OUR OFFICE
Zadarska ulica 80
10000 Zagreb, Croatia
2015-2026 Thespian d.o.o.
Zadarska ulica 80
10000 Zagreb, Croatia
2015-2026 Thespian d.o.o.
4-phase Transformation Plan
Fleet innovation roadmap
Fleet innovation roadmap
Turn your fleet from reactive to predictive with a personalized maturity-based transformation plan.
Turn your fleet from reactive to predictive with a personalized maturity-based transformation plan.
Fleets across Europe are leaving more
than €6 billion in operational savings
untapped each year because they run
reactively instead of predictively.
Fleets across Europe are leaving more than €6 billion in operational savings untapped each year because they run reactively instead of predictively.
Research from McKinsey & Company and World Economic Forum shows that predictive maintenance and integrated telematics can reduce unplanned downtime by up to 30 %, cut maintenance costs by 20–40 %, and improve fleet utilization by 10–15 %.
Research from McKinsey & Company and World Economic Forum shows that predictive maintenance and integrated telematics can reduce unplanned downtime by up to 30 %, cut maintenance costs by 20–40 %, and improve fleet utilization by 10–15 %.
Yet, most operators are stuck in manual or siloed systems — unable to bridge the gap between maintenance, routing, and asset visibility.
Yet, most operators are stuck in manual or siloed systems — unable to bridge the gap between maintenance, routing, and asset visibility.
Reduce costs
20–40% lower maintenance cost through preventive & predictive workflows.
Reduce costs
20–40% lower maintenance cost through preventive & predictive workflows.
Improve Uptime
Up to 30% less unplanned downtime from data-guided decisions.
Improve Uptime
Up to 30% less unplanned downtime from data-guided decisions.
Modernize Operations
10–15% better fleet utilization via smarter routing and dispatch.
Modernize Operations
10–15% better fleet utilization via smarter routing and dispatch.
This is your GPS for 2026. First, we’ll pin your current position on the maturity map – from manual firefighting to fully predictive. Then, we’ll lay out quarter-by-quarter moves, the tech stack that supports them, and the expected impact on uptime, maintenance savings, and asset efficiency.
This is your GPS for 2026. First, we’ll pin your current position on the maturity map – from manual firefighting to fully predictive. Then, we’ll lay out quarter-by-quarter moves, the tech stack that supports them, and the expected impact on uptime, maintenance savings, and asset efficiency.
Get clear milestones, ROI timelines, and a realistic view of the change your teams will go through on the way. You’ll walk away with a shared plan that keeps operations, IT, and finance aligned on milestones, investment, and the business impact of every step.
Get clear milestones, ROI timelines, and a realistic view of the change your teams will go through on the way. You’ll walk away with a shared plan that keeps operations, IT, and finance aligned on milestones, investment, and the business impact of every step.
Your fleet’s transformation starting point
Explore each maturity stage and identify where you are right now — from Manual to Autonomous.
Fleet maturity assessment slider
Select the stage that best describes your current operations
MANUAL
You are Manual
Your operations are mostly paper-driven and reactive.
Maintenance happens when something breaks
Lots of spreadsheets, calls, WhatsApp messages
No reliable real-time data on vehicles or equipment
Downtime surprises are common
Planning is manual and based on experience
Data Quality
Real-time accuracy and visibility
Telemetry Coverage
Sensors and asset monitoring
System Automation
Tools and workflows connected
Maintenance Intelligence
From reactive to predictive decisions
Your recommended roadmap
Your self-assessment results indicate where your organization stands today. The roadmap below highlights the phases that will help you strengthen data quality, automation, and predictive capability across your fleet.
Q1: Data foundation
Objective is to create a single source of truth for fleet and asset health. The objective is to move away from guesswork and scattered files toward real telemetry, centralized maintenance records, and measure-ready KPIs.
This phase does not optimize or automate; it establishes visibility and removes operational blind spots.
Milestone
Establish the telematics and CMMS data foundation, sensor rollout.
ROI window
6-9 months*
*Value comes from transparency; savings begin only in Q2.
Recommended tech stack
IoT & Sensor layer: Telematics units for GPS, engine health, vibration, temperature, and load.
