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An empirical investigation in the decision-making processes of new infrastructure development

Dehe, Benjamin (2014) An empirical investigation in the decision-making processes of new infrastructure development. Doctoral thesis, University of Huddersfield.

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The aim of this research is to present and discuss the development and deployment of Lean thinking models and techniques applied to improve the decision-making within the planning and design processes of new infrastructures, within a healthcare organisation.

In the UK, healthcare organisations are responsible for planning, designing, building and managing their own infrastructures, through which their services are delivered to the local population (Kagioglou & Tzortzopoulos, 2010). These processes are long and complex, involving a large range of stakeholders who are implicated within the strategic decision-making. It is understood that the NHS lacks models and frameworks to support the decision-making associated with their new infrastructure development and that ad-hoc methods, used at local level, lead to inefficiencies and weak performances, despite the contractual efforts made throughout the PPP and PFI schemes (Baker & Mahmood, 2012; Barlow & Koberle-Gaiser, 2008). This is illustrated by the long development cycle time – it can take up to 15 years from conception to completion of new infrastructure.

Hence, in collaboration with an NHS organisation, an empirical action research embedded within a mixed-methodology approach, has been designed to analyse the root-cause problems and assess to what extent Lean thinking can be applied to the built environment, to improve the speed and fitness for purpose of new infrastructures. Firstly, this multiphase research establishes the main issues responsible for the weak process performances, via an inductive-deductive cycle, and then demonstrates how Lean thinking inspired techniques: Multiple Criteria Decision Analysis (MCDA) using ER and AHP, Benchmarking and Quality Function Deployment (QFD), have been implemented to optimise the decision-making in order to speed up the planning and design decision-making processes and to enhance the fitness for purpose of new infrastructures.

Academic literatures on Lean thinking, decision theories and built environment have been reviewed, in order to establish a reliable knowledge base of the context and to develop relevant solutions. The bespoke models developed have been tested and implemented in collaboration with a local healthcare organisation in UK, as part of the construction of a £15 million health centre project. A substantial set of qualitative and quantitative data has been collected during the 450 days, which the researcher was granted full access, plus a total of 25 sets of interviews, a survey (N=85) and 25 experimental workshops. This mixed-methodology research is composed of an exploratory sequential design and an embedded-experiment variant, enabling the triangulation of different data, methods and findings to be used to develop an innovative solution, thus improving the new infrastructure development process.

The emerging developed conceptual model represents a non-prescriptive approach to planning and designing new healthcare infrastructures, using Lean thinking principles to optimise the decision-making and reduce the complexity. This Partial & Bespoke Lean Construction Framework (PBLCF) has been implemented as good practice by the healthcare organisation, to speed up the planning phases and to enhance the quality of the design and reduce the development cost, in order to generate a competitive edge. It is estimated that a reduction of 22% of the cycle time and 7% of the cost is achievable. This research makes a contribution by empirically developing and deploying a partial Lean implementation into the healthcare‟s built environment, and by providing non-prescriptive models to optimise the decision-making underpinning the planning and design of complex healthcare infrastructure. This has the potential to be replicated in other healthcare organisations and can also be adapted to other construction projects.

Item Type: Thesis (Doctoral)
Subjects: H Social Sciences > HE Transportation and Communications
Schools: The Business School > Quantitative Analysis Research Group
Depositing User: Elizabeth Boulton
Date Deposited: 09 Mar 2015 14:07
Last Modified: 04 Nov 2015 15:42


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