دانلود ترجمه مقاله پیش‌بینی موفقیت ERP: یک رویکرد شبکه عصبی مصنوعی

Enterprise Resource Planning (ERP) systems are defined as single software systems allowing the complete integration of information flow from all functional areas in companies by means of a single database, such a system is accessible through a unified interface of communication [1]. These systems have been increasingly adopted by organizations across various industries in both developed and developing countries. Organizations implement ERPs to enhance both operational efficiency and business efficacy [2–4]. They improve operational efficiency by integrating business processes and by providing better access to integrated data across the entire enterprise, while a company that wishes to enhance its efficacy may redesign its business practices by using the best practices embedded in the ERP [1,5]. Although ERP systems can bring about benefits such as competitive advantage, decision support capabilities and business intelligence competences (e.g. [6–8]), the high failure rate is a major concern [1]. The failure rate of ERP implementation has been estimated at between 60% and 90% [9]. These projects are, on average, 178% over budget, take 2.5 times longer than intended and deliver only 30% of the promised benefit [10]. Other research findings indicate that organizations do not always achieve their desired level from their ERP investments [6,11]. These statistics imply that ERP projects are one of the most difficult system development projects. They are quite complex projects and often require fundamental organizational changes. To avoid such costly failures and help organizations take more advantage of their system implementation, a great deal of effort has been made by researchers. Some researchers have provided valuable insight into the process of ERP implementation [12–16] and others have identified a variety of critical factors influencing the success or failure of the system [10,17,18]. Understanding the nature of Enterprise Systems (ESs) and ERPs success has also been the focus of scholarly research interest in recent years [10,19–25]. Despite these studies, there is little research on measuring the ERP success/failure possibility except for the work conducted by Chang et al. [26] in which they proposed a frame- work based on incomplete linguistic preference relations under a multi-criteria decision making environment to measure the success/failure possibility of initiating an ERP system. They presented a model in which three technological, environmental and organizational aspects are associated with the postimplementation success of ERP. Although their work extended the current studies on ERP from the implementation stage to the post-implementation stage, we believe that their work has limitations, in terms of the number of both factors and cases investigated. In another related work, Tsai et al. [27] developed a framework for investigating how ERP selection criteria are linked to system quality and the service provided by suppliers and consultants, and, thus, how these influenced ERP implementation success and system net benefits. Their results implied that the system, supplier and consultant selection criteria had positive influences on net benefits through the service quality process and user perspective. However, they have not taken organizational circumstances into account in their analysis. In this paper, therefore, with adopting a more holistic and comprehensive view of some of the organizational profiles (such as industry type, size, structure, management style, IT systems and so on), it has been attempted to point out the relationships between some organizational profiles and ERP system success. Meanwhile, the paper has tried to answer the following research question: • How can organizational profiles be related to ERP success? The response to the above research question would create a dynamic model to help firms determine the desired success level in ERP implementation and supply the needed organizational characteristics. Thismeans that an ERP success prediction instrument and model would serve as a facilitator to cast light on ERP preparation plans. Since this model illuminates the relationships between the desired success level and the needed organizational characteristics, it can provide proper justification for preparation plans. Generally, when ERPs are being taken into consideration, there are numerous parameters and factors affecting its success. So, it sounds difficult to answer this question by developing a precise mathematical model for eliciting the relationships. Meanwhile, another approach is required to identify such hidden patterns. Due to such complexity, the proposed model should have classification and forecasting capabilities, which makes Artificial Neural Networks (ANNs) an appropriate technique to apply to expert systems, as is now commonly used in the literature [28–30]. The objective of this research, consequently, is to classify a set of organizational parameters and factors, on the one hand, and a set of ERP success factors, on the other hand, and then develop and design the ANN-based expert system in order to predict and recommend the probable ERP success level. Validation of the expert system is also done by the authors. The paper is organized as follows: Section 2 presents literature reviews on ERP success, organizational profiles and ANN-based expert systems. Section 3 describes the proposed research method and ANN architecture. Section 4 discusses the result of ANN training, validation and testing, as well as expert system implementation. Finally, Section 5, the conclusion, offers the research findings.