Connectivity & Data pipeline: MQTT/REST API ingestion, secure gateway, stable data stream.
Integration layer: First connection to CMMS for asset identity, maintenance events, meter/engine hours.
Data Lake / warehouse: Central storage for historical and real-time fleet and asset telemetry.
ROI metrics to track
% of fleet with telemetry installed
Baseline idle hours
Baseline maintenance cost per vehicle / per equipment unit
Baseline downtime events
Change management & governance
Create steering committee (Operations, Maintenance, IT, Finance)
Define data ownership and data governance policies
Train technicians and dispatchers on digital entries and CMMS usage
Begin reducing paper, spreadsheets, scattered messages, and manual logs
Check-in anchor & Governance model
>70% of vehicles/assets with working telemetry
CMMS is receiving maintenance events consistently
A single source of truth exists for fleet operational data
Identify gaps before moving into automation
Q2: Preventive layer
The aim is to stop putting out fires – alerts flag issues early, service windows are booked in advance, and technicians know what’s coming through the workshop next week.
The entire maintenance loop becomes predictable: fewer crisis repairs, fewer last-minute breakdowns, less stress on maintenance teams.
Milestone
Rule-based alerts and automated maintenance scheduling.
ROI window
9-12 months
Recommended tech stack
Alert engine (rules-based): Time, engine hours, vibration threshold, temperature deviations
Visualization: Basic dashboards surfacing alerts and upcoming work orders
CMMS integration: Telematics → maintenance scheduling → work orders
Workflow automation: Auto-generated maintenance tasks triggered by telemetry inputs
ROI metrics to track
Preventive tasks as % of all tasks
MTBF (Mean Time Between Failures)
Emergency intervention frequency
Maintenance cost per operating unit
Change management & governance
Update SOPs from reactive → preventive
Train maintenance teams to prioritize preventive work orders
Weekly or bi-weekly KPI cadence
Define who owns alerts, who schedules tasks, and who closes them
Check-in anchor & Governance model
Noise ratio reduction (false positives under control)
Closed loop validated: ALERT → TASK → EXECUTION → CLOSED
Preventive coverage 60–80%
Stakeholders aligned on which alerts matter
Q3: Analytics & optimization
Move from mechanics to strategy. Use your own operational data to optimize maintenance windows, improve utilization, and minimize wasted time, fuel, labor, and asset life.
This is where optimization begins: fewer surprises, fewer inefficiencies, higher stability.
Milestone
Deploy analytics engine, optimize maintenance windows, and utilization.
ROI window
12-15 months
Recommended tech stack
Analytics engine: Utilization clustering, component risk scoring, trend detection
Visualization platform: Heatmaps, fleet health, busy vs idle segments
Unified integration layer: CMMS + WMS/TMS + Telematics merged into one analytics environment
ROI metrics to track
Downtime reduction % (vs baseline)
Utilization improvement %
Idle time reduction %
Maintenance cost trend downward
Change management & governance
Regular KPI review sessions
Cross-department ownership: Fleet, Maintenance, Dispatch, Warehouse Ops
Cultural shift: decisions based on evidence, not intuition
Workflow refinement based on analytics outputs
Check-in anchor & Governance model
Are optimization recommendations implemented or ignored?
Are scheduling patterns shifting based on data?
Do planners/dispatchers use dashboards, not gut feeling?
Month-over-month improvements are measurable
Q4: Predictive stage
Move from optimization to autonomy.
Maintenance becomes proactive, guided by data, and eventually automatic. Fleet managers stop firefighting and focus on cost, uptime, and growth.
Milestone
Predictive maintenance models, digital twin simulations, and autonomous scheduling.
ROI payback
12-24 months
Recommended tech stack
Digital twin platform: Real-time representation of vehicle, vessel, or warehouse asset state
ML prognostics: Remaining useful life (RUL), Failure probability forecast
ERP integration: Automated parts ordering and labor allocation
Autonomous orchestration: System creates and schedules maintenance events
ROI metrics to track
Maintenance cost reduction: 20–40%
Downtime reduction: up to 30%
Asset lifecycle extension: +2–3 years
Change management & governance
Executive steering reviews become strategic
Predictive service level agreements with vendors
ESG reporting tied to asset telemetry
Budgeting influenced by predictive analytics
Check-in anchor & Governance model
Are predictive outputs executed without human override?
