Industrial IoT is only profitable if it transforms field data into operational decisions: fewer unplanned outages, fewer trips, less energy waste, more targeted maintenance, better service quality, and faster response times. The ROI of an industrial IoT project is therefore not calculated solely based on the cost of sensors or gateways. It is derived from a comprehensive business case, measured before and after deployment, with explicit assumptions.
The Problem
Many industrial IoT projects fail economically even though they work from a technical standpoint. Sensors are sending data, dashboards are in place, and connectivity is established, but scaling up is stalled because the value created has not been sufficiently demonstrated.
These causes are common.
-
The business case accurately identifies the deployment costs but underestimates the operational benefits: avoided downtime, better-planned maintenance, eliminated travel, and optimized energy use.
-
Success metrics are not defined before the project begins, making it impossible to compare results to a baseline.
-
The evaluation timeframe is too short: an industrial IoT project often pays for itself over several years, not just a few months.
-
The debate focuses on the price of gateways, sensors, or subscriptions, even though the total cost of ownership includes installation, support, operation, cybersecurity, training, and integration.
-
Profits are distributed among several teams: maintenance, production, energy, quality, IT, industrial management, and customer service.
-
Causality is difficult to prove if other actions are being taken at the same time: a new maintenance structure, equipment replacement, a change in supplier, or process optimization.
-
Successful pilot programs are not rolled out on a wider scale due to the lack of a compelling business model for management.
-
The value of data is underestimated: data collected for maintenance purposes can also be used for energy management, quality control, compliance, or customer service.
-
The hidden costs of a lack of monitoring are rarely attributed to the absence of IoT: energy losses, unnecessary travel, excess inventory, delays in diagnostics, customer penalties, or time spent troubleshooting a failure.
A credible ROI must therefore link each piece of data collected to a decision, and then each decision to a measurable benefit.
Our Approach
The ROI of an industrial IoT project is broken down into several value streams. Each stream must be calculated using a simple formula, documented assumptions, and baseline data. Eziwan facilitates this measurement through field data collection, availability reports, remote access logs, alerts, and monitoring metrics.
The main value streams are:
-
Reduction in unplanned downtime: hours of downtime avoided multiplied by the hourly cost of downtime.
-
Condition-based or predictive maintenance: fewer emergency repairs, better planning, and replacement of parts at the right time.
-
Travel avoided: diagnostics and actions performed remotely instead of on-site visits.
-
Energy optimization: reducing energy consumption through monitoring, detecting deviations, and adjusting settings.
-
Quality and Compliance: improved traceability, fewer nonconformities, automatic verification, and alerts before limits are exceeded.
-
Team productivity: less time spent diagnosing issues, manually collecting data, or managing recurring incidents.
-
Value of data: new services, connected maintenance contracts, customer reporting, or multi-site management.
A Simple Formula for Industrial IoT ROI
ROI compares the net gains generated by the project to the total cost incurred. The simplest formula is as follows.
ROI = (gains_cumules - cout_total) / cout_total
For an annual calculation:
ROI_annuel = (gains_annuels - couts_annuels) / couts_annuels
For a return on investment timeframe:
payback = investissement_initial / gains_nets_annuels
A reliable ROI must also distinguish between:
-
CAPEX: equipment, installation, cabling, sensors, gateways, initial integration.
-
OPEX: subscriptions, connectivity, monitoring, support, maintenance, licenses, cybersecurity.
-
Direct benefits: avoided downtime, energy savings, eliminated travel, and reduced maintenance.
-
Indirect benefits: customer satisfaction, compliance, risk reduction, and better planning.
ROI Measurement Architecture
Calculating ROI depends on the quality of the data collected. An effective architecture must link field measurements to business systems: CMMS, monitoring, ERP, energy, quality, or reporting.
ROI becomes justifiable when technical indicators are linked to the company’s actual costs.
Establish a baseline before the project begins
The baseline is the reference point prior to deployment. Without a baseline, it becomes difficult to prove that the IoT generated the observed benefit.
The data to be collected before the project are:
| Category | Baseline to be measured | Possible source |
|---|---|---|
| Production | Unplanned downtime hours | MES, SCADA, ERP, production reports |
| Maintenance | Number of corrective actions | CMMS |
| Travel | Number of trips and average duration | Scheduling, expense reports, on-call logs |
| Energy | Consumption by site or line | Invoices, meters, monitoring |
| Quality | Non-conformities and scrap | QMS, ERP, quality reports |
| Support | Average diagnosis time | Tickets, CMMS, on-call logs |
| Availability | Equipment or site uptime | Monitoring, SCADA |
| Data | Frequency of available measurements | Existing exports or manual readings |
The baseline must cover a representative period. If the activity is seasonal, avoid comparing a slow month with a month of high production.
