Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Reference Models for Digital Manufacturing Platforms

[EN] This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform.This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 825631 and from the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Fraile Gil, F.; Sanchis, R.; Poler, R.; Ortiz Bas, Á. (2019). Reference Models for Digital Manufacturing Platforms. Applied Sciences. 9(20):1-25. https://doi.org/10.3390/app9204433S125920Pedone, G., & Mezgár, I. (2018). Model similarity evidence and interoperability affinity in cloud-ready Industry 4.0 technologies. Computers in Industry, 100, 278-286. doi:10.1016/j.compind.2018.05.003Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S. S., & Gisario, A. (2019). The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review. Applied Sciences, 9(18), 3865. doi:10.3390/app9183865Tran, Park, Nguyen, & Hoang. (2019). Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Applied Sciences, 9(16), 3325. doi:10.3390/app9163325Fernandez-Carames, T. M., & Fraga-Lamas, P. (2019). A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories. IEEE Access, 7, 45201-45218. doi:10.1109/access.2019.2908780Moghaddam, M., Cadavid, M. N., Kenley, C. R., & Deshmukh, A. V. (2018). Reference architectures for smart manufacturing: A critical review. Journal of Manufacturing Systems, 49, 215-225. doi:10.1016/j.jmsy.2018.10.006Sutherland, W., & Jarrahi, M. H. (2018). The sharing economy and digital platforms: A review and research agenda. International Journal of Information Management, 43, 328-341. doi:10.1016/j.ijinfomgt.2018.07.004Corradi, A., Foschini, L., Giannelli, C., Lazzarini, R., Stefanelli, C., Tortonesi, M., & Virgilli, G. (2019). Smart Appliances and RAMI 4.0: Management and Servitization of Ice Cream Machines. IEEE Transactions on Industrial Informatics, 15(2), 1007-1016. doi:10.1109/tii.2018.2867643Gerrikagoitia, J. K., Unamuno, G., Urkia, E., & Serna, A. (2019). Digital Manufacturing Platforms in the Industry 4.0 from Private and Public Perspectives. Applied Sciences, 9(14), 2934. doi:10.3390/app9142934Digital Manufacturing Platforms, Factories 4.0 and beyondhttps://www.effra.eu/digital-manufacturing-platformsZero Defect Manufacturing Platform Project 2019https://www.zdmp.eu/Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0 – An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12. doi:10.1016/j.ifacol.2016.12.002Announcing the IoT Industrie 4.0 Reference Architecturehttps://www.ibm.com/cloud/blog/announcements/iot-industrie-40-reference-architectureVelásquez, N., Estevez, E., & Pesado, P. (2018). Cloud Computing, Big Data and the Industry 4.0 Reference Architectures. Journal of Computer Science and Technology, 18(03), e29. doi:10.24215/16666038.18.e29Pisching, M. A., Pessoa, M. A. O., Junqueira, F., dos Santos Filho, D. J., & Miyagi, P. E. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering, 125, 574-591. doi:10.1016/j.cie.2017.12.029Calvin, T. (1983). Quality Control Techniques for «Zero Defects». IEEE Transactions on Components, Hybrids, and Manufacturing Technology, 6(3), 323-328. doi:10.1109/tchmt.1983.113617

Open Platform Concept for Blockchain- Enabled Crowdsourcing of Technology Development and Supply Chains

We outline the concept of an open technology platform which builds upon a publicly accessible library of fluidic designs, manufacturing processes and experimental characterisation, as well as virtualisation by a ‘digital twin” based on modelling, simulation and cloud computing. Backed by the rapidly emerging Web3 technology “Blockchain”, we significantly extend traditional approaches to effectively incentivise broader participation by an interdisciplinary ‘value network’ of diverse players. Ranging from skilled individuals (the ‘citizen scientist’, the ‘garage entrepreneur’) and more established research institutions to companies with their infrastructures, equipment and services, the novel platform approach enables all stakeholders to jointly contribute to value creation along more decentralised supply chain designs including research and technology development (RTD). Blockchain-enabled “Wisdom of the Crowds” and “Skin in the game” mechanisms secure “trust” and transparency between participants. Prediction markets are created for guiding decision making, planning and allocation of funding; competitive parallelisation of work and its validation from independent participants substantially enhances quality, credibility and speed of project outcomes in the real world along the entire path from RTD, fabrication and testing to eventual commercialisation. This novel, Blockchain-backed open platform concept can be led by a corporation, academic entity, a loosely organised group, or even “chieflessly” within a smart-contract encoded Decentralised Autonomous Organisation (DAO). The proposed strategy is particularly attractive for highly interdisciplinary fields like Lab-on-a- Chip systems in the context of manifold applications in the Life Sciences. As an exemplar, we outline the centrifugal microfluidic “Lab-on-a-Disc” technology. Rather than engaging in all sub-disciplines themselves, many smaller, highly innovative actors can focus on strengthening the product component distinguishing their unique selling point (USP), e.g., a particular bioassay, detection scheme or application scenario. In this effort, system integrators access underlying commons like fluidic design, manufacture, instrumentation and software from a more resilient and diversified supply chain, e.g., based on a verified pool of community-endorsed or certified providers

Recent advances in industrial wireless sensor networks towards efficient management in IoT

With the accelerated development of Internet-of- Things (IoT), wireless sensor networks (WSN) are gaining importance in the continued advancement of information and communication technologies, and have been connected and integrated with Internet in vast industrial applications. However, given the fact that most wireless sensor devices are resource constrained and operate on batteries, the communication overhead and power consumption are therefore important issues for wireless sensor networks design. In order to efficiently manage these wireless sensor devices in a unified manner, the industrial authorities should be able to provide a network infrastructure supporting various WSN applications and services that facilitate the management of sensor-equipped real-world entities. This paper presents an overview of industrial ecosystem, technical architecture, industrial device management standards and our latest research activity in developing a WSN management system. The key approach to enable efficient and reliable management of WSN within such an infrastructure is a cross layer design of lightweight and cloud-based RESTful web service

Understanding The Role Of IoT Technologies In Supply Ecosystems

This paper introduces the concept of supply ecosystems for exploring the role of the Internet of Things (IoT) and its technologies in value added activities. The paper argues that the IoT has a role in helping to understand the information problems that firms face, and in identifying the way in which IoT technologies can help. To achieve this, the paper examines the vision and scope of the IoT and its perceived potential value to firms. Then, based on the available literature, the paper furthers the existing concept of supply ecosystems and argues that the potential value of the IoT to businesses can only be realized if a holistic, more ecological perspective, is used. The paper hence proposes that supply ecosystems can be used to understand the value of IoT in solving information problems