Reciprocal Learning in Production and Logistics

Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates. One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of “Reciprocal Learning” (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN)

Socioeconomic inequalities in prevalence, awareness, treatment and control of hypertension: evidence from the PERSIAN cohort study

Background Elevated blood pressure is associated with cardiovascular disease, stroke and chronic kidney disease. In this study, we examined the socioeconomic inequality and its related factors in prevalence, Awareness, Treatment and Control (ATC) of hypertension (HTN) in Iran. Method The study used data from the recruitment phase of The Prospective Epidemiological Research Studies in IrAN (PERSIAN). A sample of 162,842 adults aged > = 35 years was analyzed. HTN was defined according to the Joint National Committee)JNC-7(. socioeconomic inequality was measured using concentration index (Cn) and curve. Results The mean age of participants was 49.38(SD = +/- 9.14) years and 44.74% of the them were men. The prevalence of HTN in the total population was 22.3%(95% CI: 20.6%; 24.1%), and 18.8%(95% CI: 16.8%; 20.9%) and 25.2%(95% CI: 24.2%; 27.7%) in men and women, respectively. The percentage of awareness treatment and control among individuals with HTN were 77.5%(95% CI: 73.3%; 81.8%), 82.2%(95% CI: 70.2%; 81.6%) and 75.9%(95% CI: 70.2%; 81.6%), respectively. The Cn for prevalence of HTN was -0.084. Two factors, age (58.46%) and wealth (32.40%), contributed most to the socioeconomic inequality in the prevalence of HTN. Conclusion The prevalence of HTN was higher among low-SES individuals, who also showed higher levels of awareness. However, treatment and control of HTN were more concentrated among those who had higher levels of SES, indicating that people at a higher risk of adverse event related to HTN (the low SES individuals) are not benefiting from the advantage of treatment and control of HTN. Such a gap between diagnosis (prevalence) and control (treatment and control) of HTN needs to be addressed by public health policymakers

Metaanalyse von Wissensbeständen zur kontinuierlichen Verbesserung des Kostencontrollings in der Instandhaltung

