I01-S01 Page 1. Jeffrey A. Joines (NC State, Textiles); Shu-Cherng Fang, Russell E. King, Henry L.W. Nuttle (NC State, Engineering)

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1 I01-S01 Page 1 Business-to-Business Collaboration in a Softgoods E-Supply Chain I01-S01 Jeffrey A. Joines (NC State, Textiles); Shu-Cherng Fang, Russell E. King, Henry L.W. Nuttle (NC State, Engineering) TEAM LEADER: Russell E. King WEB SITE: PROJECT GOALS Efficient and effective collaboration is critical for the success of the U.S. softgoods supply chain. The recent explosion of e-commerce technologies is providing the platform for easier communication and collaboration among the entities in the chain from fiber supplier to retail store. For a successful e-supply chain collaborative effort partner selection, contract negotiation, and dynamic pricing/cost sharing are essential issues requiring resolution. Successful resolution of such issues involves the simultaneous consideration of a number of possibly conflicting criteria including both quantitative factors such as cost and lead-time and vague qualitative factors such as quality, reliability and reputation. The goal of this project is to provide models and prototype tools to support collaborative efforts in the B2B environment. More specifically we are working to integrate new developments in Data Envelopment Analysis (DEA), cooperative game theory, and fuzzy mathematics to address not only quantitative but also qualitative data and decision criteria in selecting partners, structuring contracts, and sharing profit/cost. ABSTRACT The effort in this project to date has been directed to studying the state-of-the-art of Data Envelopment Analysis, cooperative games, and auctions and beginning to develop corresponding models which use fuzzy mathematics to capture vague and uncertain factors and demonstrate their potential for establishing collaboration and contracts in the increasingly global softgoods supply chain. I. BACKGROUND AND PROJECT OBJECTIVES With the recent advances in e-commerce technology, supply chain design and management is being rapidly drawn into the e-era. An Internet platform allows easier communication and collaboration among all business entities in the softgoods chain. A cross-industry subcommittee of VICS, which includes a number of participants from the softgoods industry, has been developing and documenting a process (protocol) for collaborative planning, forecasting, and replenishment (CPFR) between buyers and sellers in a supply chain. Software vendors such as Logility, Syncra Systems, i2 Technologies, and Manugistics have begun to provide proprietary solutions to support web-based collaboration. However, there is a need for scholarly research on fundamental issues such as building useful models and developing practical methods and strategies for all entities in the softgoods supply chain and with the results being broadly shared. The issues of partner selection, contract negotiation, and dynamic pricing and cost allocation have been identified as critical for success in CPFR implementations. Successful resolution of such issues involves the simultaneous consideration of a number of possibly conflicting measures including both quantitative factors such as cost and lead-time and vague qualitative factors such as quality, reliability and reputation.

2 I01-S01 Page 2 In many businesses performance data is collected and used in evaluating potential trading partners. Unfortunately, grading and comparing candidates based on this data is not straightforward owing to the presence of numerous and possibly conflicting evaluation criteria. For example, if one candidate outperforms all others according to one performance criterion but fails to achieve satisfactory levels on other criteria, comparison becomes difficult. The traditional approach is to assign a fixed weight to each criterion to form an aggregated, weighted score for each candidate. Usually weights are chosen to support specific business rules (i.e., weighting quality measures more heavily than financial measures to reflect their greater importance). Several problems can arise with the traditional approach, including: (1) Weights are subjective, difficult to agree on, and have a tremendous effect on the final scoring; (2) It is difficult to balance relatively strong and relatively weak performance for different criteria; (3) Differences in units of measurement for criteria can distort the influence of the weights used in scoring. Over the past two decades, Data Envelopment Analysis (DEA) has emerged as an important tool in the field of efficiency measurement. DEA is used to compare similar Decision Making Units (DMUs) such as potential suppliers in which one or more inputs secure one or more outputs. The DMUs use the same inputs and secure the same outputs but generally at varying levels. DEA provides the ability to perform objective, comparative efficiency analyses that go beyond purely financial measures of performance. For example, one bank has used DEA to substantially improve its branch productivity and profits while maintaining its service quality. It identified over $6 million of annual expense savings not identifiable with traditional analysis. Up to now, DEA has been used to evaluate and compare DMUs in a wide variety of contexts including educational departments in public schools and universities, health care units, prisons, agricultural production, and courts. Many business enterprises are beginning to explore the use of DEA. Several software companies are beginning to develop DEA solutions. Yet a fundamental study of DEA for the softgoods industry is missing and, in particular, when qualitative as well as quantitative measures are involved. Fuzzy mathematics has proven to be useful for handling both quantitative and qualitative linguistic factors in business decision making. Members of the team have been involved in developing prototype tools involving fuzzy modeling for delivery date negotiation and supply chain design for the U.S. softgoods and furniture industries. Once potential partnerships have been identified partnership formation and contract negotiation must take place. Two key issues in this context are how to function so as to increase sales to the ultimate consumer and how to rationally distribute the increased profits, which result among the entities in the supply chain. A potentially fruitful approach to addressing these issues is to view the entities in the supply chain as "players" in a "cooperative game" in which each player has its own perhaps conflicting goals, but there is potential gain for all players through cooperation. In recent years there has been increasing interest in the theory and application of cooperative games. However, little work has been done on cooperative games involving qualitative as well as quantitative data and there have been few if any applications to the softgoods supply chain. Even in the presence of partnerships, individual companies or groups of companies will typically need to acquire materials and/or distribute product to entities outside the partnership. With the rapid development of e-commerce, on-line auctions are becoming a legitimate means for reaching buyers and sellers in today's competitive world marketplace. The nature of on-line auctions is different from that of the traditional marketplace in which companies in the softgoods industry have operated. With the increased globalization of the industry, research into the structure and operation of on-line auctions and their application in the softgoods is needed. Previous work by members of this project team on a prototype software package called the "Due-Date Negotiator" is a first step in developing tools to bring customers and a manufacturer together in a contract for guaranteed delivery date at the best price.

