example One of the most successful enterprises in the area of demand chain management is Dell Computers. In the following figure indicators illustrating the tremendous success of Dell in comparison to traditional business (Dilts 1999) are shown. • Grew 25% in the 3rd Quarter • Customer base: – 15% of systems for consumers – 85% of systems for business
figure 3: Automotive supply chain complexity. In order to achieve such a successful demand chain network 1st or 2nd tier suppliers are totally connected to the manufacturer and to the trucks. As a matter of fact totally integrated distributed production planning systems processing customer’ specifications and real-time communication as described in Figure 4, do not exist, yet.
• Web site activity – 2 million visitors a month (vs. Amazon @ 11 mil.) – $ 30 million in sales a day (vs. Amazon @ 3,5 mil.)
• Return on Invested Capital – 13,7% Heavy Equipment Machinery Firms (GM, Ford, Caterpillar, Case, Deere, etc.) – 195% for Dell
figure 2: The Dell example. In the Dell example (Figure 2), mentioned above, it is predetermined which components are necessary to assemble the product. The customer clearly defines the product specifications. In average up to 20 units will be delivered to Dell where the final computer will be assembled. Many business processes are then generated out of the customer’s product specification. This, of course, results in complex business process chains with dependencies, which are still linear and cyclic and must be designed and coordinated in an appropriate manner. A centralized approach to this problem does not seem to be very promising because of the overall complexity and the amount of information.
figure 4: Real-time communication infrastructure. The question is if and how distributed, semiautomated decision support systems could be used to reduce the information management overhead. A benefit could be a better synchronization between the customer and the salesman, between different production places. Furthermore, the participants can reduce their administrational and organizational costs and can speed up the flows.
The information flows created through the customers’ product specifications are triggering chains going from customers to the sub-suppliers. Traditionally, there are information flows from the customers’ demand to the respective production place by using phone-calls, e-mail (natural language), fax, etc. Information flow in the other direction is mostly just concerned by means of material flow. Importance of information •
Necessary for production
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Necessary for administration
Kind of transmission •
Telephone
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Fax
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Letter
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Consignment note
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Electronic data interchange
But can the traditional way be considered as an appropriate and promising solution for future developments especially concerning the production control? As we have learnt in the Dell example it can be sufficient for specific supply chain but what about the automotive industry? The answer is no because there are too many transactions necessary which are expensive, slow, and errorprone. In the following part of the paper we will demonstrate the benefits gained from automated electronic data interchange. 4 INFORMATION INTERCHANGE IN DEMAND CHAINS Information Exchange has always been a hot topic both in academia and in industry. Especially the amazing success of companies like Microsoft has brought computer science in the mid-point of every days discussion. Currently one of the most promising research fields in computer science in order to support the automation of flexible process chains are multiagent systems (Sadeh, Smith, 1993). Intelligent agents represent a modern approach in Artificial Intelligence and distributed software engineering dealing with software entities which can act autonomously, communicate with other agents, are goal-oriented (pro-active) and are using explicit knowledge (Weiß, 1999). They are often used for tasks, which can be hardly solved monolithically and are showing a natural distribution. In these domains agents gain great benefit as they are able to collaborate and solve problems in a distributed manner. Especially in the field of logistics there is a huge unused potential of this technology (Knirsch, Timm 1999). Representing companies in demand networks agents are capable of organizing, controlling and managing information flows
autonomously. Even consideration of company secrets can be integrated easily. Furthermore, agents are not bound to static organizational structures and can choose their communication counterparts dynamically during runtime of the system. 4.1 Supply chain networks modeled with agents Information agents linked to distributed databases can overtake parts of the information transfer partially automatically using semantically welldefined communication (cf. Figure 5). In our opinion nearly every demand chain can effectively be supported by using agent technologies to satisfy the customer requirements as early as possible. This takes also into account some kind of crash management if the requested customer’s specification must be modified due to delivery problems or changes of the specification.