Maintenance is a combination of multilateral and cross-functional activities and processes. Maintenance processes are identified in both strategic management and operation systems. Managers, engineers, technicians and operators collaboratively contribute in conducting and performing preventive or corrective maintenance activities. Maintenance management is to provide the long-term business strategy that ensures capacity of the production, quality of the product, and the best life cycle cost. It is a decision-making activity which has been highly correlated with expertise of maintenance staff and their own practical experience. Maintenance management intends not only to keep the desired performance of machinery, but to continuously improve quality and cost effectiveness of the pertained processes. Maintenance cost management (MCM), consisting of cost planning, monitoring and controlling, thereby is an essential part of the sustainable and efficient maintenance management system. MCM is determined as a knowledge-centered and experience-driven process where exploiting existing knowledge and generating new knowledge strongly influences every instance of cost planning. Taking into account the dynamics of knowledge assets, an interdisciplinary research raises practical implications in the domain of maintenance. The key aspect of the present work is learning from past experiences for continuous improvement of the maintenance cost planning and controlling. Learning in MCM is an evolutionary and iterative process through which a chief maintenance officer (CMO) compounds and deepens his/her knowledge. CMO analyzes former experiences gained in the past maintenance planning periods, identifies facts or artifacts (i.e. evidence for improving the planning process), and finally enhances planning of the upcoming events by applying the lessons learned. This work principally constitutes a model, Costprove, for meta-analysis of maintenance knowledge assets. The knowledge assets are articulated, represented, and stored in repositories (i.e. explicit knowledge), or remain with (a group of) individuals and need to be extracted, documented, and validated (i.e. implicit knowledge). Meta-analysis is a set of methods for discovering the strength of the relation between certain predefined entities. It provides evidence for decision-makers (e.g. CMO) to discover hidden improvement potentials in cost planning, and incrementally attain desired company objectives. The main focus of this work is to establish a mathematical meta-analysis for (i) identifying the relation between cost figures (planned, unplanned and total cost), and operation parameter (number of maintenance activities), and (ii) trading-off between planned and unplanned cost. Hence, the model deploys an economic approach for identifying desired cost figures in every planning period, and ultimately defining operation-related parameters. Anticipating the trend of the fourth industrial revolution, the foremost result of this thesis is the development of an integrated and practice-oriented knowledge-based approach to maintenance cost planning and controlling.Instandhaltung ist eine Kombination aus multilateralen und bereichsübergreifenden Aktivitäten und Prozessen. Instandhaltungsprozesse bestehen sowohl aus strategischem Management als auch aus operativen Systemen. Manager, Ingenieure, Techniker und Anwender leisten gemeinsam Beiträge zur Administration und Durchführung präventiver oder wiederherstellender Instandhaltungsaktivitäten. Das Instandhaltungsmanagement dient dazu, für die langfristige Unternehmensstrategie zu sorgen, die die Produktionskapazitäten, die Produktqualität und die niedrigsten Lebenszykluskosten gewährleistet. Es handelt sich um eine Funktion der Entscheidungsfindung, die hochgradig mit der Expertise der Instandhaltungsmitarbeiter und ihren eigenen praktischen Erfahrungen verbunden ist. Das Instandhaltungsmanagement sorgt nicht nur dafür, die gewünschte Leistungsfähigkeit des Maschinenparks zu gewährleisten, sondern verbessert kontinuierlich die Qualität und Kosteneffektivität der betreffenden Prozesse. Das Instandhaltungskostenmanagement (MCM), bestehend aus Kostenplanung, -überwachung und -steuerung, ist somit ein wesentlicher Bestandteil eines nachhaltigen und effizienten Instandhaltungsmanagementsystems. MCM ist als wissenszentrierter und erfahrungsgetriebener Prozess zu verstehen, bei dem die Nutzung vorhandenen und die Generierung neuen Wissens jedes Element der Kostenplanung maßgeblich beeinflusst. Unter Einbeziehung der Dynamik der Wissensbestände erhöht ein interdisziplinärer Forschungsansatz die praktische Relevanz im Bereich der Instandhaltung. Der Schlüsselaspekt der vorliegenden Arbeit ist „aus Erfahrungen lernen“ mit dem Ziel einer kontinuierlichen Verbesserung der Instandhaltungskostenplanung und -kontrolle. Lernen im Instandhaltungskostenmanagement ist ein evolutionärer und iterativer Prozess, durch den der „Chief Maintenance Officer“ (CMO) sein Wissen verknüpft und vertieft. Der CMO analysiert bisherige Erkenntnisse, die in den vergangenen Planungsperioden erworben wurden, identifiziert Fakten oder Hypothesen (z.B. Bestätigungen für die Verbesserung des Planungsprozesses) und verbessert schließlich die Planung des zukünftigen Geschehens durch die Umsetzung des Erlernten. In dieser Arbeit wird insbesondere ein Modell, Costprove, für die Meta-Analyse von Instandhaltungswissen entwickelt. Die Wissensbestände sind artikuliert, dargestellt und abgespeichert (explizites Wissen) oder befinden sich bei Personen und müssen extrahiert, dokumentiert und validiert werden (impliziertes Wissen). Meta-Analyse ist ein Methodenpaket zur Entdeckung der Stärke der Beziehung zwischen bestimmten vordefinierten Objekten. Es liefert den Entscheidern (z.B. CMO) Hinweise, verdeckte Verbesserungspotentiale bei der Kostenplanung zu finden und schrittweise die gesteckten Unternehmensziele zu erreichen. Der Hauptfokus dieser Arbeit liegt darauf, eine mathematische Meta-Analyse zu erzeugen, die 1. das Verhältnis zwischen den Kostenarten (geplante, ungeplante, gesamte Kosten) und den Handlungsparametern (Zahl der Instandhaltungsaktivitäten) identifiziert und 2. geplante und ungeplante Kosten optimiert. So stellt das Modell einen wirtschaftlichen Ansatz zur Identifizierung der gewünschten Kostengrößen in jeder Planungsperiode bereit und definiert abschließend die damit zusammenhängenden operativen Parameter. Den Trend der vierten industriellen Revolution vorwegnehmend, besteht das Hauptergebnis dieser Dissertation in der Entwicklung eines integrierten und anwendbaren wissensbasierten Ansatzes zur Instandhaltungskostenplanung und -kontrolle

