Development of a semantic knowledge modelling approach for evaluating offsite manufacturing production processes

The housing sector in the UK and across the globe is constantly under pressure to deliver enough affordable houses to meet the increasing demand. Offsite Manufacturing (OSM), a modern method of construction, is considered to be a key aspect in meeting these demands given its potential to increase efficiency and boost productivity. Although the use of OSM to increase the supply of affordable and efficient homes is getting popular, the focus has been on ‘what’ methods of construction are used (i.e. whether implementing OSM or traditional approach) rather than ‘how’ the alternative construction approach shall be done (i.e. choice of OSM method to meet set objectives). There have been criticisms of the approaches used by professionals implementing OSM methods as some of these approaches are non-structured and these methods have been criticised for being similar to the conventional onsite methods with little process gains. There are previous studies that have compared the performance of OSM and other modern methods of construction with conventional methods of construction. However, there is hardly any attempt nor quantitative evidence comparing the performance of various competing OSM approaches (i.e. methods with standardised and non-standardised processes) in order to support stakeholders in making an informed decision on choices of methods. In pursuit of the research gap identified, this research aims to develop a proof-of-concept knowledge-based process analysis tool that would enable OSM practitioners to efficiently evaluate the performances of their choice of OSM methods to support informed decision-making and continuous improvement. To achieve this aim, an ontology knowledge modelling approach was adopted for leveraging data and information sources with semantics, and an offsite production workflow (OPW) ontology was developed to enable a detailed analysis of OSM production methods. The research firstly undertook an extensive critical review of the OSM domain to identify the existing OSM knowledge and how this knowledge can be formalised to aid communication in the OSM domain. In addition, a separate review of process analysis methods and knowledge-based modelling methods was done concurrently to identify the suitable approach for analysing and systemising OSM knowledge respectively. The lean manufacturing value system analysis (VSA) approach was used for the analysis in this study using two units of analysis consisting of an example of atypical non-standardised (i.e. static method of production) and standardised (i.e. semi-automated method of production) OSM methods. The knowledge systematisation was done using an ontology knowledge modelling approach to develop the process analysis tool – OPW ontology. The OPW ontology was further evaluated by mapping a case of lightweight steel frame modular house production to model a real-life context. A two-staged validation approach was then implemented to test the ontology which consists of firstly an internal validation of logic and consistency of the results and then an expert validation process using an industry-approved set of criteria. The result from the study revealed that the non-standardised ad-hoc OSM production method, involving a significant amount of manual tasks, contributes little process improvement from the conventional onsite method when using the metrics of process time and cost. In comparison with the structured method e.g. semi-automated OSM production method, it is discovered that the process cost and time are 82% and 77% more in the static method respectively based on a like-to-like production schedule. The study also evaluates the root causes of process wastes, accounting for non-value-added time and cost consumed. The results contribute to supporting informed decision-making on the choices of OSM production methods for continuous improvement. The main contributions to knowledge and practice are as follows: i. The output of this research contributes to the body of literature on offsite concepts, definition and classification, through the generic classification framework developed for the OSM domain. This provides a means of supporting clear communication and knowledge sharing in the domain and supports knowledge systematisation. ii. The approach used in this research, integrating the value system analysis (VSA) and activity-based costing (ABC) methods for process analysis is a novel approach that bridges that gaps with the use of the ABC method for generating detailed process-related data to support cost/time-based analysis of OSM processes. iii. The developed generic process map which represents the OSM production process captures activity sequences, resources and information flow within the process will help in disseminating knowledge on OSM and improve best practices in the industry. iv. The developed process analysis tool (the OPW ontology) has been tested with a real-life OSM project and validated by domain experts to be a competent tool. The knowledge structure and rules integrated into the OPW ontology have been published on the web for knowledge sharing and re-use. This tool can be adapted by OSM practitioners to develop a company-specific tool that captures their specific business processes, which can then support the evaluation of their processes to enable continuous improvement

A new knowledge sourcing framework to support knowledge-based engineering development

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by the industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. Current knowledge capture procedures represent one of the main constraints limiting the wide use of KBE in the industry. This is due to the extraction of knowledge from experts in high cost knowledge capture sessions. To reduce the amount of time required from experts to extract relevant knowledge, this research uses Artificial Intelligence (AI) techniques capable of generating new knowledge from company assets. Moreover the research reported here proposes the integration of AI methods and experts increasing as a result the accuracy of the predictions and the reliability of using advanced reasoning tools. The proposed knowledge sourcing framework integrates two features: (i) use of advanced data mining tools and expert knowledge to create new knowledge from raw data, (ii) adoption of a well-established and reliable methodology to systematically capture, transfer and reuse engineering knowledge. The methodology proposed in this research is validated through the development and implementation of two case studies aiming at the optimisation of wing design concepts. The results obtained in both use cases proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of each of the case studies through the implementation of structured quantitative and qualitative analyses