Are results financially measurable per vehicle/asset?
Do breakdowns become rare exceptions?
Does intervention happen before failure?
Strategic transformation stage
The aim is to build a predictive layer over the whole fleet – a smart control centre that informs every decision, keeps carbon numbers honest, and turns day-to-day operations into a revenue asset.
Milestone
Four strategic capabilities become integrated into daily operations:
Cross-fleet digital twin across truck, water, and warehouse asset
Autonomous maintenance orchestration and predictive parts ordering
ESG & carbon tracking integration
Data monetisation and SaaS extension
ROI window
Scaling value year-over-year through automation + monetisation
Recommended tech stack
Digital twin platform: Full lifecycle simulation across all fleet asset types
ML-driven orchestration: Automated scheduling, labor planning, and parts procurement
ESG and carbon intelligence layer: Telemetry-based emissions and efficiency scoring
Data exchange and Monetisation APIs: Secure external data sharing and subscription services
ROI metrics to track
Cost reduction and uptime gains become sustained and compounding
OPEX reduction measured as a recurring annual benefit
Verified CO₂ reduction per fleet unit
Revenue generated from external data services
Benchmarking performance vs. other operators in the market
Change management & governance
Enterprise-level governance and predictive-based vendor agreements
Operating model redesign: from “maintenance department” to “operations intelligence”
Incentives and budgeting tied to predictive outcomes and ESG performance
Legal and compliance frameworks for data sharing and monetisation
Check-in anchor & governance models
Are digital-twin simulations used to plan operations and investments?
Are maintenance and parts decisions autonomous without crisis mode?
Is emissions data actively used in business decisions and compliance audits?
Is fleet data delivering measurable commercial value beyond internal use?
Your fleet’s transformation starting point
Explore each maturity stage and identify where you are right now — from Manual to Autonomous.
Fleet maturity assessment slider
Select the stage that best describes your current operations
MANUAL
You are Manual
Your operations are mostly paper-driven and reactive.
Maintenance happens when something breaks
Lots of spreadsheets, calls, WhatsApp messages
No reliable real-time data on vehicles or equipment
Downtime surprises are common
Planning is manual and based on experience
Data Quality
Real-time accuracy and visibility
Telemetry Coverage
Sensors and asset monitoring
System Automation
Tools and workflows connected
Maintenance Intelligence
From reactive to predictive decisions
Your recommended roadmap
Your self-assessment results indicate where your organization stands today. The roadmap below highlights the phases that will help you strengthen data quality, automation, and predictive capability across your fleet.
Objective is to create a single source of truth for fleet and asset health. The objective is to move away from guesswork and scattered files toward real telemetry, centralized maintenance records, and measure-ready KPIs.
This phase does not optimize or automate; it establishes visibility and removes operational blind spots.
Milestone
Establish the telematics and CMMS data foundation, sensor rollout.
ROI window
6-9 months*
*Value comes from transparency; savings begin only in Q2.
Recommended tech stack
IoT & Sensor layer: Telematics units for GPS, engine health, vibration, temperature, and load.
Connectivity & Data pipeline: MQTT/REST API ingestion, secure gateway, stable data stream.
Integration layer: First connection to CMMS for asset identity, maintenance events, meter/engine hours.
Data Lake / warehouse: Central storage for historical and real-time fleet and asset telemetry.
ROI metrics to track
% of fleet with telemetry installed
Baseline idle hours
Baseline maintenance cost per vehicle / per equipment unit
Baseline downtime events
Change management & governance
Create steering committee (Operations, Maintenance, IT, Finance)
Define data ownership and data governance policies
Train technicians and dispatchers on digital entries and CMMS usage
Begin reducing paper, spreadsheets, scattered messages, and manual logs
Check-in anchor & Governance model
>70% of vehicles/assets with working telemetry
CMMS is receiving maintenance events consistently
A single source of truth exists for fleet operational data
Identify gaps before moving into automation
Q1: Data foundation
The aim is to stop putting out fires – alerts flag issues early, service windows are booked in advance, and technicians know what’s coming through the workshop next week.
