Abstract:
In the literature, a need was identified to consider the provision of drinking water to be
a ‘high reliability’ societal service. This thesis reports on an investigation into the
technical and organisational reliability of a defined section in the water utility sector
and a Regional Water Utility. Here, the organisational reliability in operations and
incident management, and, secondly, the management of technical reliability of water
supply systems arising from risk-based asset management were the emphasis of this
project.
In order to substantiate this investigation, three main research components were
designed and conducted: firstly, a characterisation of the nature of incidents and their
impact on customers; secondly, an investigation into organisational capabilities to
manage incidents and its role in maintaining a resilient water supply system that
minimises the impact of incidents on customers, and thirdly, an investigation into riskbased
asset management strategies that provide and maintain the technical reliability of
the water supply system. In the latter perspective, the opportunity to learn from previous
incidents to enhance asset risk assessments was investigated.
In this study, it was found that many HRO principles are readily observable in the water
utilities that participated in this research. Following the characterisation of incidents, it
is demonstrated that the observation of HRO principles during incident management has
a positive effect on the overall reduction of incident impacts on customers. Beyond the
immediate effect of HRO principles in incident management, it could be demonstrated
that ‘learning from failure’ provides a mechanism to understand and manage future
risks. The concept of incident meta-analysis is introduced that compares series of past
incidents with documented perceived, future risks. The statistical analysis of incident
time series facilitated the monitoring of incident trends, the validation of the risk model
used in the Regional Water Utility and the verification of risk data, in particular for the
risk components ‘probability, cause, effect and impact’.
