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Title of Paper: Integrating Business-alert Services with Supply Chain Decision Models

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Student Changxin Xu Academic Michael Ball, Satyandra K. Gupta, Edward Lin, Zhenying Zhao

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Integrating Business-alert Services with Supply Chain Decision Models

Michael Ball1, Satyandra K. Gupta2 , Edward Lin3, Changxin Xu2, Zhenying Zhao1

Institute for Systems Research University of Maryland

College Park, MD 20742

Abstract

Making business decisions quickly in response to dynamic market changes becomes essential activities in business communities. In today’s market, all kinds of unexpected events can occur. It is becoming a challenging task for managers to rapidly, effectively, and responsively response to these disturbances. In this paper, we describe an Internet-enable business-alert network that facilitates monitoring of business announcements relevant to supply chain decision-making process. We also present an implement of business alert services and a supply chain optimization model for analyzing impacts of price changes to supply chain planning.

Introduction Rapid response to customer needs for high product variety with competitive price is

becoming an increasingly important factor for ensuring success in the marketplace. Since 1980’s, opportunities for competitive advantage began shifting from inside manufacturing plant to relationships with suppliers and then to closer relationships with customers (Green 1998). Supply chain management provides capabilities to optimize the material and information flow and even restructure an organization or the entire chain. Advances in the area of supply chain management are accelerating as leading companies are trying to keep up with the demands of today's customers.

With the advent of new technologies, such as the Internet, the marketplace is additionally turning into a global village. Manufactures find themselves exposed not only to domestic but also to international competitions. In order to gain a competitive edge the participants in a supply chain have to work together and to integrate their trading partners' business practice knowledge into their own business applications quickly and painlessly (Dabbiere 1999).

Supply chains are highly complex structures and all of the activities that take place in a supply chain must be coordinated and planed. However, today’s market is not a static object. All

1 Also with the Robert H. Smith School of Business, University of Maryland, College Park 2 Also with the Department of Mechanical Engineering, University of Maryland, College Park 3 To whom correspondence should be addressed, lin@isr.umd.edu

kinds of disturbances can occur. Whenever such an altering and influential event arises, managers must evaluate their performance, analyze their long and short-term decisions, and counter react with fast and effective solution. The kind of events include, for example, a supplier not being able to deliver the ordered quantity due to an earthquake, or a promotion from a supplier. (Angerhofer and Angelides 2000) pointed out that “Rapid, effective and efficient response to changes in the market is one of the main challenges in modern supply chains.”

In a supply chain, if a participant is alerted with latest market information, the company may change its supply chain plan accordingly to incorporate these events. As a result, this might give substantial benefits to the participant, such as improvements in delivery performance or reductions in its cost. However, changing a supply chain plan will also incur substantial costs. To decide whether the benefits outweigh the costs sufficiently to warrant making modifications, the participant may need to obtain and analyze information about many different business announcements. In current practice, there are several significant problems with obtaining and analyzing that information:

• Business consumers receive too many business announcements to evaluate them adequately. Supply china partners may receive many business announcements each year, primarily through trade magazines and trade show. Since several people may need to study each business announcement to examine its potential impact, the total amount of time needed for this task over the course of a year may add up to many weeks of effort.

• Evaluating and assimilating business announcements may require reasoning about information from semantically heterogeneous sources (i.e., different component suppliers). The ability to share such information is often hindered because of differing concepts, terminology, and assumptions about the world. Often, the loosely defined natural-language definitions associated with this information will be too ambiguous to resolve the differences.

• Existing search facilities are not adequate. Existing search facilities include general-purpose search engines (Yahoo, Lycos, Infoseek, etc.) and database query engines (at component suppliers’ sites).

We believe that developing and combining new techniques for information representation and decision-making can overcome the above problems. In particular, we envision the development of Internet-enabled business-alert network that will be used by supply chain participants to communicate information about business announcements relevant to supply chain decision-making. Participants will be able to post information about new business announcements, and users of these business announcements will be able to share their experiences in using them. Participants will have sophisticated decision model to help them process and assimilate the information resulting from these interactions and exchanges. In this scenario,

• Participants will make business announcements accessible through their web interfaces in order to facilitate the use of this information within a supply chain;

• Participants will be able to create and deploy programs that will monitor postings about business, to search for information that might be relevant or useful;

• To help screen and assimilate the information resulting from these interactions and exchanges, participants will have sophisticated decision models that capture the information about business decisions needed to evaluate what impact an business might have on the supply chain.

