Equipment lifecycle management software

Equipment lifecycle management software that replaces age-based guesses with evidence

Transformers, turbines, and pumps rarely fail on the date in the spreadsheet. We build lifecycle software that uses AI to score each asset’s real condition and risk, so you repair, refurbish, or replace at the right time.

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Why age alone is a poor replacement signal

Most energy companies still plan renewals by nameplate age and expected useful life. Two transformers of the same age can be in very different shape. One ran cool at half load for 30 years. The other carried peaks every summer and has wet insulation.

Svitla builds equipment lifecycle management software that follows each asset from purchase to retirement. It combines condition data, maintenance history, loading, and failure consequence into one risk view. Planners see which assets need capital first and can defend that plan to regulators and boards.

Who we serve

Transmission and distribution networks

Thousands of transformers, breakers, poles, and cables spread across a service territory. You need a defensible renewal plan for rate cases and reliability targets.

Thermal and hydro generation fleets

Large rotating equipment with long outage windows. You need to time major overhauls against market prices and planned outages.

Wind and solar portfolios

Inverters, gearboxes, trackers, and blades from many OEMs, with varying warranty terms. You need to track warranty exposure and plan repowering.

Challenges we address

Equipment management software is becoming core infrastructure for asset-heavy operators. GM Insights values the market at $10.5 billion in 2025 and expects about 9.1% annual growth through 2035. Growth reflects a shared problem: lifecycle data sits in too many places.

Specs and warranties lost after commissioning

Challenge: Purchase specs, factory test reports, and warranty terms stay in procurement files. Field teams never see them.

How Svitla solves it: Each asset record starts at purchase order and carries its specs, test baselines, and warranty dates for life.

Loading history nobody connects to wear

Challenge: SCADA and historian data show how hard each asset worked, but planners rarely use it.

How Svitla solves it: The system links operating stress, such as overloads and thermal cycles, to each asset’s aging model.

Repairs that repeat without anyone noticing

Challenge: The same breaker gets the same fix three times. Work orders exist, but no one sees the pattern.

How Svitla solves it: AI groups recurring failure modes by asset, model, and location, and flags candidates for replacement instead of repair.

Capital plans built on age tables

Challenge: Budgets follow expected useful life, so healthy assets get replaced while risky ones wait.

How Svitla solves it: Risk scores combine probability of failure with consequence. Planners rank projects by risk reduced per dollar spent.

Our process at Svitla

STEP 1

We take stock of your asset data

We review your asset registry, work order history, inspection records, and operating data. We show where data is missing and which gaps matter most for decisions.

STEP 2

We agree on how risk is scored

Your engineers help define failure modes, health indicators, and consequence rules for each asset class. Every score is traceable to its inputs.

STEP 3

We build around one asset class first

We start where the capital stakes are highest, often power transformers or inverters. Your team tests the scores against what they know from the field.

STEP 4

We expand and plan capital together

We add asset classes, connect more data sources, and turn risk scores into a multi-year renewal plan.

Schedule a call

One record for each asset, from purchase to disposal

Holds nameplate data, specs, location, and parent-child hierarchy.

Stores factory test results as condition baselines.

Tracks warranty terms and expiry alerts by OEM.

A health score for each asset, built from real evidence

Combines inspections, test results, and sensor data.

Uses class-specific indicators, such as dissolved gas analysis for transformers.

Shows the inputs behind every score.

Predicts which assets are most likely to fail next

Learns from your failure history and operating stress.

Estimates remaining useful life with a confidence range.

Retrains as new failures and inspections arrive.

Weighs what each failure would cost you

Scores safety, outage, environmental, and financial impact.

Accounts for customers served and redundancy available.

Lets planners adjust weights to match company policy.

Compares the options for each at-risk asset

Models lifecycle cost for each option over the planning horizon.

Includes spare-parts availability and lead times.

Flags assets where repeated repairs cost more than replacement.

Builds a renewal plan that fits your budget

Ranks projects by risk reduced per dollar.

Tests budget scenarios and shows the risk each one leaves.

Exports plans for rate case and board reporting.

Keeps field data flowing into the record

Captures inspections offline with photos and readings.

Guides crews through decommissioning and disposal steps.

Records environmental testing and salvaged parts.

Is custom equipment lifecycle management software the best option for your needs?

Answer a few simple questions to find out whether you should choose a custom solution or a pre-built equipment lifecycle management platform.

1 / 9

Do you still plan most equipment replacements by asset age?

Is your asset data split across an EAM, spreadsheets, and paper files?

Do you need to connect SCADA or historian data to asset condition?

Have you tried an off-the-shelf EAM module that didn't fit your asset classes?

Do regulators or boards ask you to justify your capital renewal plan?

Do you manage equipment from many OEMs with different warranty terms?

Would AI-based remaining useful life estimates help your planning?

Do you expect your asset base to grow through new sites or acquisitions?

Important integrations for equipment lifecycle management software

EAM and CMMS

Work orders, maintenance history, and failure codes are the backbone of any aging model. We connect to systems such as IBM Maximo, SAP PM, or Infor EAM, and write approved plans back as work requests.

