Business Intelligence Methodology Research Articles

Use of Big Data in Supply Chain Management

Abstract: The rapidly expanding interest in the application of big data analytics (BDA) in supply chain management (SCM) among academics and practitioners has prompted an assessment of current research progress in order to define a new agenda. the use of sophisticated analytics tools to improve supply chain management The apps are divided into three categories: descriptive, predictive, and prescriptive analytics, as well as the supply chain operations reference (SCOR) model domains of plan, source, make, deliver, and return. This review answers to the demand by offering a new classification scheme that provides a comprehensive picture of current literature on where and how BDA has been used in SCM. The classification system is based on Mayring's (2008) content analysis method and addresses four research questions: (1) In which aspects of SCM is BDA used? (2) To what extent is BDA employed in these SCM domains in terms of analytics? (3) What are the different types of BDA models utilised in SCM? (4) How are these models developed using BDA techniques? The consideration of these four topics reveals several research gaps, pointing to future study directions. Purpose - Rapid innovation and globalisation have created a plethora of opportunities and choices for businesses and consumers in the marketplace. Due to competitive pressures, sourcing and manufacturing are now done on a global basis, resulting in a huge increase in product availability. The purpose of this article is to determine whether real-time business intelligence (BI) is required in supply chain analytics. Design/methodology/approach – The paper argues for and analyses the benefits and drawbacks of BI. Findings – The article focuses on the need to review the classic BI notion of integrating and consolidating information in an organisation in order to help service-oriented businesses that want to keep their customers. Using a BI methodology to improve the effectiveness and efficiency of supply chain analytics is a vital component of a company's ability to establish a competitive edge. Originality/value – This study contributes to a better understanding of the difficulties surrounding the usage of business intelligence tools in supply chains. Keywords: Supply chain management, Business analytics, Information systems, Big Data, Big data analysis

A Strategic Business Intelligence Framework for Sustainable Asset Management in Finance

Amidst the evolving landscape of finance, integrating sustainability principles into asset management stands as a pivotal pursuit for fostering long-term value creation. This research addresses the symbiotic relationship between business intelligence methodologies and sustainable asset management within the domain of finance. Leveraging advanced machine learning techniques including logistic regression, XGBoost, and CatBoost, this study delves into the exploration of sustainable finance practices and their implications for optimized asset management strategies. The study analyzes and models Asset data, aiming to understand the multifaceted dynamics and interdependencies shaping sustainable asset management decisions. Logistic regression serves as a foundation to model the relationships between variables, while XGBoost and CatBoost handle the complexities of categorical attributes, predicting outcomes related to sustainability metrics and financial performance indicators within the asset portfolio. Through comprehensive analyses and visualizations, this research illuminates critical insights into the influential factors driving sustainable asset management decisions. The findings underscore the significance of leveraging data-driven methodologies to optimize asset management strategies aligned with environmental, social, and governance considerations.

Twitter data analytical methodology development for prediction of start-up firms’ social media marketing level

Social media marketing is an essential and important tool for start-up firms, which can help start-up firms remedy the marketing limitations through ease and relatively low costs. Predicting start-up firms’ social media engagement level can allow them to gauge the effectiveness of their social media marketing efforts and can provide numerous benefits related to strategic marketing processes. This study focuses on developing a methodology involving data science processes and machine learning models to account for the ongoing advancement of business intelligence methodologies. This study gathered data of 8,434 start-up firms from Twitter, generated social media-based features, and created machine learning models to predict the social media engagement level of each firm. The results show that deep learning provides the best accuracy in predicting the engagement levels. The results also show that the number of tweets by the firms, the number of retweets received, and the number of likes received have the most significance in determining the effectiveness of social media marketing activities.

KEY PERFORMANCE INDICATORS FOR E-GOVERNMENT: METHODOLOGICAL APPROACH TO INDICATOR DEVELOPMENT

It is often assumed that modern information technology (IT) is a source of performance improvement. Empirical research has not univocally confirmed this assumption, since complex, extensive and resource demanding IT tools require extensive organizational restructuring, in order to be properly and successfully implemented. This generates the need to create and use business intelligence and key performance indicators (KPI) to obtain a high quality basis for making decisions in real time. In this paper, authors analyze the existence of a potential relationship between the methodological approach to defining business intelligence (BI)-related KPIs and achieving business process performance, as well as the overall organizational performance. The methodological determinants of the process, in which the KPIs are defined, are empirically analyzed and associated with the two discussed aspects of performance. The empirical analysis is conducted for the case of e-government development, with the special emphasis on countries from Central, East and South-East Europe. The E-Government development is measured by two KPIs, which have been defined, by strictly following the recommended BI methodology. The two KPIs are related to a business process, performed by the public administration, which can be greatly facilitated by the introduction of E-Government. This is the process of opening a new enterprise, which can be also viewed as one of key measures of the national economic and entrepreneurial development. The public administration performance in serving the needs of new entrepreneurs for company registration is, therefore, considered in terms of E-Government performance. Two KPIs used measure the procedural complexity and costs for opening a new enterprise in a sample, consisting of 28 European Union member-states. The statistical analysis uses secondary data, available from the official Eurostat Web pages. The empirical results confirm the existence of the hypothesized relationship between the methodological procedures for developing KPIs and the process performance, which affirms the need to develop both E-Government, as well as other processes in public administration, by using solid planning and methodological approaches.

Integrating multiple recommendation schemes for designing sales force support system: a travel agency example

This paper proposes a design of sales force support (SFS) system with business intelligence methodologies. OLAP provides embedded multi-dimensional aggregation knowledge. DM provides sequentially associated relationship knowledge. The integration of both should make the support system easier to get accepted by salespersons and helpful for them to conduct customer recommendation and self-management. Different from most recommendation systems mainly concerned with customers as the users, the proposed design of SFS is concerned with salespersons as the major users. It provides various functions by integrating multiple recommendation schemes, such as personal recommendation (based on OLAP), as well as forward recommendation and backward recommendation (based on sequential pattern discovery in DM). The design can hopefully resolve the usage motivation problem which is usually the major critical success factor of CRM, as indicated in the literature. The SFS implementation with enhanced user interface should hopefully stimulate better sales and customer/management satisfaction.

BUSINESS INTELLIGENCE FOR INSURANCE COMPANIES

The current state and future trends for the world and domestic insurance markets are analyzed. The description of business intelligence methodology, tools and their practical implication for insurance companies are provided.

Summer Profitability Challenge: A Compelling Case for Business Intelligence

To strategically assess the profitability of its summer session, Miami University began an initiative known as the Summer Profitability Challenge project, focused on managing summer enrollment while measuring and containing costs. The project team included representation from across university divisions. The challenge was to bring together pieces of technologically disconnected data and report them as usable and actionable information to support administrative decisions that would increase profitability. As the project progressed, the value and substance of the powerful business intelligence methodologies allowed the team to cross seemingly insurmountable barriers. The initial challenge—detailed and innovative reporting for effective decision making in a real-time environment—was met. Further, the team debriefing following Phase I of the project revealed the value of the collaborative effort as well as best practices in alliances across university divisional divides. In this article, members of the team share this business intelligence case study, an application to a summer session revenue-sharing and profitability model with data and reporting, and the best practices and outcomes revealed by the project.

Short Cycle Business Intelligence Methodology to Guide and Improve Process and Outcomes During Weaning From Mechanical Ventilation

Short Cycle Business Intelligence Methodology to Guide and Improve Process and Outcomes During Weaning From Mechanical Ventilation