Management approaches for Industry 4.0: A human resource management perspective

Shamim, Saqib; Cang, Shuang; Yu, Hongnian; Li, Yun

Abstract

Industry 4.0 is characterized by smart manufacturing, implementation of Cyber Physical Systems (CPS) for production, i.e., embedded actuators and sensors, networks of microcomputers, and linking the machines to the value chain. It further considers the digital enhancement and reengineering of products. It is also characterized by highly differentiated customized products, and well-coordinated combination of products and services, and also the value added services with the actual product or service, and efficient supply chain. All these challenges require continuous innovation and learning, which is dependent on people and enterprise's capabilities. Appropriate management approaches can play a vital role in the development of dynamic capabilities, and effective learning and innovation climate. This paper aims at offering a viewpoint on best suitable management practices which can promote the climate of innovation and learning in the organization, and hence facilitate the business to match the pace of industry 4.0. This paper is one of the initial attempts to draw the attention towards the important role of management practices in industry 4.0, as most of the recent studies are discussing the technological aspect. This paper also suggests empirical and quantitative investigation on these management approaches in the context of industry 4.0

Citation

Shamim, S., Cang, S., Yu, H., & Li, Y. (2016). Management approaches for Industry 4.0: A human resource management perspective. In 2016 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/cec.2016.7748365

• Conference Name: 2016 IEEE Congress on Evolutionary Computation (CEC)
• Conference Location: Vancouver, BC, Canada
• Start Date: Jul 24, 2016
• End Date: Jul 29, 2016
• Online Publication Date: Nov 21, 2016
• Publication Date: 2016
• Deposit Date: Jun 15, 2022
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2016 IEEE Congress on Evolutionary Computation (CEC)
• DOI: https://doi.org/10.1109/cec.2016.7748365
• Public URL: http://researchrepository.napier.ac.uk/Output/2879205