Developing B2B Forums Using Semantic Web Technology S. Henninger, S. Swenseth, M. Keshk, P. Ashokkumar, Z. Chen, M. Nabavi, S. Modali Dept. of Computer Science & Engineering and Dept. of Management University of Nebraska Lincoln, Lincoln, NE 68588
[email protected] Abstract Semantic Web technologies have been widely touted as the next generation of intelligent Webbased knowledge repositories. While many have discussed the potential merits of using Semantic Web to improve Business-to-Business (B2B) transactions, few demonstrate an overall approach that meets the potential of this technology. In this paper, we present a B2B architecture using Semantic Web technologies that allows for a form of business relationship we have termed “Opportunistic commerce”. Semantic Web technology provides the intelligence to achieve the level of real-time information, knowledge, and collaboration in B2B transactions and alleviates the existing difficulties of demand forecasting, providing more opportunities for businesses to find the right markets when they need it. The trucking industry domain is used to demonstrate how this technology finds business relationships that produce advantages for all parties in a manner that would be difficult to represent and process using other technologies, such as XML or EDI. 1. Introduction Current Semantic Web research has focused on language semantics [Fensel et al. 2001; Klein 2001], ontology construction [Holsapple, Joshi 2002; Kim 2002; Maedche, Staab 2001], query languages [Jena 2002], agent support [Hendler 2001], Web-based reasoning [Horrocks, Sattler 2001] and the overall Semantic Web vision and architecture [Berners-Lee et al. 2001]. Few to date have out these pieces together to create systems that take advantage of these features in a manner that changes the nature of web-based applications, such as business-to-business (B2B). Some have created pieces. [Trastour et al. 2002]. In the area of supply chain management current approaches have yet to reach the potential that Internet-based solutions have to offer. In spite of the many electronic business-to-business (B2B) technologies being employed, studies have shown that supply chain problems can cause companies to lose between 9% and 20% of their value in a six-month period. The current staple of Business-to-Business e-Commerce is Electronic Data Interchange (EDI) [R. J. Glushko 1999, R. Pyle1996], which uses a pre-defined computer-to-computer exchange of business documents in a structured and standardized format. EDI is expensive, brittle (cannot be extended easily), and complex [R. J. Glushko 1999]. Also, the proprietary nature of the technology severely inhibits distributed decision-making. The parties involved must design all potential transactions in advance, allowing very little distributed decision-making. In many ways, this describes the current state of Collaborative Commerce [J. Herman2002, B. Welty2001].
1
Web Services is a standard platform for building interoperable distributed applications [E. Newcomer2002]. Using platform-neutral standards such as XML, XSD, SOAP, WSDL, UDDI and others, a Web service usually exposes a Web-accessible API that can invoke this service programmatically over the Web without regarding cross-platform and languages issues. In terms of distributed decision-making, the open standards give Web Services a distinct advantage over proprietary EDI technologies. The data format for Web Services, XML, is often touted as the replacement for EDI’s proprietary formats [R. J. Glushko 1999]. Being an open standard, and one that allows the flexible creation of document sections (or attributes) through its metalanguage. Distributed decision-making using XML is supported as long as the standard XML schema is static. But in today’s fast-changing business milieu, this is highly improbable, and XML schemas will need to evolve, creating pressure for increasingly complex agent technology to address the shortcomings of this static data technology. It is claimed that in e-commerce communities, the Semantic Web can be used as a model for specifying a highly expressive and open language that can be used not only in advertisements, but also covering all relevant business activities (manufacturing, transportation, purchasing, etc.), of supply chain model - all in a uniform way [D. Trastour2002]. The Semantic Web is intended to facilitate a full automation of Web service tasks including automated Web service discovery, execution, interoperation, composition and execution monitoring. But, this kind of evolutional automation is has yet to be fully outlined in any form of usable detail [W. Nejdl2002]. In this paper, we present a B2B architecture using Semantic Web technologies that allows for a form of business relationship we have termed “Opportunistic commerce.” “Opportunistic commerce,” means the technology supporting dynamic business transactions based on real-time data. Businesses are allowed to create relationships on the fly based on their individual need. This brings out a kind of distributed decision-making, or distributed cognition [J. Hollan 2000, E. Hutchins 1995] in which while no one entity understands all elements of the system, the system is able to operate at a level meeting or exceeding current business techniques, such as collaborative commerce. Here we make an effort to show that large-scale semantic web based information technology provides a domain for decision-making where individuals act in their own self-interests (OC). This will lead to better efficiencies than the creation of static 