AI & BI use case · Business Intelligence

Asset lifecycle management

Plan asset investments based on data.

What it's about

Asset lifecycle management links operations, maintenance, cost and performance data to optimise replacement investments, modernisation and maintenance budgets.

  • Cost reduction
  • Higher availability
  • Better planning
Business case & ROI
−10 %

maintenance costs

+15 %

asset availability

9–12 Mon.

payback

−20 %

unplanned spend

Calculated from fewer emergency repairs, longer asset life and optimised capital budgets.

How we do it
  1. 01

    Data integration

    Consolidate data from ERP, MES and further sources for Asset lifecycle management.

  2. 02

    Data quality

    Clean, harmonise and validate data for plausibility.

  3. 03

    Model development

    Train and validate the Asset lifecycle management model on historical data.

  4. 04

    Pilot

    Pilot Asset lifecycle management in one area and collect feedback.

  5. 05

    Rollout

    Scale the solution and integrate it into operational processes.

5

Steps

6

Data sources

4

Stakeholders

From first data access to production – every step delivers a tangible interim result.

Data typically needed

Maintenance data

Faults, maintenance and spare-parts consumption.

ERP data

Master and transaction data from ERP.

Machine data

Condition, parameters and production counters.

Financial data

Cost centres, budgets, cash flows and invoices.

Quality data

Inspection results, lab values and complaints.

Energy consumption

Electricity, gas, water and steam per area.

Stakeholders
  • Maintenance

    Plans maintenance and spare parts precisely.

  • CFO

    Receives reliable financial and risk metrics.

  • Production manager

    Uses insights directly in daily operations.

  • Controlling

    Quantifies effects and supports budgeting.

Typical business value
01

Cost reduction

02

Higher availability

03

Better planning

04

More transparency

05

Better decisions

The data platform advantage

With a solid data foundation this use case gets faster, cheaper and far more stable.

Central data platform

Real-time data integration

Scalable analytics pipelines

Reusable data products

Build a data platform
Synergies & positive side effects

Predictive maintenance

Sensor data provides wear indicators.

Digital twin

Models can be reused in simulations.

Automated reporting

Metrics are provided without manual effort.