Abstract
Enterprise, project, and workforce selection models for Industry 4.0.
Rupinder Kaur
The German federal government first coined industry 4.0 in 2011. Industry 4.0 involves the use of
advanced technologies such as cyber-physical system, internet of things, cloud computing, and
cognitive computing with the aim to revolutionize the current manufacturing practices.
Automation and exchange of big data and key characteristics of Industry 4.0. Due to its numerous
benefits, industries are readily investing in Industry 4.0, but this implementation is an uphill
struggle.
In this thesis, we address three key problems related to Industry 4.0 implementation namely
Enterprise selection, Project selection and Workforce selection. The first problem involves
identification of enterprises suitable for Industry 4.0 implementation. The second problem involves
prioritization and selection of Industry 4.0 projects for the chosen digital enterprises. The third and
last problem involves workforce selection and assignment for execution of the identified Industry
4.0 projects. Multicriteria solution approaches based on TOPSIS and Genetic Algorithms are
proposed to address these problems. Industry experts are involved to prioritize the criteria used for
enterprise, project and workforce selection. Numerical applications are provided.
The proposed work is innovative and can be useful to manufacturing and service organizations
interested in implementing Industry 4.0 projects for performance improvement.