Smart Industry - Better Management

The ebook edition of this title is Open Access and freely available to read online. Smart industry requires better management. As industrial and production systems are future-proofed, becoming smart and interconnected through use of new manufacturing and product technologies, work is advancing on improving product needs, volume, timing, resource efficiency, and cost, optimally using supply chains. Presenting innovative, evidence-based, and cutting-edge case studies, with new conceptualizations and viewpoints on management, Smart Industry, Better Management explores concepts in product systems, use of cyber physical systems, digitization, interconnectivity, and new manufacturing and product technologies. Contributions to this volume highlight the high degree of flexibility in people management, production, including product needs, volume, timing, resource efficiency and cost in being able to finely adjust to customer needs and make full use of supply chains for value creation. Smart Industry, Better Management illustrates how industry can enabled by a more network-centric approach, making use of the value of information and the latest available proven manufacturing techniques

Impact of IIoT and Lean bundles configurations on proactive work behaviors

This thesis investigates the impact of IIoT and Lean bundles configurations on team proactivity, that is calculated as a mean of individual proactivity values. It is divided in four chapters: in the first chapter an introduction to Industry 4.0 and a description of its main technologies is performed, the second chapter is characterized by a literature review on the integration between Industry 4.0 an Lean Production, the third chapter consists on a description of the Qualitative Comparative Analysis approach and the last chapter discusses the results of the analysis

Capabilities of the Intelligent Manufacturing Enterprise

Manufacturing enterprises encounter pressure to digitalize and increase their intelligence as their environments demand improved productivity and agility. Based on existing research on intelligent enterprises, manufacturing enterprises, and data technologies, the authors developed an explanatory model for the derivation of a definition of the intelligent manufacturing enterprise. This paper expands the formerly developed model by presenting the characteristics of the intelligent manufacturing enterprise and the capabilities needed to become such an enterprise

Risk Prediction of Digital Human Resource Management Based on Artificial Intelligence

The latest information technologies have greatly accelerated the digitalization progress of Human Resource Management (HRM) and many useful techniques and tools have been developed for that purpose. However, in terms of risk management, effective enough tools and methods are still insufficient. Existing studies generally fail to give a turnkey solution to the operational risks in digital HRM system, and the macro measurement models are not suitable for dealing with the risks in the digital HRM system of each single enterprise. In view of these defects, this paper studied the prediction of risks in digital HRM systems based on Artificial Intelligence (AI). Firstly, the paper outlined the functions of a digital HRM system, defined the risk management mechanism of a HRM system, and built a conceptual model for it. Then, this paper proposed a novel method for predicting the risks in the digital HRM system, which innovatively integrates the digital HRM risk event chains with the risk event graph. After that, the paper elaborated on the structures and building principles of the risk event representation layer, risk event chain module, risk event graph module, and attention fusion module. At last, experimental results verified that the proposed model has obvious advantages in digital HRM risk prediction in terms of both stability and accuracy

Green human resource management, green supply chain management, green lifestyle: their effect on business sustainability mediated by digital skills

Purpose: This study aims to empirically test and analyze the role of Green Human Resource Management (GHRM) and Green Supply Chain Management (GSCM) and Green Lifestyle on business sustainability mediated by digital skills. Design/Methodology: This study involved logistics managers at manufacturing companies in DKI Jakarta and West Java, Indonesia. It is because these two provinces have the largest manufacturing companies in Indonesia. From a late survey, the researchers obtained data from 250 questionnaires that were distributed. Then from all the data, there were 218 data that could be processed for further analysis. This study utilized the Structural Equation Model (SEM) analysis technique with AMOS 24 software in the data testing process. Findings: This study has confirmed a number of findings including: With the current COVID-19 Pandemic, Green Human Resources Management (GRHM) and Green Supply Chain Management (GSCM) are needed in Business Sustainability. GSCM has a positive effect on Business Sustainability (BS). Indonesia is also facing a revolution of 4.0 and 5.0, and Green Human Resources Management (GHRM) practice has an influence on BS and is deemed having significant and positive effect. In addition, manufacturing companies support the practice of green environment so that Green Supply Chain Management (GSCM) practice needs Digital Skill (DS) in enhancing its performance. Also, it has been proven in this research that GSCM has positive and significant effect. Digital Skill (DS), however, has no direct effect on Business Sustainability (BS). Research Limitation/Implications: This study focuses on the scope of green human resource management, green supply chain management, and green lifestyle and their impact on business sustainability mediated by digital skills. The next study is expected to examine the concept of green human resource management in service companies in order to obtain unique findings and confirm the relevance of the green human resource management concept in various business phenomena. Originality/value: This study explains the concept of Green Human Resource Management (GHRM), Green Supply Chain Management (GSCM) and Green Lifestyle (GL) towards Business Sustainability (BS) which is a different concept from the concept of Human Resource Management (HRM) implemented in manufacturing companies. In addition, this study discusses the green supply chain in its influence on business sustainability. Also, this study explains the role of the concept of Digital Skills (DS) in influencing Business Sustainability (BS). Practical and Social Implication: This study is expected to be a reference for practitioners, especially in the fields of Green Human Resource Management (GHRM), Green Lifestyle (GL), and Green Supply Chain Management (GSCM) in improving Business Sustainability (BS). Furthermore, the concept of green human resource management can be used by organizations as an effective alternative to increase responsibility in increasing business sustainability. Digital skills have a very significant role in implementing the research modelPeer Reviewe

