Managing change in the delivery of complex projects: Configuration management, asset information and ‘big data’
This article provides insight into how change is managed in three organisations delivering complex projects – Airbus, CERN and Crossrail – and how those methods are evolving in the era of ‘big data’.
It has been taken from the International Journal of Project Management.
What does the paper cover?
The paper starts from the idea that asset information, as well as physical products, are becoming a deliverable of complex projects.
As digital technologies such as mobile hardware, cloud computing and integrated software become increasingly used in project delivery, they are changing established approaches, enabling more rapid and agile forms of organisation, but they bring new challenges.
The new approaches to project management are being developed in industries such as consumer electronics and biotechnology, which operate in dynamic situations where data analytics and visualisation using large digital data-sets provide the basis for more responsive, flexible and real-time decision-making.
But how can organisations manage complex projects in an era of ‘big data’?
Complex projects are high-tech, capital-intensive engineering projects that are of a significant scale, relatively long duration and require firms to work collaboratively.
Information about complex product systems is developed across multiple firms, involving diverse professions and trades as these organisations interact through digital systems.
The paper considers the emerging new practices of managing change in complex projects as asset information is becoming a project deliverable, and as internal and external data-sets become linked.
There is a tension between the need for control, to manage the configuration of complex systems (particularly in regulated and safety-critical industries), and the responsive, flexible and real-time decision-making implied by new approaches to project management.
This paper analysed the change management practices used by Airbus, CERN and Crossrail using a combination of desk-based review, interviews, visits and a cross-case workshop. It then
compared the findings to discover how the organisations delivered complex projects using digital technologies to manage the large amounts of data involved in each industry.
Each of the three organisations studied have different levels of experience of configuration management, specifically:
The aircraft manufacturer has mature processes and systems, with interest in leading development of future systems to manage and control the growing amount of data produced in the delivery of complex products.
The European organisation for nuclear research introduced configuration management in the 1990s. CERN has ongoing initiatives to update processes of change control of large data sets, has processes in place to manage information on assets through the supply chain, and is formalising feedback from operation and maintenance teams as input for new designs.
Established in 2008 to deliver a new 118km rail route through central London, Crossrail is a complex but temporary organisation. It has a configuration management team and has drawn on industry standards to rapidly embed configuration management processes in the delivery of asset information.
The regulated nature of each of the industries (aerospace, nuclear research and civil engineering) means these organisations all need to be able to track configuration items in order to revisit designs and comply with future regulation on safety-critical facilities.
- All three organisations studied use hierarchical, asynchronous and sequential processes to manage change. Therefore, the unstructured nature of ‘big data’ presents challenges to complex projects that deliver assets.
- ‘Big data’ challenges existing approaches to ensuring the integrity of assets in regulated and safety critical environments, forcing new ways of thinking and doing.
- There may be situations in which managers can benefit from ‘big data’ to implement new approaches in order to understand future scenarios. But they must maintain the validity of the information on which the delivery of complex product systems rely.