An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the proposed method.
In the past few years, thousands of companies around the world have implemented ERP systems. The number of companies that plan to implement ERP is growing rapidly. Since the early to mid-1990s, the ERP software market has been and continues to be one of the fastest growing segments of the information technology (IT) industry [7]. AMR Research, an authoritative market forecast institution in America, indicated that the ERP market would grow at annual rate of 37% in recent 5 years. The sales of the ERP packaged software are estimated to be around $20 billion by the year 2000 and the eventual market size is predicted to be around $1 trillion by the year 2010 [8]. Even in China, a developing country, ERP has also become a main product in the software market and the sales have approached 600 million RMB in the first half of 2002 ([9], [10] and [11]). Surprisingly, given the significant investment in resources and time, many companies did not achieve success in ERP implementation. It is estimated that the failure rate of ERP implementation ranges from 40% to 60% or higher [2]. Some surveys and researches indicate that successful outcome is also not guaranteed even under ideal circumstances. Researchers consider that the factors such as organizational change and process re-engineering, the enterprise-wide implications, the high resource commitment, and high potential business benefits and risks associated with ERP systems make the implementation a much complex exercise [12] and [13]. It is therefore not surprising that numerous companies have abandoned their ERP projects before completion or have failed to achieve their business objectives after implementation [14]. Many experts and scholars have investigated this issue from various angles. Some provide valuable insights into ERP implementation process and others identify a variety of factors that can be considered to be critical to the success of an ERP implementation. These factors include top management support, business plan and vision, organizational change management and culture, business process re-engineering (BPR), data accuracy, education and training, and vendor selection and support, etc. ([2], [3], [5], [12], [13], [15], [16], [17], [18], [19] and [20]). A successful ERP project involves managing business process change, selecting an ERP software system, implementing this system, and examining the practicality of the system. However, a wrong ERP system selection would either fail the project or weaken the system to an adverse impact on company performance. Due to limitations in available resources, the complexity of ERP systems, and the diversity of alternatives, it is often difficult for an organization to select a suitable ERP system [21].
Selecting a suitable ERP system is the basis of implementing ERP project successfully. This paper presents a new model for ERP selection, which is based on linguistic information processing. The main characteristics of this model are: (1) the linguistic 2-tuple representation model and computing model are taken for dealing with multi-granular linguistic assessment information. It overcomes the drawback of the loss of information in the classical linguistic computational models such as the semantic model and the symbolic model, (2) In the study, a similarity degree based algorithm is proposed to aggregate the information about ERP systems from external professional organizations. It make aggregation results reflect the collective opinions more reasonably and more objective, (3) The weights of attributes are determined by solving linear programming. They need not be provided by project team in advance, (4) It combines the objective information obtained from external professional organization and the subjective information from the member of project team. Therefore, the decision result is more credible. In a word, the method proposed in this paper provides an effective tool for dealing with ERP system selection.
