Introduction

Innovation analytics (IA) is an emerging paradigm that integrates advances in the data engineering field, innovation field, and artificial intelligence field to support and manage the entire life cycle of a product and processes. In this chapter, we have identified several possibilities where analytics can help in innovation. First, we aim to explain using a few cases how analytics can help in innovating new products to the market specifically through collaborative engagement of designers and data. Second, we will explain the use of artificial intelligence (AI) techniques in the manufacturing context, which progresses at different levels, i.e., from process, function to function interaction, and factory-level innovations.</p

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.</p

Innovation Analytics: Tools for Competitive Advantage

Innovation analytics is an emerging paradigm that integrates information/knowledge, analytics, digital twins and artificial intelligence to support and manage the entire lifecycle of a product and process from inception, through engineering design and manufacture, to service and disposal of manufactured products. Innovation analytics is set to become an integral part of the innovation lifecycle to help make smart, agile decisions and accelerate business growth. Innovation Analytics: Tools for Competitive Advantage provides a comprehensive overview of the challenges and opportunities behind the latest research surrounding technological advances driving innovation analytics; the transition of analytical ideas to interdisciplinary teams; the development of deep synchronicity of skills and production innovation; and the use of innovation analytics in multiple stages of product and process evolution. In exploring the impact of emerging developments in the current climate, researchers and academics will be able to gain insight into real-world usage of analytics for innovation and its contribution toward society. As such, students, scientists, engineers, academics, and management professionals alike will find this title beneficial

Introduction

Innovation analytics (IA) is an emerging paradigm that integrates advances in the data engineering field, innovation field, and artificial intelligence field to support and manage the entire life cycle of a product and processes. In this chapter, we have identified several possibilities where analytics can help in innovation. First, we aim to explain using a few cases how analytics can help in innovating new products to the market specifically through collaborative engagement of designers and data. Second, we will explain the use of artificial intelligence (AI) techniques in the manufacturing context, which progresses at different levels, i.e., from process, function to function interaction, and factory-level innovations.</p

Innovation Analytics: Tools for Competitive Advantage

Innovation analytics is an emerging paradigm that integrates information/knowledge, analytics, digital twins and artificial intelligence to support and manage the entire lifecycle of a product and process from inception, through engineering design and manufacture, to service and disposal of manufactured products. Innovation analytics is set to become an integral part of the innovation lifecycle to help make smart, agile decisions and accelerate business growth. Innovation Analytics: Tools for Competitive Advantage provides a comprehensive overview of the challenges and opportunities behind the latest research surrounding technological advances driving innovation analytics; the transition of analytical ideas to interdisciplinary teams; the development of deep synchronicity of skills and production innovation; and the use of innovation analytics in multiple stages of product and process evolution. In exploring the impact of emerging developments in the current climate, researchers and academics will be able to gain insight into real-world usage of analytics for innovation and its contribution toward society. As such, students, scientists, engineers, academics, and management professionals alike will find this title beneficial

Relationship analysis between greenwashing and environmental performance

This paper fills the gap in the study of the impact of Chinese companies' environmental performance (EP) on greenwashing based on the listed companies in China from 2010 to 2018. The relationship between EP and greenwashing is analyzed based on legitimacy theory and signal theory. From the empirical analysis, it is found that there exists a negative correlation between EP and greenwashing which supports the signal theory. Based on resource-based theory analysis, the impacts of environmental subsidies and political connections on the relationship between environmental performance and greenwashing are also analyzed. EP of enterprises receiving environmental protection subsidy has a greater inhibition effect on greenwashing. The negative effect of EP on greenwashing of state-owned enterprises is bigger than that of non-state-owned enterprises. This study can provide reference for government departments in deepening the reform of government environmental subsidies and environmental governance of state-owned enterprises

Relationship analysis between greenwashing and environmental performance

This paper fills the gap in the study of the impact of Chinese companies' environmental performance (EP) on greenwashing based on the listed companies in China from 2010 to 2018. The relationship between EP and greenwashing is analyzed based on legitimacy theory and signal theory. From the empirical analysis, it is found that there exists a negative correlation between EP and greenwashing which supports the signal theory. Based on resource-based theory analysis, the impacts of environmental subsidies and political connections on the relationship between environmental performance and greenwashing are also analyzed. EP of enterprises receiving environmental protection subsidy has a greater inhibition effect on greenwashing. The negative effect of EP on greenwashing of state-owned enterprises is bigger than that of non-state-owned enterprises. This study can provide reference for government departments in deepening the reform of government environmental subsidies and environmental governance of state-owned enterprises

Valkyrie-Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization

Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban‐based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the work-ing environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal‐inspired quadruped robot that incorpo-rates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed‐loop control system. To minimize errors and improve the performance of the robot due to its non‐linearity, a meta‐heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot’s current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python’s ‘Pyswarms’ package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the pro-posed algorithm and stability of the robot during various gait movements

Valkyrie-Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization

Over the past decades, developments and scientific breakthroughs in the field of robotics have shown the replacement of wheeled robots with legged robots, which are often inspired by the biological characteristics of legged animals. Many industries and urban‐based applications promote quadruped robots because of their dexterous ability to efficiently handle multiple tasks in the work-ing environment. Motivated from the recent works in the field of quadruped robots, this research aims to develop and investigate gaits for a 2 DoF mammal‐inspired quadruped robot that incorpo-rates 4 hip and 4 knee servo motors as its locomotion element. Forward and inverse kinematic techniques are used to determine the joint angle required for the locomotion and stability calculation are presented to determine the center of mass/center of gravity of the robot. Three types of gaits such as walk, trot, and pace are developed while keeping the center of mass inside the support polygon using a closed‐loop control system. To minimize errors and improve the performance of the robot due to its non‐linearity, a meta‐heuristic algorithm has been developed and addressed in this work. The fitness function is derived based on the Euclidean distance between the target and robot’s current position and kinematic equations are used to obtain the relation between joints and coordinates. Based on the literature, particle swarm optimization (PSO) was found to be a promising algorithm for this problem and is developed using Python’s ‘Pyswarms’ package. Experimental studies are carried out quantitatively to determine the convergence characteristics of the control algorithm and to investigate the distance traveled by the robot for different target positions and gaits. Comparison between experimental and theoretical results prove the efficiency of the pro-posed algorithm and stability of the robot during various gait movements

Seven recommendations for scientists, universities, and funders to embrace interdisciplinarity: Practical guidelines to enabling interdisciplinarity

Interdisciplinary research is vital for innovation. Here, we consider interdisciplinarity to mean any form of collaboration between researchers that integrates information, data, techniques, concepts, theories and/or perspectives from two or more disciplines to advance fundamental understanding or solve problems that are beyond the scope of a single discipline (Choi and Pak, 2006; National Academy of Sciences et al, 2005). Increasingly, university leaders, funders and politicians have recognised that the most pressing problems facing the world are too complex to be tackled from a single-disciplinary perspective. Despite this significance and general recognition, a recent report suggests that a high share of academic institutions only pay “lip service” to interdisciplinary research and fail to recognise staff for cross-disciplinary working. Crucially, it states that global research hubs, that is, the USA, UK and Australia, are in reality much less focused on interdisciplinarity versus their Asian counterparts as their research continues to orient itself along disciplinary boundaries and thinking. [Opening paragraph]</p