Analyzing Workforce 4.0 in the Fourth Industrial Revolution and proposing a road map from operations management perspective with fuzzy DEMATEL

The purpose of this paper is threefold: first, to present a structural competency model; second, to remark new criteria for personnel selection in Industry 4.0 environment; and third, to contribute to the operations management literature by focusing on recruitment process in Industry 4.0 environment and supporting human resources activities with Industry 4.0 related criteria and point out a new research field in Industry 4.0.,Fuzzy DEMATEL has been used in the implementation. The study is conducted in a high-tech firm, which has started to modify its processes according to Industry 4.0, and introduces a new specific department that is responsible of this transformation. In total, 11 personnel selection criteria were presented and then assessed by experts through a fuzzy linguistic scale. Both importance order and causal relation between criteria are presented at the end of the study.,According to the results, the most important criteria in the selected firm are the ability of dealing with complexity and problem solving, thinking in overlapping process, and flexibility to adapt new roles and work environments. While cause group includes criteria such as knowledge on IT and production technologies, awareness of IT security and data protection, and ability of fault and error recovery, effect group includes flexibility to adapt new roles and work environments, organizational and processual understanding, and the ability to interact with modern interfaces.,Analytical thinking and system approach are the key topics for new supporting personnel selection criteria, which lead to the need for the skills and qualifications in decision making and process management. Results of the cause group criteria also indicate the importance of technical abilities such as coding, IT security and human-machine interfaces. On the other hand, effect group of the study emphasizes on the flexibility and interdisciplinary working structure that suggests the suitability of matrix organization in the companies which follow the Industry 4.0 trends. Moreover, team work comes forward as another key concept for organizations transforming to Industry 4.0.,The originality of this study appears on modeling of a competency structural model for Workforce 4.0 which is proposed as a road map, including the suggested set of related criteria and the fuzzy MCDM-based methodology for companies which alter their organizations according to Industry 4.0.

[1]  Gwo-Hshiung Tzeng,et al.  Defuzzification within a Multicriteria Decision Model , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[2]  Malte Brettel,et al.  How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective , 2014 .

[3]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[4]  Mourad Oussalah,et al.  On the compatibility between defuzzification and fuzzy arithmetic operations , 2002, Fuzzy Sets Syst..

[5]  Wei-Wen Wu,et al.  Developing global managers' competencies using the fuzzy DEMATEL method , 2007, Expert Syst. Appl..

[6]  Wen-Hsien Tsai,et al.  Selecting management systems for sustainable development in SMEs: A novel hybrid model based on DEMATEL, ANP, and ZOGP , 2009, Expert Syst. Appl..

[7]  Shouzhen Zeng,et al.  Group multi-criteria decision making based upon interval-valued fuzzy numbers: An extension of the MULTIMOORA method , 2013, Expert Syst. Appl..

[8]  Serhat Burmaoglu,et al.  A fuzzy hybrid MCDM approach for professional selection , 2012, Expert Syst. Appl..

[9]  Remco M. Dijkman,et al.  Business models for the Internet of Things , 2015, Int. J. Inf. Manag..

[10]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[11]  Hu-Chen Liu,et al.  Personnel Selection Using Interval 2‐Tuple Linguistic VIKOR Method , 2015 .

[12]  Alvydas Balezentis,et al.  Personnel selection based on computing with words and fuzzy MULTIMOORA , 2012, Expert Syst. Appl..

[13]  Detlef Zühlke,et al.  Lean Automation enabled by Industry 4.0 Technologies , 2015 .

[14]  Gwo-Hshiung Tzeng,et al.  An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method , 2011, Expert Syst. Appl..

[15]  C. Kahraman,et al.  Multi‐criteria supplier selection using fuzzy AHP , 2003 .

[16]  Pieter J. Mosterman,et al.  Industry 4.0 as a Cyber-Physical System study , 2016, Software & Systems Modeling.

[17]  Ying Liu,et al.  A categorical framework of manufacturing for industry 4.0 and beyond , 2016 .

[18]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[19]  Günther Schuh,et al.  Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0 , 2014 .

[20]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[21]  Hans-Georg Kemper,et al.  Application-Pull and Technology-Push as Driving Forces for the Fourth Industrial Revolution , 2014 .

[22]  Jorge Posada,et al.  9th International Conference on Knowledge Based and Intelligent Information and Engineering Systems a Perspective on Knowledge Based and Intelligent Systems Implementation in Industrie 4.0 , 2022 .

[23]  E. Ertugrul Karsak,et al.  A fuzzy MCDM approach for personnel selection , 2010, Expert Syst. Appl..

[24]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..

[25]  Edward A. Lee Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[26]  Dimitris Askounis,et al.  A new TOPSIS-based multi-criteria approach to personnel selection , 2010, Expert Syst. Appl..

[27]  Tobias Wagner,et al.  Mental Strain as Field of Action in the 4th Industrial Revolution , 2014 .