Smooth and Resilient Human–Machine Teamwork as an Industry 5.0 Design Challenge

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems

Assessing the Accuracy of Task Time Prediction of an Emerging Human Performance Modeling Software - CogTool

There is a need for a human performance modeling tool which not only has the ability to accurately estimate skilled user task time for any interface design, but can be used by modelers with little or no programming knowledge and at a minimal cost. To fulfill this need, this research investigated the accuracy of task time prediction of a modeling tool – CogTool - on two versions of an interface design used extensively in the petrochemical industry – DeltaV. CogTool uses the KeyStroke Level Model (KLM) to calculate and generate time predictions based on specified operators. The data collected from a previous study (Koffskey, Ikuma, & Harvey, 2013) that investigated how human participants (24 students and 4 operators) performed on these interfaces (in terms of mean speed in seconds) were compared to CogTool’s numeric time estimate. Three tasks (pump I, pump II and cascade system failures) on each interface for both participant groups were tested on both interfaces (improved and poor), on the general hypothesis that CogTool will make task time predictions for each of the modeled tasks, within a certain range of what actual human participants had demonstrated. The 95% confidence interval (CI) tests of the means were used to determine if the predictions fall within the intervals. The estimated task time from CogTool did not fall within the 95% CI in 9 of 12 cases. Of the 3 that were contained in the acceptable interval, two belonged to the experienced operator group for tasks performed on the improved interface, implying that CogTool was better in predicting the operators’ performance than the students’. A control room monitoring task, by its nature, places great demand on an operator’s mental capacity. This also includes the fact that operators work on multiple screens and/or consoles, sometimes requiring them to commit information to memory that they have to revisit a screen to check on some vital information. In this regard, it is suggested that the one user mental operator for “think time” (estimated as 1.2sec), should be revised in CogTool to accommodate the demand on the operator. For this reason, the present CogTool prediction did not meet expectations in estimating control room operator task time, but it however succeeded in showing where the poor interface could be improved by comparing the detailed steps to the improved interface

Human factors consideration in the automation design of a safety-critical installation

M.Ing. (Engineering Management)Abstract: Human factors consideration should form an integral part of any system’s design. The aim is to ensure the designed system is compatible with human skills and limitations. Benefits of this consideration include reduction in the required level of training once the system is deployed. Unfortunately, even though the requirement of humans in systems design is well known, systems are continuously designed with little or no input from the eventual operators. This study aims to investigate the human factors aspect in the automation design of a safety-critical installation. Automation in its noble form is intended to improve factors such as safety, efficiency, and costs. However, this is not always the case. Part of the problem is that human operators are not always adequately considered during the design. It is the aim of this study to elicit the important human factors that must be considered in the automation design. This is done using a case study method. The case study was undertaken at the major radioisotopes production institution in the Republic of South Africa. The use of this study method is adopted as it provides enough in-depth knowledge that can be used in other safety-critical facilities

Modelling the causation of accidents: human performance separated system and human performance included system

