Special Issue "The Role of Digitalization and Industry 4.0 Technologies for Product and Production Development in the Manufacturing Industry"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Koteshwar Chirumalla
E-Mail Website
Guest Editor
Division of Product Realization, Mälardalen University, 631 05 Eskilstuna, Sweden
Interests: industrialization; process innovation; product and production development; digital transformation; business models; knowledge management
Prof. Dr. Jessica Bruch
E-Mail Website
Guest Editor
Division of Product Realization, Mälardalen University, 631 05 Eskilstuna, Sweden
Interests: industrialization; process innovation and smart factories; digitalization of manufacturing; information management; intra- and interorganizational collaboration
Prof. Dr. Anna Öhrwall Rönnbäck
E-Mail Website
Guest Editor
Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, 97187 Luleå, Sweden
Interests: technology and business development; product service systems (PSS); business models; innovation management; product and production development; production innovation
Special Issues and Collections in MDPI journals
Dr. Alessandro Bertoni
E-Mail Website
Guest Editor
Department of Mechanical Engineering, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden
Interests: engineering design; product service systems development (research and education); value-driven design; data driven design; design for circular economy; systems engineering (research and education)
Special Issues and Collections in MDPI journals
Prof. Dr. Anna Syberfeldt
E-Mail Website
Guest Editor
Department of Production and Automation Engineering, University of Skövde, P.O. Box 408, SE-541 28 Skövde, Sweden
Interests: evolutionary algorithms; multi-objective optimization; industrial optimization; industrial decision support systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue (SI) focuses on advancing knowledge on the role of digitalization and Industry 4.0 technologies for product and production development in manufacturing industry. The goal of the SI is to make theoretical and practical contributions to the areas of product development, production development, operations management, and sustainability.

In recent years, among the most common terms used to describe the technology shift both in academia and practice are digitalization, digital transformation (Verhoef et al., 2021), and Industry 4.0 technologies (I4.0) (Liao et al., 2017). Some underlying digital technologies discussed in literature include the Internet of People, the Internet of Things (IoT), cloud computing, big data technologies, cyberphysical systems (CPS), blockchain, augmented reality (AR), automation, advanced robotics, additive manufacturing (AM), simulation, and semantic technologies (e.g., Klingenberg, Borges, and Antunes, 2019; Oztemel and Gursev, 2020). These technologies promise to provide many novel opportunities and benefits to industrial firms, such as increased product quality, process reliability, and improved flexibility and productivity. The role and impact of the new digital technologies have been explored and described from multiple perspectives. Some researchers viewed them from a perspective of data management (Gölzer and Fritzsche, 2017; Tao et al., 2018), dynamic capabilities (Warner and Wäger, 2019; Chirumalla, 2021), digital capability building (Li et al., 2019), strategic roadmaps (Ghobakhloo, 2018), reformation of organizational structures (Balakrishnan and Das, 2020), business models (Parida et al., 2019), strategy (Kane et al., 2015), maturity models (Santos and Martinho, 2019), readiness (Castelo-Branco et al., 2019; Gürdür et al., 2019), and sustainability and the circular economy (Ghobakhloo, 2020; Nascimento et al., 2019; Hallstedt et al., 2020). The most common dimensions discussed in the above contributions are strategy, organizational structures, infrastructure, leadership, resources, culture, and work processes. Hence, the digital transformation or I4.0 should not just be limited to technology; they also need to be related to firms’ strategic change and organizational management, including strategy, structures, processes, resources, and cultural readiness (Gürdür et al., 2019; Li et al., 2019; Balakrishnan and Das, 2020).

In the context of product or production development (PPD), most studies have focused on the role and impact of I4.0 technologies, such as digital twins, CPS, cloud computing, IoT, AI, big data, etc. on operations and manufacturing, e.g., smart factory (Osterrieder et al., 2020), smart manufacturing (Kusiak, 2018; Taoa et al., 2018), digital twin-based CPS (Ding et al., 2019), or AI-based manufacturing systems (Lee et al., 2018). However, deeper analysis studies on the role and impact of I4.0 technologies on product or production system design and development or in combination are still limited. For instance, Tao et al.’s (2019) study on digital twin-driven product design is a good example in this direction.

This Special Issue (SI) aims to provide a forum for researchers and practitioners to provide a comprehensive overview of how I4.0 enabling technologies can be adopted and implemented to support product or production system development, or combinations. Papers are expected to contribute with the latest results on sociotechnical and managerial understanding, design processes, the implementation of use cases or demonstrations of specific or combination of I4.0 technologies, and theoretical and methodological conceptualizations. The SI welcomes papers that not only focus on technology aspects but also on organizational, managerial, and social aspects related to I4.0 in product and production development.

The expected contribution to the SI includes but is not limited to the following topical areas:

  • Barriers and challenges for adoption and implementation of digitalization and I4.0 for PPD;
  • Contextual factors influencing on the adoption and implementation of I4.0 for PPD;
  • Impact of data-driven approaches or Big Data on PPD and its implications;
  • Mapping of technologies and capabilities for adoption and implementation of I4.0 for PPD;
  • Adoption of base technologies such as IoT, cloud services and data analytics for PPD and its integration with the existing or other relevant technologies;
  • Digitalization and I4.0 for supporting the early phases of PPD;
  • Structural changes and managerial actions associated with adoption of I4.0 for PPD;
  • Work processes and process excellence for the adoption and implementation of I4.0 for PPD;
  • Organization change management in the adoption and implementation of I4.0 for PPD;
  • Role of competences for successful adoption and implementation of I4.0 for PPD;
  • Dynamic capabilities for implementation of I.40 for product, process, and production development;
  • Influence of digital technologies on concept study, prototypes, pre-series, production ramp-up, serial production;
  • Influence of digital technologies on the interfaces such as R&D manufacturing, production system design, and operations;
  • Influence of digital technologies on industrialization and introduction of new products and production technologies in the existing production plants;
  • Readiness and maturity of companies in the adoption and implementation of I4.0 for PPD;
  • System architecture of I4.0 for PPD;
  • Different conceptualizations and perspectives of I4.0 adoption and implementation for PPD;
  • Value chain and ecosystem collaborations in the adoption and implementation of I4.0 for PPD;
  • Guidelines and methodologies for supporting companies in defining their digitalization and I4.0 roadmaps for PPD.