1:1 relationships (CC), provided by other technology alternatives like EDI and XML. The trucking industry domain is used to demonstrate how this technology finds business relationships that produce advantages for all parties in a manner that would be difficult to represent and process using other technologies, such as XML or EDI. 2. Supply Chain Management Implications The term supply chain refers to all the activities involved in supplying an end user with a product or service, from producing raw materials to the final product or service and also the operations that support these activities, such as financing, accounting, design, transportation, marketing, etc. There are not just goods that flow along the chain; this flow includes information, funds, people, and other such items that flow in both directions of a supply chain. Supply chain management coordinates and integrates all the supply chain activities into a seamless process and links all of the partners in the chain, including departments within an organization as well as the external 2
suppliers, carriers, third-party companies, and information system providers [Walker and Alber 1999]. Flow of information along the supply chain however, is not usually smooth. One of the problems in information flow in supply chain is about demand forecasting which is not trivial for retailers, wholesalers, and manufacturers. Products and services that have an uncertain, variant market demand cause a distorted demand forecast up the supply chain, known as bullwhip effect. The bullwhip effect refers to the phenomenon where orders to the supplier tend to have larger variance than sales to the buyer, and the distortion propagates upstream in an amplified form [Lee, Padmanabhan, and Whang, 1997]. The implications of bullwhip effect include inventory buildups or service providing capabilities with no demand for them. Solutions to alleviate this demand distortion emphasize the role of speed in information exchange and decision making in supply chain. Swaminathan [Swaminathan 1998] proposed a multi agent framework, where all parties in a supply chain communicate through their agents to speed up the communications and reduce lead times. Cao and Siau [Cao and Siau, 2000] proposed the multi-agent supply chain framework (parallel decision-making) to replace the traditional sequential supply chain management. Chen et al. [Chen, Drezner, Ryan, and Simchi-Levi, 2000] quantified the bullwhip effect, considering two factors that are commonly assumed to be the cause of the bullwhip effect demand forecasting and lead-times. Kumar et al. [S.Kumar, C.Chandra, A.V.Smirnov, 2002] explain the characteristics of an effective supply chain network (SCN) as: 1. Increased connectivity between its units 2. Alignment of its inter-organizational support systems 3. Sharing of information resources among its units One of the common ideas among all solutions for alleviating bullwhip effect is increasing the speed of information interchange and designing logistics and operational strategies that reduce to lead times, direct purchasing and logistics outsourcing. There are a number of solutions for supporting supply chain operations and increasing the speed of information exchange through electronic commerce, such as electronic data interchange, enterprise systems, using web services, and the Semantic Web. 3. System Architecture Figure 1 shows an overview of
the system architecture, which is based on a peer-to-peer model [Nejdl et al. 2002] in which a Forum supplies a set of URLs to organizations that can then communicate directly, peer-topeer. The dark, thicker lines in the Figure refer to information supplied to Forum members, including ontologies, member URLs, and other third-party data. The thinner blue lines represent data passed during
3
B2B transactions between the companies. The architecture in Figure 2 shows that each organization has three common kinds of components that facilitate B2B transactions. The SWD (Semantic Web Document)1 contains real-time data that is accessed by other companies through the peer-to-peer network. This data is processed by the B2B agent used by the organization, which may be a third-party solution provided by a vendor, such as the Forum, or an in-house solution using standards set by the Forums. Information found or decisions made by the agent that match business needs are then displayed in a user interface. Elements of this overall architecture are further explained in the following sections. 3.1 Forums The role of a Forum in our architecture is to serve as a third-party entity that provides services to member organizations that facilitate B2B transactions. The forums are independent organizations that organizations subscribe to for their services. Organizations can belong to as many Forums as desired, depending on the kinds of ontologies, services, and etc. provided by the Forum. Services provided by the Forum include: 1) A list of URLs for the SWDs of authenticated companies participating in B2B transactions. This also implies that the Forum entity provides the authentication services necessary to ensure that only trustworthy organizations are included. 2) Third-party performance data. In our example shipping domain, these would include data such as on-time delivery performance, payment records, security policies (organizationspecific security policies to ensure data does not fall into competitors hands, yet are available to potential customers), and other attributes of organizations that need to be collected by a third party to ensure accuracy. 