Artificial intelligence applied to potential assessment and talent identification in an organisational context

França, T. J. F., São Mamede, J. H. P., Barroso, J. M. P., & Santos, V. M. P. D. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organisational context. Heliyon, 9(4), 1-25. [e14694]. https://doi.org/10.1016/j.heliyon.2023.e14694Our study provides valuable insights into the relationship between artificial intelligence (AI) and Human Resource Management (HRM). We have minimised bias and ensured reliable findings by employing a systematic literature review and the PRISMA statement. Our comprehensive synthesis of the studies included in this research, along with a bibliometric analysis of articles, journals, indexes, authors' affiliations, citations, keyword co-occurrences, and co-authorship analysis, has produced robust results. The discussion of our findings focuses on critical areas of interest, such as AI and Talent, AI Bias, Ethics and Law, and their impact on Human Resource (HR) management. Our research highlights the recognition by organisations of the importance of talent management in achieving a competitive advantage as higher-level skills become increasingly necessary. Although some HR managers have adopted AI technology for talent acquisition, our study reveals that there is still room for improvement. Our study is in line with previous research that acknowledges the potential for AI to revolutionise HR management and the future of work. Our findings emphasise the need for HR managers to be proactive in embracing technology and bridging the technological, human, societal, and governmental gaps. Our study contributes to the growing body of AI and HR management knowledge, providing essential insights and recommendations for future research. The importance of our study lies in its focus on the role of HR in promoting the benefits of AI-based applications, thereby creating a larger body of knowledge from an organisational perspective.publishersversionpublishe

