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A cost-based decision framework for advanced manufacturing research

Jeavons, Claire E (2018) A cost-based decision framework for advanced manufacturing research. EngD thesis, University of Sheffield.

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Abstract

Advanced Manufacturing research centres bridge the gap between fundamental academic research and high value manufacturing. There are complexities in terms of decision making and knowledge management across these interfaces in particular surrounding the uncertainties in data. This research provides a solution to this combining cost engineering and Bayesian methods into a framework for use within these contexts. The research aim is to provide; A framework to improve value- related decision making when selecting novel manufacturing technologies. The framework consists of four elements; Elicit — Ensure that cost related drivers and input parameters are identified early using expert elicitation techniques to capture soft evidence. Consolidate — Map all cost and value related parameters, uncertainties and their interrelationships. Analyse — Identify the sensitivities to cost of all parameters. Communicate —- Provide results as multi-objective outputs useful to a range of decision makers. Feedback — Ensure that when new evidence emerges this is incorporated into the knowledge base. Mixed methods were used in this research using a pragmatic approach, incorporating both quantitative and qualitative methods. The novel framework offers an extension to the field of knowledge management and cost estimation, providing a mechanism for dynamic evidence and uncertainty propagation with feedback loops. The research demonstrates that providing multi-objective decision making support enhances the ‘buy-in’ from multiple stakeholder groups. The research builds on existing cost estimation research into cutting fluids to include many parameters not previously considered. The case study 1 activity identified the value of robust coolant management and helped to initiative companywide investigation of coolant filtration technologies to enable improved coolant life and quality. This is now yielding significant cost reduction and improved life and sustainability to coolant practices across the company. The results of case study 1, helped resolve the mitigating factors of inconsistent test results seen in case study 2. New research and industrial investment will now be conducted into coolant filtration and also adoption of improved filtration control in the research environment is commencing.

Item Type: Thesis (EngD)
Keywords: Advanced manufacturing Decision making Cost estimation Bayesian Networks
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Automatic Control and Systems Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.792038
Depositing User: Mrs Claire E Jeavons
Date Deposited: 10 Dec 2019 09:50
Last Modified: 23 Dec 2019 11:05
URI: http://etheses.whiterose.ac.uk/id/eprint/25255

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