3) Software ontologies, agent templates, Reasoners, and other tools needed by participants to engage in B2B transactions. Our working assumption is that, for example, independent truckers or some IT organizations in larger trucking firms, lack the resources to develop their own Reasoners, query engines, and other technologies necessary to participate in this approach. These would most likely be supplied by third-party vendors such as, the entity developing the Forum. This structure is not uncommon in other industries like RosettaNet [RosettaNet. 2002]. Other parts of this architecture also have natural ties to a third party. For instance, agreement on the ontology structure is critical to the success of a Semantic Web application. Having a third party to administer the base ontology and to make it available to participants assures ontology agreement, and organizations can use that base ontology to create Reasoner rules that specialize the ontology for the organization’s needs. In addition, to propagate necessary ontology refinement and evolution, Forums provide a venue for extending and modifying the ontology.
1
The term “Semantic Web Document,” or SWD is used to signify that while current standards, such as the Resource Description Framework (RDF) [Klein 2001} are currently the most widespread Semantic Web format used, the standards are current in a great deal of flux. Our intention is to support these emerging standards, such as the Defense Agency Markup Language (DAML) [Burke 2003], and the Ontology Language (OWL) [Costello, Jacobs, 2003; McGuinness, van Harmelen 2002] as they become available, and their use becomes widespread.
4
Performance data and trust are also a critical element of B2B transactions that require the
Figure 2: Opportunistic Commerce Process from a Carrier’s Perspective.
involvement of a third party.
3.2 A B2B Process Using Semantic Web Technologies Figure 2 shows a system behavior diagram from the perspective of an example carrier or carrier organization. The figure shows that the process is a cycle in which real-time data is made available through Semantic Web Documents (SWDs) updated locally by the organization. The overall cycle has the following main steps: 1) A user submits settings, either for a recurring B2B transaction or a single transaction. This request is converted to a Semantic Web query. 2) A query engine, such as Jena for RDF [Jena 2003], uses the URLs provided by the Forum(s) to search SWDs at organizations to find instances meeting query criteria. Note that the realtime data is held at the participating organization’s Web server, not the Forum, which only needs to be consulted to periodically update the list of URLS. 3) The resulting knowledge base, consisting of real-time data matching search criteria, in SWD format, such as RDF, are fed to a Reasoner, such as the Java Theorem Prover (JTP) [Frank 1999], which uses a rule base to infer new facts and relationships between SWD instances. 5
4) Matching instances and facts found by the query engine and refined by the Reasoner, are fed to a GUI translator for selection by user and/or to inform the user of automated decisions that have been made by the system. 5) Given the new facts derived by the Reasoner, and potential reformulated queries by the user, the query is modified and the query is re-dispatched. The process is then repeated from step 2. 3.3
Opportunistic Commerce
The most significant part of this process is that it is not a simple one-time search, but a continuous search that is repeated with new, real-time, data. This allows a kind of market-based, opportunistic, behavior in which users can construct rules or monitor their queries to maximize their value. For example, a truck driver with a half-full load with a know route in a week from Denver to Chicago can use the GUI interface to specify this need, resulting in both a query and an update to the organization’s Semantic Web Document (see Figure 2). Focusing on profit maximization for the moment (there may be many other factors, such as the type of load, etc.) the user can either set price requirements at a threshold where the system, acting as an agent, will agree to deliver a load, or the user can monitor the market for this transaction, and choose the best available offer. As the time to leave the pick-up point in Denver nears, the user may lower the asking price to see if there are any entities willing to accept the new offer. Again, this can be done manually through the GUI, or by selecting agent options that modify price criteria over time. 3.4 Unique features of this architecture The uniqueness of this architecture lies firstly in the fact that it tries to bring together existing semantic web technologies to form an overall approach to improving B2B communications. Secondly, an open market form of negotiation takes place, which is different from direct agent-toagent negotiation processes suggested in other architectures. For example, a trucking firm may create a strategy to price their half-full capacity high in the beginning. Shippers looking for low bids may therefore ignore some of the high bids. But as the date to ship the merchandise comes closer, the shipper may raise (through automatic settings) their price. Likewise, as the time to move the truck nears, the trucker will lower the price of shipping the merchandise. In this scenario, over time, a match is found merely through waiting. Other forms of indirect negotiation can also be built into the system that, for example, barters discounts on merchandise for lowering bids. 4.
