Proposed Framework Of Next Generation Supply Chain Management Using Big Data Analytics
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A Proposed Framework of Next Generation Supply Chain Management Using Big Data Analytics
Over last few years the definition of competitive strategy has been substantially modified due to the rapidly changing nature of global market and the technology revolutions. Data driven supply chain management has become one of the competitive advantages for organizations as it helps in integration of all the activities across the entire supply chain. It also enables organizations to take informed strategic decisions while mitigating risks in uncertain business environment. Data is being generated from every stage of a supply chain resulting in gigantic volume of data having diverse nature ranging from transactional data to subjective opinions of the customers. Analytics plays a significant role in handling 'Big Data' today and will be of increasing importance tomorrow. This paper attempts to identify the sources of data generation, the nature and the volume of the data being generated, the consumption of the data in a typical next generation supply chain. Furthermore, it also examines the effect of big data on supply chain and the role of analytics in extracting useful information which can be suitably leveraged for achieving more efficiency and robustness in supply chain operations. A brief discussion on future scope of work is also included.
Big Data Analytics and Intelligent Techniques for Smart Cities
Big Data Analytics and Intelligent Techniques for Smart Cities covers fundamentals, advanced concepts, and applications of big data analytics for smart cities in a single volume. This comprehensive reference text discusses big data theory modeling and simulation for smart cities and examines case studies in a single volume. The text discusses how to develop a smart city and state-of-the-art system design, system verification, real-time control and adaptation, Internet of Things, and testbeds. It covers applications of smart cities as they relate to smart transportation/connected vehicle (CV) and intelligent transportation systems (ITS) for improved mobility, safety, and environmental protection. It will be useful as a reference text for graduate students in different areas including electrical engineering, computer science engineering, civil engineering, and electronics and communications engineering. Features: Technologies and algorithms associated with the application of big data for smart cities Discussions on big data theory modeling and simulation for smart cities Applications of smart cities as they relate to smart transportation and intelligent transportation systems (ITS) Discussions on concepts including smart education, smart culture, and smart transformation management for social and societal changes
Predictive Intelligence Using Big Data and the Internet of Things
With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers.