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Full Description
Business Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making.
BA and Business Intelligence (BI) generate capabilities for companies to compete in the market effectively and has become one of the main functional areas in most companies. BA tools are used in diverse ways, for example, to identify consumer behaviour patterns and market trends, to derive valuable insights on the performance of stocks, to find information on the attrition rate of employees, to analyse and solve healthcare problems, to offer insight into inventory management and supply chain management, to analyse data from social networks, and to infer traffic behaviour and develop traffic management policy, among others.
BA and BI have become one of the most popular research areas in academic circles, as well as in the industry, driven by the increasing demand in the business world. This book aims to become a stimulus for innovative business solutions covering a wide range of aspects of business analytics, such as management science, information technology, descriptive, prescriptive, and predictive models, machine learning, network science, mathematical and statistical techniques. The book will encompass a valuable collection of chapters exploring and discussing computational frameworks, practices, and applications of BA that can assist industries and relevant stakeholders in decision-making and problem-solving exercises, with a view to driving competitive advantage.
Contents
SECTION I: OPERATIONS AND SUPPLY CHAIN ANALYTICS. Wholesale Price Strategy of a Manufacturer under Collusion of Downstream Channel Members: A Game-Theoretic Approach. AI and ML in Supply Chain Decision Making - A Pragmatic Discussion. Assessing Relations of Lean Manufacturing, Industry 4.0 and Sustainability in Manufacturing Environment. Role of Artificial Intelligence in Supply Chain Management. Impact of Blockchain in Creating Sustainable Supply Chain. Exploring Adoption of Blockchain Technology for Sustainable Supply Chain Management. SECTION II: DATA MINING, COMPUTATIONAL FRAMEWORK, AND PRACTICES. Mathematical Model of Consensus and its Adaptation to Achievement Consensus in Social Groups. Data to Data Science: A Phenomenal Journey. Application of Algorithm on Computational Intelligence and Machine Learning for Product Design: Emerging Needs and Challenges. SECTION III: BUSINESS INTELLIGENCE AND ANALYTICS APPLICATIONS. HR ANALYTICS: Galvanizing the Organizations with Prowess of Technology. Marketing Analytics - Concept, Applications, Opportunities, and Challenges Ahead. Effect of Social Media Usage on Anxiety During a Pandemic: An Analytical Study on the Young Adults. An Exploratory Study of Understanding Consumer Buying Behaviour Towards Green Cosmetics Products in the Indian Market. Analytical Study of Factors Affecting Adoption of Blockchain by Fintech Companies. A Study of the Performances of Small Cap, Large Cap and Banking & Financial Sector Funds of Nippon India AMC, ICICI Prudential AMC and Tata AMC.