The 5 Main Value Streams
Reducing Unplanned Downtime
Unplanned downtime often represents the largest value stream. Early warnings, connectivity monitoring, condition-based maintenance, or remote access can reduce the duration or frequency of downtime.
Formula:
gain_arrets = heures_arret_evitees * cout_horaire_arret
The hourly downtime cost should be calculated conservatively. It may include:
-
Lost production.
-
Tied-up labor.
-
Rejected material.
-
Customer delays.
-
Restart and cleanup.
-
Contractual penalties.
-
Power consumption in idle mode.
When this cost is unknown, it is best to develop three scenarios: conservative, realistic, and ambitious.
Condition-Based and Predictive Maintenance
IoT maintenance creates value by transforming emergency repairs into scheduled maintenance. The benefits come from several factors: fewer breakdowns, fewer on-call duties, fewer parts replaced prematurely, better preparation, and less emergency outsourcing.
Simplified formula:
gain_maintenance = interventions_correctives_evitees * cout_moyen_correctif
+ interventions_planifiees_optimisees * gain_par_intervention
- surcout_capteurs_et_analyse
The relevant data comes from the CMMS: type of service call, duration, urgency, part replaced, cause, equipment, cost, and date.
Trips Avoided
Secure remote access makes it possible to diagnose a PLC, HMI, SCADA system, router, or Modbus device without having to travel on a routine basis. Not all travel is eliminated, but much of it is better targeted.
Formula:
gain_deplacements = deplacements_evites * cout_moyen_deplacement
The average cost of a trip may include:
-
Travel time.
-
Technician time.
-
Mileage allowance.
-
Accommodations, if needed.
-
On-call duty.
-
Downtime before arrival.
-
Enlisting the help of a rare expert.
Eziwan’s remote access logs make it possible to objectively document work performed without on-site presence.
Energy Optimization
Industrial IoT can reduce energy consumption through continuous monitoring: detection of deviations, equipment left running, compressed air, pumping, HVAC, oversized motors, improper reactive power compensation, or suboptimal process parameters.
Formula:
gain_energie = consommation_reference - consommation_apres_optimisation
Then:
gain_financier = kWh_economises * prix_kWh
The results must be adjusted for the level of activity. A decrease in consumption due to a decrease in production does not constitute an energy savings.
Data Value
The value of the data is harder to quantify, but it can be significant. IoT data can create new services, improve maintenance contracts, automate reporting, reduce disputes, or speed up quality analyses.
Example values:
-
Automatic customer reporting.
-
Maintenance contracts that include monitoring.
-
Proof of temperature, energy, or availability.
-
Faster diagnostics for support.
-
Multi-site optimization.
-
Product improvement for an OEM.
-
Reduced time spent on manual reporting.
This value should be estimated using conservative assumptions and revised after deployment.
Comprehensive Calculation Model
An ROI model should aggregate gains and costs over a given period, ideally three to five years.
hypotheses:
periode: 3_ans
sites: 50
cout_solution_annuel: 30000
investissement_initial: 60000
gains_annuels:
arrets_evites:
heures: 20
cout_horaire: 5000
gain: 100000
deplacements_evites:
nombre: 120
cout_unitaire: 350
gain: 42000
energie:
kwh_economises: 80000
prix_kwh: 0.14
gain: 11200
maintenance:
interventions_urgence_evitees: 15
gain_unitaire: 900
gain: 13500
calcul:
gains_annuels_total: 166700
couts_annuels_total: 30000
gain_net_annuel: 136700
payback_mois: 5.3
These figures are provided solely as an example of the method. They should be replaced with the company’s actual data.
Total Cost of Ownership
TCO, or total cost of ownership, helps prevent underestimating the project. It includes both visible costs and operating costs.
| Cost Item | Examples |
|---|---|
| Hardware | Gateways, sensors, antennas, power supplies, enclosures |
| Installation | Cabling, installation, testing, relocation, commissioning |
| Connectivity | SIM cards, data plans, VPN, satellite if necessary |
| Platform | Cloud subscription, storage, users, reports |
| Integration | CMMS, ERP, SCADA, APIs, dashboards |
| Operations | Support, monitoring, updates, administration |
| Cybersecurity | Certificates, segmentation, logs, audits |
| Training | Maintenance, operations, support, administrators |
| Expansion | New sites, new sensors, new rules |
A credible ROI does not hide these costs. It shows that the operational gains offset them.