3 I01-S01 Page 3 Specific objectives of the project include: 1. Investigate existing DEA formulations and expand to best fit softgoods supply chain scenarios. 2. Integrate fuzzy mathematics into DEA to reflect both qualitative and quantitative criteria. 3. Conduct research on fuzzy cooperative game theory and fuzzy auctions for partnership formation and contract negotiation. 4. Identify a specific segment of the softgoods supply chain for an internet-based prototype software system to demonstrate the effectiveness of DEA and cooperative games. II. ACCOMPLISHMENTS TO DATE We have conducted literature surveys on DEA, cooperative games, and auctions. Some of these are summarized in the references at the end of the report. Reference lists can be found at the project web site. We have begun developing fuzzy DEA models for performance assessment of softgoods supply chain firms and potential trading partners. To facilitate illustration and explanation, we have implemented some of the models in a prototype software package. We have done preliminary work on interval computations for fuzzy relational equations for use in the modeling of cooperative games for contract negotiation. We have begun to examine the formulation of auctions using fuzzy sets and fuzzy logic. We have also made some enhancements to a previously developed prototype due-date negotiation tool. 1. Fuzzy Data Envelopment Analysis (Fuzzy DEA) Productivity and efficiency analysis plays a significant role for firms or organizations in assessing their performance or that of potential trading partners. A well-known technique for the performance measurement is Data Envelopment Analysis (DEA). It evaluates the performance of business units (DMUs) performing similar functions (i.e., similar resources, or inputs, are consumed, and similar outputs are produced). DEA is a special application of linear programming based on frontier methodology as shown in Figure 1 for the simple case of one input and one output. DEA compares the inputs and outputs of a group of decision-making units and assesses their relative efficiency. A given DMU is inefficient if at least one other DMU can produce the same or more output by using less input. Figure 1. Efficient frontier of DEA

4 I01-S01 Page 4 DEA is becoming increasingly popular as a decision making tool owing to its ability to convert multiple inputs and multiple outputs into a single productive efficiency measure, to measure performance based on peer-group comparisons, to identify the most efficient performer(s), and to pinpoint opportunities for improvement. DEA has been shown to be applicable in a wide range of settings, including banking, logistics, public administration, manufacturing, and retailing. In the softgoods industry, DMUs can be manufacturing systems, production processes, distribution centers, or retailers. While the traditional DEA requires precise data for its analysis, the evaluation environment often involves vagueness and uncertainty. As system complexity increases, measuring precise data measurement becomes a difficult task. Furthermore decision-makers often think and operate on the basis of vague linguistic data (e.g., quality is "good", on time performance is "poor"). In a softgoods supply chain, many evaluations must be based on vague and uncertain data and measures. Furthermore, the vagueness and uncertainty is often amplified as information is passed along the chain. In order to provide powerful tools for assessing the performance of a set of DMUs in the softgoods supply chain, we are integrating fuzzy modeling and possibility theory with traditional DEA analysis. Simply introducing fuzzy sets into the traditional DEA models in order to represent vague or imprecise data leads to fuzzy linear programs which are not well defined due to the ambiguity which occurs in the ranking of fuzzy sets. As a way to resolve this ambiguity we have been exploring the use of "possibility theory". This approach transforms fuzzy DEA models into possibility DEA models by using possibility measures of fuzzy events (fuzzy constraints). For the case in which the membership functions of fuzzy data are trapezoidal, possibility DEA models become linear programming models. This work is documented in the second and third references at the end of this report. To communicate our results, a software package utilizing Fuzzy DEA is being developed. This software uses fuzzy sets to quantify imprecise and vague data, and analyze the data by the DEA approach. The software provides insights into how well the individual DMUs are performing their activities, as well as how their efficiency can be enhanced. We are using Microsoft Visual Basic to build the user interface that is linked with Microsoft Access database. The Fuzzy DEA engine is written in C++ and dynamically linked to the interface. The software includes several key features helping users to implement the analysis. These features include data displays for data input, graphical tools for setting membership functions of fuzzy inputs and fuzzy outputs, alternative methods for solving fuzzy DEA linear program, and efficiency score table for data displays. Figures 2 and 3 illustrate the interfaces for input and output (i.e., performance) data entry.

