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Special Issue "JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Dr. Javier Villalba-Diez
E-Mail Website
Guest Editor
1. Department of Computer Science, Technische Universität München, 80333 Munich, Germany
2. Department of Computer Science, Universidad Politécnica de Madrid, 28001 Madrid, Spain
Interests: artificial intelligence; deep learning; cyberphysical systems; business intelligence; strategic organizational design
Special Issues and Collections in MDPI journals
Dr. Joaquin Ordieres Meré
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, 28006 Madrid, Spain
Interests: big data analytics; IIoT; smart sensors; digital transformation of industry; artificial intelligence; machine learning; distributed computing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Today’s manufacturing industry is increasingly subject to a global competition due to falling transportation and communication costs as well as a faster transportation of goods. Adding to this, it is becoming more and more important for companies to reduce their environmental footprint by cutting down energy and material usage in order to reach a sustainable level of the consumed resources. This all increases the pressure to find ways in which to reduce the costs but, at the same time, increase the speed of the delivered goods.

While it is quite generally accepted that higher automation naturally improves the quality in manufacturing, the effects are not necessarily positive and, at least on their own, should not be seen as a sufficient step towards higher quality in manufacturing. In some cases when the complex interplay between machine and human operator is not fully taken into consideration, this can even expand the problems due to the complexity of the task. In fact, automatization ought to respond to the call of integrating the advantageous capabilities of both human and cyberphysical assets. This is where JIDOKA comes into play: “automation with a human touch”.

This allows for two general strategies to deal with the complexity that do not—and should not—need to be used exclusively. The first is to reduce the complexity of a process step by analyzing it and using tools such as lean management. A different approach is to use tools with a higher complexity than the problem in order to try to control it. At this point, artificial intelligence (AI) comes into play. This includes a big range of subfields with different goals: from the integration of smart wearable sensors to ease human decision making in the value creation process to distributed ledger technologies that increase the trustworthiness of the systems in place; from the ubiquitous acquisition of relevant data with industrial internet of things to the online computation of such data to sharpen strategic market positioning and responsiveness to customer needs; and from systematic human problem-solving empowerment to discriminating deep learning algorithms.

The contributions presented in this Special Issue should combine the application of lean management systematics with artificial intelligence methodologies within the context of cyberphysical systems.

Some of the areas of interest (amongst others) include:

  • JIDOKA—intelligent automation with a human touch;
  • Predictive maintenance—online monitoring, condition-based maintenance, risk-based maintenance;
  • IIoT related to production, safety, and/or health in the workplace, including pollution;
  • Industrial applications of smart sensors in, e.g., cloud computing, mobile technologies, 3D printing, advanced robotics, big data, internet of things, RFID technology, and cognitive computing, that enable better value stream performance;
  • Applications of smart sensors that optimize the energy consumption of value creation processes and reduce the CO2 manufacturing footprint, i.e., smart grids;
  • IIoT and integration between workers and process automation to produce a more comprehensive perspective;
  • Industrial applications of cloud computing, artificial intelligence, machine learning, and deep learning that enable a better value stream performance within smart sensor networks;
  • Applications of smart sensor networks to cyberphysical production systems;
  • Applications of deep learning to industrial problem solving and value stream continuous improvement;
  • Complex networked lean production systems;
  • Trust and accountability through DLT (distributed ledger technology) in industrial applications.
Dr. Javier Villalba Diez
Dr. Joaquin Ordieres Meré
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. Sensors 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 2200 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

  • JIDOKA
  • lean management
  • cyberphysical production systems
  • deep learning applied to smart sensor networks
  • predictive maintenance
  • smart sensors in industrial applications
  • industrial internet of things
  • cloud computing
  • 3D printing
  • advanced robotics
  • big data in industrial applications
  • RFID technology
  • cognitive computing
  • deep learning
  • smart grid
  • artificial intelligence in industrial applications
  • distributed ledger technology in industrial applications

Published Papers (5 papers)

