Online Exclusives
March / April 2021

Industry 4.0 & the Future of the Pharmaceutical Industry

Ingrid Carla Reinhardt
Jorge Oliveira
Denis Ring, PhD, CEng, FIChemE
Industry 4.0 & the Future of the Pharmaceutical Industry

Industry 4.0 is the recent movement toward intelligent automation technology. In this new era, the integration of modern manufacturing skills and novel information technologies plays an important role on economic competitiveness.1  In this review, technological trends for the future of the pharmaceutical industry are explored.

  • 1Zawadzki, P., and K. Żywicki. “Smart Product Design and Production Control for Effective Mass Customization in the Industry 4.0 Concept.” Management and Production Engineering Review 7, no. 3 (June 2016): 105–112. doi:10.1515/mper-2016-0030

In the pharma industry, Industry 4.0 may support augmented manufacturing, personalized medicine, additive manufacturing, localized 3D printing of treatments, and even a future where humans are no longer intimately involved with production. This future is possible if industry and education facilities collaborate to encourage progressive research and implementation of Industry 4.0.

With the advent of the new cyber-physical system design paradigm, the variety of systems that need to work together in the future enterprises has significantly increased.2 Industry 4.0 is an industrial approach that addresses all aspects associated with an industrial operational model, including culture, management responsibility, and interaction with regulators.

Manufacturing has shifted from mass production to mass customization,3 encouraging Industry 4.0–related trends such as data integrity by design, holistic control strategies, personalized treatments, and end-to-end supply chain integration across the industry. Industry 4.0 allows cyber and physical systems to cooperate profitably, aiming to build smart factories by redefining the role of humans in production.

The emerging technologies of Industry 4.0 facilitate sustainable value creation4 and lead to a more agile, smart, and personalized pharma industry, thereby enabling pharmaceutical companies to obtain competitive advantages. A more sustainable pharmaceutical supply chain should be implemented to match future operations and management of the pharmaceutical products across the entire life cycle.

Emerging 4.0 Technologies

Industry today strives for flexible and localized production situated close to customers. It manufactures “on demand” and tries to avoid large inventories.

Adaptive and innovative technologies will help pharmaceutical companies establish more robust and agile manufacturing processes characterized by fewer interruptions, fewer defects, and higher levels of quality management.5 ,6 ,7  The vertical integration of Industry 4.0 will upgrade the pharma production plant to a “reconfigurable factory” in which a highly flexible, agile, and smart production line can support mass customization of personalized drugs for different demands.6 ,8  Efficient communication across company boundaries and big data analytics can improve process monitoring performances, and identify and reduce material waste, overproduction, and energy consumption.9 ,10

The topics to be discussed in this article include augmented reality (AR) and virtual reality (VR); 3D printing and personalized medicine; digital twin and simulation; and cyber-physical systems, cloud computing, and big data. We have selected these issues based on findings from an unpublished 2019 industry survey of executives in the Irish pharmaceutical and biopharmaceutical sectors conducted prior to this review as well as other feedback from industry professionals.

Augmented and Virtual Reality

AR and VR form an integral part of Industry 4.0, helping manufacturing companies to face challenges related to volatile demand and changing requirements from customers and suppliers.11 . The literature identifies the primary advantages of AR and VR applications as follows:

  • Production plant control and error diagnostics
  • Production system safety and security
  • Product design and reconfiguration
  • Provision of required information at operational and enterprise level
  • Complex task training to increase safety and productivity

The concepts of AR and VR have existed for more than 20 years, but these technologies have only recently entered the industrial environment. VR applications in pharma allow companies to decrease design and production costs, maintain product quality, and reduce the time needed to go from product concept to production.