Conservative, Realistic, and Ambitious Scenarios
A single ROI figure creates a false sense of certainty. It is better to present three scenarios.
| Assumption | Conservative | Realistic | Ambitious |
|---|---|---|---|
| Avoided Outages | Minor Reduction | Moderate Reduction on Critical Equipment | Reduction Extended to Entire Fleet |
| Avoided Travel | Simple Diagnostics | Routine Diagnostics and Configuration | Frequent Remote Expert Support |
| Energy | Limited gains | Optimization of identified deviations | Advanced energy management |
| Team Adoption | Partial | Regular use | Integrated into processes |
| CMMS Integration | Manual | Automated tickets | Comprehensive feedback |
This method helps you make a decision even when there is a reasonable degree of uncertainty.
Sample Calculation by Sector
The examples below are calculation models, not guarantees of results. They are intended to help structure the business case using data specific to each organization.
| Sector | Dominant value stream | Calculation example |
|---|---|---|
| Water and wastewater | Travel and remote stations | Travel avoided + pump alerts + energy |
| Electrical substations | Faults and grid quality | Incidents avoided + reduced service rounds + compliance |
| Manufacturing | Line downtime | Avoided downtime hours + condition-based maintenance |
| Retail and refrigeration | Merchandise losses | Temperature alerts + refrigeration maintenance |
| Machine OEMs | Connected services | Remote support + new contracts |
| Agriculture | Water and Pumping | Optimized irrigation + avoided trips |
| Smart Buildings | Energy | HVAC, lighting, schedules, deviations |
| Refrigerated Logistics | Temperature Compliance | Avoided non-compliance + traceability |
The same IoT device can support multiple value streams. It is often this combination that leads to a strong ROI.
Water Supply and Sanitation
In the water system, the sites are scattered throughout the network: pumping stations, reservoirs, lift stations, pressure sensors, meters, and remote management cabinets. The benefits often come from reduced travel, anomaly detection, and energy optimization of the pumps.
Variables to be measured:
-
Number of inspection visits.
-
Pump failures and emergency repairs.
-
Power consumption of the stations.
-
Average time to detect an alarm.
-
Recovery times following an incident.
-
Leaks or flow irregularities.
A sample calculation might combine the trips avoided and the reduction in emergency response time. The ROI will depend heavily on the distance between sites and the criticality of the structures.
Power Grids and Substations
In substations, industrial IoT is used to monitor network analyzers, protection relays, temperature, power quality, and communication availability. The benefits include incident prevention, reduced inspection rounds, and improved operational data quality.
Variables to be measured:
-
Number of inspection rounds.
-
Electrical faults detected before an incident occurred.
-
Diagnostic time after triggering.
-
Availability of measurements.
-
Cost of a transformer or switchgear incident.
-
Time spent preparing reports.
Data stored in InfluxDB, Grafana, or a similar platform can also facilitate post-incident analysis.
Manufacturing Industry
In a factory, ROI is often heavily influenced by production line downtime. A single breakdown that is prevented or minimized can finance a significant portion of an IoT project, especially on high-speed production lines.
Variables to be measured:
-
Hourly downtime cost per line.
-
Number of unscheduled stops.
-
Average downtime.
-
Equipment responsible for the malfunctions.
-
Diagnostic time.
-
Remote procedures.
-
Scrap related to restarts.
The key is to focus first on critical equipment: rotating machinery, conveyors, compressors, utilities, power, industrial refrigeration, PLCs, and OT network connections.
Retail and Multi-Location Sites
In the retail sector, value comes from multi-site monitoring: refrigeration, energy, availability, technical alarms, and reduced service calls. The losses avoided can be significant when refrigeration equipment is critical.
Variables to be measured:
-
Cold spells.
-
Average value of merchandise on display.
-
Time to detection.
-
Maintenance trips.
-
Energy consumption per retail location.
-
Breakdown history by equipment.
An early warning is only valuable if it triggers a specific action: a call, a ticket, an intervention, a local directive, or a change in procedure.
System Integrators and Maintenance Providers
For a system integrator, ROI is also measured in terms of operational capacity. The same team can manage more sites if it has remote access, centralized monitoring, and automated reporting.
Variables to be measured:
-
Number of sites managed per technician.
-
Average time to diagnosis.
-
Travel expenses billed or absorbed.
-
Number of remote interventions.
-
SLA compliance rate.
-
Value of supervision contracts.
-
Customer retention rate.
Here, the IoT becomes a driver of profit margins and a source of competitive differentiation.
Smart Agriculture and Irrigation
In agriculture, the benefits come mainly from savings in water, energy, and time spent in the field. Sites are often remote, and pumping or irrigation equipment can be difficult to monitor manually.
Variables to be measured:
-
Water consumption.
-
Power consumption during pumping.
-
Number of inspection trips.
-
Pump failures.
-
Yield or losses related to irrigation.
-
Pressure, level, or flow alerts.
