TOWARDS AN ONTOLOGY-BASED APPROACH TO MEASURING PRODUCTIVITY FOR OFFSITE MANUFACTURING METHOD

Conference paper


Ayinla, K., Cheung, F and Tawil, A (2019). TOWARDS AN ONTOLOGY-BASED APPROACH TO MEASURING PRODUCTIVITY FOR OFFSITE MANUFACTURING METHOD. Proceedings of the 35th Annual ARCOM Conference, 2-4 September 2019. Leeds, UK, 02 - 04 Sep 2019
AuthorsAyinla, K., Cheung, F and Tawil, A
TypeConference paper
Abstract

The steady decline of manual and skilled trades in the construction industry has increased the recognition of offsite manufacturing (OSM), an aspect of Design for Manufacture and Assembly (DFMA) methods as one way to boost productivity and performance. However, existing productivity estimation approaches are carried out in isolation thus limiting the sort of result obtained from such systems. Also, there is yet to be a holistic approach that enables productivity estimation using different metrics and integrates experts’ knowledge to predict productivity and guide decision making at the early development stage of a project. This study aims to develop a method that can be used to generate multiple estimations for all these metrics simultaneously through linking their relationships. An ontology-based knowledge modelling approach for estimating productivity at the production stage for OSM projects is proposed. A case study of panel system offsite is used as a proof-of-concept for data collection and knowledge modelling in an ontology. Results from the study through the use of rules and semantic reasoning retrieved cost estimates and time schedule for a panel system production with considerations for different design choices. It is thus proven that systemising the production process knowledge of OSM methods enables practitioners to make informed choices on product design to best suit productivity requirements. The developed method helps to reduce the level of uncertainty by encouraging measurable evidence and allows for better decision-making on productivity.

KeywordsDFMA; estimating; offsite-manufacturing; ontology; productivity
Year2019
Web address (URL)https://www.arcom.ac.uk/-docs/proceedings/6319b483bc16f3ca561b2dd5069c24af.pdf
Accepted author manuscript
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Publication dates
Print02 Sep 2019
Publication process dates
Accepted02 Aug 2019
Deposited11 Feb 2022
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ARCOM 2019 - Paper.pdf
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File access level: Open

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