AR, defined as a system “in which 3D virtual objects are integrated into a 3D real environment in real-time,” 12 can “increase the operator’s natural feedback with virtual clues.”13 In other words, AR enables users to visualize and interact with 3D objects, in the real environment, more easily than they can through a simulation or a computer screen.14

AR is recognized as a support for industrial applications including product assembly, improving assembly quality and efficiency, and reducing assembly costs and maintenance.15 ,16 However, AR visualization tools must be improved to allow for seamless and complete integration into manufacturing, as current technology is not suitable for continuous use and harsh working environments, such as those found in the pharma industry. 17

3D Printing and Personalized Medicine

3D printing is a form of additive manufacturing in which computer-aided design software is used to generate a digital model and a 3D object is printed from raw materials in either liquid or particle form.18 ,19 ,20  3D printing offers the possibility of fabricating solid oral dosage forms, including complex modified-release products, by layering thin strands of polymer to quickly and efficiently produce a customized and repeatable unit. Thus, this will become a key technology for fabricating personalized products and sophisticated objects with advanced attributes and complex, lightweight designs.21 ,22  High-performance, decentralized additive manufacturing systems also reduce transport distances and stock on hand.23

Intrinsically linked with 3D printing and additive manufacturing is the field of personalized treatments. As each unit is produced individually, 3D printing can allow manufacturers to achieve complete control with regards to dosage, material used, or build of item, among other characteristics.24 ,25 ,26 Thus, 3D printing could be extended throughout the drug development process, ranging from preclinical development and clinical trials to frontline medical care.

Digital Twin and Simulation

A digital twin is a virtual digital equivalent of a physical product or physical system. Grieves and Vickers describe it as a virtual mirroring of what exists in the real world.27 It can provide real-time, simulated, or historical information on movements, machine operations, and process parameters.

Lee and colleagues launched the first research on the digital twin in the industrial sector in 2013.28 They presented it as the virtual equivalent of production resources, not just the product, and emphasized its core technologies—big data analytics and cloud platforms.

Real-time simulation with a digital twin allows for testing and process optimization prior to the task being conducted, thereby driving down machine setup times and increasing quality.29 Because the digital twin can be an accurate model of systems used on the shop floor, its benefits include decreased process development time, decreased cycle time, increased confidence in the system, and therefore decreased regulatory timelines.

Cyber-Physical Systems, Cloud Computing, and Big Data

The modern manufacturing system has cyber-physical, cyber, and human components, linked together by the Internet of Things (IoT).30 Together, Industry 4.0 and cloud manufacturing have the potential to unleash the full potential of the pharmaceutical manufacturing industry.31

Cloud computing has been one of the most significant advances in the virtual industrial world. It allows analysts to extract and analyze huge quantities of information (i.e., big data) from the supply chain, products, machines, and production lines. Thus, big data analytics and the IoT help companies anticipate and shape future customer demands, bringing greater efficiencies to the distribution of final goods.23

Industry 4.0 Needs in the Pharma Industry

Pharmaceutical companies require the integration of analytical techniques such as simulation, data analytics, and optimization to derive better understanding of the data generated. However, the necessary technologies are primarily proofs of concept that have generally not yet been adopted on a commercial scale. Full adoption of Industry 4.0 will stall until such technology becomes cost-effective to implement.

Industry 4.0 and digital transformation promise to increase efficiency and improve pharmaceutical manufacturing processes. Although the current machine-operator interface mainly provides the operator with easy control over production processes and convenient access to related information, workstation–operator interactions could become a principal component of human–machine interactions in the Industry 4.0 era.32 The shift toward digitalized production thereby not only involves technological progress and empowerment but also requires target-oriented qualification of personnel.33 In this context, employee management and leadership play an important role.

AR is one of the most promising tools for improving technical manuals in the context of Industry 4.0. AR manuals can present the technical information registered to the object in the real workspace with potential benefits that increase with more complex operations.34 ,35  Using AR is also a valid way to create GxP-compliant technical documentation.21 However, implementing AR documentation in industry is challenging due to incomplete standards and guidelines.36 Static documentation (often saved as PDFs) is difficult to update, translate, and access when needed.

Industry 4.0 “smart” factories will become more intelligent, flexible, and dynamic as they are equipped with sensors, actors, and autonomous systems, and machines and equipment achieve higher levels of self-optimization and automation. These innovations will help pharma companies fulfill more complex standards and requirements for products.37 ,38

Industry 4.0 involves dynamic changes to the industrial infrastructure and requires higher-level mechanization, digitalization, networking, and miniaturization.39 Intertwined digital and physical processes are needed to create smart products.40 Therefore, big data, cloud computing, mass customization, IoT, and production time improvement collectively dominate the development of Industry 4.0.37 ,41

Education 4.0

To implement Industry 4.0, employers need personnel with creativity and decision-making skills as well as technical expertise in multiple areas. For this reason, it is vital that students and workers are well educated and dedicated to lifelong learning.

