Abstract
The study explores cloud manufacturing readiness in the context of aerospace composite manufacturing. Cloud manufacturing has been identified as a novel, service-oriented manufacturing paradigm that has the potential to transform the manufacturing industry from the traditional ‘product-oriented’ model to a ‘service-oriented’ one, offering on-demand manufacturing as a service. While the popularity of the concept has steadily grown over the years, its commercial-level applications remain limited. The inherent complexity of cloud manufacturing systems and the lack of tools to support transition have been identified in the literature as two of the key challenges. Both industry and academia believe that to increase adoption, cloud manufacturing system readiness-maturity levels and evaluation indexes should be developed. The literature review revealed not only the absence of a practical readiness assessment model that could used by manufacturing organisations to assist in their transition but also the lack of a systematic methodology for conducting readiness assessment to provide step-by-step guidance.To address these key gaps in the literature and practice, the study developed a firm-level cloud manufacturing readiness assessment model by adopting a systems development research methodology and research process, supported by an embedded single case study approach to validate and test the proposed model. This comprehensive multi-methodological research approach enabled a systematic enquiry, leading to the iterative development and refinement of the proposed model and its accompanying systematic methodology, in collaboration with an aerospace composite manufacturer. The case study revealed that small and medium enterprises struggle to adopt complex manufacturing innovations such as cloud manufacturing due to the lack of understanding, ad-hoc efforts, uncoordinated strategies, and unavailability of tools to aid complex decision-making, reaffirming the findings in the literature.
By utilising a three-stage factors identification process, the study identified 45 factors affecting the readiness of manufacturing organisations to adopt cloud manufacturing through the Technology-Organisation-Environment (TOE) lens. Among these, one of the factors (i.e., process qualification) was context-specific, while others were generalisable. The inter relationship and the hierarchy of these factors were systematically identified and categorised under 6 core dimensions: Strategic Leadership, Business Model, Digitalisations, Compliance, Operations, and Service Delivery, in order of their relative importance, using the analytic hierarchy process (AHP) technique. Cloud manufacturing readiness levels were defined and standardised through an integrated CMMI-DOI framework, which can be customised for specific industrial contexts.
In addition to developing a cloud manufacturing readiness model comprising a 3-level hierarchical architecture capable of both qualitative and quantitative assessment, the study contributed to the cloud manufacturing domain by developing a systematic methodology for conducting readiness assessment in manufacturing organisations. By testing the model in an industrial setting, the study demonstrated the model’s capability to assess not only the ‘current readiness’ status of a company but also the ‘future readiness’ level that is realistically achievable based on the company’s expectations and capabilities. It simultaneously provides step-by-step guidance for the cloud manufacturing transition, supporting informed decision making for the future. The study further extended the application of systems development methodology into the cloud manufacturing domain. Further research into industry-agnostic implementation is recommended to test and fine-tune the model and to explore techniques for transforming the model from a ‘static’ to a dynamic and adaptive model.
| Date of Award | 15 Jul 2025 |
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| Original language | English |
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| Supervisor | Nai Yeen Gavin Lai (Supervisor), Kok Wong (Supervisor), Kulwant S. Pawar (Supervisor) & Yingdan Zhu (Supervisor) |