Footer Image
Arrow
Arrow

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Specialist Teams

Capabilities

Greenvolt Case Study

How Greenvolt is Redefining Asset Performance

From manual reporting and reactive maintenance to predictive fleet control: Evora built GreenVolt’s remote Monitoring & Prediction Center for real-time visibility and better performance across its European biomass plants.
Greenvolt logo white

Scalable

Monitoring & Prediction Center across renewable Biomass Plants

Up to

5%

Boiler Efficiency Improvement through AI optimization models

Improved

asset reliability and plant performance

"Evora is not only supporting us in a technical perspective, but they are also challenging us in a business perspective, which is critical in this topic."

Business technology speaker, Henrique Gomes, on stage at an SAP event discussing digital transformation

Henrique Gomes

Greenvolt · IT Director

From a plant solution to a scalable renewable portfolio blueprint.
Purple quotation mark graphic used as a decorative testimonial design element

Real case

From local routines to
one shared way of working

Different routines by region

Maintenance execution varied across countries, sites, and local teams.

Manual evidence handling

Build compliance, security, and ownership into the model early — not as an afterthought.​

High audit preparation effort

Preparing evidence required coordination instead of being part of daily execution.

Limited KPI visibility

Performance, maintenance status, and improvement signals were not visible in one consistent view.

Local workarounds filled the gaps

Teams kept operations moving, but too much depended on individual routines.

Improvements were hard to scale

What worked in one site or country did not automatically become a repeatable standard elsewhere.

Our point of view

Built around responsibility.

Proven by outcomes.

1

Map reality
Maintenance routines, evidence, and reporting differed across sites.

2

Define standards
Evora clarified what consistent execution should look like.

3

Build governance
KPIs, roles, and evidence logic became part of daily work.

4

Prepare audits
Proof became easier to capture, access, and use.

5

Improve after rollout
The model was stabilized, measured, and improved over time.

The Transformation

From local maintenance routines to
governed execution across regions.

Speak with an expert

Every inquiry reaches the right specialist.

Abstract purple wave background for modern technology and digital transformation design

Sales Americas

Abstract purple wave background for modern technology and digital transformation design

Sales EMEA

Abstract purple wave background for modern technology and digital transformation design

Sales APAC

Request a meeting

Tell us your challenge — right expert within 24h.

GDPR compliant. Data never shared.

Request a meeting

Tell us your challenge — right expert within 24h.

GDPR compliant. Data never shared.

FAQ

Questions enterprise leaders ask before they commit.

The challenge was fragmented asset data and limited transparency. Asset information was locked in siloed systems, maintenance was reactive, dashboards were missing, and risks remained difficult to see before they became operational problems.
Tilbury Green Power operates critical infrastructure with high availability requirements. Without reliable asset visibility, predictive insight, and real-time performance data, unplanned downtime, manual reporting effort, and delayed decisions become business risks.
Evora helped connect the asset-data foundation, lifecycle information, predictive maintenance logic, and management dashboards. The solution architecture referenced SAP Datasphere for the data foundation, SAP PLM for lifecycle management, SAP APM for asset intelligence, and SAP Analytics Cloud for decision dashboards.
The target model moved from siloed ERP, CMMS, and GIS data, manual Excel reports, and delayed decisions toward a unified data foundation, predictive insights, lifecycle-based decisions, and real-time dashboards for operations and management.
The case primarily supports Asset & Maintenance Excellence and Data, AI & Automation. It also connects to Architecture & Integration, because the value depends on bringing data, systems, dashboards, and operational decision-making into one governed model.
It means moving beyond fragmented reports and reactive troubleshooting. Data becomes useful when it helps teams identify failure risks earlier, understand asset lifecycle context, monitor live KPIs, and make better maintenance and operational decisions.

More Questions?