To achieve Industry 4.0 goals, undergraduate education will need to change dramatically. Universities must develop updated curricula that combine real- and virtual-world information,42 and they must enhance their approaches to technological advancements and educational methods to reflect Industry 4.0 needs and guidelines; Mourtzis and colleagues43 refer to this as “Education 4.0.” Shifting from the traditional teaching framework to Education 4.0 requires careful design. A holistic approach to transitioning from a traditional model would combine education in traditional manufacturing techniques with active student training in Industry 4.0 technologies,41 such as big data analytics, AI, AR, IoT, and cloud computing.

An Industry 4.0–enabled classroom can be used as a physical demonstrator, consisting of different modules from different departments integrated into an Industry 4.0–compliant information-communication technologies architecture.40 The Industry 4.0 classroom can also be used as a practical testbed, applying 4.0 concepts to real-world applications.

Education programs should also focus on integrating engineering with liberal arts studies to increase creativity, which would ultimately prepare engineers for an ambiguous future. Students and workers able to transition to Industry 4.0 may find greater autonomy and more interesting work,44 highlighting the need to broaden knowledge of basic concepts related to Industry 4.0.45

In future manufacturing, employee qualifications and skill requirements will be higher than at present, due to new technology. Physically demanding positions will be replaced by automated production, requiring upskilled personnel for facility operation, maintenance, and restoration in case of obsolescence.46 Workforce education will therefore need to extend beyond the university level; throughout their careers, production staff will benefit from a learning factory environment that helps them hone their technical and professional expertise, including decision-making, group work, and performance-monitoring skills.41

Research Priorities

Practice-oriented learning environments, such as LeanLab at Graz University of Technology and similar laboratories in third-level facilities worldwide, aim to facilitate effective knowledge transfer and address future challenges in manufacturing.47 Such facilities can serve as a platform for demonstrating relevant parts of the Industry 4.0 vision. The LeanLab curriculum aims to increase industrial management skills and qualifications of students and industrial personnel, with industrial engineering topics such as workplace organization, logistics management, and planning as well as methodologies such as Lean manufacturing and energy efficiency.

In a special issue on 21st-century quality management, the International Journal of Production Economics48 uncovered many underexplored research pathways, including:

  • Changes in quality management to lessen human involvement with respect to new technologies
  • Quality-related performance measures and metrics for futuristic supply chains
  • Development of sophisticated methods to capture emerging quality challenges
  • Encouraging radical quality innovation
  • Development of contemporary Lean management techniques
  • Creating leadership emphasis and process agility to achieve economic sustainability
  • Engaging employees to meet quality standards

Similarly, Ding notes that future research should focus on:4

  • Cross-linking coordination and cooperation
  • Ecofriendly end-of-life product disposal
  • Proactive product recall management
  • New benchmarks and sustainable performance measurements
  • New regulation system design
  • Effects of incentives for sustainable activities

The pharmaceutical industry would also benefit from research to support innovation in the following areas related to supply chain sustainability:

  • Improving supply chain collaboration to more effectively control and manage the entire product life cycle22 ,49
  • Increasing the response speed of the drug recall supply chain through cross-company symbiosis to control and mitigate dissemination of defected products to the public49
  • Collecting data from broader regions and quantitatively investigating hospital procurement50
  • Designing appropriate indicators and assessment systems that integrate extant sustainable indicators to holistically evaluate supply chain performance51
  • Completing systematic reviews of legal frameworks of the primary regulatory authorities to integrate environmental and social concerns, as detailed by Ding4

Until sufficient research is conducted on these topics, the pharma industry will lack the full array of tools it needs.

