Название: Data Analytics and Business Intelligence: Computational Frameworks, Practices, and Applications
Автор: Vincent Charles, Pratibha Garg, Neha Gupta
Издательство: CRC Press
Год: 2023
Страниц: 275
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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.

In Chapter 2, “Artificial Intelligence (AI) and Machine Learning (ML) in Supply Chain Decision Making – A pragmatic Discussion”, AI and ML are essential strategic tools used in the predictive analytics domain of supply chain management. The chapter gives an overview of the present trend of using data analytics through AI and ML in making supply chain decisions and its contribution to making the current supply chain lean and agile. The adoption of AI and ML is quite slow but can be quite promising for business growth, provided it is being adopted by making the current system and environment fit for its implementation. This chapter aims to present a theoretical idea of supply chain segmentation based on decision-making by considering dynamic demand factors applicable across various industries.

In Chapter 3, “Assessing Relations of Lean Manufacturing, Industry 4.0 and Sustainability in manufacturing Environment”, reflects on how sustainability in the manufacturing environment relates to the production philosophy of Lean Manufacturing (LM) and Industry 4.0.

In Chapter 4, “Role of Artificial Intelligence in Supply Chain Management”, Artificial intelligence (AI) continues to dominate the business and non-business environments amid various criticisms due to the fear that AI technology will endanger the role of people in future management and business operations. In this chapter, the authors seek to identify different approaches through which AI and some other technologies transform the jewellery business.

The fifth chapter, “Impact of Blockchain in Creating Sustainable Supply Chain”, highlights how blockchain technology can be a game-changer in building sustainable supply chains with an overall low carbon footprint per unit of product, eliminating possibilities of fraud, double-counting in the carbon credit market and in bringing authentication and transparency in audits and certification of sustainability practices. Blockchain technology will help in building a sustainable supply chain.

The sixth chapter, “Exploring Adoption of Blockchain Technology for Sustainable Supply Chain Management”, highlights how the Shared ledger, Smart contract, Privacy, Trust, and Transparency, the five pillars of Blockchain, can ease some of the global difficulties faced in supply chain management and create sustainable supply chains. It further discusses the application of blockchain and challenges in the adoption of blockchain technology.

The second section of this book is based on chapters about Data Mining, Computational Framework, and Practices. The seventh chapter, “Mathematical Model of Consensus and its Adaptation to Achievement Consensus in Social Groups”, discusses how this approach ensures that all the opinions of the group members, their ideas, and needs will be considered.

Chapter 8, “Data to Data Science: A Phenomenal Journey”, helps the readers to gain insights about the data warehouse, data mining, and big data and its challenges. Data has been crucial for humans since the pre-historic age, from counting to bartering and other activities. With evolution, data has occupied a very important position in human life which can be used for future decision-making. The approach to recording and storing data has moved from the conventional approach to a recent manner of digitalization of data.

Chapter 9, “Application of Algorithm on Computational Intelligence and Machine Learning for Product Design: Emerging Needs and Challenges”, Computational intelligence is one of the most fascinating techniques that has recently joined the material science toolkit for Machine Learning. This set of statistical tools has already demonstrated its ability to significantly accelerate both fundamental and practical research. Right now, several attempts are being placed into growing and device studying (machine learning) implemented to solid-nation devices. Machine Learning concepts, algorithms, descriptors, and databases are the starting points in materials science. Various Machine Learning algorithms for detecting stable materials and predicting their crystal structure are being debated all the time, on a range of quantitative structure-property correlations, as well as a few more ideas for using Machine Learning to replace first-principal methods.