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Adaptive Decision Support for Shop-floor Operators using Function Blocks
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatisering)ORCID iD: 0000-0002-1699-3778
2017 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

In manual and semi-automation production systems, flexibility and adaptability are affected by the shop-floor operators’ skills, abilities and knowledge. Such dependencies highlight the vital importance of developing and utilising the knowledge, achievements and abilities of the operators working with production on the shop-floor. Teamwork, including both novice and highly experienced shop-floor operators, in a production environment with a high level of automation, is essential already today and is predicted to increase, when the complexity and demands of future production systems intensify. This trend is confirmed in both the research literature and by specialists within industry.

The key to future competitiveness and effectiveness of the manufacturing industry is the shop-floor operators who handle the production systems. In addition, the future information intensive working environment, with its increasing complexity and less time available for decision-making, demands adaptive decision support and adaptive control systems that facilitate collaborative work on the shop-floor. It is therefore important to emphasise how decisions are supported in the time-limited working environment of the shop-floor, because this has a large impact on production output and quality and is vital to the success of the company. Consequently, this dissertation presents a framework for an adaptive decision support system that concentrates on shop-floor operators, in order to enhance their development and future contribution to leading edge production systems.

The overall aim of the research presented is to define a framework for an Adaptive Decision Support System, to address the scope and demands of the future shop-floor, as indicated in the research literature, and confirm its relevance, as well as further elaborate it on the basis of interviews with production managers and HR specialists

The research presented uses the design science research process. In parallel, decision support systems and the industrial shop-floor have been studied in the research literature and the current state of industrial practice has been assessed. These areas together form the basis for the research on adaptive decision support for shop-floor operators. A framework enabling adaptive decision support and adaptive system control, based on event-driven function block technology and Augmented Reality technology, is formulated.

The gap of research on decision support for shop-floor operators, indicated in the research literature is addressed by the research preformed.  Adaptive and dynamic decision support and system control able to process vast amounts of information in real time demonstrates utility for shop-floor operators. The research presenting the Adaptive Decision Support System has demonstrated its utility for shop-floor systems and production operatives in two extensive studies using demonstrators based on real-life production environments.

A methodology, the ‘User group’, has been formulated for research collaboration and bi-directional knowledge transfer between academia and the industrial partners. It provides tools that enable cooperation between the experienced research partner and the novices, despite their different levels of engagement in the same project, without dividing them into separate groups. The ‘user group’ case study presented describes how both the inexperienced and the research mature companies gain new knowledge and engage in ongoing research. By doing so, the industrial project partners have extensively supported the research presented and will subsequently be the expected beneficiaries.

Place, publisher, year, edition, pages
Högskolan i Skövde , 2017. , 192 p.
Series
Dissertation Series, 17
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering
Identifiers
URN: urn:nbn:se:his:diva-13503ISBN: 978-91-982690-8-6 (print)OAI: oai:DiVA.org:his-13503DiVA: diva2:1089364
Public defence
2017-01-11, 09:30 (English)
Supervisors
Available from: 2017-04-20 Created: 2017-04-19 Last updated: 2017-04-20Bibliographically approved

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