SCADA and data historians

Loading, temperature, and switching operations show how hard each asset has worked. We pull this data from historians such as AVEVA PI and turn it into asset-level stress indicators.

GIS

Location and network connectivity drive failure consequences. GIS data shows how many customers each asset serves and what backup exists.

ERP and finance

Finance provides asset book values, depreciation, and project costs. Capital plans flow back to ERP so budgets and plans stay in step.

Condition monitoring and test labs

Online monitors and lab results, such as oil analysis or vibration data, feed directly into health scores. No more retyping results from PDF reports.

Capital spent where risk is highest. Budgets go to the assets most likely to fail with the worst consequences

Healthy assets stay in service longer.

High-risk assets get replaced before they fail.

Fewer unplanned failures. Planned replacement costs less than emergency work

Fewer outages and overtime call-outs.

Lower cost of rush equipment orders.

Stronger regulatory filings. Every renewal decision has documented evidence behind it

Rate case filings rest on transparent risk data.

Audits take less time to prepare.

Recovered warranty value. Claims get filed before terms expire

Alerts flag failures that fall under warranty.

OEM performance data supports future purchasing.

Asset data audit and taxonomy

We profile your registry, work orders, and test records for completeness and duplicates. We then agree on one asset taxonomy and failure code structure. This decides how far the first models can reach.
Risk model design with your engineers

For each asset class, we define health indicators, failure modes, and consequence factors. Transformer models weigh dissolved gas and moisture. Inverter models weigh fault codes and thermal stress. Engineers sign off on every rule.
Data pipelines and integration

We connect EAM, historian, GIS, and finance data into one model. Pipelines clean and reconcile records on a schedule, so scores refresh without manual work.
Planning tools and pilot

We build the scoring, option analysis, and capital planning views. A pilot on one asset class tests scores against known failures and field experience.
Rollout, training, and model upkeep

We extend the system to more asset classes and sites. Planners and field crews get role-based training. Models retrain as new failure and inspection data arrive.

How much does equipment lifecycle management software cost?

A focused solution for one utility or plant, covering an asset registry, health scoring, and replacement planning for one or two asset classes, typically costs $120,000 to $220,000. A multi-site platform with several asset classes, live sensor data, AI risk models, and capital plan optimization typically costs $250,000 to $500,000 or more. The final cost depends on the number of asset classes, the state of your data, and the systems we connect.

Want to understand the cost of your equipment lifecycle management software?

Calculate the cost

Check our client success cases

Energy Saving Solution

Project Type Web Development

Industry Energy

Learn how we overhauled a secure client portal for a solar energy company, modernizing its technology stack, implementing a fresh UI/UX, migrating to Microsoft Azure, and introducing a new billing system, thereby enhancing user satisfaction and streamlining operational efficiency.

Electricity IoT Platform

Project Type Big Data & Analytics, IoT

Industry Energy, Hi-Tech Devices

Learn how we enhanced the client's capabilities in electricity generation and distribution by developing a new IoT platform for comprehensive device management, implementing a solution for big data analytics with modern technologies, thus delivering a robust platform for real-time monitoring.

SaaS Platform For Solar PV Provider

Project Type Web Development

Industry Energy

Discover how we upgraded a leading US manufacturer's SaaS platform for solar PV products, which operates across over 35 countries. Our team transitioned the platform to a serverless architecture with microservices, automated critical QA processes, and utilized advanced AWS cloud services.

Consulting

We assess your asset data, planning process, and tools, then recommend a lifecycle approach and roadmap.

Engineering

We design, build, and test lifecycle software around your asset classes and planning cycle.

Modernization

We audit your current spreadsheets or legacy EAM modules and plan the migration. The upgraded system adds risk scoring the old setup lacked. We cut over in phases and validate data after migration.

Data pipeline and partner integration engineering

We build and maintain the pipelines that connect EAM, historian, GIS, and lab data, so risk scores stay current.

FAQ

How is this different from the asset module in our EAM?

Your EAM records what was done to an asset. Lifecycle software decides what should happen next. It combines condition, stress, and consequence data to rank repair, refurbish, and replace options. Your EAM stays in the system of record for work.

Our asset data is incomplete. Can we still start?

Yes. Most utilities have gaps. We score assets with the data available and show how confident we are in each score. The system then points to the inspections that would improve the weakest scores most.

How do engineers learn to trust an AI risk score?

Every score shows the inputs behind it, such as test results, loading, and failure history. Engineers can compare scores against assets they know well. They can also override a score and record why.

What happens when an asset fails earlier than predicted?

The failure is recorded with its cause and fed back into the model. Similar assets are rescored right away. Over time, these surprises make the model more accurate for your fleet.

How do you handle equipment from many OEMs?

We build one taxonomy that maps each OEM’s models and failure codes to common classes. Warranty terms are stored per contract. You can compare reliability by OEM and model.

Can we start small and scale later?

Yes. Many clients start with one asset class at one site. The data model supports more classes, sites, and users from day one, so growth doesn’t require a rebuild.

What if your renewal budget followed risk instead of age?

Tell us about your assets and planning process. We’ll show you what a custom equipment lifecycle management solution could change.

Equipment lifecycle management software