5 I01-S01 Page 5 Figure 2. DEA Data Interface Figure 3. Performance Measure Specification The fuzziness of inputs and performance measures can be modeled by entering all parameters directly or by simply scrolling to set parameters of the membership functions based on a visual of the function. Membership function types available in this package at this point are triangular and trapezoidal. Figure 4 illustrates the screen that is presented to the users for setting membership functions. Figure 4. Membership Function Interface for Inputs Figure 5. Results from Best-Best Method Another feature of the prototype software is that it allows the users to select from among four alternative methods to analyze the performance of DMUs. These alternatives capture the user's outlook (optimistic or pessimistic) relative to the business environment. Efficiency scores are reported in a tabular form, indicating the most efficient DMU(s). Figure 5 illustrates the output of the relative efficiencies of a group of five potential fabric suppliers as determined by the so called Best-Best method. All of the inputs, outputs and results are stored in the database permitting later manipulation and analysis. Currently, we are enhancing the fuzzy DEA software to include more functionality such as graphical displays and report generator.

6 I01-S01 Page 6 2. Cooperative Games and Auctions "Game theory" refers to the analysis of situations involving conflicting interests (as in business or military strategy) in terms of gains and losses among opposing players. In a cooperative game, groups of players are allowed to form teams or coalitions. Members of a team may cooperate with each other to the mutual benefit of the team. A team may in turn act as a single player in the game to achieve a bigger piece of pie. Contracts are formed within a team to determine players' actions and returns. Some contracts focus on how to increase the total return of this team. Others provide a means to rationally distribute the pie among the players. A softgoods supply chain involves the activity and interaction of many entities. In this situation, each entity is a player in a game. Cooperative game theory is a potentially useful tool for achieving efficient and effective collaboration in the supply chain. It can be used in selecting partners, structuring contracts, and sharing profit/cost. Cooperative game theory can also be used to optimize the performance of a supply chain, i.e., increase the total profit of the supply chain. In this project to date we have conducted a thorough study of the theory of cooperative games for potential application in the softgoods supply chain. As with DEA, when vagueness is taken into account data is represented by interval-valued fuzzy numbers. Preliminary work on interval computations for fuzzy relational equations and applications in cooperative game theory is documented in the dissertation proposal cited (seventh reference) at the end of this report. "Auction theory" refers to the analysis of situations in which goods are sold on the basis of a competitive bidding process. The traditional auction is one in which a single seller accepts bids sequentially from multiple potential buyers with the winner being the last (highest) bidder. However there are actually many structures of auctions involving single buyers/sellers, multiple buyers/sellers, single/multiple items, closed/open bid, etc. The rapid increase in the use of the Internet as a means of information gathering and communication has lead to a new interest in the practice( e.g., ebay, Onsale,Ubid) and theory of auctions since now buyers and sellers around the world can easily participate. As the sourcing and marketing in the softgoods industry becomes increasingly global, on-line auctions provide a potentially useful mechanism for purchasing and marketing of product and capacity. As part of our work on this project we have conducted a thorough study of the literature on auction theory. This is documented in references 4 and 5 at the end of this report. Much of the early work on auction theory is based on game theory which turns out to be difficult to implement in practical situations and not a viable approach for the more complicated auction structures that are coming into use. Further, as with DEA and cooperative games, realistic auction scenarios involve vague and uncertain factors such as a bidder's estimation of the price an object will probably bring and the bidder's perception of the value of the object to him/her. This has lead us to examine the formulation of auctions using fuzzy sets and fuzzy logic. The initial work is documented in reference 6 at the end of the report. 3. Due-Date Negotiation Tool In today's competitive markets, price and delivery date are of particular importance for make-to-order manufacturing systems. In order to win new orders, salespersons often promise potential customers to meet their desired delivery date without adequate consideration of the availability of production capacity. In fact, the sales department often lacks detailed information about available manufacturing resources. This often results in tardy deliveries, unhappy customers, and low utilization of some resources.

