Gunjan Soni – författare

Risk, Reliability and Resilience in Operations Management examines measurement tools and techniques and their real-world application. The book provides a resource that is needed to help solve complex business operations and global supply chains and their important requirements for the accurate measurement of risk, reliability, and resilience to inform decisions and reduce risk. In addition, the book discusses advancements in technology and data analytics, with final sections covering the COVID-19 pandemic and how it has put greater emphasis on the importance of risk, reliability, and resilience in business operations.This book provides a timely overview of measurement techniques and their application in operations management, offering insights into future directions in this field.

Provides a comprehensive overview on the measurement of risk, reliability, and resilience in operations managementDelves into the practical application of risk, reliability, and resilience management techniques in real-world scenarios, providing case studies and examples that demonstrate how businesses can effectively measure and manage these factors to make informed decisionsExplores emerging trends, technological advancements, and potential developments that may impact risk measurement, reliability, and resilience

Risk, Reliability and Resilience in Operations Management examines measurement tools and techniques and their real-world application. The book provides a resource that is needed to help solve complex business operations and global supply chains and their important requirements for the accurate measurement of risk, reliability, and resilience to inform decisions and reduce risk. In addition, the book discusses advancements in technology and data analytics, with final sections covering the COVID-19 pandemic and how it has put greater emphasis on the importance of risk, reliability, and resilience in business operations.This book provides a timely overview of measurement techniques and their application in operations management, offering insights into future directions in this field.- Provides a comprehensive overview on the measurement of risk, reliability, and resilience in operations management- Delves into the practical application of risk, reliability, and resilience management techniques in real-world scenarios, providing case studies and examples that demonstrate how businesses can effectively measure and manage these factors to make informed decisions- Explores emerging trends, technological advancements, and potential developments that may impact risk measurement, reliability, and resilience

AI-Powered Supply Chains: Balancing Risk, Reliability, and Sustainability explores tools and techniques for assessing risk, reliability, resilience, and sustainability in Supply Chain Management. As business operations become more complex and globalized, understanding these factors is crucial for informed decision-making and reducing risk. Recent technological advancements-such as AI, machine learning, data analytics, IoT, and blockchain-offer innovative methods for improving visibility and sustainability in supply chains. Additionally, the COVID-19 pandemic has underscored the importance of effective risk management and resilience strategies.AI-Powered Supply Chains: Balancing Risk, Reliability, and Sustainability provides a comprehensive overview of measurement techniques, their real-world applications, and insights into future developments in the field.Provides an in-depth exploration of measuring risk, reliability, resilience, and sustainability in supply chain managementExamines various tools and techniques, demonstrating their application in real-world scenarios to aid businesses in informed decision-making and risk reductionContent is tailored for researchers, academics, and practitioners in supply chain managementHighlights how emerging technologies, such as AI, machine learning, data analytics, IoT, and blockchain, can be implemented to enhance supply chain resilience and sustainability

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.

The book

Discusses basic as well as advance research in the field of prognostics Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning

The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.

The book

Discusses basic as well as advance research in the field of prognostics Explores integration of data collection, fault detection, degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning

The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

The text discusses the latest data-driven, physics-based, and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, electrical engineering, and computer science.The bookDiscusses basic as well as advance research in the field of prognosticsExplores integration of data collection, fault detection, degradation modeling and reliability prediction in one volumeCovers prognostics and health management (PHM) of engineering systemsDiscusses latest approaches in the field of prognostics based on machine learningThe text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior, based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate, graduate students, and academic researchers in areas such as industrial and production engineering, manufacturing science, electrical engineering, and computer science.

This volume gathers the latest advances, innovations and applications in the field of condition monitoring, damage assessment and maintenance engineering, as presented by leading international researchers and engineers at the UNIfied Conference of DAMAS (International Conference on Damage Assessment of Structures), IncoME  (International Conference on Maintenance Engineering) and TEPEN (The Efficiency and Performance Engineering), held in Jaipur, India on November 26-28, 2024. Topics include sensors and measurement systems, condition monitoring and predictive maintenance, machine health monitoring, maintenance organisation & performance measurement, Industrial Internet of Things (IIoT), cyber physical systems, machine learning in maintenance and production environment, plant maintenance, asset management, reliability, artificial intelligence and related areas, life cycle cost optimisation, health management. The contributions, which were selected through a rigorous internationalpeer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.

This volume gathers the latest advances, innovations and applications in the field of condition monitoring, damage assessment and maintenance engineering, as presented by leading international researchers and engineers at the UNIfied Conference of DAMAS (International Conference on Damage Assessment of Structures), IncoME  (International Conference on Maintenance Engineering) and TEPEN (The Efficiency and Performance Engineering), held in Jaipur, India on November 26-28, 2024. Topics include sensors and measurement systems, condition monitoring and predictive maintenance, machine health monitoring, maintenance organisation & performance measurement, Industrial Internet of Things (IIoT), cyber physical systems, machine learning in maintenance and production environment, plant maintenance, asset management, reliability, artificial intelligence and related areas, life cycle cost optimisation, health management. The contributions, which were selected through a rigorous internationalpeer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.

This volume gathers the latest advances, innovations and applications in the field of condition monitoring, damage assessment and maintenance engineering, as presented by leading international researchers and engineers at the UNIfied Conference of DAMAS (International Conference on Damage Assessment of Structures), IncoME  (International Conference on Maintenance Engineering) and TEPEN (The Efficiency and Performance Engineering), held in Jaipur, India on November 26-28, 2024. Topics include sensors and measurement systems, condition monitoring and predictive maintenance, machine health monitoring, maintenance organisation & performance measurement, Industrial Internet of Things (IIoT), cyber physical systems, machine learning in maintenance and production environment, plant maintenance, asset management, reliability, artificial intelligence and related areas, life cycle cost optimisation, health management. The contributions, which were selected through a rigorous internationalpeer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.