In this paper, we describe our approach for constructing Internet-enabled business alert services. We discuss two notification models for information sharing. We present our development of a business scout agent system for monitoring computer products to demonstrate our approach. We also describe a mathematical optimization model for analyzing price and capacity in a supply chain.

This paper is organized as follows. Section 2 describes Internet-enabled Business Alert Services. Section 3 presents our development of a web-based business-alert service. Section 4 introduces a supply chain decision model for a notebook computer company. Section 5 summarizes our research work and directions for further study.

Internet-enabled Business Alert Services In order to facilitate evaluating what impact a business announcement might have on a

supply chain, decision models should be constructed in order to represent the supply chain options that might potentially be used in supply chain plans, and the decision-making criteria needed to decide which of those options may be feasible or preferable. From the decision model, supply chain planner can identify what changes of decision variables may affect supply chain plan decisions and what decision variables are likely to changed by business announcements.

Business announcements provide an opportunity to improve supply chain performance. However, a large number of announcements are announced every year. Monitoring them manually is very time consuming. Internet-based business alert services facilitate the monitoring of business announcements with desired changes of decision variables. A business request specifies the condition or constraints that a business announcement may become attractive for incorporation into a supply chain plan. In order to develop a successful business alert service, we need to consider three issues.

• How to properly represent a business monitoring request that contain numerical/symbolic values, constraints, and business alternatives?

• How is business information delivered with proper format?

• How to determine whether a business is worthwhile to re-evaluate the design options? In other words, does a business announcement meet the thresholds of decision variables?

In the following section, first, we describe how supply chain participants may use this Internet-enable business-alert network. Secondly, we discuss two notification models for sharing business information. Finally, we describe our representation of business-alert requests.

Business-Alert Workflow

Supplier Distributor

Events

Manufacturer

EEvveenntt

ERP and SCM Decision Models

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Business-Alert Services Cycle Time

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Figure 1 An Internet-enabled Business Alert Services

The Internet-enabled business-alert services facilitate the monitoring of business announcements for supply chain partners/participants. In this framework, participants will make business announcements available to be accessed through their web interfaces. For example, a supplier may publish its price changes like price discount for a promotion, or supply location changes like new warehouse. A participant requests a business alert service by specifying the condition or constraints that a business announcement may become attractive for incorporating into a supply chain planning decision. The business-alert services provider notifies the participant once there is a change. By using these information, supply chain optimization models considering various constraints like production status, transportation, inventory, tax and sales, may enable new decisions to adjust a participant’s procurement plan, delivery plan and/or production schedule over the supply chain, to increase total profit and provide better customer service. Figure 1 illustrates this framework.

Business-alert Message Models

Traditionally, business announcements are posted on participants’ web pages. The Internet-enabled business-alert services provider needs to retrieval business announcements from traditional web pages and extract relevant information and notify requestors if necessary. On the other hand, relatively recent advent of web services technologies, such as XML, Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), provide a way to simplify the integration of a large number of participants in an increasing dynamic distributed supply chain. We envision that these technologies will help to evolve our thinking about how the partners may work together to share and deliver information.

In the following, we describe two models that will probably be implemented for business announcements retrieval.

A Pull Model

In this model, the business-alert services search web pages of participant’s web sites, filter out retrieved information, compare them with business alert requests, and notify requestor if there is a match. With the huge amount of information available online, the web information extraction has been a popular research area. Several terminologies, such as web mining, information retrieval (IR) and information extraction (IE), are commonly used in this research area.

Based on our observations, most of web pages for business announcements are in structured format or semi-structured format. There is no difficulty in retrieving information from structured web pages. For semi-structured web document, wrapper techniques are mostly used. However, for number of web pages, efficiency is becoming a significant problem. For the web resources, there may be a large number of wrappers. An automatic or semi-automatic way to generate wrapper is needed. (Sahuguet and Azayant 1999) built W4F toolkit to help building wrappers that translate HTML pages into XML. This toolkit provides a visual wizard for user to define web source, extraction rules and test the wrapper. Myllymaki developed another approach that uses reference point (anchor) in the HTML page instead of extraction rule in the case of extracting small amount of information from large source (Myllymaki 2001). This approach can be quickly implemented. (Gupta et. al. 2002) developed a language to describe layout of business information in a web page. They also developed a way to reason and map retrieved information to XML data model.

A Push Model

In this model, the business-alert services provider subscribes its interests of business announcements through web interfaces of supply chain participants. This step can be achieved through traditional web interface or web services interface. The participant web server will deliver interested announcement to the alert services provider whenever there is a change, update, or new announcement.

