Knowledge support for problem-solving in a production process: A hybrid of knowledge discovery and case-based reasoning

https://doi.org/10.1016/j.eswa.2006.04.026Get rights and content

Abstract

Problem-solving is an important process that enables corporations to create competitive business advantages. Traditionally, case-based reasoning techniques have been widely used to help workers solve problems. However, conventional approaches focus on identifying similar problems without exploring the information needs of workers during the problem-solving process. Such processes are usually knowledge intensive tasks; therefore, workers need effective knowledge support that gives them the information necessary to identify the causes of a problem and enables them to take appropriate action to resolve the situation. In this paper, we propose a mining-based knowledge support system for problem-solving. In addition to adopting case-based reasoning to identify similar situations and the action taken to solve them, the proposed system employs text mining (information retrieval) techniques to extract the key concepts of situations and actions. These concepts form profiles that model workers’ information needs when handling problems. Effective knowledge support can thus be facilitated by providing workers with situation/action-relevant information based on the profiles. Moreover, association rule mining is used to discover hidden knowledge patterns from historical problem-solving logs. The discovered patterns identify frequent associations between situations and actions, and can therefore provide decision-making knowledge, i.e., appropriate actions for handling specific situations. We develop a prototype system to demonstrate the effectiveness of providing situation/action relevant information and decision-making knowledge to help workers solve problems.

Introduction

Problem-solving is an important process that enables corporations to create competitive advantages, especially in the manufacturing industry. Case-based reasoning (CBR) techniques (Chang et al., 1996, Kohno et al., 1997, Park et al., 1998, Yang et al., 2004) have been widely used to help workers solve problems. For example, based on these techniques, a decision support system was developed to facilitate problem-solving in a complex production process (Park et al., 1998). CBR techniques have also been used to implement a self-improvement helpdesk service system (Chang et al., 1996), and integrated with the ART-Kohonen Neural Network (ART-KNN) to enhance fault diagnosis in electric motors (Yang et al., 2004).

Conventional CBR approaches focus on identifying similar problems without exploring the information needs of workers during problem-solving tasks. Problem-solving is a complex process that includes a series of uncertain situations and operational actions. Moreover, it is usually knowledge intensive and workers need to access relevant information in order to identify the causes of a situation and take appropriate action to solve it. Due to the uncertain characteristics of situations, several causes and possible solutions may exist for a specific situation. For example, in a production process, a significant decline in performance may be due to poor materials, inexperienced workers, or faulty machinery. Thus, possible solutions would include replacing the poor materials, retraining the workers, or repairing the faulty machinery. The causes and possible solutions are usually hidden in relevant data resources and difficult to extract. In such uncertain environments, workers need to use knowledge gathered from relevant information and previous problem-solving experience to clarify the causes and take appropriate action. Thus, identifying similar cases through CBR is not sufficient to solve problems effectively. An effective knowledge support system is essential so that workers have the information necessary to identify the causes of a problem and take appropriate action to solve it.

In this work, we propose a mining-based knowledge support system for problem-solving. Besides adopting case-based reasoning to identify similar situations and the action taken to solve them, we adopt text mining methods to compensate for the shortcomings of CBR. For specific situations or actions, relevant information (documents) accessed by workers is recorded in a problem-solving log. Historical codified knowledge (textual documents), i.e., experience and know-how extracted from previous problem-solving logs, can provide valuable knowledge for solving the current problem. The proposed system employs Information Retrieval (IR) techniques to extract the key concepts of relevant information necessary to handle a specific situation or action. The extracted key concepts form a situation/action profile that models the information needs of workers for a specific problem-solving task. The system can then uses the situation/action profile to gather existing and new relevant knowledge documents for specific situation/action.

Moreover, we employ association rule mining methods to discover decision-making knowledge rules about frequently adopted actions taken to handle specific situations. These rules are generated as knowledge support to help workers take the appropriate action to solve a specific situation. Furthermore, the problem-solving process includes a series of uncertain situations and operational actions, and preceding situations or actions may trigger subsequent problem situations. Therefore, workers need to gather such triggering information (chain reactions) to determine appropriate action. For example, if an unstable system causes production to decline, the solution may be to reboot the system. However, this may result in breakage of materials, which would increase production costs. The proposed approach applies sequential pattern mining methods to discover dependency knowledge which represents frequent chain-reactions. The knowledge helps workers make appropriate action plans.

The discovered profiles and knowledge rules are used to construct a knowledge support network, which provides workers with relevant situation/action information, as well as decision-making and dependency knowledge. Finally, a prototype system is developed to demonstrate the effectiveness of the knowledge support network.

The remainder of this paper is organized as follows. Section 2 reviews related works on knowledge discovery and problem-solving. Section 3 introduces the proposed framework of knowledge support for problem-solving. Section 4 describes the discovery of knowledge patterns, including situation/action profiles and knowledge rules. The knowledge support network and its usage are discussed in Section 5. Section 6 presents an implementation of the prototype system. Finally, in Section 7, we present our conclusions and indicate the direction of future work.

Section snippets

Related work

The related literature covers knowledge management, problem-solving, case-based reasoning, information retrieval, and data mining techniques.

The system framework of knowledge support for problem-solving

In this section, we describe the proposed system framework, including the concepts of the problem-solving process, the knowledge required for problem-solving, and the proposed knowledge support framework. A wafer manufacturing process in a semiconductor foundry is used to illustrate the proposed approach. The process comprises the following steps: crystal growing, wafer cutting, edge rounding, lapping, etching, polishing, cleaning, final inspection, packaging and shipping. The wafer cleaning

Discovery of problem-solving knowledge

This section describes the procedure of discovering knowledge from historical problem-solving logs, as shown in Fig. 3. To illustrate the proposed approach, we use data from the log file of a semiconductor foundry’s intranet portal, which contains the problem-solving log for handling problems on the production line. The company operates wafer manufacturing fabs to provide the industry with leading-edge foundry services. The log file records the encountered situation and the action taken at each

Knowledge support for problem-solving

This section describes the construction of the knowledge support network, which provides knowledge recommendations for problem-solving. The procedure is illustrated in Fig. 4.

System implementation

We developed a prototype system to demonstrate the effectiveness of the proposed knowledge support system for problem-solving. The implementation is conducted using several software tools, including the Java(TM) 2 Platform Standard Edition Runtime Environment Version 5.0, Java Server Page, and Macromedia Dreamweaver MX. A web and application server is setup on Apache Tomcat 5.5.7, and Microsoft SQL Server 2000 is used as the database system for storing data related to the problem-solving

Conclusion

In this work, we have developed a novel knowledge support system for problem-solving on a production-line. Case-based reasoning is used to identify similar situations/actions. Text mining techniques are then applied to discover the key terms of a situation/action. The terms form situation/action profiles that model the information needed to handle a problem. Association rule mining and sequential pattern mining are used to discover decision-making and dependency knowledge patterns,

Acknowledgement

This research was supported in part by the National Science Council of the Taiwan (Republic of China) under the grant NSC 94-2416-H-009-015.

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