Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics

Abstract

Nearly all companies need to retrieve valuable information from business data to increase its efficiency or value, and the rising interests of research in this domain could be named as business analytics. Because most of the problems (obstacles) faced by business have to consider a group of complex and interrelated factors, conventional statistics models (e.g., regression) have constraints in resolving these interrelated and complex problems. Therefore, this study proposes a novel multiple attribute decision-making model to resolve—from ranking/selection to improvement planning—the problems of business analytics in finance, based on the similarity with positive contexts (rules) and the dissimilarity with negative ones. The proposed model not only enhances the previous method (i.e., dominance-based rough set approach, DRSA) on ranking within the same decision class, but also provides a contextual approach to guide businesses for systematic improvements. Infused with the modified VIKOR method, the proposed model could support a company to transform analytics into priority contexts, which may guide improvement planning. To show the proposed model, a group of semiconductor companies in Taiwan is analyzed as an empirical case, and three companies are taken as examples to illustrate the ranking and improvement planning processes. The obtained findings thus contribute to bridge the applications of data-driven business analytics to the field of decision science in practice.