Transport System Model
Shippers need to transport their products to their customers including wholesalers, distributors, retailers, or directly to the final consumer. Shippers in a supply chain are shipment requesters who could be a consignee, broker, agent, freight forwarder, consignor, etc. The cargo for shipment always has particular specifications including cargo class (type of cargo), weight and volume, specific methods of loading and unloading, and perhaps special treatments such as
6
controlled atmosphere. The Carrier, which is the transport service provider, offers this service based on the standard or negotiated rates that basically depend on the cargo class, weight and volume, and distance of shipment. The transport service hauls cargo between two locations, from an origin to a destination according to the schedule required by shipper. Different types of transport vehicles may be used for shipment, based on the weight and volume, the schedule, distance, and the cost. The transport vehicles have attributes that should match with the shipped cargo and shipper requirements. Carriers can bid for shipper requests and offer their rates and service, and shippers can select the carrier based on the offered rate and terms, besides the credibility of carriers including their performance records. Based on the explained description of the business entities we came up with a class diagram that represents the interrelationships among these entities. An extension of class “Truck” is presented in a later section to explain some reasoner examples. Then, based on the class diagram we developed an ontology representation for our business model as in the following figure.
Figure 3 Transport System Ontology
4.1 Query Engine A knowledge base of transport system is first modeled and created in the form of an ontology containing a set of classes, properties and instances interrelated to each other. The model results 7
in two files with RDF and RDFS extension names. The RDFS file represents the schema that defines the terms (vocabularies) used in RDF statements and provides a simple data type model for RDF in the form of classes and properties. RDF file represents the facts of the knowledge base in the form of instances that represent the actual data of transport system. Then, a query engine is built on top of that ontology to retrieve and extract grounded facts that are already declared in the ontology. The query engine uses a Java-based API called Jena and its query language RDQL [Jena]. The idea is to provide a data-oriented query model so it would be easy to use a declarative approach to retrieve and traverse RDF models. RDQL is used to retrieve RDF classes and properties, so it would be enough for the user to just place queries to the query engine to get at the information by traversing the RDF model. RDQL treats RDF as data model and provides query with triple patterns and constraints over a single RDF model. The query provides one way in which the programmer can write a more declarative statement of what is wanted, and have the system retrieve it. 4.1.1 Query scenario In a typical scenario, we assume that a Carrier has a number of trucks and among them three trucks moving along the following routes within the specified schedule: Truck1:
Denver Æ 02/15
Omaha Æ 02/17
Lincoln Æ 02/18
Kansas City 02/20
Æ
Minneapolis 02/23
Omaha Æ 02/18
Lincoln Æ 02/19
Chicago 02/24
Æ
New York City 02/29
Omaha Æ 02/19
Lincoln Æ 02/20
Detroit 02/25
Æ
New York City 02/30
Truck2:
Denver Æ 02/16 Truck3:
Denver Æ 02/17
A sample RDF representation of these paths looks like the following:
Now, the Carrier can send the following two queries to the query engine: SELECT ?x, ?truck WHERE (?x, , "Omaha"), (?x, , "02-18-03"), (?truck, , ?x) SELECT ?x, ?truck WHERE (?x, , "Chicago"), (?x, , "02-24-03"), (?truck, , ?x)
