Asset Management and CAFM Best Practice at the University of Wolverhampton

A university building with sign outside

14 stakeholders engaged, identifying 12 consistent themes and 5 root causes

22 process gaps identified

23-Pprocess governance framework developed

12 prioritised recommendations delivered

Great work. Thank you, CI Projects.

Anonymous feedback

Define it

The University of Wolverhampton had invested in Planon as its computer-aided facilities management system and begun a building condition survey programme. However, two years into the implementation, Planon was not yet operating as the single source of truth for asset and planned preventative maintenance data.

Asset information remained dispersed across spreadsheets, personal drives and supplier systems, while planned maintenance was not being driven through Planon. Statutory compliance was also being tracked manually by 12 people using spreadsheets, creating a resource-intensive process and limiting visibility across the Estates and Facilities function.

The University commissioned an independent review to understand the underlying causes and establish a clear, evidence-based roadmap for its asset management and CAFM programme.

Our initial assessment found a capable and committed team managing a complex, cross-functional programme alongside its day-to-day operational responsibilities. However, progress was constrained by the absence of a documented Asset Management Strategy, a central process repository and clearly embedded governance. There was also a critical dependency on two individuals for Planon configuration and planned maintenance administration. CI Projects applied the Lean Six Sigma DMAIC methodology to assess the current position, capture the Voice of the Customer and establish a prioritised improvement roadmap.

Improve it

We completed a 13-day diagnostic engagement structured around the Define, Measure and Analyse stages of DMAIC.

During the Define stage, we established the scope, success criteria and stakeholder map before planning a comprehensive Voice of the Customer exercise across the Facilities Management operation.

The Measure stage examined the current state of asset data, planned maintenance, Planon configuration, reporting capability and process governance. This provided a clear baseline against which the University’s arrangements could be assessed.

Nine structured interviews involving 14 stakeholders, from senior management to engineers and helpdesk colleagues, identified 12 consistent themes. These findings were triangulated against the system, process and data review, highlighting issues including the absence of an asset hierarchy standard, interim planned maintenance arrangements and dependency on two individuals for Planon configuration and administration.

Rather than treating each gap as an isolated technical problem, we used root cause analysis to identify five interconnected causes: strategy, resource, process, data quality and culture. The absence of a documented Asset Management Strategy was identified as the principal root cause, with 22 supporting process gaps and many of the operational challenges flowing from it. This gave the University’s leadership team a clear, evidence-based critical path for improvement.

Sustain it

To help the University embed and maintain the improvements, we developed a practical governance framework rather than providing diagnostic findings alone.

The framework set out 23 supporting processes across asset data management, planned maintenance and compliance, contractor management, Planon governance and financial planning. A supporting RACI matrix assigned clear process ownership and accountability across the Estates and Facilities team.

We also produced 12 recommendations sequenced into immediate, short-term and medium-term priorities. This reflected the dependencies between actions and provided a deliberately paced roadmap, rather than an overwhelming list of parallel activities.

The University identified the Asset Management Strategy and Asset Hierarchy Standard as its first priorities. Supporting guidance included a three-tier asset classification model, maintenance strategy selection guidance and asset management strategy options to inform the continuing survey programme and future planned maintenance regime. Together, these outputs provided a reusable framework for improving data quality, strengthening accountability and reducing reliance on key individuals as the programme progresses.

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