The entire maintenance loop becomes predictable: fewer crisis repairs, fewer last-minute breakdowns, less stress on maintenance teams.
Milestone
Rule-based alerts and automated maintenance scheduling.
ROI window
9-12 months
Recommended tech stack
Alert engine (rules-based): Time, engine hours, vibration threshold, temperature deviations
Visualization: Basic dashboards surfacing alerts and upcoming work orders
CMMS integration: Telematics → maintenance scheduling → work orders
Workflow automation: Auto-generated maintenance tasks triggered by telemetry inputs
ROI metrics to track
Preventive tasks as % of all tasks
MTBF (Mean Time Between Failures)
Emergency intervention frequency
Maintenance cost per operating unit
Change management & governance
Update SOPs from reactive → preventive
Train maintenance teams to prioritize preventive work orders
Weekly or bi-weekly KPI cadence
Define who owns alerts, who schedules tasks, and who closes them
Check-in anchor & Governance model
Noise ratio reduction (false positives under control)
Closed loop validated: ALERT → TASK → EXECUTION → CLOSED
Preventive coverage 60–80%
Stakeholders aligned on which alerts matter
Q2: Preventive layer
Move from mechanics to strategy. Use your own operational data to optimize maintenance windows, improve utilization, and minimize wasted time, fuel, labor, and asset life.
This is where optimization begins: fewer surprises, fewer inefficiencies, higher stability.
Milestone
Deploy analytics engine, optimize maintenance windows, and utilization.
ROI window
12-15 months
Recommended tech stack
Analytics engine: Utilization clustering, component risk scoring, trend detection
Visualization platform: Heatmaps, fleet health, busy vs idle segments
Unified integration layer: CMMS + WMS/TMS + Telematics merged into one analytics environment
ROI metrics to track
Downtime reduction % (vs baseline)
Utilization improvement %
Idle time reduction %
Maintenance cost trend downward
Change management & governance
Regular KPI review sessions
Cross-department ownership: Fleet, Maintenance, Dispatch, Warehouse Ops
Cultural shift: decisions based on evidence, not intuition
Workflow refinement based on analytics outputs
Check-in anchor & Governance model
Are optimization recommendations implemented or ignored?
Are scheduling patterns shifting based on data?
Do planners/dispatchers use dashboards, not gut feeling?
Month-over-month improvements are measurable
Q3: Analytics & optimization
Move from optimization to autonomy.
Maintenance becomes proactive, guided by data, and eventually automatic. Fleet managers stop firefighting and focus on cost, uptime, and growth.
Milestone
Predictive maintenance models, digital twin simulations, and autonomous scheduling.
ROI payback
12-24 months
Recommended tech stack
Digital twin platform: Real-time representation of vehicle, vessel, or warehouse asset state
ML prognostics: Remaining useful life (RUL), Failure probability forecast
ERP integration: Automated parts ordering and labor allocation
Autonomous orchestration: System creates and schedules maintenance events
ROI metrics to track
Maintenance cost reduction: 20–40%
Downtime reduction: up to 30%
Asset lifecycle extension: +2–3 years
Change management & governance
Executive steering reviews become strategic
Predictive service level agreements with vendors
ESG reporting tied to asset telemetry
Budgeting influenced by predictive analytics
Check-in anchor & Governance model
Are predictive outputs executed without human override?
Are results financially measurable per vehicle/asset?
Do breakdowns become rare exceptions?
Does intervention happen before failure?
Q4: Predictive stage
The aim is to build a predictive layer over the whole fleet – a smart control centre that informs every decision, keeps carbon numbers honest, and turns day-to-day operations into a revenue asset.
Milestone
Four strategic capabilities become integrated into daily operations:
Cross-fleet digital twin across truck, water, and warehouse asset
Autonomous maintenance orchestration and predictive parts ordering
ESG & carbon tracking integration
Data monetisation and SaaS extension
ROI window
Scaling value year-over-year through automation + monetisation
Recommended tech stack
Digital twin platform: Full lifecycle simulation across all fleet asset types
ML-driven orchestration: Automated scheduling, labor planning, and parts procurement
ESG and carbon intelligence layer: Telemetry-based emissions and efficiency scoring
Data exchange and Monetisation APIs: Secure external data sharing and subscription services
ROI metrics to track
Cost reduction and uptime gains become sustained and compounding
OPEX reduction measured as a recurring annual benefit
Verified CO₂ reduction per fleet unit
Revenue generated from external data services
Benchmarking performance vs. other operators in the market
Change management & governance
Enterprise-level governance and predictive-based vendor agreements
Operating model redesign: from “maintenance department” to “operations intelligence”
Incentives and budgeting tied to predictive outcomes and ESG performance
Legal and compliance frameworks for data sharing and monetisation
Check-in anchor & governance models
Are digital-twin simulations used to plan operations and investments?
