Research focus

Asset management systems

Asset life-cycle renewal and capex decisions should be aligned with strategic corporate objectives; our work examines how innovative tools can be used to support enterprise wide asset management systems that comply with PAS-55, ISO15686 and ISO31000.

Multi-agent systems (MAS)

Multi-agent systems (MAS) are well established although not used widely in practice. Working with our colleagues in MBS, we are looking at the applications of MAS in 'ramp-up' risks,  high complexity, low-volume manufacturing.

Private finance risk allocation

We continue to explore the economic, political and sociological aspects of PPP in infrastructure delivery. Our earlier studies in emerging economies illustrate a complex landscape and current research at Manchester seeks to identify how the technical and engineering knowledge, both tacit and explicit, can be used to inform effective risk allocation across all parties.

Whole-life cost and performance risk management

We work on whole-life cost studies; the announcement that UK Government will be seeking Level 2 BIM compliance by 2016 illustrates the importance of taking a holistic approach to building design and performance evaluation. 

Understanding risk and uncertainties through 'storytelling'

This is a technique that is gaining increasing recognition in knowledge sharing and scenario planning research (see late 2011 edition of Harvard Business Review - ‘living with complexity’). We are looking at how story-telling can complement ‘hard risk analysis’ to make potentially important future possibilities ‘more real’. Storytelling can also drive a reflective approach to the practice of project management and this ethos underpins our MSc Project Mmanagement Professional Development Programme PMPDP. 

Coping with complexity of risk using bayesian networks

Bayesian networks are very useful to us in terms of understanding risk in complex organisations but our evidence suggests hat one of the main challenges to implementing effective ERM strategies is the perception and understanding of risk. Bayesian Networks can help us deal with this problem whilst still retaining a rigorous analytical approach. The emphasis here is not so much on the theoretical aspects of BN's - bur rather the steps required to implement such methods across complex organisations and supply-chains.

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