Sarah Fletcher

Towards a theoretical framework for human performance modelling within manufacturing systems design

The performance of direct workers has a significant impact on the competitiveness of many manufacturing systems. Unfortunately, system designers are ill equipped to assess this impact during the design process. An opportunity exists to assist designers by expanding the capabilities of popular simulation modelling tools, and using them as a vehicle to better consider human factors during the process of system design manufacture. To support this requirement, this paper reports on an extensive review of literature that develops a theoretical framework, which summarizes the principal factors and relationships that such a modelling tool should incorporate.

Servitization of manufacture: Exploring the deployment and skills of people critical to the delivery of advanced services

The servicisation agenda is arguably one of the most important facing western manufacturers. In a world of severe international competition, it can be the basis of distinct competitive advantage, and a means to differentiate a companys products from goods produced in low cost economies. The concept of Product Service Systems (PSS) is, in particular, a special case of servicisation. With PSS, the market proposition is designed as a system that exploits synergies between products and services while, at the same time, seeks to reduce the economic and environmental costs of delivery. Although key to sustaining western manufacturing operations, major issues arise with design and management of engineering, manufacturing and supply chain activities for successful implementation of such a service-led competitive strategy. This session includes presentations which examine the opportunities and challenges associated with servicisation in more detail.

The challenge of regional accents for aviation English language proficiency standards: A study of difficulties in understanding in air traffic control–pilot communications

Although English has been the international aviation language since 1951, formal language proficiency testing for key aviation personnel has only recently been implemented by the International Civil Aviation Organization (ICAO). It aims to ensure minimum acceptable levels of English pronunciation and comprehension universally, but does not attend to particular regional dialect difficulties. However, evidence suggests that voice transmissions between air traffic controllers and pilots are a particular problem in international airspace and that pilots may not understand messages due to the influence of different accents when using English. This study explores the potential impact of ‘non-native English’ in pilot–air traffic control transmissions using a ‘conversation analysis’ technique to examine approach phase recordings from Bangkok International Airport. Results support that communication errors, defined by incidents of pilots not understanding, occur significantly more often when speakers are both non-native English, messages are more complex and when numerical information is involved. These results and their possible implications are discussed with reference to the development of ICAOs new language proficiency standards. Statement of Relevance: This study builds on previous work and literature, providing further evidence to show that the risks caused by language and linguistics in aviation must be explored more deeply. Findings are particularly contemporary and relevant today, indicating that recently implemented international standards would benefit from further exploratory research and development.

The Development of a Scale to Evaluate Trust in Industrial Human-robot Collaboration

Trust has been identified as a key element for the successful cooperation between humans and robots. However, little research has been directed at understanding trust development in industrial human-robot collaboration (HRC). With industrial robots becoming increasingly integrated into production lines as a means for enhancing productivity and quality, it will not be long before close proximity industrial HRC becomes a viable concept. Since trust is a multidimensional construct and heavily dependent on the context, it is vital to understand how trust develops when shop floor workers interact with industrial robots. To this end, in this study a trust measurement scale suitable for industrial HRC was developed in two phases. In phase one, an exploratory study was conducted to collect participants’ opinions qualitatively. This led to the identification of trust related themes relevant to the industrial context and a related pool of questionnaire items was generated. In the second phase, three human-robot trials were carried out in which the questionnaire items were applied to participants using three different types of industrial robots. The results were statistically analysed to identify the key factors impacting trust and from these generate a trust measurement scale for industrial HRC.

Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions.

A study to trial the use of inertial non-optical motion capture for ergonomic analysis of manufacturing work

It is going to be increasingly important for manufacturing system designers to incorporate human activity data and ergonomic analysis with other performance data in digital design modelling and system monitoring. However, traditional methods of capturing human activity data are not sufficiently accurate to meet the needs of digitised data analysis; qualitative data are subject to bias and imprecision, and optically derived data are hindered by occlusions caused by structures or other people in a working environment. Therefore, to meet contemporary needs for more accurate and objective data, inertial non-optical methods of measurement appear to offer a solution. This article describes a case study conducted within the aerospace manufacturing industry, where data on the human activities involved in aircraft wing system installations was first collected via traditional ethnographic methods and found to have limited accuracy and suitability for digital modelling, but similar human activity data subsequently collected using an automatic non-optical motion capture system in a more controlled environment showed better suitability. Results demonstrate the potential benefits of applying not only the inertial non-optical method in future digital modelling and performance monitoring but also the value of continuing to include qualitative analysis for richer interpretation of important explanatory factors.

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

Manual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator—precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks.

Industrial Robot Ethics: The Challenges of Closer Human Collaboration in Future Manufacturing Systems

As a result of significant advances in information and communications technology the manufacturing industry is facing revolutionary changes whereby production processes will become increasingly digitised and interconnected cyber-physical systems. A key component of these new complex systems will be intelligent automation and human-robot collaboration. Industrial robots have traditionally been segregated from people in manufacturing systems because of the dangers posed by their operational speeds and heavy payloads. However, advances in technology mean that we will soon see large-scale robots being deployed to work more closely and collaboratively with people in monitored manufacturing sytems and widespread introduction of small-scale robots and assistive robotic devices. This will not only transform the way people are expected to work and interact with automation but will also involve much more data provision and capture for performance monitoring. This paper discusses the background to these developments and the anticipated ethical issues that we now face as people and robots become able to work collaboratively in industry.

Investigating the Effects of Signal Light Position on Human Workload and Reaction Time in Human-Robot Collaboration Tasks

Critical to a seamless working relationship in human-robot collaborative environments is effective and frequent communication. This study looked to assess whether placing a light source on a robot was more effective for informing the human operator of the status of the robot than conventional human-machine interfaces for industrial system signaling such as light towers. Participants completed an assembly task while monitoring a robot and changes to the light sources: either from one of two light towers or LED strip lights attached to the robot. Workload was assessed by measuring reaction times to light changes and by counting number of completed assemblies. Although both the ANOVA and Friedman tests returned none significant results, total misses per condition showed that the participants did not miss any of the robot lights, whereas signals were missed for the light towers.