سیستم کارشناسی فازی معنایی برای کارت امتیازی متوازن فازی

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF–THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.

Knowledge management plays a key role in the search for success in the current business world. Increasing specialization and complexity of companies has given raise to the necessity of an integral management of own and foreign resources, which involves and generates huge amounts of valuable data. Empresarial intelligence must be consequently more a cornerstone of the corporative strategy than simply an amalgam of disperse tools and procedures, if decision processes are expected to be faced with guarantees in order to achieve a joint and balanced global performance. Balanced scorecard (BSC) (Kaplan & Norton, 1992) is a decision support tool at the strategic management level which improves the satisfaction of the strategic objectives. Since it was proposed in the early 1990s, it has demonstrated its suitability to assist decision making in management. Nevertheless current balanced scorecard-based systems suffer from two problems. Firstly, variables which are to be measured have associated vagueness, being much more natural to refer to their values using a linguistic label instead a numerical value as frequently is done. Secondly, data do not have an explicit representation of their semantics; ad hoc solutions are usually implemented for each problem, making developers duplicate efforts and users cope with their specific details. Some solutions have been proposed to the first problem. Since fuzzy set theory and fuzzy logic (Zadeh, 1965) have proved to be successful in handling imprecise and vague knowledge, they have been combined with the BSC leads to fuzzy balanced scorecard (see Section 5 for details). However, such approaches also leave room for improvements in several aspects such as interpretability, modularity and accuracy. On the other hand, to the very well of our knowledge, there has not been any effort in the other direction. Thus we have represented balanced scorecard data using an ontology, which allows to add semantics to them making easier knowledge base maintenance as well as reuse of components among different organizations. In this paper we present a new approach to a fuzzy BSC which improves the state of art by extending the number of variables and perspectives. We also present a fuzzy expert system for this fuzzy BSC. Its knowledge base relies on an ontology and its inference system derives new knowledge from fuzzy rules. The system is general and reusable, so every company can personalize it by providing their own meaning for the linguistic labels defined over the variables (e.g. what they consider a “high” value of some variable) and their own rules. The results of the expert system are highly interpretable pieces of information ready to be incorporated to managers’ decision making processes. The remainder of this paper is structured as follows. Section 2 provides some preliminaries on the fundamental theoretical aspects underlying this paper: balanced scorecard, fuzzy logic and ontologies. In Section 3 we present our fuzzy balanced scorecard, describing the variables which take part in it. Implementation details are set out in Section 4. The description of our intelligent system starts by sketching the ontology and then we show how the rule-based engine computes the value of the output variables of the system. Section 5 evaluates our proposal with regard to the related work. Finally, some conclusions and ideas for future research are drawn in Section 6.

This paper proposes a semantic fuzzy expert system for the balanced scorecard. We have presented a novel approach to a fuzzy balanced scorecard, extending the number of variables and perspectives with respect to previous works. We have built an OWL ontology to encourage reusing and sharing of this model. We have also developed a fuzzy expert system with a knowledge-based relying on this ontology and an inference system using fuzzy IF–THEN rules to derive new knowledge. The output of the expert system is appropriate to take part in a decision making process which improves the achievement of the strategic objectives of the company. The whole system is very general and may be used by different companies, which can customize it to make it suitable to their own needs. An interesting direction of further research would be to use a more standard representation for fuzziness, with a more solid theoretical basis such as fuzzy concrete domains (Straccia, 2005). Fuzzy concrete domains were proposed in the context of some fuzzy extension of description logics and offer the possibility to represent some explicit membership functions for fuzzy sets such as triangular and trapezoidal. However, for the concerns this paper, it would be sufficient to have a crisp description logic with fuzzy concrete domains, as proposed in Schockaert et al. (2006). Another interesting consideration would be the representation of the fuzzy rules using some of the recently proposed fuzzy semantic rule languages, such as fuzzy SWRL (Pan, Stoilos, Stamou, Tzouvaras, & Horrocks, 2006) or fuzzy RuleML (Damasio, Pan, Stoilos, & Straccia, 2006), as an alternative to FuzzyJess.