Several web services standards are published, proposed, and being developed (Oasis). In addition to these efforts, in order to share business announcements and business requests, two organizations must agree to a common set of assumptions that provide a semantic basis for their descriptions. The specifications of such common assumptions have become known as domain ontologies (Gruber 1993).

Representation of Business Alert Request

In order to facilitate evaluating what impact a business announcement may have on a supply chain, we need to represent business announcement alternatives that may potentially be used in designs. A business announcement can be specified by a finite set of business specification items (attributes). Thus, a set of business alternatives can be represented as follows:

• Attributes represent product specifications or manufacturing process specifications. Each specification item is represented as an attribute.

• Alternatives represent possible values of an attribute. Values may contain numeric and/or symbolic values. Numeric values may have discrete and continuous values.

• Constraints represent constraints between attributes or constraints applicable to all attributes.

AND/OR tree can be used to represent a set of business alternatives. OR nodes represent attributes, whereas AND nodes represent alternatives of an attribute, children of the OR node. However, AND/OR tree doesn’t efficiently represent continuous variables. Representing every continuous value as an AND node is not a sound approach. Thus, we have extended basic

AND/OR tree and represent continuous values as constraints associated with attribute nodes. For example, an attribute has continuous values ranging from 20 to 30. These values can be represented by “20≤value≤30”. This expression can then be further decomposed into the lower and upper limits and an operator pair (“≤&≤”). An operator pair (“<&<” or “<&≤” or “≤&<” or “≤&≤”) is specified as a constraint of the attribute while the lower and upper limits are regarded as the children of the attribute node. In addition, complex relationships between attribute nodes can be easily represented in a hierarchical structure using AND and OR nodes. A dependent relationship between attributes is represented as a constraint to the common node of the attributes. An extended AND/OR tree is shown in the Figure 2.

A business request specifies the conditions under which a business might have an impact on a design. Thus, a typical business request has the following parts:

• Alert conditions specify the nature of the business being sought.

• Monitoring preferences include monitoring start time, end time and frequency by which the monitoring job is executed. Monitoring frequency may also be assigned by the system in order to sample the web as few times as possible, without loss of data.

We also use the above extended AND/OR tree to represent business alert requests. Alert conditions are specified as constraints of attribute nodes.

A Business-Alert Service – Pull Model A web-based business alert service is a web-enabled application that assists supply chain

participants to efficiently monitor business announcements posted on the Internet, effectively process business announcements that are relevant, and alert users with potential announcements

Root OR Node

OR Node

Business Requirements

Attribute

Value Option 1

Decomposition Option 1

Decomposition Option n

Attribute Attribute

Value Option 1

Value Option 1

Decomposition Option 2

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Leaf Node (AND Node)

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Value Option 1

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Value Option 2

…

Leaf Node (AND Node)

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AND Node

AND Node

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Figure 2 An Extended AND/OR Tree

that may benefit participants. In the following sections, we first present general system architecture of web business alert services. Then, we discuss important design issues.

A web based-business alert service requires performing three operations. It allows participants to specify business-alert requests through web browser. The system manages these requests in data warehouse. It deploys information scouts to search and filter relevant business announcements posted on the Internet. Business announcements that meet alert conditions are forwarded to requestors. Therefore, a web-based business-alert service generally contains three essential modules:

• Web-based Interface: allows users to enter business alert requests. Each request may contain business requirements (including business alert conditions) and monitoring preferences (monitoring time or frequency, and notification method). This interface program analyzes business requests, transforms requests to appropriate representations, and stores them in persistent storage.

• Data warehouse: stores business alert requests and user profiles. It also stores retrieved business information if necessary

• Intelligent control center: is the central part of a business alert service. It monitors business providers’ web contents at the time and frequency specified by requestors, extracts information from web contents, filters out information based on user’s business alert conditions, and notifies requestors if necessary.

Figure 3 describes the relationship of these modules.

Control center controls the execution of monitoring, extracting, and filtering of business announcements. Once a requestor submits a business alert request, the system stores the business alert request in persistent storage. The system scheduler establishes first monitoring event in the system future event list. Each element in the list is an event that is to be triggered in the future. Each request only has one instance of the request in the future event list. When an event time is up, the system scheduler removes the event from the future event list. Since it’s possible that several events are triggered at the same time, the removed events are actually stored in another current events list. An information scout agent is generated for each event in the current event list. Since each event keeps a reference to the corresponding request in the database, the agent

Partner

Data Warehouse

Web document

Control Center

Notification

Web based Interface

Request Request

request, information

Extracted information

can retrieve the request and business providers’ information from the database and generate a wrapper for each website. After the web document is retrieved, the agent verifies each business announcement if it satisfies the conditions of the business alert request. The system notifies the requestor with matched business announcements. A framework of the control center is shown in Figure 4.

