Towards artificial intelligence in production: A competence profile for shop floor managers

Rosemeyer, Jannik; Bardy, Sebastian; Pinzone, Marta; Bosani, Enrica; Schubert, Tobias; Metternich, Joachim

doi:10.2139/ssrn.4072671

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…In addition, top managers need the ability and skill to depoliticize and professionalize public administration (8). The ability to evaluate personnel professionally (9), resilience, spontaneity, and emotional management (10), the competence to face modern technology, the age of intelligence (11), professional development and behavior and leadership attitude (12), competency-based human resource management (13), internationalization competencies (14), understanding artificial intelligence technologies (15) are considered essential competencies of senior managers. Some evidence has listed superior cognitive and analytical abilities, the ability to create and use external contacts, project talent, and the ability to develop multicultural interaction as the special competence of top managers (16).…”

Section: Introductionmentioning

confidence: 99%

Managerial Competencies: A Pattern for Top Managers' Competency of Medical Sciences Universities

2022

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Background and aims: Managers’ competencies can influence the organization’s development and effectively promote and improve management and implementation of planned changes. This study addresses developing the pattern of specific competence of senior managers of universities of medical sciences. Methods: This research was conducted in two qualitative and quantitative phases in 2021-2022. First, it was done using a qualitative approach using conventional content analysis. The research tool was a semi-structured interview. The data from the interviews with 17 participants through the purposeful sampling method depended on the criteria. In the second phase, the variables identified in the first phase were measured through the correlation method and structural equation model using AMOS software. The tool for collecting information in this phase was a researcher-made questionnaire based on the first phase of the research. The respondents, Lawshe’s content validity ratio coefficient, and factor analysis confirmed the questionnaire’s face, content, and construct validity. Its reliability was estimated using Cronbach’s alpha coefficient at higher than 0.7. The study population of this department consists of managers in the departments of a medical sciences university (n=196). Results: The findings showed that 43 subscales were placed in 18 general categories in a paradigm model (causal factors, contextual conditions, intervening conditions, strategies, and consequences). The causal factor coefficient of the main category was 0.57, background factors on strategies were 0.59, intervening factors on strategies were 0.43, and the main category on strategies was 0.52. Finally, the factor coefficient of the strategies on the results was obtained at 0.46. Conclusion: The results of this research may contribute to knowledge about the unique competencies of top managers in the development and improvement of human resources to achieve the organization’s goals and success. These competencies can be considered in evaluating and selecting talented human resources.

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Section: Introductionmentioning

confidence: 99%

Managerial Competencies: A Pattern for Top Managers' Competency of Medical Sciences Universities

2022

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ML Pro: digital assistance system for interactive machine learning in production

Neunzig,

Möllensiep,

Kuhlenkötter

et al. 2023

J Intell Manuf

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The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.

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Towards artificial intelligence in production: A competence profile for shop floor managers

Cited by 2 publications

References 26 publications

Managerial Competencies: A Pattern for Top Managers' Competency of Medical Sciences Universities

Managerial Competencies: A Pattern for Top Managers' Competency of Medical Sciences Universities

ML Pro: digital assistance system for interactive machine learning in production

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