Jedes Jahr ereignen sich weltweit Millionen von Arbeitsunfällen, die zahlreiche Opfer fordern und enorme wirtschaftliche Verluste zur Folge haben. Vorangegangene Studien aus dem Feld der Risikoeinschätzung zeigten, dass es wichtig ist die Wahrscheinlichkeit von Faktoren, welche zum Auftreten von Unfällen beitragen, zu quantifizieren. Mehrere Methoden, wie z. B. die Technik zur Vorhersage der menschlichen Fehlerrate (Technique for Human Error Rate Prediction, THERP), wurden dafür vorgeschlagen, potenzielle Risikofaktoren zu bewerten und die Systemsicherheit zu verbessern. Diese Methoden haben jedoch einige Einschränkungen, wie z.B. ihre geringe Generalisierbarkeit, die Behandlung von Unfallursachen und menschlichem Einfluss als zwei voneinander getrennte Forschungsthemen, die Notwendigkeit ausgiebiger Datensätze, oder die ausschließliche Abhängigkeit von Expertenwissen. Um diese Einschränkungen zu überwinden, 1) klassifiziert diese Dissertation die Systeme in zwei Kategorien. Zum einen in von menschlichem Einfluss separierte Systeme (Human Performance Separated System, HPSS) und zum anderen in Systeme mit menschlichem Einfluss (Human Performance Included System, HPIS); 2) entwickelt ein auf Bayes‘schen Netzwerken (BN) basierendes Unfallkausalitätsmodell, das auf beide Arten von Systemen angewendet werden kann, um den Einfluss menschlicher Wahrnehmung in HPSS und den Einfluss menschlichen Versagens in HPIS zu untersuchen; 3) untersucht zwei Methoden zur Analyse menschlichen Versagens. Die erste Methode geht von einer kognitiven Wahrnehmung aus und die zweite behandelt das menschliche Versagen als essenziellen Teil des Systems. 4) schlägt eine innovative Taxonomie namens Contributors Taxonomy for construction Occupational Accidents (CTCOA) für HPIS vor, die nicht nur auf die Unfallkausalität abzielt, sondern auch zur Rückverfolgung menschlichen Versagens im Bauwesen verwendet werden kann. 5) erstellt BN-Beispielmodelle aus unterschiedlichen Industriesektoren. Dazu zählen Gasturbinenausfälle als typisches Beispiel für HPSS-Maschinenversagen, das Multi-Attribute Technological Accidents Dataset (MATA-D) für einfaches HPIS-Systemversagen und das Contributors to Construction Occupational Accidents Dataset (CCOAD) für komplexes HPIS-Systemversagen. Diese drei BN-Modelle zeigen, wie die von uns vorgeschlagene Methode in Bezug auf spezifische Probleme aus verschiedenen Industriesektoren angepasst und angewendet werden kann. Unsere Analyse zeigt die Effizienz der Kombination von Expertenwissen und mathematischer Unabhängigkeitsanalyse bei der Identifizierung der wichtigsten Abhängigkeitsbeziehungen innerhalb der BN-Struktur. Vor der Parameteridentifizierung auf Basis von Expertenwissen sollten die Auswirkungen der menschlichen Wahrnehmung auf die Modellparameter gemessen werden. Die vorgeschlagene Methodik basierend auf der Kombination der menschlichen Zuverlässigkeitsanalyse mit statistischen Analysen kann zur Untersuchung menschlichen Versagens eingesetzt werden.Millions of work-related accidents occur each year around the world, leading to a large number of deaths, injuries, and a huge economic cost. Previous studies on risk assessment have revealed that it is important to calculate the probabilities of factors that can contribute to the occurrence of accidents. Several methods, such as the Technique for Human Error Rate Prediction (THERP), have been proposed to evaluate potential risk factors and to improve system safety. However, these methods have some limitations, such as their low generalizability, treating accident causation and human factor as two separate research topics, requiring intensive data, or relying solely on expert judgement. To address these limitations, this dissertation 1) classifies systems into two types, Human Performance Separated System (HPSS) and Human Performance Included System (HPIS), depending on whether the system involves human performance; 2) develops accident causal models based on Bayesian Network (BN) that can be applied to both types of systems while examining the influence of human perception in HPSS and human errors in HPIS; 3) examines two methods for the analysis of human errors with the first method based on the cognitive view and the other method treating human errors as an essential part of the system; 4) proposes an innovative taxonomy as an example for HPIS, known as the Contributors Taxonomy for Construction Occupational Accidents (CTCOA), which not only targeting accident causation, but can also be used for tracking human error in construction; 5) builds example BN models in the different industrial sectors, including gas turbine failures as a typical example of HPSS machine failures, Multi-Attribute Technological Accidents Dataset (MATA-D) as simple HPIS failures, and Contributors to Construction Occupational Accidents Dataset (CCOAD) as complex HPIS failures. These three types of BN models demonstrate how our proposed methodology can be adapted to specific questions and how it can be applied in various industrial sectors. Our analysis demonstrates that it is efficient to combine expert judgement with mathematical independence analysis to identify the main dependency links for the BN structure in all models. The influence of human perception on model parameters should be measured before these parameters being identified based on expert judgement. Our proposed methodology can be used to study human errors by combining traditional human reliability analysis with statistical analysis

