Jayanthi Ranjan

Bio: Jayanthi Ranjan is an academic researcher from Institute of Management Technology, Ghaziabad. The author has contributed to research in topics: Customer relationship management & Customer retention. The author has an hindex of 15, co-authored 75 publications receiving 1231 citations. Previous affiliations of Jayanthi Ranjan include Techno India.

Real time business intelligence in supply chain analytics

Abstract: Purpose – Rapid innovation and globalization have generated tremendous opportunities and choices in the marketplace for firms and customers. Competitive pressures have led to sourcing and manufacturing on a global scale resulting in a significant increase in products. The paper tries to identify the need for real time business intelligence (BI) in supply chain analytics.Design/methodology/approach – The paper provides argument and analysis of the advantages and hurdles in BI.Findings – The paper focuses on the necessity to revisit the traditional BI concept that integrates and consolidates information in an organization in order to support firms that are service oriented and seeking customer loyalty and retention. Enhancing effectiveness and efficiency of supply chain analytics using a BI approach is a critical component in a company's ability to achieve its competitive advantage.Originality/value – This paper furthers understanding of the issues surrounding the use of BI systems in supply chains.

Business intelligence: concepts, components, techniques and benefits

Abstract: For companies maintaining direct contact with large numbers of customers, however, a growing number channel-oriented applications (e.g. e-commerce support, call center support) create a new data management challenge: that is effective way of integrating enterprise applications in real time. To learn from the past and forecast the future, many companies are adopting Business Intelligence (BI) tools and systems. Companies have understood the importance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts. It describes the insights on the role and requirement of real time BI by examining the business needs. The paper explores the concepts of BI, its components, emergence of BI, benefits of BI, factors influencing BI, technology requirements, designing and implementing business intelligence, and various BI techniques.

Business justification with business intelligence

Abstract: Purpose – The paper intends to find out the business justifications and requirements for incorporating business intelligence (BI) in organizations because many organizations that already have systems in place to collect data and gather information, often find themselves in a situation where they have no tools or roadmaps to put their vast data and information into use for strategic decision making.Design/methodology/approach – In this paper BI and the growing potential for implementing BI is explained. The paper also explains a checklist for implementing BI.Findings – During the last ten years, the approach to business management in the entire globe has deeply changed. Firms have understood the importance of enforcing achievement of the goals defined by their strategy through metrics‐driven management. Firms are evolving into new forms based on knowledge and networks in response to an environment characterized by indistinct organizational boundaries and fast‐paced change. New and complex changes are emerg...

Data mining techniques for better decisions in human resource management systems

Abstract: This paper presents the role of data mining in Human Resource Management Systems (HRMS). A deep understanding of the knowledge hidden in Human Resource (HR) data is vital to a firm's competitive position and organisational decision making. Analysing the patterns and relationships in HR data is quite rare. The HR data is usually treated to answer queries. Because HR data primarily concerns transactional processing getting data into the system, recording it for reporting purposes it is necessary for HRMS to become more concerned with the quantifiable data. We show how data mining discovers and extracts useful patterns from this large data set to find observable patterns in HR. The paper demonstrates the ability of data mining in improving the quality of the decision-making process in HRMS and gives propositions regarding whether data-mining capabilities should lead to increased performance to sustain competitive advantage.

Educational Data Mining: A Review of the State of the Art

Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Data-mining for sulfur and fluorine: an evaluation of pharmaceuticals to reveal opportunities for drug design and discovery.

Abstract: Among carbon, hydrogen, oxygen, and nitrogen, sulfur and fluorine are both leading constituents of the pharmaceuticals that comprise our medicinal history. In efforts to stimulate the minds of both the general public and expert scientist, statistics were collected from the trends associated with therapeutics spanning 12 disease categories (a total of 1969 drugs) from our new graphical montage compilation: disease focused pharmaceuticals posters. Each poster is a vibrant display of a collection of pharmaceuticals (including structural image, Food and Drug Administration (FDA) approval date, international nonproprietary name (INN), initial market name, and a color-coded subclass of function) organized chronologically and classified according to an association with a particular clinical indication. Specifically, the evolution and structural diversity of sulfur and the popular integration of fluorine into drugs introduced over the past 50 years are evaluated. The presented qualitative conclusions in this arti...

Review: Data mining techniques and applications - A decade review from 2000 to 2011

Abstract: In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.