Are maintenance and parts decisions autonomous without crisis mode?
Is emissions data actively used in business decisions and compliance audits?
Is fleet data delivering measurable commercial value beyond internal use?
Strategic transformation stage
Your fleet’s transformation starting point
Explore each maturity stage and identify where you are right now — from Manual to Autonomous.
Fleet maturity assessment slider
Select the stage that best describes your current operations
MANUAL
You are Manual
Your operations are mostly paper-driven and reactive.
Maintenance happens when something breaks
Lots of spreadsheets, calls, WhatsApp messages
No reliable real-time data on vehicles or equipment
Downtime surprises are common
Planning is manual and based on experience
Data Quality
Real-time accuracy and visibility
Telemetry Coverage
Sensors and asset monitoring
System Automation
Tools and workflows connected
Maintenance Intelligence
From reactive to predictive decisions
Your recommended roadmap
Your self-assessment results indicate where your organization stands today. The roadmap below highlights the phases that will help you strengthen data quality, automation, and predictive capability across your fleet.
Q1: Data foundation
Objective is to create a single source of truth for fleet and asset health. The objective is to move away from guesswork and scattered files toward real telemetry, centralized maintenance records, and measure-ready KPIs.
This phase does not optimize or automate; it establishes visibility and removes operational blind spots.
Milestone
Establish the telematics and CMMS data foundation, sensor rollout.
ROI window
6-9 months*
*Value comes from transparency; savings begin only in Q2.
Recommended tech stack
IoT & Sensor layer: Telematics units for GPS, engine health, vibration, temperature, and load.
Connectivity & Data pipeline: MQTT/REST API ingestion, secure gateway, stable data stream.
Integration layer: First connection to CMMS for asset identity, maintenance events, meter/engine hours.
Data Lake / warehouse: Central storage for historical and real-time fleet and asset telemetry.
ROI metrics to track
% of fleet with telemetry installed
Baseline idle hours
Baseline maintenance cost per vehicle / per equipment unit
Baseline downtime events
Change management & governance
Create steering committee (Operations, Maintenance, IT, Finance)
Define data ownership and data governance policies
Train technicians and dispatchers on digital entries and CMMS usage
Begin reducing paper, spreadsheets, scattered messages, and manual logs
Check-in anchor & Governance model
>70% of vehicles/assets with working telemetry
CMMS is receiving maintenance events consistently
A single source of truth exists for fleet operational data
Identify gaps before moving into automation
Q2: Preventive layer
The aim is to stop putting out fires – alerts flag issues early, service windows are booked in advance, and technicians know what’s coming through the workshop next week.
The entire maintenance loop becomes predictable: fewer crisis repairs, fewer last-minute breakdowns, less stress on maintenance teams.
Milestone
Rule-based alerts and automated maintenance scheduling.
ROI window
9-12 months
Recommended tech stack
Alert engine (rules-based): Time, engine hours, vibration threshold, temperature deviations
Visualization: Basic dashboards surfacing alerts and upcoming work orders
CMMS integration: Telematics → maintenance scheduling → work orders
Workflow automation: Auto-generated maintenance tasks triggered by telemetry inputs
ROI metrics to track
Preventive tasks as % of all tasks
MTBF (Mean Time Between Failures)
Emergency intervention frequency
Maintenance cost per operating unit
Change management & governance
Update SOPs from reactive → preventive
Train maintenance teams to prioritize preventive work orders
Weekly or bi-weekly KPI cadence
Define who owns alerts, who schedules tasks, and who closes them
Check-in anchor & Governance model
Noise ratio reduction (false positives under control)
Closed loop validated: ALERT → TASK → EXECUTION → CLOSED
Preventive coverage 60–80%
Stakeholders aligned on which alerts matter
Q3: Analytics & optimization
Move from mechanics to strategy. Use your own operational data to optimize maintenance windows, improve utilization, and minimize wasted time, fuel, labor, and asset life.