The above queries help to find the available truck to ship cargo from “Omaha” at “02-18-03” to “Chicago” on “02-24-03”. The Carrier comes back with a result that; only “Truck2” is the available one to ship that cargo. In general, we can build the query based on whatever criteria that fits the situation, such as: looking for a truck with specific weight_capacity and size to carry the specific cargo, within a desired range of shipment rate. 4.2 Reasoner One of the benefits of creating ontologies to represent formal semantics and descriptions for a Web resource in RDF/DAML is that interpretations and assumptions of the language elements can be identified, characterized, and validated. Reasoning over such descriptions will be essential if Web resources are to be more accessible to automated processes [Ian Horrocks 2002]. A knowledge representation and reasoning system is assumed to provide a number of reasoning 9
facilities that can be separated into two groups - terminological and instance reasoning. Throughout the ontology design phase and runtime phase, examples of reasoning functionalities were checked out with “Truck” sub-hierarchy as following:
Figure 4 Truck sub-Hierarchy
Consumption check is used to check if one class is a subclass of another; in this case, reasoner can check if class “Refrig” is a subclass of class “Covered” or not. Consistency check is used to check for inconsistent class definitions. In some situations, if the reasoner is asked to verify the class model, the subclass hierarchy could be replaced with a different hierarchy determined by the reasoner without changes made to the definitions of the classes. In our example, the reasoner might find that it is more consistent for class “Container” to be a subclass of “NonCovered” rather than being a subclass of “Covered”. Taxonomy construction computes all subclass relations, including those which are not explicitly stated but that are implied by the given definitions. In our example, reasoner can find that class “Refrig” is not only a subtype of class “Covered” but also a subtype of class “Trailer”. Realization finds the most specific class that describes a given description of an individual (instance), for example, reasoner can find the class “instance5” is a type of – in this case it is class “Refrig”. Instance checking is used to check whether or not the class describes a given description of an individual (instance); for example, reasoner can check if “instance1” is a type of class “Trailer” or not. Finally, Consumption Individual retrieval could be used to find all individuals (instances) that are described by a given concept. For example, reasoner can enumerate all instances that belong to a specific class such as “Trailer”. In this case, not only will “instance1” be a type of class “Trailer” but also “instance2”, “instance4” and “instance5” will be enumerated, even though they are not explicitly stated in the knowledge base as direct instances of class “Trailer”.
10
5.
A sub route Scenario
In the example shown below we have a Truck that is half-full, it’s full capacity is 300 units and currently filled with 200 units. It starts from Denver on 20th and arrives at Omaha on 23rd, the truck stops at Lincoln on the 22nd. There is another shipment request that needs to be sent to Omaha on 23rd from Lincoln on 22nd. Shipment Origin
Destination
Pick-up Date
Delivery Date Size shipment
Lincoln
Omaha
02-22-03
02-23-03
Truck Route From To Denver Omaha 02-20-03 02-23-03 Truck TruckNumber Capacity Truck_0001 300
of
100
Via Lincoln 02-22-03 CurrentCapacity Route TruckRoute 100
The goal is to increase the truck utility and the rules are as following: No. 1
Rules Time Range
2
Space Range
3
Capacity Range
4
Temperature Range
5
Truck Type Range
Specification The shipment’s time range should be inside the truck’s time range The route of the shipment should be a subroute of the truck. The size of the shipment should be under the truck’s current capacity. The temperature of the shipment should be inside the temperature range of the truck. The truck type of the shipment should be the same as the type of the truck.
Reasoner provides the interface shown in Figure 5 to apply the rules: In the example, we apply the three rules, Time Range, Space Range and capacity Range.