Industrial maintenance service quality evaluation and improvement strategies: A case study of a corporation

Improving industrial maintenance service quality is not only essential for service providers to acquire and retain customers, but also plays a critical role in the shift from “Made in China” to “Intelligent Manufacturing in China”. The study focuses on the case of Company A to explore how to boost industrial maintenance service quality. Firstly, factor analysis is used to construct an industrial maintenance service quality scale, which is adopted to evaluate the service quality through analytic hierarchy process. Secondly, taking repurchase intention and recommendation intention as the measurement dimensions of customer behavioural intentions, structural equation model is used to explore the effect of industrial maintenance service quality on customer behavioural intentions. Last but not the least, the study explores the key factors that affect the quality of industrial maintenance service, and offers suggestions on how to improve industrial maintenance service quality. The study develops the industrial maintenance service quality scale with four dimensions, namely service professionalism, service reliability, service customization, and service digitization, all of which have direct, significant and positive effects on repurchase intention and recommendation intention. It is also found that industrial maintenance service quality can be improved through successful customer relationship maintenance, brand-building management, and information technology upgrading. Such improvement will contribute to the development of industrial maintenance service providers and promote their transformation and upgrading.A melhoria da qualidade do serviço de manutenção industrial é não só importante para as empresas fornecedoras destes serviços como também desempenha um papel crítico na mudança da política “Produzido na China” para a política “Manufatura Inteligente na China”. Esta tese tem como objeto de estudo a empresa A e analisa o modo de impulsionar a qualidade de serviço de manutenção. Primeiramente, a análise fatorial é utilizada para construir uma escala de qualidade de serviço de manutenção industrial, que será adoptada para avaliar a qualidade do serviço segundo o processo analítico hierárquico. Seguidamente, tomando a intenção de recompra e intenção de recomendação como dimensões de medida das intenções comportamentais dos clientes, utilizamos o modelo de equações estruturais para estudar o efeito da qualidade do serviço de manutenção industrial nas intenções comportamentais dos clientes. Por último, mas não menos importante, este estudo explora os fatores chave que afetam a qualidade do serviço de manutenção industrial e propõe sugestões para melhoria da qualidade do serviço de manutenção industrial. Esta tese desenvolve a escala de qualidade de serviço de manutenção industrial com quatro dimensões, nomeadamente profissionalismo do serviço, confiabilidade do serviço, serviço personalizado e digitalização do serviço, todas estas dimensões têm efeitos positivos diretos e significantes nas intenções de recompra e recomendação. Esta tese concluiu também que a qualidade do serviço de manutenção industrial pode ser melhorada através da manutenção de relacionamento com o cliente, a gestão de construção da marca e atualização da tecnologia de informação. Estas melhorias irão contribuir para o desenvolvimento das empresas de serviços de manutenção industrial e promoverão a sua transformação e atualização

Emerging Informatics

The book on emerging informatics brings together the new concepts and applications that will help define and outline problem solving methods and features in designing business and human systems. It covers international aspects of information systems design in which many relevant technologies are introduced for the welfare of human and business systems. This initiative can be viewed as an emergent area of informatics that helps better conceptualise and design new world-class solutions. The book provides four flexible sections that accommodate total of fourteen chapters. The section specifies learning contexts in emerging fields. Each chapter presents a clear basis through the problem conception and its applicable technological solutions. I hope this will help further exploration of knowledge in the informatics discipline