A knowledge-based decision support system for measuring enterprise performance

Abstract

This paper presents a knowledge-based decision support system for measuring enterprise performance. The KDSS system provides not only company’s various financial data query, but also enterprise performance based on knowledge reasoning. Additionally, an artificial neural network is adopted to predict future total sales. The system integrates a database, a knowledge base, an inference engine, and a model base. It can offer a wide range of different queries and all rules in the knowledge base are explained in detail to illustrate the process of reasoning. Meanwhile, in order to reduce subjective judgment on performance measurement, a group assessment is used to assess the scores of each dimension for measuring enterprise performance. Finally, the result of enterprise performance evaluation is presented and some suggestions are given to managers for making decisions.

Introduction

With the increasing complexity of globalization, there has been a rapid growing importance and discussion for more research on how to use information technology to gain competitive business advantages. However, this form of competition has been studied largely on a firm-to-firm level and not at an industry level that would provide greater insight into the relationship between information technology and enterprise performance. Additionally, the establishment of a company is for profits and sustainable development. Therefore, periodical performance evaluation is very important to keep the firm growing. According to the evaluation, managers can learn the effect of resource utilization and operation. Enterprise performance presents how a manager achieves an enterprise’s goal with available resources. Additionally, the performance is not only about the expected goal, but also the future development of the corporation. For the above reasons, business activities should be periodically analyzed and evaluated by using formulas and measures based on the data internally or externally collected during operations. Generally, a corporation has a long-term plan based on its long-term goals and past performances. It has put all possible variables into consideration and amends its future direction and business plan gradually. The trends can therefore be predicted. In recent years, the electronics industry of Taiwan has become a focus of the domestic stock market. The electronic stocks lead this feverish market. Usually, the daily amount accounts for more than half of the total market transactions. The electronics industry is not only a mainstay of Taiwan’s domestic economics, but it also plays an important role in the international market. Any improper operation may result in serious crisis. Therefore, the performance of the electronics industry is an intense focus for managers, administrations, and investors.

Financial statements can reflect the operation of a company. They provide necessary data to evaluate enterprise performance. The analysis of financial statements is a process to help managers make decisions. Its goal is to evaluate the current and past financial situation and operation, and to predict the future of the company. Furthermore, in today’s age with innovations and variances, decision-making and efficiency are considered the highest priority when managers try to improve operational processes. Yamin et al. [29] devoted themselves to relationships among general policies and the predominance in the competitive and organizational performances under different situations. Hoque [8] discussed an impact on performance of two factors: policy and environment uncertainty. Their results showed that policy was positively related to performance. But, there was no evidence to prove the relationship between environment uncertainty and performance.

With the fast development of information technology, there are more and more tools available to decision makers. The most widely deployed systems are decision support systems. For financial applications, Wen et al. [28] proposed a decision support system based on an integrated knowledge base for acquisition and merger. It not only provides merger processes, major problems, and related regulations practically or procedurally, but also gives rational suggestions according to related regulations. Finally, it can make suggestions on how to deal with uncertain growth rate and current evaluations. Zopounidis et al. [31] also developed a knowledge-based decision support system for financial management. The system integrated the technologies of decision support systems and professional systems, and was employed to deal with the past and current problems that occurred frequently. Besides, decision support systems are also used in the HR planning and decision, financial management, and marketing decision-makings. They are widely utilized and accepted [12], [22], [18], [16], [2], [13], [24], [5], [17], [9].

Additionally, more and more attention has been paid to the application of artificial neural networks (ANN). They have been utilized to solve problems such as identification, classification, and forecasting. Their application includes industry and engineering, business and finance, science and information etc. According to the related research [30], [25], the neural network model is much better than the traditional statistical model in the efficiency of prediction. ANNs have a precise mathematical base, parallel massive data process capability, fault-tolerant capability, and association and noise filtering capability. They can also be used to make up many assumptive conditions required by statistical modeling. Therefore, much literature has been published on market partitioning, stock index prediction, exchange rate prediction, bankruptcy prediction, credit prediction, credit evaluation, and moral crisis in the insurance cases [21], [6], [27], [30], [25], [10].

In this paper, we focus on measuring business performance for an electronics company and then comparing its performance with that of the Taiwan electronics industry. Next, through knowledge reasoning, some actions or suggestions can be given for conducting the company. The remainder of the paper is organized as follows. Section 2 presents the vital measures of evaluating enterprise performance for an electronics company. In Section 3, we propose the structure and functions of the decision support system. Then, the functions and processes of the database and knowledge base, knowledge reasoning, and neural network forecasting are explained, respectively, in Sections 3.1 The functions and processes of the database and the knowledge base, 3.2 The group assessment method, 3.3 Knowledge reasoning. Section 4 demonstrates the system’s functions and evaluates its benefits through system implementation. Finally, in Section 5, some suggestions for future directions are also given.