A Price and Capacity Analysis Supply Chain Optimization Decision Model

An industrial notebook (PC) global supply chain shown in Figure 5, which comprises four final assembly and testing (FAT) factories and six sales subsidiaries, including locations in Japan, The Philippines, United States and Germany, will be used for this demonstration. The focal company buys PC components, including CPU, hard disk drive, keyboard, LCD, CD-ROM, and DVD-ROM, directly from suppliers, and buys the motherboard subassembly from subassembly factories; these are shipped to the FAT factories for assembly, and finished PCs are finally shipped to the different sales subsidiaries. The motherboard components are bought directly from the subassembly factories of the suppliers, which are the locations to monitor in our business scenario alert system. The supplier price and maximum supply availability may be changed. There are over 3,500 different product models offered across the different sales subsidiaries.

Wrapper CScout Agent C

Scout Agent Scout Agent A

Run-Time Obj

Index of a new request

Current Event CCurrent Event

Current Event A

Current Events i

Future Events List

New future event

Event trigger by timeNew current

event

Wrapper BWrapper A

Stored Innovations

Scheduler

Wrapper CScout Agent C

Scout Agent Scout Agent A

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Figure 4: Structure of Control Center

Current Event CCurrent Event

Current Event A

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event

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Stored Innovations

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SupplierSingapore

SupplierJapan

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Germany FAT

SalesAustralia

SaleCanada

SalesSingapore

SalesJapan

SalesUSA

SubsidiariesIn Europe

Customer

Customer

Customer

Customer

Customer

Customer

SupplierGerman

Suppliers FAT Sales

Figure 5 A Simplified Supply Chain of an Industrial Notebook Manufacturer

Our supply chain model spans a time horizon of 13 weeks. Unmet demand for a sales subsidiary at any period cannot be backordered; consequently committed quantity is equal or lower than demand. Except the motherboard suppliers, the other suppliers are not considered in this model and thus we ignore holding costs at the suppliers.

This model takes a closer look at the short-term - operational - decision-making processes a manager has to make when faced with unforeseen events occurring and influencing the efficiency of the supply chain. The integrated supply chain model is a multi-period, multi-echelon and multi-product system that can be formulated as an MIP model (integer variables are necessary because of minimum lot size requirements). The objective function maximizes a weighted combination of tangible profit––revenue from promised orders minus transportation, production, duty, component, and inventory costs––and a penalty for under–utilization of capacity. Constraints include: demand commitment and fill rate constraints, inventory balance constraints at factories and sales subsidiaries, minimum lot size, and production and transportation capacity constraints.

Objective function:

Maximize profit

Subject to: 1. Demand commitment 2. Minimum fill rate constraints 3. Inventory balance constraints at sales subsidiaries 4. Flow conservation constraints at factories 5. Inventory balance constraints for components at factories 6. Flow conservation constraints at supplier

7. Inventory balance constraints for raw material at sub-assembly factories 8. Minimum lot size constraints at factories 9. Production and transportation capacity constraints 10. Cost and profit calculation constraints 11. Integrality and Non-negativity

Note that inventory balance constraints (5) and (7) ensures inventory balancing at each factory and subassembly factory, respectively, based on BOM relationships, with initial inventory specified in constraint; factories don’t hold finished product inventory, only sales subsidiaries do, the penalty variables in the objective function are zero if production is above the minimum lot size requirements. We have implemented this model using CPELX MIP solver. We plan to perform experiments to study impacts of component prices and available quantities to this supply chain planning decision model.

Summary With rapid change in information technology, the Internet has become the electronic

communication channel for business consumers and providers to share information. In this new era, supply chain participants require better tools to improve their supply chain performance in a timely manner. We envision the development of Internet-enabled business alert services that will be used to communicate and analyze business information relevant to supply chain planning. In this paper, we discuss the required infrastructure and issues related to the design of the Internet-enabled business alert services. We also describe a pull-model implementation of business alert services. A supply chain short term planning model is also presented. Integration of the business alert service and the optimization model is being developed.

