ایجاد نقشه‌ های دانش‌ آنتالوژی‌ مبنا برای کمک به مهندسی مجدد فرآیند

Business Process Re-engineering (BPR) is being used to improve the efficiency of the organizational processes, however, a number of obstacles have prevented its full potential from being realised. One of these obstacles is caused by an emphasis on the business process itself at the exclusion of considering other important knowledge of the organization. Another is due to the lack of tools for identifying the cause of the inefficiencies and inconsistencies in BPR. In this paper we propose a methodology for BPR that overcomes these two obstacles through the use of a formal organizational ontology and knowledge structure and source maps. These knowledge maps are represented formally to facilitate an inferencing mechanism which helps to automatically identify the causes of the inefficiencies and inconsistencies. We demonstrate the applicability of this methodology through the use of a case study of a university domain.

Business process re-engineering (BPR) has been defined as the fundamental rethinking and radical design of business processes to achieve dramatic improvements in critical, contemporary measures of performance such as cost, quality, service, and speed [18]. Although there were high expectations for the improvements in performance that BPR would bring about for organizations, in many cases these benefits were not realised and high failure rates (e.g. 70%) have been reported [2], [5] and [27]. The reasons for these high failure rates have been debated and a number of factors have been posited as to why these expected benefits have not been realised. One factor is the focus on the steps in the business process (e.g. business process diagrams) at the exclusion of the environment within which the process is carried out [42]. In considering the environment organizations will be faced with the challenge of making certain types of knowledge visible to relevant stakeholders. Another factor is that although there are a number of tools for modelling the business processes, many of these tools only support diagrammatic and mathematical modelling [39]. While these models are useful for understanding the business processes, they do not support the automated analysis for identifying the cause of inefficiencies in the business process, which is considered to be one of the most time consuming stages of BPR [1] and [39]. This research addresses the issues of the lack of understanding of the environment within which the business process exists and of automating the identification of the inefficiencies and inconsistencies in the business process. The lack of understanding of the environment suggests the need for the integration of knowledge management models and techniques [3]. One such knowledge management technique that could be relevant is knowledge mapping, as knowledge maps can be used for several purposes, including finding sources of knowledge or opportunities for knowledge creation, identifying expertise and increasing knowledge-sharing, and helping to determine the knowledge competencies that exist within an organization [9] and [37] and how they interact. The lack of automated methods can be addressed by representing the business processes and the environment using a formal notation. Alleviating these problems will improve the BPR efforts and ultimately help organizations realise the benefits that have been anticipated. In this paper we use a design science approach [19] to develop a methodology that incorporates a number of existing techniques, namely ontologies and knowledge maps, to ensure that both the process and its environment are modelled when re-engineering is being undertaken by an organization. The methodology also proposes an automated mechanism for analysing these domain models by using a formally represented ontology which facilitates automated inferencing. The applicability of the methodology will be demonstrated by applying it to a university domain. The following section describes the BPR, ontology and knowledge literature. The methodology is then described and evaluated using a case study. The applicability of the methodology for this domain is then discussed and finally the conclusion and suggestions for future directions for this research are presented.

In this paper we proposed an ontology-driven methodology for business process re-engineering that includes the development and analysis of knowledge maps. It was found that is not enough to simply reengineer by examining only the process but rather organizations must consider the environment within which the process is carried out. The formal representation of the knowledge of the domain also facilitated an automated inferencing mechanism which improves the identification of the causes of inefficiencies. We demonstrated the applicability of the methodology using a case study of a university. The success of this methodology depends on the quality of the ontology (e.g. accuracy and completeness) and therefore the instantiation becomes extremely important. Creating and maintaining this instantiation can be a tedious process. The choice of the formal notation will impact the level of automated reasoning that can be achieved and these limitations must be understood. In this study we applied our methodology to the university domain, however, the steps are not specific to a particular organization. Any organization adopting the proposed methodology will be required to instantiate the given ontology. Once instantiated the knowledge maps generated from the ontology will be representative of the knowledge in the new domain. Therefore, this methodology is generalizable, which will be demonstrated by applying it to a number of other domains (e.g. healthcare, crime). The methodology proposed is based on an instantiated ontology. The focus in this study was on the business process entity (as BPR was being considered), however, the focus could be on other entities in the ontology which would then benefit other decision making situations (e.g. disaster recovery planning (DRP) where the focus would be on the resource entity). This will be explored in future work.