تحقیقات درباره استفاده از ANP برای ایجاد یک مدل ارزیابی عملکرد برای سیستم های هوش کسب و کار(هوش تجاری)
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|676||2009||12 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 36, Issue 2, Part 2, March 2009, Pages 4135–4146
In order to compete in the rigorous environment, the electronization has enabled business to deploy business intelligence (BI) systems for the purpose of decision-making. However, to avoid the ineffective experiences during the deployment, it is important to clarify the impact factors of a BI system and find out a suitable assessment method to evaluate the performance of BI systems. In this paper, an analytic network process (ANP) based assessment model was constructed to assess the effectiveness of BI systems. Furthermore, an expert questionnaire was used to filter out useful performance matrices, used as the sub-criteria of the ANP model. Finally, a real case was analyzed using the constructed ANP-based effectiveness assessment model for Business Intelligence systems. The results indicate that the most critical factors that impact the effectiveness of a BI system are: output information accuracy, conformity to the requirements, and support of organizational efficiency. Utilizing this model to assess the BI performance of the studied case, it reveals that 24% improvement in effectiveness has been reached, which consists with the perception of the management level. Therefore, this effectiveness assessment model can be used to evaluate the performances of a BI system. It can also provide performance indices and improvement directions for BI users and vendors, respectively, for the total succession in system effectiveness and satisfaction.
Traditional enterprises may normally face issues such as the overflow of data, the lack of information, the lack of knowledge and insufficiency of reports. Therefore, in order to make prompt decision within the shortest period of time possible to keep pace with the situation, high levels of management commonly make decisions based on their experiences, leading to the ever-increasing risk of decision failure while lowering the value of the decision itself. As worldwide competition is maturating, past decision-making modes can no longer satisfy the requirements of enterprises for decision efficiency and benefits; enterprises must make good use of electronic tools to quickly extract useful information from huge volume of data by providing the skills of fast decision-making (Rakar & Jovan, 2004). The way to promote the electronization solutions from the operational level to the decision making level is a topic enterprises cannot avoid in the face of the next wave of electronization. The information system applied within the enterprises should be able to demonstrate the data or information with accuracy and in real-time, in order to expedite the speed of processing and decision-making. Existing electronization software package can provide a set of complete solutions for the operation and management processes of enterprises. However, the effects of the implementation of electronization tools vary that the probability of failure is higher than that of the success (Ward, Hemingway, & Daniel, 2005). Therefore, defining the performance of information tool and laying down related assessment criteria is an important issue that has to be tackled for the deployment of electronization. Business intelligence (BI) is the tool used by enterprises to collect, manage and analyze structural and non-structural data and information by taking advantage of modern information technology (IT). It utilizes a substantial amount of collected data during the daily operational processes, and transforms the data into information and knowledge to avoid the supposition and ignorance of the enterprises (Wang, 2005). Under the speed-oriented operation mode, in order to improve management effects and performance, BI will surely become the tool enterprises would like to actively deploy as well as the solution that can bring enterprises competitive edge. However, current BI application is still at its fledging stage and most of the enterprises fall short of sufficient understanding towards BI (Wang, 2005); currently, research on conducting performance evaluation for the implementation of BI system is scarce, not to mention the analysis of on-line performance. Beside that, managers usually have to measure all the pros and cons to achieve a balance in assessing the performances of BI/IT systems. Different end users and IT people adopt different performance measurement criteria. Therefore, it is a significant issue to implement across-the-board considerations to incorporate different viewpoints and perspectives from manifold experts in BI development and usage into the choice for assessing BI performance effectiveness.
نتیجه گیری انگلیسی
In recent years, all enterprises look for an efficient and effective information system as the tool to obtain competitive advantage. To lower operational costs and retain competitiveness, many enterprises expect to implement the BI system, integrate the internal and external data of the enterprises, interpret the data, and transfer them into useful information. However, the implementation of information system can not make distinctive effectiveness without suitable evaluation indicators. Thus, defining suitable indicators for evaluating the performances of a BI system is necessary. In this research, the ANP structure for the evaluation of a BI system is used as the assessment model. The key factors that impact on the effect of BI system are discussed, and the interview cases are used to explain the feasibility of the model. The structure can be not only used by the enterprises that have implemented the system, but also can be referenced by the prospective enterprises for lowering risk and reducing future failure possibility. Suggestions and conclusions are given as follows: 1.The ANP assessment model for BI systems built up in this research has been subjected to the judgment of 12 experts for the comparison among the performance indicators and criteria. In the process of creating the ANP decision model, the discussion with experts and end users was made before the relationships among levels and aspects are given. Therefore, the ANP structure in this research is reliable and valid. 2.The appraisal result has shown that ‘meeting enterprises requirements’ (MER) is the most concerned criteria that senior experts evaluate, followed by ‘meeting user’s needs’ (MUN). 3.To build the assessment model of a BI system, this paper chooses nine indicators, which are system response time (SRT), system security (SS), output information accuracy (OIA), implementing experience of consultant (IEC), comprehension degree to implementer’s business (CDIB), support degree of users and high-level (SDUH), conformity to the requirements (CR), support of organizational efficiency (SOE) and support in decision-making in organization (SDM). 4.The comprehensive assessment result has shown that the critical factors used to evaluate the effect of a BI system include, in their priority sequence, output information accuracy (OIA), conformity to the requirement (CR), support of organizational efficiency (SOE), and system response time (SRT). This is to say that in the process of implement ting a BI system, the users do not care about the advanced functions that the system has, but emphasize on the accuracy of information they have. Excessive pursuit of the system response time (SRT) may give adverse results. To software suppliers and IT personnel, they should focus on the fast and correct information acquisition when they develop or sell the software. In addition, most enterprises will check the enterprise service and integration ability (ESIA) according to the experiences and expertises of consultants, so the supplier should stress the training and cultivation of consultants. To most enterprises, the key for the success of implementing a BI project lies in the right choice of consultants and the full-time participation of consultants during the whole process.