Implementation Challenges

Glass has identified potential barriers to the progression of Industry 4.0 in the near future.52 First, enterprises need technological criteria regarding the compatibility of various interfaces and data formats. Second, there is a high demand for skilled workers with industry know-how. To confront the human resources challenges, companies, governments, and education facilities should increase their efforts to offer interdisciplinary apprenticeships and degree courses, such as mechanical engineering combined with aspects of information technology. The aim of such initiatives should be to improve efficiency of production processes, which would help pharmaceutical enterprises align technologies and solutions more closely to their individual industry- and company-specific needs.

The major challenges that inhibit the sustainability in pharmaceutical supply chains are:4

  • High cost and time consumption
  • Insufficient expertise and training
  • Poor regulation enforcement
  • Scarcity of business incentives
  • Ineffective collaboration and coordination across the supply chain
  • Lack of objective benchmarks
  • Poor end-customer awareness

Industry 4.0 technologies and innovations can lift these barriers in four ways: (a) by enhancing supply chain flexibility for patient-centric drug supplies; (b) by improving the effectiveness of coordination and communication across different entities in the supply chain; (c) by mitigating waste and pollution at different stages; and (d) by enabling a more autonomous decision-making process for supply chain managers.


Because it is so highly regulated to ensure patient safety, product quality, and data integrity, the pharmaceutical/biopharmaceutical industry struggles to adopt Industry 4.0. Technology must meet stringent GxP criteria imposed by corporations and regulators. Currently, many technologies are unsuitable for the pharmaceutical sector because they do not meet those criteria.

Nevertheless, Betz and colleagues53 predict that the combination of medical, biomedical, and biotech advances with developments in AI will have the greatest impact on business and society in the next 35 years. Ultimately, we expect that smart technology will become fully integrated into the pharmaceutical industry as well as everyday life. This future is possible if industry stakeholders and educators collaborate to ensure that the understanding being generated now is nourished and further research is encouraged.