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Open AccessArticle
Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems
Sensors 2020, 20(20), 5856; https://doi.org/10.3390/s20205856 - 16 Oct 2020
Cited by 5 | Viewed by 861
Abstract
The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that [...] Read more.
The strategic design of organizations in an environment where complexity is constantly increasing, as in the cyber-physical systems typical of Industry 4.0, is a process full of uncertainties. Leaders are forced to make decisions that affect other organizational units without being sure that their decisions are the right ones. Previously to this work, genetic algorithms were able to calculate the state of alignment of industrial processes that were measured through certain key performance indicators (KPIs) to ensure that the leaders of the Industry 4.0 make decisions that are aligned with the strategic objectives of the organization. However, the computational cost of these algorithms increases exponentially with the number of KPIs. That is why this work makes use of the principles of quantum computing to present the strategic design of organizations from a novel point of view: Quantum Strategic Organizational Design (QSOD). The effectiveness of the application of these principles is shown with a real case study, in which the computing time is reduced from hundreds of hours to seconds. This has very powerful practical applications for industry leaders, since, with this new approach, they can potentially allow a better understanding of the complex processes underlying the strategic design of organizations and, above all, make decisions in real-time. Full article
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Open AccessArticle
Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology
Sensors 2020, 20(11), 3046; https://doi.org/10.3390/s20113046 - 27 May 2020
Cited by 6 | Viewed by 2685
Abstract
Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary [...] Read more.
Information-intensive transformation is vital to realize the Industry 4.0 paradigm, where processes, systems, and people are in a connected environment. Current factories must combine different sources of knowledge with different technological layers. Taking into account data interconnection and information transparency, it is necessary to enhance the existing frameworks. This paper proposes an extension to an existing framework, which enables access to knowledge about the different data sources available, including data from operators. To develop the interoperability principle, a specific proposal to provide a (public and encrypted) data management solution to ensure information transparency is presented, which enables semantic data treatment and provides an appropriate context to allow data fusion. This proposal is designed also considering the Privacy by Design option. As a proof of application case, an implementation was carried out regarding the logistics of the delivery of industrial components in the construction sector, where different stakeholders may benefit from shared knowledge under the proposed architecture. Full article
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Open AccessArticle
Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning
Sensors 2020, 20(10), 2860; https://doi.org/10.3390/s20102860 - 18 May 2020
Cited by 4 | Viewed by 1313
Abstract
Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). [...] Read more.
Achieving the shift towards Industry 4.0 is only feasible through the active integration of the shopfloor into the transformation process. Several shopfloor management (SM) systems can aid this conversion. They form two major factions. The first includes methodologies such as Balanced Scorecard (BSC). A defining feature is rigid structures to fixate on pre-defined goals. Other SM strategies instead concentrate on continuous improvement by giving directions. An example of this group is the “HOSHIN KANRI TREE” (HKT). One way of analyzing the dissimilarities, the advantages and disadvantages of these groups, is to examine the neurological patterns of workers as they are applying these. This paper aims to achieve this evaluation through non-invasive electroencephalography (EEG) sensors, which capture the electrical activity of the brain. A deep learning (DL) soft sensor is used to classify the recorded data with an accuracy of 96.5%. Through this result and an analysis using the correlations of the EEG signals, it has been possible to detect relevant characteristics and differences in the brain’s activity. In conclusion, these findings are expected to help assess SM systems and give guidance to Industry 4.0 leaders. Full article
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Open AccessArticle
Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces
Sensors 2020, 20(7), 2011; https://doi.org/10.3390/s20072011 - 03 Apr 2020
Cited by 7 | Viewed by 1864
Abstract
Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing [...] Read more.
Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits. Full article
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Open AccessLetter
Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One
Sensors 2020, 20(23), 6977; https://doi.org/10.3390/s20236977 - 06 Dec 2020
Cited by 2 | Viewed by 820
Abstract
In this paper we investigate how the relationship with a subordinate who reports to him influences the alignment of an Industry 4.0 leader. We do this through the implementation of quantum circuits that represent decision networks. In fact, through the quantum simulation of [...] Read more.
In this paper we investigate how the relationship with a subordinate who reports to him influences the alignment of an Industry 4.0 leader. We do this through the implementation of quantum circuits that represent decision networks. In fact, through the quantum simulation of strategic organizational design configurations (QSOD) through five hundred simulations of quantum circuits, we conclude that there is an influence of the subordinate on the leader that resembles that of a harmonic under-damped oscillator around the value of 50% probability of alignment for the leader. Likewise, we have observed a fractal behavior in this type of relationship, which seems to conjecture that there is an exchange of energy between the two agents that oscillates with greater or lesser amplitude depending on certain parameters of interdependence. Fractality in this QSOD context allows for a quantification of these complex dynamics and its pervasive effect offers robustness and resilience to the two-qubit interaction. Full article
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