7 I01-S01 Page 7 To provide the sales management with a support system for negotiation and decision making, we have developed and prototyped a tool dubbed the Due Date Negotiator (DDN). The prototype DDN consists of a database, an order scheduler, and report generator. The database contains all needed information regarding customers, orders, products, bill of material, manufacturing resources, production processes (e.g., cutting, sewing), shop calendar, etc. The database can be edited and reports viewed through an interface written in Visual Basic 6. The scheduler module provides the manager with great flexibility in exploring alternatives. A real-time due-date assignment approach is combined with MRP-II based on the concept of integrating the due-date assignment process with the production planning process. The scheduler module uses a genetic algorithm along with an order-loading algorithm for capacity allocation. Both fuzzy and crisp logic loading algorithms have been developed. Potential new customer orders are inserted into a rough-cut capacity plan which details the implied time phased work load on each key resource and the associated estimated order completion dates. First, leaving the plan for currently active orders undisturbed, earliest possible completion times for new orders that do not overload production resources are determined. If the resulting estimated completion times satisfy the customers requested delivery dates, the order promise dates can be quoted as requested. However, in many instances some of these estimated completion dates may not meet customers requirements. In this case the prototype software allows the manager to determine the impact on the plan selectively scheduling overtime on one or more resources and/or of forcing the loading of one or more orders to meet specific delivery dates. Exploring a number of options permits the manager to make informed delivery date quotations. While exploring such alternatives, the loading for selected customer orders can be left undisturbed. Figure 6 illustrates the screen that is presented to the user upon the completion of a loading run. The recent work on the DDN has been directed to broadening its applicability to manufacturing systems that involve assembly in addition to a sequence of processing operations. Figure 6. Due-Date Negotiator Output Report

8 I01-S01 Page 8 III. RESOURCE MANAGENENT AND TECHNOLOGY TRANSFER The research team is drawn from the Department of Textile and Apparel Management in the College of Textiles and from Industrial Engineering and Operations Research in the College of Engineering bringing together a wide array of expertise. To date one masters and three doctoral students have participated in the research. "Ph.D. Dissertation proposals entitled "Fuzzy Data Development Analysis in Supply Chain Modeling and Analysis", and "Interval Computations for Fuzzy Relational Equations and Cooperative Game Theory," have been submitted and approved. As described above, we have developed a prototype software package to facilitate illustration and explanation of the Fuzzy DEA approach. As our research proceeds, this prototype will be enhanced to provide a useful decision making tool. In collaboration with Professor Dingwei Wang of the Department of Systems Engineering of Northeastern University in P.R. China, we have conducted an in-depth survey of literature on modeling and optimization for E-Commerce, including recent developments in auction theory. A working survey paper has been written and is being reworked for publication. A second paper on the application of fuzzy modeling and reasoning in multi-object auctions will be submitted for publication soon. Various aspects of this research has been discussed with and demonstrated to personnel from a number of companies, including Burlington Industries and Milliken & Company. Technology developed in this project is also being used in a project with the U.S. furniture industry. Relevant research papers authored by the project team are listed below. OTHER CONTRIBUTORS Students: Negar Arefi, Yue Dai, Saowanee Lertworasirikul, Shumin Wang (NC State, Engineering). Visiting Scholar: Dingwei Wang, (Dept. of Systems Engineering, Northeastern University, Shenyang. P.R. China.) FOR FURTHER INFORMATION: Fang, S-C., H.L.W. Nuttle, R.E. King, and J.R. Wilson. "Soft Computing for Softgoods Supply Chain Analysis and Decision Support," to appear in Soft Computing in Textile Sciences (C. Patore and L Sztandera, editors), Physica-Verlag, Lertworasirikul, S., "Fuzzy Data Envelopment Analysis in Supply Chain Modeling and Analysis," Dissertation Proposal, Dept. of Industrial Engineering, N.C. State University, May Lertworasirikul, S., S-C. Fang, J.A. Joines, and H.L.W. Nuttle, "Fuzzy Data Envelopment Analysis (Fuzzy DEA): A Possibility Approach," Technical Report 01-02, Dept. of Industrial Engineering, N.C. State University, September Wang, D., S-C. Fang, and H.L.W. Nuttle, "Survey on Modeling and Optimization for E-Commerce," Working Paper, Dept. of Industrial Engineering, N.C. State University, May Wang, D., S-C. Fang, and H.L.W. Nuttle, "Notes on the Theory and Current Developments in Auctions," Working Paper, Dept. of Industrial Engineering, N.C. State University, May 2001.

9 I01-S01 Page 9 Wang, D., S-C. Fang, and H.L.W. Nuttle, "A Fuzzy Formulation of Auctions and Optimal Sequencing of Multiple Auctions," Working Paper, Dept. of Industrial Engineering, N.C. State University, August Wang, S., "Interval Computations for Fuzzy Relational Equations and Cooperative Game Theory, " Dissertation Proposal, Graduate Program in Operations Research, N.C. State University, June 2001.