  • 2Weichhart, G., A. Molina, D. Chen, L. Whiteman, and F. Vernadat. “Challenges and Current Developments for Sensing, Smart and Sustainable Enterprise Systems.” Computers in Industry 79 (August 2015): 34–46. doi:10.1016/j.compind.2015.07.002
  • 3Burger, N., M. Demartini, F. Tonelli, F. Bodendorf, and C. Testa. “Investigating Flexibility as a Performance Dimension of a Manufacturing Value Modeling Methodology (MVMM): A Framework for Identifying Flexibility Types in Manufacturing Systems.” Procedia CIRP 63 (2017): 33–38. doi:10.1016/j.procir.2017.03.343
  • 4 a b c d Ding, B. “Pharma Industry 4.0: Literature Review and Research Opportunities in Sustainable Pharmaceutical Supply Chains.” Process Safety and Environmental Protection 119 (2018): 115–130. doi:10.1016/j.psep.2018.06.031
  • 5Herwig, C., C. Wölbeling, and T. Zimmer. “A Holistic Approach to Production Control from Industry 4.0 to Pharma 4.0™ .” Pharmaceutical Engineering 37, no. 3 (2017): 44–49.
  • 6 a b Tjahjono, B., C. Esplugues, E. Ares, and G. Pelaez. “What Does Industry 4.0 Mean to Supply Chain?” Procedia Manufacturing 13 (2017): 1175–1182. doi:10.1016/j.promfg.2017.09.191
  • 7Lawrence, X. Y., and M. Kopcha. “The Future of Pharmaceutical Quality and the Path to Get There.” International Journal of Pharmaceutics 528, no. 1–2 (2017): 354–359. doi:10.1016/j.ijpharm.2017.06.039
  • 8Qin, J., Y. Liu, and R. Grosvenor. “A Categorical Framework of Manufacturing for Industry 4.0 and Beyond.” Procedia CIRP 52 (2016): 173–178. doi:10.1016/j.procir.2016.08.005
  • 9Waibel, M., L. Steenkamp, N. Moloko, and G. Oostheizen “Investigating the Effects of Smart Production Systems on Sustainability Elements.” Procedia Manufacturing 8 (2017): 731–737. doi:10.1016/j.promfg.2017.02.094
  • 10Wang, G., A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos. “Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications.” International Journal of Production Economics 176 (June 2016): 98–110. doi:10.1016/j.ijpe.2016.03.014
  • 11Damiani, L., M. Demartini, G. Guizzi, R. B. Revetria, and F. Tonelli. “Augmented and Virtual Reality Applications in Industrial Systems: A Qualitative Review Towards the Industry 4.0 Era.” IFAC-PapersOnLine 51, no. 11 (2018): 624–630. doi:10.1016/j.ifacol.2018.08.388
  • 12Azuma, R. T. “A Survey of Augmented Reality.” Presence: Teleoperators and Virtual Environments 6, no. 4 (August 1997): 355–385. doi:10.1162/pres.1997.6.4.355
  • 13Milgram, P., H. Takemura, A. Utsumi, and F. Kishino. “Augmented Reality: A Class of Displays on the Reality-Virtuality Continuum.” Telemanipulator and Telepresence Technologies 2351 (December 1995). doi:10.1117/12.197321
  • 14Michalos, G., P. Karagiannis, S. Makris, and Ö. Tokçalar. “Augmented Reality (AR) Applications for Supporting Human-Robot Interactive Cooperation.” Procedia CIRP 41 (2016): 370–375. doi:10.1016/j.procir.2015.12.005
  • 15Rabah, S., A. Assila, E. Khouri, et al. “Towards Improving the Future of Manufacturing Through Digital Twin and Augmented Reality Technologies.” Procedia Manufacturing 17 (2018): 460–467. doi:10.1016/j.promfg.2018.10.070
  • 16Masoni, R., F. Ferrise, M. Bordegoni, and M. Gattullo. “Supporting Remote Maintenance in Industry 4.0 through Augmented Reality.” Procedia Manufacturing 11 (2017): 1296–1302. doi:10.1016/j.promfg.2017.07.257
  • 17Jetter, J., J. Eimecke, and A. Rese. “Augmented Reality Tools for Industrial Applications: What Are Potential Key Performance Indicators and Who Benefits?” Computers in Human Behavior 87 (October 2018): 18–33. doi:10.1016/j.chb.2018.04.054
  • 18Oesterreich, T. D., and F. Teuteberg. “Understanding the Implications of Digitisation and Automation in the Context of Industry 4.0: A Triangulation Approach and Elements of a Research Agenda for the Construction Industry.” Computers in Industry 83 (December 2016): 121–139. doi:10.1016/j.compind.2016.09.006
