Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Rehe, Grit; Schneider, Robert; Thimm, Denny

doi:10.24251/hicss.2020.563

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…However, learning factories have often been criticized for representing an idealized industrial environment with a narrow focus on specific tasks. To address this concern, the LTA qualification model was developed by Rehe et al [7].…”

Section: Related Workmentioning

confidence: 99%

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Bringing a Natural Language-enabled Virtual Assistant to Industrial Mobile Robots for Learning, Training and Assistance of Manufacturing Tasks

Hansen²,

Chrysostomou

et al. 2022

2022 IEEE/SICE International Symposium on System Integration (SII)

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Nowadays, industrial companies want to enhance their Industry 4.0 competencies. Therefore, they need to help employees master state-of-the-art technologies and gain the necessary knowledge to stay relevant and competitive. As a result, there is a global demand for learning and training tools that assist the employees at all levels. In this paper, we propose a natural language-enabled virtual assistant (VA) integrated with an industrial mobile manipulator to fulfill this target in manufacturing tasks. The latest Learning, Training, Assistance -Formats, Issues, Tools (LTA-FIT) model is leveraged to guide the design and development of a pilot version of the VA. To validate its performance, three manufacturing scenarios are analyzed based on the learning, training, and assistance phases, respectively. In our system, the human-robot interaction is achieved through conversation and a dashboard implemented as a web application. This intuitive interaction enables operators of all levels to control a industrial mobile manipulator easier and use it as a complementary tool for developing their competencies. The pilot experiments show that the proposed VA is able to respond to operator commands flexibly within the LTA-FIT model.

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Section: Related Workmentioning

confidence: 99%

“…Aalborg University, 9220 Aalborg, Denmark {cl, akhan, dimi, sb, om}@mp.aau.dk A cornerstone of our work is the Learning, Training, Assistance -Formats, Issues, Tools (LTA-FIT) model proposed by [7]. The model specifies the requirements that can qualify workers and guide companies toward a digital transformation.…”

Section: Introductionmentioning

confidence: 99%

Bringing a Natural Language-enabled Virtual Assistant to Industrial Mobile Robots for Learning, Training and Assistance of Manufacturing Tasks

Hansen²,

Chrysostomou

et al. 2022

2022 IEEE/SICE International Symposium on System Integration (SII)

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“…What sorts of machine ‘personalities’ lead to desired humanistic and instrumental outcomes, and what are ethical and appropriate decisions, actions, and behaviours in various contexts? These and other challenging tensions suggest a range of research questions, which IS researchers have begun to address (Engelmann & Schwabe, 2018; Mantello et al, 2021; Rehe et al, 2020; Strich et al, 2021; You & Robert, 2022). Nonetheless, further scientific knowledge is needed to provide robust approaches to support humanistic outcomes appropriate for specific contexts.…”

Section: Illustrative Applicationsmentioning

confidence: 99%

Putting humans back in the loop: An affordance conceptualization of the 4th industrial revolution

Melville

Robert

Xiao

2022

Information Systems Journal

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The current technology epoch-sometimes called the fourth industrial revolution (4IR)-involves the innovative application of rapidly advancing digital technologies such as artificial intelligence. Societal implications of the 4IR are significant and wide ranging, from life-saving drug development to privacy loss and app addiction. A review of the information systems literature, however, reveals a narrow focus on technology-enabled business benefits. Scant research attention has been paid to the role of humans and humanistic outcomes. To spur new research addressing these issues, formalized affordance theory is employed to develop a new 4IR conceptualization. Four groupings of affordances that capture salient 4IR action possibilities are developed within two categories: machine emulation of human cognition (expansive decision making and creativity automation) and machine emulation of human communication (relationship with humans and intermachine teaming). Implications are explored in the context of human-machine coworking and the development of artificial intelligence safety regulations. Overall, the affordance conceptualization of the 4IR advances a new sociotechnical lexicon of action possibilities and their joint enactment in achieving humanistic and instrumental outcomes, enabling alignment of the scope of 4IR research with the scope of 4IR phenomena-and bringing humans back into the loop.

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“…acquisition and dissemination of learned experience should be utilized [3]. One revolutionary model used to assist workers in their learning and training is the Learning, Training, Assistance -Formats, Issues, Tools (LTA-FIT) model proposed by [4]. The model offers a flexible approach to assist the requalification and training of workers in new digital tools.…”

Section: Introductionmentioning

confidence: 99%

Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots

Li¹,

Chrysostomou²,

Pinto³

et al. 2022

Preprint

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<p>Assisting employees in acquiring the knowledge and skills necessary to grasp and use new services and technologies on the shop floor is critical for manufacturers to adapt to Industry 4.0 successfully. In this paper, we employ a Learning, Training, Assistance (LTA) approach and propose a framework for a Language-enabled Virtual Assistant (VA) to facilitate this adaptation. In our system, the human-robot interaction is achieved through spoken natural language and a dashboard implemented as a web-based application. This type of interaction enables operators of all levels to control a collaborative robot intuitively in several industrial scenarios and use it as a complementary tool for developing their competencies. Our proposed framework has been extensively tested with 29 users who completed various tasks while interacting with the proposed VA and industrial robots. Through three different scenarios, we evaluated the usability of the system for LTA based on an established System Usability Scale and the cognitive effort required by the users based on the standardised NASA TLX questionnaire. The qualitative and quantitative results of the study show that users of all levels found the VA user friendly with low requirements for physical and mental effort during the interaction. Additionally, the study demonstrates that the VA enables operators to streamline the learning and training phases of new tasks and improve their user experience during the assistance phase for daily tasks. The source code of the proposed VA and the supplementary material of the user study are accessible at https://bit.ly/VA MAX to support the reproducibility of the proposed framework.</p>

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Transformation towards Human Workforce 4.0 through the Qualification Model LTA-FIT

Cited by 10 publications

References 12 publications

Bringing a Natural Language-enabled Virtual Assistant to Industrial Mobile Robots for Learning, Training and Assistance of Manufacturing Tasks

Bringing a Natural Language-enabled Virtual Assistant to Industrial Mobile Robots for Learning, Training and Assistance of Manufacturing Tasks

Putting humans back in the loop: An affordance conceptualization of the 4th industrial revolution

Hey Max, can you help me? An Intuitive Virtual Assistant for Industrial Robots

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