This is where optimization begins: fewer surprises, fewer inefficiencies, higher stability.
Milestone
Deploy analytics engine, optimize maintenance windows, and utilization.
ROI window
12-15 months
Recommended tech stack
Analytics engine: Utilization clustering, component risk scoring, trend detection
Visualization platform: Heatmaps, fleet health, busy vs idle segments
Unified integration layer: CMMS + WMS/TMS + Telematics merged into one analytics environment
ROI metrics to track
Downtime reduction % (vs baseline)
Utilization improvement %
Idle time reduction %
Maintenance cost trend downward
Change management & governance
Regular KPI review sessions
Cross-department ownership: Fleet, Maintenance, Dispatch, Warehouse Ops
Cultural shift: decisions based on evidence, not intuition
Workflow refinement based on analytics outputs
Check-in anchor & Governance model
Are optimization recommendations implemented or ignored?
Are scheduling patterns shifting based on data?
Do planners/dispatchers use dashboards, not gut feeling?
Month-over-month improvements are measurable
Q4: Predictive stage
Move from optimization to autonomy.
Maintenance becomes proactive, guided by data, and eventually automatic. Fleet managers stop firefighting and focus on cost, uptime, and growth.
Milestone
Predictive maintenance models, digital twin simulations, and autonomous scheduling.
ROI payback
12-24 months
Recommended tech stack
Digital twin platform: Real-time representation of vehicle, vessel, or warehouse asset state
ML prognostics: Remaining useful life (RUL), Failure probability forecast
ERP integration: Automated parts ordering and labor allocation
Autonomous orchestration: System creates and schedules maintenance events
ROI metrics to track
Maintenance cost reduction: 20–40%
Downtime reduction: up to 30%
Asset lifecycle extension: +2–3 years
Change management & governance
Executive steering reviews become strategic
Predictive service level agreements with vendors
ESG reporting tied to asset telemetry
Budgeting influenced by predictive analytics
Check-in anchor & Governance model
Are predictive outputs executed without human override?
Are results financially measurable per vehicle/asset?
Do breakdowns become rare exceptions?
Does intervention happen before failure?
Strategic transformation stage
The aim is to build a predictive layer over the whole fleet – a smart control centre that informs every decision, keeps carbon numbers honest, and turns day-to-day operations into a revenue asset.
Milestone
Four strategic capabilities become integrated into daily operations:
Cross-fleet digital twin across truck, water, and warehouse asset
Autonomous maintenance orchestration and predictive parts ordering
ESG & carbon tracking integration
Data monetisation and SaaS extension
ROI window
Scaling value year-over-year through automation + monetisation
Recommended tech stack
Digital twin platform: Full lifecycle simulation across all fleet asset types
ML-driven orchestration: Automated scheduling, labor planning, and parts procurement
ESG and carbon intelligence layer: Telemetry-based emissions and efficiency scoring
Data exchange and Monetisation APIs: Secure external data sharing and subscription services
ROI metrics to track
Cost reduction and uptime gains become sustained and compounding
OPEX reduction measured as a recurring annual benefit
Verified CO₂ reduction per fleet unit
Revenue generated from external data services
Benchmarking performance vs. other operators in the market
Change management & governance
Enterprise-level governance and predictive-based vendor agreements
Operating model redesign: from “maintenance department” to “operations intelligence”
Incentives and budgeting tied to predictive outcomes and ESG performance
Legal and compliance frameworks for data sharing and monetisation
Check-in anchor & governance models
Are digital-twin simulations used to plan operations and investments?
Are maintenance and parts decisions autonomous without crisis mode?
Is emissions data actively used in business decisions and compliance audits?
Is fleet data delivering measurable commercial value beyond internal use?
Join our senior Mobility & Logistics experts for a free 1-hour strategy session. Walk away with a tailored priority action plan and specific next steps, built from real industry data and your own systems.
Join our senior Mobility & Logistics experts for a free 1-hour strategy session.