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Business area digitalisation analysis

Building maintenance and technical service digitalisation

How to connect asset register, work orders, technicians, BMS signals, parts and SLA

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.

medium
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.

88/100
Biggest challenge
Maintenance is reactive to failures
Biggest opportunity
Condition-based equipment and technician work management

In this business area, the greatest value will be created by condition-based equipment and technician work management. It is recommended to start with a clearly bounded first process and expand the solution in stages.

Building maintenance and technical service operating model

The technical maintenance service combines equipment registers, planned work, fault signals, dispatcher decisions, technician call-outs, parts and client commitments. Service quality is determined not by the number of signals, but by the ability to convert them into an appropriate and timely completed task.

High volume of planned and emergency work

Each asset generates periodic tasks, signals, faults and inspections.

Technician competence must match the equipment

Qualifications, manufacturer knowledge, location and parts availability are important for correct assignment.

Asset context determines first visit outcome

The technician needs history, instructions, measurements, warranty and compatible parts before departure.

Importance of SLA and evidence

The client cares not only about the closed task, but also response time, actual work, measurements and confirmation.

Market and technology context

The maintenance operating model is shifting from periodic inspections and reactive response to condition-based work planning. CMMS, BMS, IoT and mobile technician workstations form a single chain when signals are linked to specific equipment and accountable action.

  • Shortage of techniciansCompanies need to reduce unproductive travel, searching and report filling.
  • Outcome-based contractsCustomers increasingly value system uptime and energy performance rather than merely the number of visits completed.
  • BMS and IoT data expansionSignals are multiplying, but value emerges only when they are linked to equipment, priority and workflow.
  • Predictive maintenanceHistory and sensors enable work to be brought forward when data is reliable and the economics of failure and intervention are clear.

Typical operational chain

01

Equipment and contract register

Equipment, criticality, maintenance plans, warranties, documents and SLAs are managed.

02

Signal, failure or planned task

Work requirements are created from schedules, customer requests, technician inspections or BMS events.

03

Priority and assignment

Criticality, competency, location, deadline, parts and technician availability are taken into account.

04

Diagnostics and on-site work

The technician views history, instructions, records time, measurements, parts, photos and the result.

05

Quality confirmation and SLA

It is verified that the cause has been resolved, measurements have been completed, the customer has confirmed and the contract has been adhered to.

06

Analysis and prevention

Recurring failures, parts requirements, technician performance, energy anomalies and equipment replacement priorities are evaluated.

Digital maturity model for the business area

0

Failures and maintenance not recorded consistently

Failures are received by phone, equipment history is not accumulated, and technicians' work, parts and measurements are recorded on paper or after the fact.

1

Unplanned callouts and paper reports

Failures are received by phone, equipment history is fragmented, and technician reports are entered after the work.

2

Digital tasks and schedules

A CMMS or request system is in use, but BMS signals, parts, competences and financial data are not linked.

3

Integrated technician workflow Typical current situation

Equipment, tasks, technicians, parts, time, measurements and SLAs are managed in a single process.

4

Condition-based maintenance Siektina

Plans are adjusted based on signals, measurements, failure history, criticality and actual equipment usage.

5

Predictive building performance

Analytics and AI help anticipate failure risk, energy anomalies, parts requirements and optimal technician workload.

Key finding

Downtime and repeat visits often arise from missing equipment context, incorrect prioritisation or unavailable parts. A single facility's critical equipment failure process enables linking the signal, technician work and agreed service level.

First priority is the critical equipment failure process for one facility, from signal or request to technician visit, parts, measurements, confirmation and SLA.

Related digitalisation topics

Digitalisation solutions: building maintenance and technical servicingProcess digitalisationData analytics
Next step

Assessing the digitalisation opportunities in Building Maintenance and Technical Services operations

The business value of condition-based equipment and technician work management can be assessed, and a realistic first version defined with measurable business KPIs.

Learn more