Adoption Factors of Artificial intelligence in Human Resource Management

Tesis por compendio[ES] El mundo es testigo de nuevos avances tecnológicos que afectan significativamente a las organizaciones en diferentes departamentos. La inteligencia artificial (IA) es uno de estos avances, visto como una tecnología revolucionaria en la gestión de recursos humanos (RRHH). Profesionales y académicos han discutido el brillante papel de la IA en RRHH. Sin embargo, el análisis profundo de esta tecnología en el proceso de RRHH es aún escaso. Con todo ello, el objetivo principal de esta tesis es investigar el estado de la IA en RRHH y así identificar factores clave de implementación concretos. Primero, construyendo un marco académico para la IA en RRHH; segundo, analizar las aplicaciones de IA más utilizada en los procesos de RRHH; tercero, identificar las formas óptimas de transferir el conocimiento en los procesos de implementación de IA. La metodología utilizada para la investigación combina la revisión sistemática de la literatura y técnicas de investigación cualitativa. Como base y medida preparatoria para abordar las preguntas de investigación, se llevó a cabo un extenso análisis de la literatura en el campo AI-RRHH, con un enfoque particular en las publicaciones de algoritmos de IA en HRM, análisis de HR-Big data, aplicaciones/soluciones de IA en HRM e implementación de IA. En la misma línea, el autor publicó artículos en varias conferencias que contribuyeron a mejorar la madurez de las preguntas de investigación. Con base en este conocimiento, los estudios publicados ilustraron la brecha entre la promesa y la realidad de la IA en RRHH, teniendo en cuenta los requisitos técnicos de la implementación de la IA, así como las aplicaciones y limitaciones. Posteriormente, se entrevistó a expertos en recursos humanos y consultores de IA que ya habían adquirido experiencia de primera mano con los procesos de recursos humanos en un entorno de IA para descubrir la verdad de la aplicación de la IA dominante en el proceso de RRHH. Los principales hallazgos de esta tesis incluyen la derivación de una definición completa de IA en RRHH, así como el estado de las estrategias de adopción de aplicaciones de IA en RRHH. Como resultado adicional, se explora la utilidad y las limitaciones de los chatbots en el proceso de contratación en la India. Además, factores clave para transferir el conocimiento del proceso de implementación de IA a los gerentes y empleados de recursos humanos. Finalmente, se concluye identificando desafíos asociados con la implementación de IA en el proceso de recursos humanos y el impacto de COVID-19 en la implementación de IA.[CA] El món és testimoni de nous avanços tecnològics, que afecten significativament les organitzacions en diferents departaments. La intel·ligència artificial (IA) és un d'aquests avanços que s'anuncia àmpliament com una tecnologia revolucionària en la gestió de recursos humans (HRM). Professionals i acadèmics han discutit el brillant paper de la IA en HRM. No obstant això, encara és escàs l'anàlisi profund d'aquesta tecnologia en el procés de HRM. Per tant, l'objectiu principal d'aquesta tesi és investigar l'estat de la IA en HRM i derivar factors clau d'implementació concrets. Primer, construint un marc acadèmic per a la IA en HRM; segon, analitzar l'aplicació de IA més utilitzada en el procés de recursos humans; tercer, identificar les formes òptimes de transferir el coneixement dels processos d'implementació de IA. La metodologia utilitzada per a la investigació es combina entre una revisió sistemàtica de la literatura i una tècnica d'investigació qualitativa. Com a base i mesura preparatòria per a abordar les preguntes d'investigació, es va dur a terme una extensa anàlisi de la literatura en el camp IA-HRM, amb un enfocament particular en les publicacions d'algorismes de IA en HRM, anàlisis de HR-Big data, aplicacions/soluciones de IA en HRM i implementació de IA. En la mateixa línia, l'autor va publicar articles en diverses conferències que van procedir a millorar la maduresa de les preguntes d'investigació. Amb base en aquest coneixement, els estudis publicats van illustrar la bretxa entre la promesa i la realitat de la IA en HRM, tenint en compte els requisits tècnics de la implementació de la IA, així com les aplicacions i limitacions. Posteriorment, es va entrevistar experts en recursos humans i consultors de IA que ja havien adquirit experiència de primera mà amb els processos de recursos humans en un entorn de IA per a descobrir la veritat de l'aplicació de la IA dominant en el procés de recursos humans. Les principals troballes d'aquesta tesi són la derivació d'una definició completa de IA en HRM, així com l'estat de les estratègies d'adopció d'aplicacions de IA en HRM. Com a resultat addicional, explore la utilitat i les limitacions dels chatbots en el procés de contractació a l'Índia. A més, factors clau per a transferir el coneixement del procés d'implementació de IA als gerents i empleats de recursos humans. També es van concloure els desafiaments associats amb la implementació de IA en el procés de recursos humans i l'impacte de COVID-19 en la implementació de IA.[EN] The world is witnessing new technological advancements, which significantly impacts organizations across different departments. Artificial intelligence (AI) is one of these advancements that is widely heralded as a revolutionary technology in Human Resource Management (HRM). Professionals and scholars have discussed the bright role of AI in HRM. However, deep analysis of this technology in the HR process is still scarce. Therefore, the main goal of this thesis is to investigate the status of AI in HRM and derive concrete implementation key factors. Through, first, building an academic framework for AI in HRM; second, analyzing the most commonly used AI applications in HR process; third, identifying the optimal ways to transfer the knowledge of AI implementation processes. The methodology used for the investigation combines a systematic literature review and a qualitative research technique. As a basis and preparatory measure to address the research questions, an extensive literature analysis in the AI-HRM field was carried out, with a particular focus on publications of AI in HRM, HR-Big data analysis, AI applications/solutions in HRM and AI implementation. Along similar lines, the author published papers in several conference proceedings to improve the maturity of research questions. Based on this work, the published studies illustrate the gap between the promise and reality of AI in HRM, taking into account the requirements of AI implementation as well as the applications and limitations. Subsequently, HR experts and AI consultants, who had already gained first-hand experience with HR processes in an AI environment, were interviewed to find out the truth of the dominant AI's application in HR process. The main findings of this thesis are the derivation of a complete definition of AI in HRM as well as the status of the adoption strategies of AI applications in HRM. As a further result, it explores the usefulness and limitations of chatbots in the recruitment processes in India. In addition, derived the key factors to transfer the knowledge of AI implementation process to HR managers and employees. Challenges associated with AI implementation in the HR process and the impact of COVID-19 on AI implementation were also concluded.Tuffaha, M. (2022). Adoption Factors of Artificial intelligence in Human Resource Management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185909Compendi

L’implantation de la robotique collaborative et la gestion des ressources humaines dans le secteur manufacturier : soutenir le changement et l’adoption

Ce mémoire de maîtrise explore l’implantation de la robotique collaborative en entreprise sous l’angle des pratiques de gestion et des facteurs humains. La visée initiale de ce projet de recherche visait préalablement à circonscrire l’apport que peut prendre la gestion des ressources humaines (GRH) lors de ce type d’implantation technologique, qui implique une collaboration humain-machine plus accrue qu’auparavant. Initialement, l’objectif était donc d’identifier les pratiques de GRH à mettre en place lors de l’implantation de robots collaboratifs. Cela dit, comme ce projet de recherche présente une démarche exploratoire semi-inductive, l’objectif de recherche a évolué vers plusieurs objectifs. Cette ouverture sur de nouveaux objectifs est subséquente aux résultats obtenus lors de la revue systématique de la littérature et de la collecte de données afin de dresser un portrait plus juste, adapté à l’état des connaissances et au terrain. Les objectifs poursuivis sont les suivants : 1) identifier les pratiques de GRH et d’autres pratiques organisationnelles en matière de gestion du changement facilitant l’implantation et l’adoption des robots collaboratifs 2) identifier les facteurs associés à l’humain, au robot et à l’environnement qui influencent l’implantation des robots collaboratifs, l’adoption et la collaboration entre l’opérateur et le robot