  • 19Goyanes, A., F. Fina, A. Martorana, D. Sedough, S. Gaisford, and A. W. Basit. “Development of Modified Release 3D Printed Tablets (Printlets) with Pharmaceutical Excipients Using Additive Manufacturing.” International Journal of Pharmaceutics 527, no. 1 (July 2017): 21–30. doi:10.1016/j.ijpharm.2017.05.021
  • 20Ngo, T.D., A. Kashani, G. Imbalzano, K. T. Q. Nguyen, and D. Hui. “Additive Manufacturing (3D printing): A Review of Materials, Methods, Applications and Challenges.” Composites Part B: Engineering 143 (June 2018): 172–196. doi:10.1016/j.compositesb.2018.02.012
  • 21 a b Gerbert, P., M. Lorenz, M. Rüßmann, et al. “Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries.” Boston Consulting Group.
  • 22 a b Stock, T., and G. Seliger. “Opportunities of Sustainable Manufacturing in Industry 4.0.” Procedia CIRP 40 (2016): 536–541. doi:10.1016/j.procir.2016.01.129
  • 23 a b Strange, R., and A. Zucchella. “Industry 4.0, Global Value Chains and International Business.” Multinational Business Review 25, no. 3 (2017): 174–184. doi:10.1108/MBR-05-2017-0028
  • 24Kadry, H., S. Wadnap, C. Xu, and F. Ahsan. “Digital Light Processing (DLP) 3D-Printing Technology and Photoreactive Polymers in Fabrication of Modified-Release Tablets. European Journal of Pharmaceutical Sciences 135 (July 2019): 60–67. doi:10.1016/j.ejps.2019.05.008
  • 25Khaled, S. A., M. R. Alexander, R. D. Wildman, et al. “3D Extrusion Printing of High Drug Loading Immediate Release Paracetamol Tablets.” International Journal of Pharmaceutics 538, no. 1 (2018): 223–230. doi:10.1016/j.ijpharm.2018.01.024
  • 26Khaled, S. A., J. C. Burley, M. R. Alexander, J. Yang, and C. J. Roberts. “3D Printing of Tablets Containing Multiple Drugs with Defined Release Profiles.” International Journal of Pharmaceutics 494, no. 2 (October 2015): 643–650. doi:10.1016/j.ijpharm.2015.07.067
  • 27Grieves, M., and J. Vickers, “Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems.” In Transdisciplinary Perspectives on Complex Systems, edited by F. J. Kahlen, S. Flumerfelt, and A. Alves, 85–113. Basel, Switzerland: Springer International, 2017. doi:10.1007/978-3-319-38756-7_4
  • 28Lee, J., E. Lapira, B. Bagheri, and H. Kao. “Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment.” Manufacturing Letters 1, no. 1 (2013): 38–41. doi:10.1016/j.mfglet.2013.09.005
  • 29Bahrin, M. A. K., M. F. Othman, N. H. N. Azli, and M. F. Talib. “Industry 4.0: A Review on Industrial Automation and Robotic.” Journal Teknologi 78, no. 6–13 (2016): 137–143. doi:10.11113/jt.v78.9285
  • 30Thramboulidis, K., and F. Christoulakis. “UML4IoT—A UML-Based Approach to Exploit IoT in Cyber-Physical Manufacturing Systems.” Computers in Industry 82 (2016): 259–272. doi:10.1016/j.compind.2016.05.010
  • 31Yue, X., H. Cai, H. Yan, C. Zuo, and K. Zhou. “Cloud-Assisted Industrial Cyber-Physical Systems: An Insight.” Microprocessors and Microsystems 39, no. 8 (September 2015): 1262–1270. doi:10.1016/j.micpro.2015.08.013
  • 32Cohen, Y., M. Golan, G. Singer, and M. Faccio. “Workstation‒Operator Interaction in 4.0 Era: WOI 4.0.” IFAC-PapersOnLine 51, no. 11 (2018): 399–404. doi:10.1016/j.ifacol.2018.08.327
  • 33Hildebrandt, A., and W. Landhäußer. CSR und Digitalisierung—Der digitale Wandel als Chance und Herausforderung für Wirtschaft und Gesellschaft. Berlin: Springer Gabler, 2017. doi:10.1007/978-3-662-53202-7
  • 34Eschen, H., T. Kötter, R. Rodeck, M. Harnisch, and T. Schüppstuhl. “Augmented and Virtual Reality for Inspection and Maintenance Processes in the Aviation Industry.” Procedia Manufacturing 19 (2018): 156–163. doi:10.1016/j.promfg.2018.01.022
  • 35Yew, A., S. Ong, and A. Nee. “Towards a Griddable Distributed Manufacturing System with Augmented Reality Interfaces.” Robotics and Computer-Integrated Manufacturing 39 (2016): 43–55. doi:10.1016/j.rcim.2015.12.002
  • 36Gattullo, M., G. W. Scurati, M. Fiorentino, A. E. Uva, F. Ferrise, and M. Bordergoni. “Towards Augmented Reality Manuals for Industry 4.0: A Methodology.” Robotics and Computer-Integrated Manufacturing 56 (2019): 276–286. doi:10.1016/j.rcim.2018.10.001
