The Effect of Industry 4.0 Concepts and E-learning on Manufacturing Firm Performance: Evidence from Transitional Economy

With the application of smart technology concepts, the fourth stage of industrialization, referred to as Industry 4.0, is believed to be approaching. This paper analyzes the extent to which smart factory concepts and e-learning have already deeply affected manufacturing industries in terms of performances in transitional economy. Empirical results indicate that manufacturing companies that have introduce both e-learning and selected smart factory technology concepts differ significantly. E-learning is mainly applied on graduates in production. Results reveal that two smart factory concepts are significantly and positively related to the firm performance when e-learning is applied.

[1]  Mohamed Khamis,et al.  Introduction and establishment of virtual training in the factory of the future , 2017, Int. J. Comput. Integr. Manuf..

[2]  R. Koren,et al.  The impact of technical and organisational innovation concepts on product characteristics , 2015 .

[3]  Rainer Drath,et al.  Industrie 4.0: Hit or Hype? [Industry Forum] , 2014, IEEE Industrial Electronics Magazine.

[4]  Kinshuk,et al.  Research on e-learning in the workplace 2000–2012: A bibliometric analysis of the literature , 2014 .

[5]  Steffen Kinkel,et al.  Trends in production relocation and backshoring activities , 2012 .

[6]  Dragutin M. Zelenović,et al.  Flexibility—a condition for effective production systems , 1982 .

[7]  Carmen Constantinescu,et al.  Smart Factory - A Step towards the Next Generation of Manufacturing , 2008 .

[8]  Sang Do Noh,et al.  Smart manufacturing: Past research, present findings, and future directions , 2016, International Journal of Precision Engineering and Manufacturing-Green Technology.

[9]  Heidi Schweizer,et al.  E-Learning in Business , 2004 .

[10]  X. Sala-i-Martin,et al.  The global competitiveness report 2016–2017 , 2016 .

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

[12]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[13]  Bojan Lalic,et al.  Developing a model to assess the success of e-learning systems: evidence from a manufacturing company in transitional economy , 2016, Inf. Syst. E Bus. Manag..

[14]  Iztok Palcic,et al.  Exploring the impact of energy efficiency technologies on manufacturing firm performance , 2013 .

[15]  Parisa Ghodous,et al.  Digital factory system for dynamic manufacturing network supporting networked collaborative product development , 2016, Data Knowl. Eng..

[16]  Wilhelm Dangelmaier,et al.  Parallel scheduling for evolving manufacturing systems , 2012, IEEE 10th International Conference on Industrial Informatics.

[17]  Richard F. Hartl,et al.  Supply chain dynamics, control and disruption management , 2016 .

[18]  Joaquín B. Ordieres Meré,et al.  Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm , 2014, 2014 IEEE International Conference on Industrial Engineering and Engineering Management.

[19]  Alexandre Dolgui,et al.  A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 , 2016 .

[20]  Eduardo Salas,et al.  E-Learning in Organizations , 2005 .

[21]  Siu Cheung Kong,et al.  An evaluation study of the use of a cognitive tool in a one-to-one classroom for promoting classroom-based dialogic interaction , 2011, Comput. Educ..