A Framework For Workforce Management An Agent Based Simulation Approach

In today\u27s advanced technology world, enterprises are in a constant state of competition. As the intensity of competition increases the need to continuously improve organizational performance has never been greater. Managers at all levels must be on a constant quest for finding ways to maximize their enterprises\u27 strategic resources. Enterprises can develop sustained competitiveness only if their activities create value in unique ways. There should be an emphasis to transfer this competitiveness to the resources it has on hand and the resources it can develop to be used in this environment. The significance of human capital is even greater now, as the intangible value and the tacit knowledge of enterprises\u27 resources should be strategically managed to achieve a greater level of continuous organizational success. This research effort seeks to provide managers with means for accurate decision making for their workforce management. A framework for modeling and managing human capital to achieve effective workforce planning strategies is built to assist enterprise in their long term strategic organizational goals

Analysis of knowledge flows in university-industry collaboration: a materials innovation case

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência e Engenharia de Materiais, Florianópolis, 2021.Materiais avançados são fundamentais para a inovação, pois desempenham um papel importante no desenvolvimento de todos os tipos de novos produtos e processos. Materiais avançados, entretanto, apresentam vários desafios devido à sua posição a montante na cadeia de valor. O desenvolvimento de materiais requer longos períodos de desenvolvimento e altos investimentos antes de obter os primeiros feedbacks dos clientes, aumentando assim os riscos de investimento. Portanto, a cooperação universidade-indústria (CUI), apoiada por políticas científicas nacionais e programas de fomento, desempenha um papel importante no apoio ao desenvolvimento de materiais avançados e na construção de vantagem competitiva no mercado. Na CUI, o compartilhamento de conhecimento é um dos principais objetivos, portanto, o gerenciamento dos fluxos de conhecimento (FC) entre a universidade e a indústria com práticas de gestão do conhecimento (GC) é uma questão importante para a eficácia da colaboração. A maioria dos estudos anteriores avalia a CUI e o fluxo de conhecimento no nível organizacional, deixando o nível de equipe (nível micro) desses construtos obscuros. O objetivo deste trabalho qualitativo foi analisar, usando uma abordagem de método misto, como o conhecimento flui em uma colaboração universidade-indústria para a inovação de materiais, a fim de propor uma estrutura de análise e um conjunto de práticas para melhorar a colaboração. A caracterização do fluxo de conhecimento mostra redes distintas de conhecimento técnico, gerencial e de mercado, nós principais espalhados pelas redes e uma série de práticas de gestão do conhecimento. Os resultados evidenciaram a relação entre o fluxo de conhecimento, CUI e fatores de influência, mas a relação quantitativa entre o desempenho da CUI e as práticas de GC não pôde ser identificada com os instrumentos empregados. A estrutura de análise sugere investigar FC pela rede, densidade, atividade do intermediador, capacidade absortiva e práticas; resultados da CUI, por seus principais produtos como tecnologias, componentes, publicações, patentes, pessoas treinadas, ganhos técnicos e econômicos e continuidade de parcerias; e fatores de influência, por setor, área de conhecimento, nível de maturidade tecnológica, posição da cadeia de valor, sobreposição de conhecimento e velocidade das mudanças. Os resultados podem ser gerados mapeando a rede usando a técnica da bola de neve e entrevistando os principais participantes da colaboração. A avaliação de nível micro forneceu informações da gestão da colaboração de nível operacional que permitiu uma visão mais profunda da colaboração e, portanto, a proposição da estrutura de análise e práticas para ajudar no sucesso da CUI. A partir dos fatores influenciadores encontrados neste trabalho, duas práticas foram concebidas e ainda não testadas: (i) implementar uma estrutura de encontros periódicos para conectar pesquisadores de todas as áreas; e (ii) dividir a colaboração em projetos de curto e longo prazo.Abstract: Advanced materials are fundamental for innovation as they play a major role in all sorts of new products and processes development. Advanced materials however present several challenges due to its upstream position in the value chain. Materials development requires long periods of development and high investments before getting the first customers? feedbacks, thus increasing investment risks. Therefore, university-industry cooperation (UIC) supported by national science policies and granting programs plays an important role supporting the development of advanced materials and building market competitive advantage. In UIC, knowledge sharing is one of the main objectives, thus managing knowledge flows (KF) between university and industry with knowledge management (KM) practices is an important issue for collaboration effectiveness. Most of previous studies assess UIC and knowledge flow at organizational-level, leaving team-level (micro-level) of these constructs unclear. The objective of this qualitative work was to analyze, using a mixed method approach, how knowledge flows in a university-industry collaboration for materials innovation, in order to propose a framework of analysis and a set of practices to improve collaboration. Knowledge flow characterization show distinct networks of technical, managerial and market knowledge, key nodes scattered across the networks and a series of knowledge management practices. Results evidenced the relationship between knowledge flow, UIC and influencing factors, but the quantitative relationship between UIC performance and KM practices couldn?t be identified with the instruments employed. The framework of analysis suggests investigating KF by its network, density, broker activity, absorptive capacity and practices; UIC outcomes, by its main outputs such as technologies, components, publications, patents, people trained, technical and economic gains and partnership continuity; and influencing factors, by industry, knowledge field, technology readiness level, value chain position, knowledge overlap and speed of changes. Results can be generated by mapping the network using the snowball technique and interviewing key participants of the collaboration. Micro-level assessment provided information from bottom-level collaboration management that allowed a deeper view of the collaboration and thus the proposition of the framework of analysis and practices to help UIC success. Based on influencing factors found in this work, two practices were conceived and not tested: (i) implement a structure of periodic meetings to connect researchers across areas; and (ii) split collaboration in short-term and long-term projects

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure