Abstract
The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and thus increase the integrability of the human–machine binomial, enabling appropriate management strategies. Therefore, the major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes. It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. By covering cases from different sectors and involving different value stream architectures, with different levels of human influence and organisational requirements, it will be possible to assess the transparency increases reached as well as the benefits of analysing processes with higher level of integration between them.
Highlights
The aim of this work is to use industrial internet of things (IIoT) technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and increase the integrability of the human–machine binomial, enabling appropriate management strategies
Value chains associated with Industry 4.0 (I4.0) are formed by complex cyber-physical networks in which humans and machines process information efficiently to supply a customer with the desired product [1,2,3]
In an I4.0 context, such as the IIoT [7,8], these data are produced by decentralized sources such as thousands of sensors in factories [9], i.e., the data are distributed over networks [10]
Summary
The aim of this work is to use IIoT technology and advanced data processing to promote integration strategies between these elements to achieve a better understanding of the processing of information and increase the integrability of the human–machine binomial, enabling appropriate management strategies. The major objective of this paper is to evaluate how human–machine integration helps to explain the variability associated with value creation processes It will be carried out through an action research methodology in two different case studies covering different sectors and having different complexity levels. Since machines autonomously make many decisions and interact with production planning and manufacturing systems, the integration of human users requires new paradigms [6]. Application developers often struggle to integrate and plug in novel sensor technologies, becoming available on the market at a fast pace [14]. These technologies enable describing processes in a more integrated way, including many more potential sources of variability [8]. The advantages are rather evident, still there are significant challenges to be better identified and faced when new useful solutions regarding knowledge and management are foreseen
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