Management of knowledge intelligence in human-centered cyber physical production systems

Enthält Originalbeitrag und 8 Veröffentlichungen16

Human-Machine Shared Driving: Challenges and Future Directions

Distraction, misjudgement and driving mistakes can significantly affect a driver, resulting in an increased risk of accidents. There are diverse factors that can cause mistakes in driving such as unfamiliarity with the road, situation unawareness, fatigue, stress, and drowsiness. In emerging smart cars, sensing, actuation, advanced signal processing and machine learning are deployed to reduce the impact of driving errors by monitoring the state of the driver in real-time, detecting the mistakes, and deploying necessary actions to counteract them. Such strategies are collectively known as human-machine shared driving. Towards a better understanding of the developments taken place in this domain, as well as identifying gaps and trends in this discipline, a systematic review of the major studies and developments reported in the literature is conducted. The study is based on 155 papers of human-machine shared driving, selected through a thorough and comprehensive search of the literature. The review demonstrates that shared control approaches are mostly dependent on vehicle and environmental data obtained through various sensors. The majority of methods deploy active shared control by leveraging longitudinal and lateral dynamics. However, the precise recognition of driver\u27s intent and actions, accurate estimation of situation awareness, and modelling the trust between driver and automation are still major challenges preventing timely transition of control from the driver to machine or vice-versa, and resulting in fatal accidents. Major challenges in human-machine shared driving are identified and potential future directions of the work are explored

Rethinking Human-Machine Learning in Industry 4.0: How Does the Paradigm Shift Treat the Role of Human Learning?

The paradigm shift in production system known as Industry 4.0 imposes changes on work division between human and machine. A human labor on the one side is assisted by smart devices and machines (human-machine cooperation) and on the other should interact and exchange information with intelligent machines (human-machine collaboration). This paper addresses the challenges of mutual human-machine learning in factories of the future. The ultimate goal is to identify new learning patterns in highly digitalized industrial work scenarios. To this end, we give a definition of mutual human-machine learning in digitalized work scenarios; provide exemplary scenarios in the TU Wien Pilot Factory Industry 4.0, and finally identify future research potentials

Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation

Introduction: With the announcement of novel Coronavirus disease 2019 (Covid-19) as a pandemic by World Health Organization (WHO) in March 2020, the whole world went into a lockdown that heavily affected human economic and social life. Since December 2020, with the discovery of effective vaccines, the world is now returning to some normality, particularly for those who are vaccinated. The multimodal transportation has resumed with majority of vaccinated drivers being back on road, driving to their work, and providing transport services. However, there are still several long-term Post-Covid-19 factors, affecting driver health and psychology. Methods: The study deployed a systematic search strategy and selected 62 research publications after rigorous evaluation of the literature. The review was based on (1) forming the inclusion and exclusion criteria, (2) selecting the appropriate keywords, and (3) searching of relevant publications and assessing the eligible articles. Results: A broad perspective study is carried out to gauge the impact of Post-Covid-19 scenarios on the driver physical health and mindset in the context of road safety and pandemic-sustained transportation. It was found that the Post-Covid-19 factors such as wearing face-mask during driving, taking oral anti-viral drugs, and fear of contracting disease, significantly impact the driver\u27s performance and situation awareness skills. The analysis suggested that driver\u27s health vitals and psychological driving awareness can be precisely detected through hybrid driver state monitoring methods. Conclusions: The paper conducts a comprehensive review of the published work and provides unique research opportunities to counteract the challenges involved in precise monitoring of driver behaviour under the effects of different Post-Covid-19 factors. The perspective suggested the possible solutions to live with the pandemic in the context of pandemic-sustained transportation