Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach.

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.

Human-Machine Integration in Processes within Industry 4.0 Management.

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.

Improvement of Quantum Approximate Optimization Algorithm for Max-Cut Problems.

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value-stream network resilience. The main interest is to improve the Max-Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.

Quantum JIDOKA. Integration of Quantum Simulation on a CNC Machine for In-Process Control Visualization.

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of "uncertain knowledge". Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of 3 Qubits: Two Report to One.

The goal of this work is to explore how the relationship between two subordinates reporting to a leader influences the alignment of the latter with the company's strategic objectives in an Industry 4.0 environment. 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 quantum circuit simulations. We conclude that the alignment probability of the leader is never higher than the average alignment value of his subordinates, i.e., the leader never has a better alignment than his subordinates. In other words, the leader cannot present asymptotic stability better than that of his subordinates. The most relevant conclusion of this work is the clear recommendation to the leaders of Industry 4.0 not to add hierarchical levels to their organization if they have not achieved high levels of stability in the lower levels.

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of 3 Qubits: One Reports to Two.

In this work we explore how the relationship between one subordinate reporting to two leaders influences the alignment of the latter with the company's strategic objectives in an Industry 4.0 environment. We do this through the implementation of quantum circuits that represent decision networks. This is done for two cases: One in which the leaders do not communicate with each other, and one in which they do. Through the quantum simulation of strategic organizational design configurations (QSOD) through 500 quantum circuit simulations, we conclude that in the first case both leaders are not simultaneously in alignment, and in the second case that both reporting nodes need to have an alignment probability higher than 90% to support the leader node.

Quantum Strategic Organizational Design: Alignment in Industry 4.0 Complex-Networked Cyber-Physical Lean Management Systems.

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.

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of Two Qubits: One Reports to One.

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.

Data Handling in Industry 4.0: Interoperability Based on Distributed Ledger Technology.

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.

Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber-Physical Complex Networks.

In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber-physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber-physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber-physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber-physical environment.

Industry 4.0 Lean Shopfloor Management Characterization Using EEG Sensors and Deep Learning.

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.

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners' brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits.

Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people's comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.

Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0.

Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.

Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective.

This study primarily aimed to explore the capabilities of digitalisation in the healthcare context, focusing on a specific disease. In this case, the study examined the potential of remote monitoring of gait to address the sensitivity of multiple sclerosis progression to gait characteristics by adopting a non-invasive approach to remotely quantify gait disturbances in a patient's daily life. To better understand the managerial aspects associated with this approach, the researchers conducted a literature review along with a set of semi-structured interviews. The target population included MS patients as well as the key agents involved in their care: patients' family members, neurologists, MS nurses, physiotherapists, medical directors, and pharmacist. The study identifies the perceived barriers and drivers that could contribute to the successful deployment of PSS remote gait monitoring as a healthcare service: i) At mega-level governance. Implications on privacy and security data are notable barriers missing on the speech. ii) At macro level, funding is highlighted as main barrier. The cost and lack of health system subsidies may render initiatives unsustainable, as emphasised by the interviewees. iii) At meso level, useable data is recognised as a driver. The data collection process can align with diverse interests to create value and business opportunities for the ecosystem actors, enhance care, attract stakeholders, such as insurers and pharma, and form partnerships. iv) At micro-level processes, we find two potential barriers: wearable device and app usability (comfort, navigation, efficiency) and organisational/behavioural aspects (training, digital affinity, skills), which are crucial for value creation in innovation ecosystems among patients and healthcare professionals. Finally, we find an interesting gap in the literature and interviews. Stakeholders' limited awareness of technological demands, especially from information technologies, for a successful long-term service, can be consider two key barriers for PSS.

Socio-Technical Analysis of the Benefits and Barriers to Using a Digital Representation of the Global Horse Population in Equine Veterinary Medicine.

There is a consensus that future medicine will benefit from a comprehensive analysis of harmonized, interconnected, and interoperable health data. These data can originate from a variety of sources. In particular, data from veterinary diagnostics and the monitoring of health-related life parameters using the Internet of Medical Things are considered here. To foster the usage of collected data in this way, not only do technical aspects need to be addressed but so do organizational ones, and to this end, a socio-technical matrix is first presented that complements the literature. It is used in an exemplary analysis of the system. Such a socio-technical matrix is an interesting tool for analyzing the process of data sharing between actors in the system dependent on their social relations. With the help of such a socio-technical tool and using equine veterinary medicine as an example, the social system of veterinarians and owners as actors is explored in terms of barriers and enablers of an effective digital representation of the global equine population.

Quantum cyber-physical systems.

This paper aims to promote a quantum framework that analyzes Industry 4.0 cyber-physical systems more efficiently than traditional simulations used to represent integrated systems. The paper proposes a novel configuration of distributed quantum circuits in multilayered complex networks that enable the evaluation of industrial value creation chains. In particular, two different mechanisms for the integration of information between circuits operating at different layers are proposed, where their behavior is analyzed and compared with the classical conditional probability tables linked to the Bayesian networks. With the proposed method, both linear and nonlinear behaviors become possible while the complexity remains bounded. Applications in the case of Industry 4.0 are discussed when a component's health is under consideration, where the effect of integration between different quantum cyber-physical digital twin models appears as a relevant implication.