References [Angerhofer and Angelides 2000] Angerhofer, Bernhard and Angelides, Marios, “System

dynamics modeling in supply chain management: Research review”, Proceedings of the 2000 Winter Simulation Conference, pages 342-351, on www.informs-cs.org /wsc00papers/049.PDF

[Dabbiere 1999] Dabbiere, Alan: “Business Process and Supply Chain Synchronization” in White Papers, Montgomery Research Inc., on http://www.ascet.com/ascet/wp/wpDabbiere.html, 1999

[Green 1998] Ken Green, Jr. (1998): “Supply Chain Management: Literature review”, on http://www.hsu.edu/faculty/greenk/scm.htm

[Gruber 1993] Gruber, T.R., “Toward Principles of the Design of Ontologies Used for Knowledge Sharing.” In International Workshop on Formal Ontology in Conceptual Analysis and Knowledge Representation, Padova, Italy, 1993. Also available as Report KSL 93-04, Knowledge Systems Laboratory, Stanford University.

[Gupta et. al. 2002] Gupta S.K., Lin, E., Lo, A., and Xu, C., “Web-based Innovation-Alert Services to Support Product Design Evolution.” DETC2002/CIE-34462, Proceedings of DETC’02 ASME 2002 Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada, Sept. 29 – Oct. 2, 2002.

[Myllymaki 2001] Myllymaki, J., “Effective Web Data Extraction with Standard XML Technologies.” In WWW10, May 2-5, 2001, Hong Kong. ACM 1-58113-348-0/01/0005.

[Oasis] OASIS, http://www.oasis-open.org [Sahuguet and Azavant 1999] Sahuguet, A. and Azavant F., “Building light-weight wrappers for

legacy web datasources using w4f.” In International Conference on Very Large Databases (VLDB) (1999), pages 738--741, 1999.

[Trichur 1999] Trichur, V.S., “Integer Programming Models for Product Design.” PhD Dissertation, University of Maryland in College Park, 1999.

Biography Michael Ball is the Orkand Professor of Management Science in the Robert H. Smith School of Business at the University of Maryland. He also holds a joint appointment within the Institute for Systems Research in the Clark School of Engineering. He is Director of Research for the Smith School and is co-Director of NEXTOR, the National Center of Excellence for Aviation Operations Research. Dr. Ball received his PhD in Operations Research in 1977 from Cornell University. His research interests are in network optimization and integer programming particularly as applied to problems in supply chain management, transportation systems and manufacturing. Satyandra Gupta is an Associate Professor in Mechanical Engineering Department and the Institute for Systems Research at the University of Maryland. Dr. Gupta has authored or co-authored more than eighty articles. He has organized and served in several conference sessions as Program Co-Chair, Papers Chair, Exhibit Chair and Program Chair. He has received a Best Paper Award in 1994 ASME International Conference on Computers in Engineering, Best Paper Award in 1999 ASME Design for Manufacturing Conference, Young Investigator Award from Office of Naval Research in 2000, Robert W. Galvin Outstanding Young Manufacturing Engineer Award from Society of Manufacturing Engineers in 2001, CAREER Award from National Science Foundation in 2001, Outstanding Systems Engineering Faculty Award from Institute for Systems Research in 2001, and Presidential Early Career Award for Scientists an Engineers (PECASE) in 2001.. Edward Lin is a research engineer and the manager of computer integrated manufacturing laboratory in the Institute for Systems Research at the University of Maryland. He received his BS in mechanical engineering and an MS in automatic control engineering from Feng Chia University in Taiwan. He received an MS in operation research and a PhD in industrial and systems engineering from the Georgia Institute of Technology. His research interests include distributed manufacturing systems, Internet-based applications, product design and process planning, production/scheduling, simulation, and data mining applications. Changxin Xu is a research assistant in Mechanical Engineering Department and the Institute for Systems Research at the University of Maryland. He received his MS and BS from Tsinghua University in 2000 and 1995. He is now working with Dr. S.K.Gupta in the University of Maryland for Ph.D. His research interests are in developing theoretical foundations for next generation of CAD/CAM systems. Zhenying Zhao is an Associate Research Scientist in R. H. Smith School of Business at the University of Maryland. His research interests are in the areas of mathematical modeling and optimization, supply chain management, computer simulation, production and operations management and supply chain management systems and enterprise resource planning (ERP) systems. He had worked with Toshiba, Maxtor and Compaq on business process improvement, available-to-promise decision support and supply chain management. He received his PhD degree in Supply Chain Management from Nanyang Technological University, Singapore.