Special Issue "JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems"
Deadline for manuscript submissions: 30 September 2021.
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
Interests: big data analytics; IIoT; smart sensors; digital transformation of industry; artificial intelligence; machine learning; distributed computing
Special Issues and Collections in MDPI journals
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. Joaquin Ordieres Meré
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.
- 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