  • 37 a b Roblek, V., M. Mesko, and A. Krapez. “A Complex View of Industry 4.0.” Sage Open 6, no. 2 (June 2016). doi:10.1177/2158244016653987
  • 38Sanders, A., C. Elangeswaran, and J. Wulfsberg. “Industry 4.0 Implies Lean Manufacturing: Research Activities in Industry 4.0 Function as Enablers for Lean Manufacturing.” Journal of Industrial Engineering and Management 9, no. 3 (2016): 811–833. doi:10.3926/jiem.1940
  • 39Lasi, H., P. Fettke, H. Kemper, T. Feld, and M. Hoffman. “Industry 4.0.” Business and Information Systems Engineering 6, no. 4 (June 2014): 239–242. doi:10.1007/s12599-014-0334-4
  • 40 a b Wermann, J., A. W. Colombo, A. Pechmann, and M. Zarte. “Using an Interdisciplinary Demonstration Platform for Teaching Industry 4.0.” Procedia Manufacturing 31 (2019): 302–308. doi:10.1016/j.promfg.2019.03.048
  • 41 a b c Schmidt, R., M. Möhring, R. Härting, et al. “Industry 4.0—Potentials for Creating Smart Products: Empirical Research Results.” In Business Information Systems: 18th International Conference, BIS 2015, Poznań, Poland, June 24-26, 2015, Proceedings, edited by W. Abramowicz. doi:10.1007/978-3-319-19027-3_2
  • 42Ellahi, R.M., M.U. Ali Khan, and A. Shah. “Redesigning Curriculum in Line with Industry 4.0.” Procedia Computer Science 151 (2019): 699–708. doi:10.1016/j.procs.2019.04.093
  • 43Mourtzis, D., E. Vlachou, G. Dimitrakopoulos, and V. Zogopoulos. “Cyber-Physical Systems and Education 4.0—The Teaching Factory 4.0 Concept.” Procedia Manufacturing 23 (2018): 129–134. doi:10.1016/j.promfg.2018.04.005
  • 44Motyl, B., G. Baronio, S. Uberti, D. Speranza, and S. Filippi. “How Will Change the Future Engineers’ Skills in the Industry 4.0 Framework? A Questionnaire Survey.” Procedia Manufacturing 11 (2017): 1501–1509. doi:10.1016/j.promfg.2017.07.282
  • 45Fernández-Miranda, S. S., M. Marcos, M.E. Peralta, and F. Aguayo. “The Challenge of Integrating Industry 4.0 in the Degree of Mechanical Engineering.” Procedia Manufacturing 13 (2017): 1229–1236. doi:10.1016/j.promfg.2017.09.039
  • 46Benešová, A., and J. Tupa. “Requirements for Education and Qualification of People in Industry 4.0.” Procedia Manufacturing 11 (2017): 2195–2202. doi:10.1016/j.promfg.2017.07.366
  • 47Karre, H., M. Hammer, M. Kleindienst, and C. Ramsaur. “Transition Towards an Industry 4.0 State of the LeanLab at Graz University of Technology.” Procedia Manufacturing 9 (2017): 206–213. doi:10.1016/j.promfg.2017.04.006
  • 48Gunasekaran, A., N. Subramanian, and W. T. E. Ngai. “Quality Management in the 21st Century Enterprises: Research Pathway Towards Industry 4.0.” International Journal of Production Economics 207 (September 2018): 125–129. doi:10.1016/j.ijpe.2018.09.005
  • 49 a b De Man, J. C., and J. O. Strandhagen. “An Industry 4.0 Research Agenda for Sustainable Business Models.” Procedia CIRP 63 (2017): 721–726. doi:10.1016/j.procir.2017.03.315
  • 50Oruezabala, G., and J. C. Rico. “The Impact of Sustainable Public Procurement on Supplier Management—The Case of French Public Hospitals.” Industrial Marketing Management 41, no. 4 (May 2012): 573–580. doi:10.1016/j.indmarman.2012.04.004
  • 51Yawar, S. A., and S. Seuring. “Management of Social Issues in Supply Chains: A Literature Review Exploring Social Issues, Actions and Performance Outcomes.” Journal of Business Ethics 141, no. 3, (March 2017): 621–643. doi:10.1007/s10551-015-2719-9
  • 52Glass, R., A. Meissner, C. Gebauer, S. Stürmer, and J. Metternich. “Identifying the Barriers to Industrie 4.0.” Procedia CIRP 72 (2018): 985–988. doi:10.1016/j.procir.2018.03.187
  • 53Betz, U. A. K., F. Betz, R. Kim, B. Monks, and F. Phillips. “Surveying the Future of Science, Technology and Business—A 35 Year Perspective.” Technological Forecasting and Social Change 144 (July 2019): 137–147. doi:10.1016/j.techfore.2019.04.005