11
Figure 5 Reasoner User Interface
The result from the reasoner is: Shipment Truck-number Arrive-date Current-capacity Stop-date truck end via
|http://www.cse.unl.edu/~zchen/TruckExample#|::|Requirement| Truck_0001 23 100 22 |http://www.cse.unl.edu/~zchen/TruckExample#|::|Truck| Omaha Lincoln
Which means Truck_0001 can pickup the shipment request at Lincoln and deliver to Omaha in time. In the example above, the defined rules are applied by the reasoner onto the RDF information in the knowledge base, which is retrieved by the Query engine explained above. And the goal of increasing the truck utility is achieved obviously. The reasoner first inferences that the time range of the shipment request is inside the truck’s time range, and then it decides the route of the shipment is a sub-route of the truck’s route, when at last it finds that the size of the shipment is under the truck’s current capacity, it comes out with a result that the truck can pickup the shipment at Lincoln. The conclusion is that the reasoner can improve the efficiency of the truck and help to solve the full truckload problem in a very simple way. 6.
Conclusion
The system developed implements Semantic Web technology and provides the intelligence to achieve the level of speed, knowledge, and collaboration expected in B2B transactions. The ultimate goal of using semantic web in B2B is to allow any enterprise requiring a business interaction with another enterprise to automatically discover and select the appropriate optimal web services based on some constraints. This technology alleviates the existing difficulties of 12
demand forecasting, providing more opportunities for businesses to find the right markets when they need it. The system also promotes significant changes to the communicative structure of the supply chain, provides advancement, not only for business organizations, but also for development of supply networks. The entire way in which supply chains are developed and modeled will be affected. Future work will include a detailed study of the Forum and a generalization of the current system to include other industries. 7.
References
[Berners-Lee et al. 2001] Berners-Lee, T., Hendler, J., and Lassila, O., "The Semantic Web." Scientific American, May 2001, 2001. [B. Welty2001] B. Welty and I. Becerra-Fernandez, "Managing trust and commitment in collaborative supply chain relationships," Comm. of the ACM, 44(6), pp. 67 -73, 2001. [Booker, E. 1999] Booker, E. (1999). “Web to ERP – ERP Stage II: Outsiders Invited In.” Internet Week, October 25, (www.techweb.com) [Cao, Q and Siau 2000] Cao, Q. and Siau, K., “Modeling the Bullwhip Effect in Supply-chains Using Group Problem Solving Approach”, Information Resource ?Management, Vol. 13, 2000, pp.123-140 [Chen F Drezner Z., Ryan J. K., and Simchi-Levi D 2000]Chen F., Drezner Z., Ryan J. K., and Simchi-Levi D., “Quantifying the Bullwhip Effect in a Simple Supply-chain: The Impact of Forecasting, Lead times, and Information”, Management Science, Vol. 46, No. 3, March 2000, pp.436-443 [D. Trastour2002] D. Trastour, C. Bartolini, and C. Preist, "Semantic Web Support for Business-to-Business ECommerce Lifecycle," World-Wide Web 2002, Honolulu, HI, pp. 89-98, 2002. [E. Newcomer2002] E. Newcomer, Understanding Web Services: XML, WSDL, SOAP and UDDI: Addsion-Wesley, 2002. [E. Hutchins 1995] E. Hutchins, Cognition in the Wild. Cambridge, MA: The MIT Press, 1995. [Fensel et al. 2001] Fensel, D., van Harmelen, F., Horrocks, I., McGuinness, D., and Patel-Schneider, P., "OIL: An Ontology Infrastructure for the Semantic Web." IEEE Intelligent Systems, 16 (2), 2001, pp. 38-45. [Fisher, Raman, A., and McClelland, A. S 2000] Fisher, M., Raman, A., and McClelland, A. S., “Rocket Science Retailing is Almost Here, Are You Ready?” Harvard Business Review, July 01, 2000 [Fisher 1997] Fisher, M., “What is The Right Supply Chain for Your Product?” Harvard Business Review, March 01, 1997 [Hendler 2001] Hendler, J., "Agents and the Semantic Web." IEEE Intelligent Systems, 16 (2), 2001, pp. 30-37. [Holsapple, Joshi 2002] Holsapple, C.W., Joshi, K.D., "A Collaborative Approach to Ontology Design." Comm. of the ACM, 45 (2), 2002, pp. 42-47. [Horrocks, Sattler 2001] Horrocks, I., Sattler, U., "Ontology reasoning in the SHOQ(D) description logic." Proc. of the 17th Int. Joint Conf. on Artificial Intelligence (IJCAI-01), (2001), 2001, pp. 199-204. [Ian Horrocks 2002], Reasoning with expressive description logics: Theory and practice. In Andrei Voronkov, editor, Proc. of the 18th Int. Conf. on Automated Deduction (CADE-18), number 2392 in Lecture Notes in Artificial Intelligence, pages 1-15. Springer-Verlag, 2002.
