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Details of Grant 

EPSRC Reference: EP/G038171/1
Title: Information Quality for Asset Management
Principal Investigator: Parlikad, Professor A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
BAE Systems Pullman Fleet Services
Department: Engineering
Organisation: University of Cambridge
Scheme: First Grant Scheme
Starts: 01 April 2009 Ends: 30 September 2012 Value (£): 328,145
EPSRC Research Topic Classifications:
Manufact. Business Strategy
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
26 Nov 2008 Engineering Socio-Technical Systems Announced
Summary on Grant Application Form
In the current economic scenario, organisations in the UK and around the world are under increasing pressure to reduce costs, meet tougher performance and production targets, comply with regulatory requirements, and maximize return on assets. Therefore, these organisations are looking for opportunities to reduce the cost of maintaining their assets, improve the performance of those assets through improved decision making, and gain competitive advantage. It is widely acknowledged that decisions made are only as good as the information available at hand to make those decisions. Consequently, it becomes imperative to these manufacturers to gather useful information about their assets throughout their lifecycle for efficient management and control. The overall vision for the research proposed here is to help UK industries maximize value generation from their assets through effective management of information quality and decision-making. There is a need for a comprehensive framework for quantitative assessment of information quality in the context of asset lifecycle decisions. The following research questions define the research directions for this project. 1. What information quality dimensions are critical for managing and improving asset performance and how can they be measured?2. How can we maximize asset performance by optimizing decision strategies during the asset lifecycle?The objective of the project is to develop quantitative measures for information quality in the context of asset management decisions and to develop methodologies to optimize information quality and decision strategies throughout the asset lifecycle. The novelty in the decision optimisation problem considered here is that the aim is not to simply optimize immediate decisions, but will consider the impact of one decision on subsequent decisions and hence the complete lifecycle of the asset, thereby minimizing total cost of ownership of the asset to its user. In addition, this project addresses a key gap in current body of knowledge - optimisation of IQ. Here we aim to examine the cost implications of improving various IQ dimensions, and optimise IQ with an objective to support optimal decision-making and hence asset performance. Issues such as possible trade-offs that may exist between the different IQ dimensions will also be examined in this task. In addition, an information quality audit tool will be built based on a robust theoretical model for use by industry to assess the information quality performance of asset management systems. The research will benefit industry practitioners, especially those who manage complex industrial assets through the development of performance measurement and improvement tools. In addition, it will help technology providers to define next generation asset management solutions that optimize asset performance. To address this challenge, this proposal defines a three-year programme aimed at the development of tools and methodologies to assess asset information systems and to optimize asset lifecycle decisions. The research activities are grouped into four Work Packages:WP1 - Modelling Asset Lifecycle Management DecisionsWP2 - Information Quality Assessment WP3 - Asset Performance OptimizationWP4 - Industrial PracticeThe first three Work Packages represent the research activities and the fourth work package groups together the industrial activities within the project. WP1 and WP2 build the foundation for this research by developing models and tools for asset lifecycle decisions and assessment of IQ dimensions. WP3 forms the core development activity in this programme, where we develop optimization tools for asset performance as well as IQ. Finally, WP4 consolidates the activities related to industrial interaction within the project.
Key Findings
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Organisation Website: http://www.cam.ac.uk