Year: 2018
Type: dissertation
Abstract: \nEnterprise, project, and workforce selection models for Industry 4.0. \nRupinder Kaur \nThe German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of \nadvanced technologies such as cyber-physical system, internet of things, cloud computing, and \ncognitive computing with the aim to revolutionize the current manufacturing practices. \nAutomation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous \nbenefits, industries are readily investing in Industry 4.0, but this implementation is an uphill \nstruggle. \nIn this thesis, we address three key problems related to Industry 4.0 implementation namely \nEnterprise selection, Project selection and Workforce selection. The first problem involves \nidentification of enterprises suitable for Industry 4.0 implementation. The second problem involves \nprioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and \nlast problem involves workforce selection and assignment for execution of the identified Industry \n4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are \nproposed to address these problems. Industry experts are involved to prioritize the criteria used for \nenterprise, project and workforce selection. Numerical applications are provided. \nThe proposed work is innovative and can be useful to manufacturing and service organizations \ninterested in implementing Industry 4.0 projects for performance improvement.
Author: Rupinder Kaur
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: Concordia University
OpenAlex ID: W2907070826