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Business area digitalisation analysis

Digitalisation of civil engineering and infrastructure projects

How to connect BIM, GIS, project controls, construction as-built and long-term infrastructure asset management

Digital maturity

Typical digital maturity

Shows the level of technological and process digitalisation at which companies in the sector or business area typically operate today.

A typical market situation is assessed, not the most advanced companies.

The assessment consists of five equally weighted dimensions:

Core system usage
Whether ERP, CRM, WMS, MES, customer portals or other operationally important systems are widespread in companies.
Process digitalisation
How many core processes run in systems and how many are still managed manually.
Systems integration
Whether core systems exchange data between themselves or whether employees transfer information manually.
Data quality and readiness
Whether core data is structured, up-to-date, consistent and suitable for automation and analytics.
Advanced data use
Whether real-time analytics, forecasting, automated alerts, optimisation models or AI are used.

The final score is the average of the five dimensions.

1–5 scale

  • 1 very low maturity
  • 2 low maturity
  • 3 medium maturity
  • 4 high maturity
  • 5 very high maturity

A low maturity score does not necessarily indicate low potential. On the contrary, low maturity and a high level of manual work may indicate significant untapped digitalisation value.

average
Skaitmenizacijos potencialas

Digitalisation potential

Shows how much significant business value a typical sector or business area company can create by systematically digitalising core processes.

The rating is calculated on a 100-point scale across five dimensions:

Process frequency and scale 20 %
An assessment of how frequently the digitalised processes recur and what proportion of operations they represent.
Manual work intensity 20 %
An assessment of the extent to which processes depend on email, telephone, Excel, paper documents and repeated data entry.
Impact on revenue and costs 25 %
An assessment of the potential effect on sales, margin, customer retention, administrative costs, errors, downtime or inventory.
Growth and scale potential 20 %
An assessment of whether digitalisation would enable operational capacity to be increased without expanding headcount and costs at the same rate.
Impact on decisions and risk 15 %
An assessment of the potential effect on data reliability, decision-making speed, customer experience, and the reduction of errors and operational risk.

The final score is calculated according to the assessments and weights of all dimensions.

100-point scale

  • 0–20 very low potential
  • 21–40 low potential
  • 41–60 moderate potential
  • 61–80 high potential
  • 81–100 very high potential

A high score does not mean the solution will be easy to implement. It indicates the size of the potential value, not the implementation complexity.

80/100
Biggest challenge
Project data is fragmented
Biggest opportunity
Seamless infrastructure data chain

In this business area, the greatest value will be created by a seamless infrastructure data chain. It is recommended to start with a clearly bounded first process and expand the solution in phases.

Operating model for civil engineering and infrastructure projects

Infrastructure projects manage linear and point assets, coordinates, design models, actual measurements, quantities and future asset attributes. Data value depends on whether the same object remains recognisable as the stage and organisation change.

Long lifecycle

Data must remain useful from feasibility study through decades of operation.

Geographical distribution

Design solutions, existing networks, plots and actual construction geometry must be visible in a common spatial context.

Many institutions and contractors

Information passes through different contracts, responsibilities and technological environments.

High handover value

An unstructured project archive is insufficient if the operator requires asset hierarchy, attributes and maintenance data.

Market and technology context

Infrastructure digitalisation is expanding from design models to a whole-lifecycle data chain. The link between BIM, GIS, surveying, progress and asset register is becoming important both for investment control and for reliable asset handover to the operator.

  • Importance of lifecycle costsInvestment decisions are increasingly assessed not only by construction cost, but also by future asset maintenance and resilience.
  • Spatial data integrationBIM models must be linked to territory, plots, networks and actual geometry.
  • Remote progress measurementDrones, point clouds and surveying data enable more frequent assessment of actual quantities and deviations.
  • Structured asset handoverAsset managers expect not an archive of documents, but a reliable asset register and attributes suitable for operations.

Typical value chain

01

Investment planning and site analysis

Need, alternatives, sites, existing networks, permits and funding constraints are assessed.

02

Design and BIM–GIS coordination

Technical models are created, geometry, environmental constraints and utility networks are coordinated.

03

Procurement and construction preparation

Work packages, information requirements, schedule and contractor responsibilities are established.

04

Construction progress and actual geometry

Quantities, work locations, geodetic measurements, changes and quality evidence are recorded.

05

Acceptance and reporting

The contractual outcome, funding conditions, documents and actual asset condition are verified.

06

Asset handover and operation

Design and actual data are converted into a useful asset register, GIS layers and maintenance information.

Digital maturity model for the business area

0

Project and spatial data uncontrolled

Drawings, maps, measurements, quantities and asset information are stored separately, so relationships for the same infrastructure asset are reconstructed manually.

1

Process based on documents and separate maps

Designs, drawings, GIS layers and reports are stored in separate environments, and relationships are reconstructed manually.

2

BIM and GIS used separately

Design and territorial data are digital, but object identity and changes between systems are not linked.

3

Coordinated project data environment Typical current situation

Models, documents, spatial data and approvals are managed according to common statuses and information requirements.

4

Lifecycle data chain Siektina

Project object linked to actual geometry, quantity, asset attributes and handover status.

5

Forecast infrastructure portfolio

Portfolio history, condition and spatial data are used to forecast investment, risk and maintenance scenarios.

Key finding

The greatest risk arises when the design object, geographical location, geodetic as-built and asset register do not share a common identifier. A single-asset design-to-as-built scenario allows this chain to be validated in practice before the entire project is completed.

First priority is the chain connecting the design model, GIS location, as-built geometry, quantities and handover attributes for a single infrastructure asset.

Related digitalisation topics

Digitalisation solutions: civil engineering and infrastructure projectsProcess digitalisationData analytics
Next step

Assessing digitalisation opportunities for the 'Civil Engineering and Infrastructure Projects' business area

The potential value of an integrated infrastructure data chain can be evaluated, and a realistic first version can be defined with measurable business KPIs.

Introduction