figure 5:Agent-mediated information interchange. How do we define agents: an agent is a software entity that assist people and can act on their behalf (Maes, 1991). In order to do so it senses its environment and reacts to changes. Currently many projects to implement agent-based solutions in business process are running. Kirn (1995) e. g. describes how co-operating agents can support virtual organizations. Concerning this paper, we regard to those that deal especially with information, the so-called information agents. Each logistical subsystem has such a representative that is able to communicate with others on its own. Therefore, agents have knowledge about internal structures and organizations, are aware of other agents situated in the environment, and have a defined economic scope of representing its enterprise. Agents are using a special communication language, which is semantically well-defined, i.e. FIPA-ACL 2.0 (FIPA, 1998), or KQML (Knowledge Query and Manipulation Language, c.f. Bradshaw, 1997). Nowadays most of the companies have local and interconnected networks, so it is possible to communicate using this media, e.g. agents can use e-mails or virtual blackboards. An easy example of such a communication act is a database query with controlled authentication. Further tasks for agents can be filtering of incoming and outgoing information, database queries, and acknowledgement. These tasks can be pursued autonomously. The communication acts are enabling cooperation between agents and consequently business-to-business cooperation. Promising application fields are distributed production planning and control, transportation
routing and scheduling, supply chain optimization, and price negotiations. The management of an extended or virtual enterprise is difficult without efficient information flow support. But how can one achieve this? The production process within a distributed production network using agents can support online resource management due to agents’ real-time communication capability. Therefore, the person who initializes the respective demand chain (mostly an OEM) is able to check the availability of all necessary components in advance. The identification of the “state of production” is the underlying objective. The interaction with the customer and the respective resources managing agents could convince the customer to modify his specification in order to enable or speed up the production process. Here time to market should be the main driver for the customers’ needs. Another benefit of using agent technology to optimize the demand chain is characterized by the monitoring of the automated information flows between the involved demand chain partners. A first application area is the identification of the state of the progress of the respective product in the distributed assembly halls. This problem can be overcome by automated information flows. The respective agent sends a message that it just shipped the product and the receiving agent acknowledges i.e. if a bar-code scanner detects the incoming product. If an agent notices that the actual production state deviates from the production plans, it can alert the others or regulate by changing priorities. A very dynamic application area is the adjustment of customers’ modifications coming up during the production process. If a customer changes his requirement specification a huge amount of information that just concern the manufacturing of one product have to be carried around in the distributed network of production places. A customer could e.g. wish to change the interior decoration of his specified car. One can imagine that this is hard to manage with the use of normal types of communication, phone, fax, letters, which are too slow and expensive. But doing it automatically will cause a fast adjustment of the complete production process. Considering global distributed production processes we can find further benefits of agent technology in the easy information interchange even between different languages and cultural bargaining behavior as this is standardized and automated by them. Further agents are available 24-hours a day, seven days a week, so they can react to different working times of different continents without problems. As a last benefit of the approach security and efficient information flow should be discussed. Constraints have to be created in order to check e.g. if a requesting agent is allowed to get the requested information. One could think of both a routing and a filtering of information. If we consider information flows we think of e.g. product specific
data, available inventory or just an adjustment of delivery amounts one can easily imagine agents of both manufacturers and suppliers exchanging automatically information about the availability of respective parts. One of the fundamental problems in such a setting is the quality of the information, which is strongly related to the quality of information transfers in a distributed setting. As a matter of fact decision makers often have to face imprecise, incomplete, incorrect, and inconsistent information. This can be overcome by using agents that communicate nonambiguously, and fast. One major bottleneck of this technique is the missing standardization of the software engineering process, programming languages and collaboration behaviors. But upcoming standards like CORBA, XML, and JINI, designed to implement and support distributed systems are going to improve the situation drastically. The following case study from the automotive industry will show how such an agent-based information system could look like. 5 DEMAND CHAIN OPTIMISATION The strong worldwide competition and the highly specified customers’ requirements towards product quality, delivery time, and services forces the automotive industry to a permanent optimization of their production. In consequence this branch is strongly forced to consider the customers’ specifications. Nowadays the delivery time of a customer specified automobile could be still weeks or even months. It really depends if the customer would like to have extra equipment or not. At the store, for instance, such information are not available at all. What does that mean to the demand chain? The demand chain optimization example is presented more precisely in Figure 5 (more information in Eschenbächer, 1999). The demand chain management case study presented here is about the improvement of logistic supply chain between suppliers, carriers and plants. This refers very clearly to the descriptions of the previous chapter. 5.1 Reengineering of demand chain networks without agent technology Many companies are undergoing corporate-wide reengineering. A major element of these programs involved is rethinking of current business processes (Teng, Grover and Fiedler, 1991). The terms reengineering or business process reengineering (BPR) defined as a fundamental change to existing business processes are rapidly gaining attention from both business and academic communities. Because of the increase in customer requirements especially in service and transportation reengineering in the logistics is an important topic for industrial enterprises to stay competitive in the market. The discussion about
efficient supply chains illustrates this argument because the effectiveness of the logistic is responsible for the success of the enterprise (Fischer, 1997). In addition Fischer argues that the efficiency of the supply chain strongly depends on the respective product itself causing different chains. Best-class enterprises do have a positive understanding of reengineering (Osterloh and Frost, 1994). They change their business processes continuously in order to stay competitive in the market so that organizational change is a mandatory challenge for those companies. Therefore, companies have reengineered their supply chains in 1999 and were very successful. This example demonstrated possibilities in supply chain management but did not consider multiagent technology. Substantial improvements have been achieved but there was no semi-automation of business processes. In the next example we give a concrete example for the application of multiagent systems in supply chains. 5.2 Vision Every reengineering activity starts with a vision of the new process. The vision here is based on the integration of advanced information technology (AIT) in the logistic supply chain. The project partner planned to establish a new logistic network between supplier, carrier and plants (information about the network planning process. It can be described as a major demand chain management problem, because not only technology is changing. By this we look from the focus on the customer who thinks about weather or not taking leather seats. Furthermore, people will have to act in a network, which is quite different for all participating partners. Figure 7 summarizes the vision and business objectives for the change management project in the logistic. Reengineering of the logistic supply chain by introduction and use of AIT to optimise the delivery process by
• Application of telematic systems, – PDF-Barcode Scanning, – controlled pick-up of material,
• increased use of information systems, – internet or e-mail, – CSCW applications,
• re-definition of roles from forwarding agents, local carrier, truck driver and logistic employees.