13
[J. Hollan 2000]J. Hollan, E. Hutchins, and D. Kirsh, "Distributed Cognition: Toward a New Foundation for HumanComputer Interaction Research," ACM Transactions on Human-Computer Interaction, 7(2), pp. 174-196, 2000. [J. Herman2002] J. Herman, "Making Collaborative Commerce Happen," Business Communications Review, 32(10), pp. 18-20, 2002. [Jena 2002] HP Labs Semantic Web Research, http://www-uk.hpl.hp.com/people/bwm/rdf/jena/, 2002. [Kim 2002] Kim, H., "Predicting How Ontologies for the Semantic Web Will Evolve." Comm. of the ACM, 45 (2), 2002. [Klein 2001] Klein, M., "XML, RDF, and Relatives." IEEE Intelligent Systems, 15 (2), 2001, pp. 26-28. [Lee H L Padmanabhan, V. and Whang, S 1997] Lee, H, L., Padmanabhan, V. and Whang, S., “Information Distortion in Supply-Chain: The Bullwhip Effect”, Management Science, Vol. 43, No. 4, 1997, pp. 546-558 [Marc J. Schniederjans 2002] Marc J. Schniederjans , Qing Cao, e-commerce operations management, World Scientific, 2002 [Maedche, Staab 2001] Maedche, A., Staab, S., "Ontology Learning for the Semantic Web." IEEE Intelligent Systems, 16 (2), 2001, pp. 72-79. [R. J. Glushko 1999]R. J. Glushko, J. M. Tenenbaum, and B. Meltzer, "An XML Framework for Agent-based Ecommerce," Comm. of the ACM, 42(3), pp. 106-114, 1999. [R. Pyle1996] R. Pyle, "Electronic commerce and the Internet," Comm. of the ACM, 39(6), pp. 22 - 23, 1996. [S.Kumar] S.Kumar, C.Chandra, A.V.Smirnov, “E-management of supply chain: general models taxonomy, Human Systems Management 21 (2002) 95-113 [Swaminathan, J. M 1998] Swaminathan, J. M., “Modeling Supply Chain Dynamics: A Multi-agent Approach”, Decision Science, Vol. 29, No.3, 1998, pp.607-632 [Teresko, J, 1998] Teresko, J. (1998). “Replacing Inventory with IT.” Industry Week, April 6, (www.industryweek.com) [Trastour et al. 2002] Trastour, D., Bartolini, C., and Preist, C., "Semantic Web Support for Business-to-Business E Commerce Lifecycle." World-Wide Web 2002, (Honolulu, HI, 2002), 2002, pp. 89-98. [W. Nejdl2002] W. Nejdl, B. Wolf, C. Qu, S. Decker, M. Sintek, A. Naeve, M. Nilsson, M. Palmer, and T. Risch, "EDUTELLA: A P2P Networking Infrastructure Based on RDF," World Wide Web, Honolulu, HI, pp. 604-615, 2002. [Wagner, M, 1999] Wagner, M. (1999). “Next Step: Remove All Human Intervention.” Internet Week, October 25. [Walker, W. T and K.L. Alber 1999] Walker, W. T., and K.L. Alber. “Understanding Supply Chain Management.” APICS-The Performance Advantage, 9 (January 1999), pp.38-43
14
15
