An Expert System for Selecting Manufacturing Workers for Training

Ntuen, Celestine A.

doi:10.1016/0957-4174(95)00007-v

Cited by 11 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firms can use intelligent systems to select the right employees for training and avoid over-or under-training (Ntuen and Chestnut 1995). Firms can also computerize decision aids by incorporating expert systems to acquire more knowledge (Rose and Wolfe 2000).…”

Section: Technology: Expert Systemsmentioning

confidence: 99%

Intellectual Capital and Knowledge Management Research towards Value Creation. From the Past to the Future

Abeysekera

2021

JRFM

View full text Add to dashboard Cite

Over the past decade, the body of literature on intellectual capital and knowledge management has grown exponentially, responding to the growing knowledge-based and digital economy. This article reviews the concepts and applications proposed at the initial stages of the launch of these two research paradigms. It then examines the interconnection between the two. The study selected a set of articles on intellectual capital and knowledge management that forward concepts and applications vital to the inception of these two research domains. The analytical review demonstrates that these concepts and applications, although considered separately in research, are interconnected and inform each other. The paper analyzes, discusses, and forms conclusions based on the self-selected literature with informed research knowledge.

show abstract

Section: Technology: Expert Systemsmentioning

confidence: 99%

Intellectual Capital and Knowledge Management Research towards Value Creation. From the Past to the Future

Abeysekera

2021

JRFM

View full text Add to dashboard Cite

show abstract

“…For example, Hooper et al (1998) developed an expert system to screen officer personnel records to derive decision rules and compared the derived decisions with domain experts for validation. Ntuen and Chestnut (1995) developed a trainability selection expert system to facilitate the selection of right employees to avoid the problem of over training or under training. Tavana et al (1996) proposed a group decision support system that integrates the Analytic Hierarchy Process (AHP) with Delphi process of anonymous feedback and iteration to facilitate technical managers on personnel selection and hiring decisions.…”

Section: Personnel Selection Decisionsmentioning

confidence: 99%

Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries

Chen

Chien

2011

Flex Serv Manuf J

View full text Add to dashboard Cite

Human capital is one of the critical resources for high-tech industries such as semiconductor manufacturing to maintain their competitive advantages, yet it is seldom addressed in literature. Owing to the changing nature of knowledge workers in high-tech industries, jobs cannot be easily delineated. Thus, conventional personnel selection approaches based on static job characteristics no longer suffice. Focusing on the needs in real settings, this study aims to develop a manufacturing intelligence framework that integrates the rough set theory, support vector machine, and decision tree to extract useful patterns and intelligence from huge human resource data and production data to enhance the decision quality of human resource management that include identifying high-potential talents who fit the company culture and allocating the job with functional nature that matches the characteristics of the talent. To assess the validity of this approach, empirical studies were conducted on the basis of real data collected from semiconductor companies for comparison. The results have shown the practical viability of this approach.

show abstract

“…DSS has been used for human resource management (HRM) problems, in order to solve the problem of personnel scheduling (Ernst et al 2004). A number of DSS applications were also developed to deal with other areas of human resource management, for example, productivity improvement (Young 1989), performance analysis (Ntuen et al 1994), career management (Bellone et al 1995), and personnel selection problem (Ntuen and Chestnut 1995;Nussbaum et al 1999).…”

mentioning

confidence: 99%

Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction

Aouadni

Rebaï

2016

Ann Oper Res

View full text Add to dashboard Cite

In this paper, we propose a Decision Support System based on the MUSA method and the continuous genetic algorithm in order to measure job satisfaction. The objective is to help organizations evaluate and measure their employees' satisfaction. Our study is composed of two parts. Firstly, we propose to combine continuous genetic algorithm and the MUSA method in order to obtain a robust solution of good performance. The aim of the development of this algorithm is to verify its efficiency regarding the classical MUSA algorithm. Therefore, we compare the result of continuous genetic algorithm with that of the MUSA algorithm. In the second part, we present our Decision Support Systems called "GMUSA System", it was developed in order to facilitate the applications and the use of the GMUSA tools and overcome the increasing complexity of managerial contexts. Our new system "GMUSA" is applied at the University of Sfax to measure teachers' job satisfaction. Keywords Continuous genetic algorithm • Decision support system • Job satisfaction • MUSA method 1 Introduction Although, job satisfaction was studied widely in psychology, it has no single definition but different opinions about it. Furthermore, it is not clear if there are specific measurement dimensions. The problems concerning a clear definition of job satisfaction lie mostly in the complexity of the topic, the subjectivity and the quality nature of the satisfaction concept (Grigoroudis and Siskos 2009).

show abstract

An Expert System for Selecting Manufacturing Workers for Training

Cited by 11 publications

References 22 publications

Intellectual Capital and Knowledge Management Research towards Value Creation. From the Past to the Future

Intellectual Capital and Knowledge Management Research towards Value Creation. From the Past to the Future

Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries

Decision support system based on genetic algorithm and multi-criteria satisfaction analysis (MUSA) method for measuring job satisfaction

Contact Info

Product

Resources

About