References

Balakrishnan, R. & Das, S. (2020). How do firms reorganize to implement digital transformation? Strategic Change, 29, 531-541.

Castelo-Branco, I., Cruz-Jesus, F. & Oliveira, T. (2019). Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union, Computers in Industry, 107, 22–32.

Chirumalla, K. (2021). Building digitally-enabled process innovation in the process industries: A dynamic capabilities approach. Forthcoming.

Ding, K., Chan, F.T.S., Zhang, X., Zhou, G & Zhang, F. (2019). Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors, International Journal of Production Research, 57:20, 6315-6334.

Gölzer, P. & Fritzsche, A. (2017). Data-driven operations management: organisational implications of the digital transformation in industrial practice, Production Planning & Control, 28:16, 1332-1343.

Ghobakhloo, M. (2018). The future of manufacturing industry: a strategic roadmap toward Industry 4.0, Journal of Manufacturing Technology Management, 29:6, 910-936.

Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869.

Gürdür, D., El-khoury, J., & Törngren, M. (2019). Digitalizing Swedish industry: What is next? Data analytics readiness assessment of Swedish industry, according to survey results. Computers in Industry, 105, 153-163.

Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review, 14, 1–25.

Hallstedt, S. I., Isaksson, O., & Öhrwall Rönnbäck, A. (2020). The need for new product development capabilities from Digitalization, Sustainability, and Servitization Trends, Sustainability, 12(23), 10222.

Kusiak, A. (2018). Smart manufacturing, International Journal of Production Research, 56:1-2, 508-517.

Klingenberg, C, O., Borges, M.A.V. and Antunes Jr, J, A, V, A. (2019). Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies. Journal of Manufacturing Technology Management, DOI 10.1108/JMTM-09-2018-0325.

Liao, Y., Deschamps, F., Loures, E.de. F.R, & Ramos, L.F-P. (2017). Past, present and future of Industry 4.0: a systematic literature review and research agenda proposal, International Journal of Production Research, 55:12, 3609-3629.

Li, J., Zhou, J., & Cheng, Y. (2019). Conceptual Method and Empirical Practice of Building Digital Capability of Industrial Enterprises in the Digital Age. IEEE Transactions on Engineering Management, DOI: 10.1109/TEM.2019.2940702.

Lee, J., Davari, H., Singh, J. & Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing Systems, Manufacturing Letters, 18, 20–23.

Nascimento, D. L. M., Alencastro, V., Quelhas, O.L.G., Caiado, R.G.G., Garza-Reyes, J.A., Rocha-Lona, L. & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal. Journal of Manufacturing Technology Management, 30:3, 607-627.

Osterrieder, P., Budde, L. & Friedli, T. The smart factory as a key construct of industry 4.0: A systematic literature review, International Journal of Production Economics, 221, 107476.

Oztemel, E & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31, 127–182.

Parida, V., Sjödin, D. & Reim, W. (2019). Reviewing Literature on Digitalization, Business Model Innovation, and Sustainable Industry: Past Achievements and Future Promises, Sustainability, 11, 391.

Santos, R.C. & Martinho, J.L. (2019). An Industry 4.0 maturity model proposal. Journal of Manufacturing Technology Management, 31: 5, 1023-1043.

Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-Y. and Nee, A.Y.C. (2019). Digital twin-driven product design framework, International Journal of Production Research, 57:12, 3935-3953.

Taoa, F., Qia, Q., Liub, A. & Kusiak, A. (2018). Data-driven smart manufacturing, Journal of Manufacturing Systems, 48, 157–169

Verhoef, P.C., Broekhuizen, T., Bart, Y., et al., (2020). Digital transformation: A multidisciplinary reflection and research agenda, Journal of Business Research, 122, 889-901.

Warner, K.S.R. & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326-349.

Dr. Koteshwar Chirumalla
Prof. Dr. Jessica Bruch
Prof. Anna Rönnbäck
Dr. Alessandro Bertoni
Prof. Dr. Anna Syberfeldt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digitalization
  • production development
  • smart factories
  • Industry 4.0
  • digital transformation
  • product development
  • data management
  • manufacturing industry
  • product and production innovation

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Improving Production Efficiency with a Digital Twin Based on Anomaly Detection
Sustainability 2021, 13(18), 10155; https://0-doi-org.brum.beds.ac.uk/10.3390/su131810155 - 10 Sep 2021
Viewed by 455
Abstract
Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they [...] Read more.
Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This paper presents the implementation of a Digital Twin module in an industrial case study, applying a concept for guiding companies on their way from data to value. A standardized use case template and a procedure model support the companies in (1) formulating a value proposition, (2) analyzing the current process, and (3) conceptualizing a target process. The presented use case entails an anomaly detection algorithm based on Gaussian processes to detect defective products in real-time for the extrusion process of aluminum profiles. The module was initially tested in a relevant environment; however, full implementation is still missing. Therefore, technology readiness level 6 (TRL6) was reached. Furthermore, the effect of the target process on production efficiency is evaluated, leading to significant cost reduction, energy savings, and quality improvements. Full article
Show Figures

Figure 1

Back to TopTop