Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Using business intelligence to improve performance.

Abstract: Cleveland Clinic's enterprise performance management program offers proof that comparisons of actual performance against strategic objectives can enable healthcare organization to achieve rapid organizational change. Here are four lessons Cleveland Clinic learned from this initiative: Align performance metrics with strategic initiatives. Structure dashboards for the CEO. Link performance to annual reviews. Customize dashboard views to the specific user.

Predicting Corporate Financial Sustainability Using Novel Business Analytics

Abstract: Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

Combining balanced scorecard with data envelopment analysis to conduct performance diagnosis for Taiwanese LED manufacturers

Abstract: Light emitting diode (LED) is a popular component to replace the traditional lighting source or advertising sign display. In 2014, high-brightness LED has a strong growth in backlight display, mobile appliances, automotive devices and outdoor illumination. However, emerging technologies in compound materials, epitaxying, packaging and new entrants result in a scale-based economy and intensively competitive environment. Inspired by the concept of business analytics, this paper proposes a novel framework to conduct corporate diagnosis for Taiwanese LED manufacturers: (1) balanced scorecard is fused with data envelopment analysis to address the impact of operational efficiency on performance outcomes, (2) financial and non-financial indicators are incorporated into the process of performance measurement, (3) the intricate causalities between key performance indicators (KPIs) and multiple outcomes (i.e. earnings per share and return on equity) are captured and (4) managerial insights are provided to indicate ...

Business understanding, challenges and issues of Big Data Analytics for the servitization of a capital equipment manufacturer

Abstract: One of the most promising areas where Big Data Analytics can be integrated into business-oriented projects-allowing research and development teams to work hand in hand with industry representatives — is the digitalization of manufacturing industry. There are two main driving forces for the interest in this area: the promotion of key strategies such as German Government's Industrie 4.0 or General Electric's Industrial Internet, and the use of servitization strategies to transform manufacturing business models. This paper presents a case study based on a Big Data Analytics applied research project developed for a capital equipment manufacturer. Their current business model is based on selling machinery and storage infrastructure for larger chemical manufacturing companies spread worldwide. The project is developed in the context of a servitization strategy where this capital equipment manufacturer aims at attaching valued-added services to their products, leveraging the use of Big Data Analytics to assist their customers in order to optimize their production process. The paper uses the CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology as a framework to organize and present our main findings related to the Business Understanding phase. This allow us to provide pragmatic, business-oriented considerations that can be leveraged by research and development teams when exploring opportunities to develop Big Data Analytics projects in the context of manufacturing servitization. Thus, they can face the initial steps of those projects with a better understanding of the specificities of this application field, as well as an a priori identification of problematic situations that may arise and required competencies to be covered by their team members.

Adoption of Business Intelligence & Analytics in Organizations – An Empirical Study of Antecedents

Abstract: Although Business Intelligence& Analytics (BIA) systems are increasingly seen as a source of competitive advantage, limited research, to our knowledge, has examined the factors driving their organizational adoption. Drawing on TechnologyOrganization-Environment framework, we present a theoretical model of factors associated with the extent of organizational adoption of BIA technologies and test it with a large cross-sectional sample. We find that an organization’s perceived benefits, technology sophistication in terms of data infrastructure and organization size are positively associated with the extent of BIA adoption. In addition, we find that firms in knowledge-intensive industries are likely to more extensively adopt BIA and the lack of industry standards hinders adoption. This study can inform researchers and practitioners on the enabling conditions in organizations that drive the adoption of BIA technologies.