figure 6: The vision. 5.3 Scenario The scenario refers to a logistic supply chain problem between 1st and 2nd tier suppliers, carriers and automobile manufacturers. These suppliers do not belong to the group of system suppliers, which can synthesize their deliveries to complete truckloads. Therefore, local carriers pick-up the material, which cannot be more than 1-5 pallets or containers per supplier, at various supplier plants
in order to collect the material in their own consolidation center. The local carrier has therefore two opportunities, which is first delivering the material directly to the end-customer (“milkrun-direct delivery”) and second delivery by using a consolidation center (CC). The achievement of a higher share of milk-run-deliveries can hence be considered as one main objective of this case study. The demand chain problem is presented more precisely in Figure 8. It refers directly to a cooperation problem between all three automobile manufacturers (plants), local carriers, and the supplier network. The reason for this cooperation problem is the missing information transparency during the transportation process between the three involved parties.
figure 7: Demand chain problem. 5.4 Solution by using agent technology In this example the demand chain information flow from manufacturers to suppliers is shown triggered by the demand of the customer. Suppliers, carriers and plants do have information agents who ensure an effective cooperation and can automatically exchange the requested information if available. The so-called supply chain pyramid (Handfield, Nichols, 1999) will be supported via communicating information agents having distributed databases. Some authors talk about flow management systems, which are described more precisely in Dornier et al, 1998. The main reason for this cooperation problem is the missing availability of information during the customer’s specification of the product. It is simply not possible to advice the customer to take rather a leather interior if the desired one has a long delivery time. These chains triggered by the customer’s order are demonstrated by an industrial case study showing the advantages of using intelligent agents.
Timm, 1999, by changing the information flows or in other words information flows partially substitute material flows. Before achieving this goal there is still a long way to go which includes more detailed analysis of extended enterprise networks and what kind of dynamic information infrastructure efficiently supports them. In the future, it will be necessary to launch research projects taking into account especially the requirements for the organizational and technical infrastructure of such an information flow management system.
Figure 8: Demand chain optimization case. In the above example the complete optimization case is sketched. Agents can be used at many places to automate mostly questions, which are always recurring. The following list illustrates the most important areas: •
Check up of inventory stocks at the manufacturers plant
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Availability suppliers
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Progress on production
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Truck loading
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Truck position
of
material
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different
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Information for the customer and the retailer Information agents representing companies this scenario are not only supporting the standard procedures and providing a higher transparency to the overall system. As a result of the ability of direct communication through agents the geographical distance between companies, which leads to high transaction costs, can be reduced in the way that a virtual neighborhood is established where needed information are available in realtime. Furthermore, agents can be used to realize information flows, which are not correlating with the structure of the demand network. E.g. this could enable customer to get information about availability of parts directly from the supplier or in the other direction a supplier can inform a customer about unexpected situations directly.
6 CONCLUSION AND FUTURE WORK As a result an easier adjustment of the production network driven by later customer specifications can be achieved through a more sophisticated information exchange. In the near future the organization of such dynamic demand chain networks will be a prerequisite to stay competitive in the market and increase ones influence. It will be possible to restructure demand chains or even the enterprises structure as proposed in Knirsch and
Demand chain management with support of multiagent technology is going to have an major impact on the European industry. Balou (1999) argues that the integration of new ICT solutions will play an essential role by re-designing supply chain networks. The overall development of the extended enterprise will provide an additional push for these developments. The case study presented here shows the importance of these elements for the European automotive industry. The involved parties are going to redefine their objectives because such supply chain reengineering projects put a co-operative approach in the forefront of European logistic managers attention. In the described case study was proved that the strive to an overall optima is the best solution for all involved parties. Nowadays one slight problem is still the ICT integration into the various system architectures. The integration process still takes a very long time, which is not satisfying the fast increasing industrial needs. Further research projects should focus on these ideas.
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