Influence of Industry 4.0 on the Production and Service Sectors in Pakistan: Evidence from Textile and Logistics Industries

This research aims to investigate the role of Industry 4.0 in the production and service sector in Pakistan. It therefore considers five Industry 4.0 factors, namely big data, smart factory, cyber physical systems (CPS), Internet of things (IoT), and interoperability. In order to analyze the role of Industry 4.0, the textile industry is taken as a production industry, while the logistics industry is considered as a service industry. Both are facing various challenges in production and services causing below standard overall performance. To address this issue, a quantitative research approach with cross-sectional research design was selected. First hand data was collected through a survey questionnaire from a total of 224 employees of textile and logistics companies. Smart partial least square-structural equation modeling (PLS-SEM) was preferred to analyze the collected data. Findings of the study revealed that Industry 4.0 has a key role in promoting the production and services sector in Pakistan, as it has a significant impact on the overall performance of the considered sectors. This research is one of the pioneer studies that examines the role of Industry 4.0 on the textile and logistics industry of Pakistan. Thus, this research also contributes in a practical dimension by explaining the implementation of Industry 4.0 for improving the performance of the textile and logistics industries.

[1]  Seymour Geisser,et al.  The Predictive Sample Reuse Method with Applications , 1975 .

[2]  N. B. Anuar,et al.  The rise of "big data" on cloud computing: Review and open research issues , 2015, Inf. Syst..

[3]  Alasdair Gilchrist.  Industry 4.0 , 2016, Apress.

[4]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[5]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[6]  Javier Solana,et al.  Big Data: A Revolution that Will Transform How We Work, Live and Think , 2014 .

[7]  C. Stoicescu.  Big Data, the perfect instrument to study today's consumer behavior , 2016 .

[8]  Wayne H. Wolf,et al.  Cyber-physical Systems , 2009, Computer.

[9]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[10]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[11]  B. Newbould.  The effect on industry , 1981 .

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

[13]  Maria R. Lee,et al.  Leveraging Big Data and Business Analytics , 2013, IT Prof..

[14]  Prashant Pandey,et al.  Cloud computing , 2010, ICWET.

[15]  J. Popp,et al.  Information technology developments of logistics service providers in Hungary , 2018 .

[16]  Wynne W. Chin.  How to Write Up and Report PLS Analyses , 2010 .

[17]  R. Rajnoha,et al.  Strategic Performance Management System and Corporate Sustainability Concept - Specific Parametres in Slovak Enterprises , 2016 .

[18]  Aleksander Gwiazda,et al.  Application of the Advanced Engineering Systems for Modeling Logistics Processes , 2015 .

[19]  Jan Ola Strandhagen,et al.  Operationalizing lean principles for lead time reduction in engineer-to-order (ETO) operations: A case study , 2018 .

[20]  Jo Wessel Strandhagen,et al.  The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study , 2017 .

[21]  Melanie Tory,et al.  Rethinking Visualization: A High-Level Taxonomy , 2004, IEEE Symposium on Information Visualization.

[22]  A. Chaudhry,et al.  Innovation in the Textiles Sector: A Firm-Level Analysis of Technological and Nontechnological Innovation , 2016 .

[23]  Kagermann Henning.  Recommendations for implementing the strategic initiative INDUSTRIE 4.0 , 2013 .

[24]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[25]  Bojan Lalic,et al.  The Effect of Industry 4.0 Concepts and E-learning on Manufacturing Firm Performance: Evidence from Transitional Economy , 2017, APMS.

[26]  M. Pohludka,et al.  Business Organisational Structures of Global Companies: Use of the Territorial Model to Ensure Long-Term Growth , 2018, Social Sciences.

[27]  Shahriar Akter,et al.  An Evaluation of PLS Based Complex Models: the Roles of Power Analysis, Predictive Relevance and GoF Index , 2011, AMCIS.

[28]  A. Cockrill,et al.  THE RELATIONSHIP BETWEEN FEMALE REPRESENTATION AT STRATEGIC LEVEL AND FIRM'S COMPETITIVENESS: EVIDENCES FROM CARGO LOGISTIC FIRMS OF PAKISTAN AND CANADA , 2017, Polish Journal of Management Studies.

[29]  W. Hameed,et al.  Determinants of E-Logistic Customer Satisfaction: A Mediating Role of Information and Communication Technology (ICT) , 2018 .

[30]  Christian Brecher,et al.  Advances in Production Technology , 2014 .

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

[32]  Examining the Relationship Among Economic Growth, Exports and Total Productivity for OECD Countries Using Data Envelopment Analysis and Panel Data Analyses , 2017 .

[33]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[34]  Felix Wortmann,et al.  Internet of Things , 2015, Business & Information Systems Engineering.

[35]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[36]  J. Popp,et al.  Future challenges and areas of development for supply chain management , 2018 .

[37]  Eric Gossett,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2015 .

[38]  Mark Lycett,et al.  ‘Datafication’: making sense of (big) data in a complex world , 2013, Eur. J. Inf. Syst..

[39]  Rainer Ruggaber,et al.  ATHENA - Advanced Technologies for Interoperability of Heterogeneous Enterprise Networks and their Applications , 2006 .

[40]  S. Lorincová,et al.  Strategic Management of Business Performance Based on Innovations and Information Support in Specific Conditions of Slovakia , 2015 .

[41]  Howard B. Lee,et al.  A First Course in Factor Analysis 2nd Ed , 1973 .

[42]  Gordon Sammut,et al.  Perspective-Taking and the Attribution of Ignorance , 2012 .

[43]  Nael B. Abu-Ghazaleh,et al.  A taxonomy of wireless micro-sensor network models , 2002, MOCO.

[44]  Michael R. Mullen,et al.  Structural equation modelling: guidelines for determining model fit , 2008 .

[45]  Marcel Bogers,et al.  on Intelligent Manufacturing and Automation , 2013 The Smart Factory : Exploring Adaptive and Flexible Manufacturing Solutions , 2014 .

[46]  Lida Xu,et al.  Big data for cyber physical systems in industry 4.0: a survey , 2019, Enterp. Inf. Syst..

[47]  David Romero,et al.  Enterprise information systems state of the art: Past, present and future trends , 2016, Comput. Ind..

[48]  S. Arabia,et al.  Pakistan Textile Industry Facing New Challenges , 2010 .

[49]  B. Ślusarczyk.  INDUSTRY 4.0 – ARE WE READY? , 2018, Polish Journal of Management Studies.

[50]  E. Bulut,et al.  The Use of Partial Least Squares Path Modeling in Investigating the Relationship between Leadership, Motivation and Rewarding , 2015 .

[51]  Adnan ul Haque,et al.  The Impact of Stressors on Organizational Commitment of Managerial and Non-Managerial Personnel in Contrasting Economies: Evidences from Canada and Pakistan , 2018 .

[52]  B. Price.  A First Course in Factor Analysis , 1993 .

[53]  S. Geisser.  A predictive approach to the random effect model , 1974 .

[54]  Amit P. Sheth,et al.  Changing Focus on Interoperability in Information Systems:From System, Syntax, Structure to Semantics , 1999 .

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

[56]  Shahriar Akter,et al.  How to improve firm performance using big data analytics capability and business strategy alignment , 2016 .

[57]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .