Track: Decision Sciences

Abstract

In the evolving competitive environment, characterized by competition, rapid market change, and globalization, companies have to enhance their competitive advantage to survive. In such a context, they have to continuously monitor and process information on their competitive environment in order to have an informational advantage over their competitors (their capabilities, vulnerabilities, intentions and potential moves). Getting better informational and intelligence support is critical and vital. Competitive Intelligence (CI) appears then as a vital component of strategic planning and management process. Developing a CI process within a company leads to several benefits such as anticipating moves of the other competitors by shedding light on competitor strategies, discovering new competitors or potential customers, identifying and analyzing situations, from competitors, customers, suppliers and others factors that influence the success or the failure of the company.

The competitive intelligence concept has attracted growing attention in the last two decades. Existing literature shows that CI is a multi-disciplinary concept studied by researchers with different fields of expertise and from different points of view either as a concept, a product, a process, a practice/discipline, a method, or a system. Despite this great diversity in the body of knowledge related to the CI, CI solutions proposed to the decision-maker are limited. The objective of this paper is to propose a practical CI solution for the anticipation of competitor’s decisions in order to support the decision-making of a company. Since anticipation evolves under uncertainty, our proposed solution adapts the rough set theory, which has the ability to deal with vagueness, uncertainty, and inconsistencies. A modified rough set and LEM2 algorithms were used to generate rules that help the decision maker anticipating competitor actions. The proposed solution goes further with the aggregation of the generated decision rules using a new proposed algorithm that will contribute to enhancing manager’s decision-making process. To motivate the research, the whole proposed approach is illustrated considering a case study in the field of telecommunications.