The ROI must take seasonality and weather conditions into account, as they have a significant impact on water needs.
Commercial Buildings and Smart Buildings
In buildings, the IoT can improve energy monitoring, HVAC maintenance, technical alarms, and service quality. The benefits are often gradual but long-lasting.
Variables to be measured:
-
Electrical and thermal power consumption.
-
Actual hours of occupancy.
-
Temperature deviations.
-
HVAC operating time.
-
Maintenance trips.
-
Resident complaints or tickets.
-
Potential regulatory non-compliance.
ROI must be adjusted for weather conditions, occupancy, and changes in building use.
Refrigerated Transportation and Logistics
In the cold chain, the benefits include compliance, reduced losses, and traceability. IoT data also serves as evidence.
Variables to be measured:
-
Temperature nonconformities.
-
Average value of the goods on display.
-
Time to alert.
-
Number of manual checks.
-
Customer disputes.
-
Time required to produce HACCP documentation.
-
Availability of refrigeration units.
Value comes not only from preventing a failure, but also from the ability to prove that the temperature remained within the expected range.
Measuring Causality
To avoid debates, it is important to establish a method for measuring ROI right from the pilot phase. The goal is to compare comparable periods or groups.
Useful methods:
-
Compare "before" and "after" on the same websites.
-
If possible, include an unequipped control group during the pilot study.
-
Normalize the data by production volume, weather, season, or number of operating hours.
-
Document any other changes that occurred at the same time.
-
Use Eziwan logs to link an alert, a remote connection, or an action to an event.
-
Validate results with the business teams, not just using a dashboard.
ROI doesn’t have to be perfect to be useful. It must be honest, traceable, and robust enough to support decision-making.
Metrics to Monitor After Deployment
| Metric | Why Track It |
|---|---|
| Unplanned downtime | Measure production gains |
| Average diagnostic time | Assess responsiveness |
| Remote interventions | Quantify remote maintenance |
| Avoided field trips | Calculate field cost savings |
| Confirmed alerts | Measure alert quality |
| False positives | Improve thresholds |
| Energy consumption | Track energy savings |
| Site availability | Measure service continuity |
| CMMS tickets created | Verify operational usage |
| User adoption | Confirm that the solution is being used |
ROI can only be sustained over time if these metrics are reviewed regularly.
Common Mistakes in Calculating ROI
Forget About Internal Costs
The time spent by project, maintenance, IT, and production teams has value. It must be factored into the TCO.
Overestimating the gains right from the pilot phase
A pilot project at a few sites may yield good results, but scaling up introduces new challenges: training, support, on-site variability, and missing data.
Counting the Same Winnings Twice
An avoided stoppage and an avoided trip may be related to the same event. You should avoid counting them twice if one is already included in the other.
Ignoring on-the-ground adoption
An unaddressed alert does not create value. ROI depends on procedures, training, and team confidence.
Focusing Solely on Technology
The availability of sensors or the cloud is not enough. We need to evaluate the decisions made based on the data.
How Eziwan Helps Demonstrate ROI
| ROI Need | Eziwan’s Solution | Measurable Value |
|---|---|---|
| Measure Downtime | Monitoring and Alerts | Reduced Detection Time |
| Reduce Travel | Secure Remote Access | Interventions Without Travel |
| Optimize Maintenance | Field Data and Trends | Better-Planned Interventions |
| Track energy | Meter and sensor data collection | Drifts detected |
| Prove usage | Logs and reports | Action traceability |
| Deploy quickly | Gateway and ZTP | Reduced rollout costs |
| Manage multiple sites | Eziwan Cloud | Centralized management |
| Integrate CMMS | APIs and webhooks | Operational processes |
Eziwan provides the data needed to move from an estimated ROI to an ROI tracked over time: events, availability, access, alerts, usage, reports, and business integrations.
Example of a 6-Step Process
This approach avoids starting with the technology. It begins with measurable losses and works backward to identify the necessary data.
IoT Business Case Checklist
| Question | Why is this important? |
|---|---|
| What business problem does the project solve? | Avoid a showcase project |
| What baseline is available? | Measure before and after |
| What benefits are quantifiable? | Calculate the ROI |
| What costs are included in the TCO? | Avoid surprises |
| Who captures the value? | Align budgets |
| What metrics will be tracked? | Prove the results |
| What payback period is used? | Compare accurately |
| What conservative scenario is acceptable? | Make decisions despite uncertainty |
| How will teams use alerts? | Ensure adoption |
| How will the pilot be scaled up? | Prepare for scaling |
This checklist must be completed before deployment and then reviewed after the pilot.
Conclusion
The ROI of industrial IoT is calculated using a simple yet rigorous method: start with actual losses, establish a baseline, link each data point to a decision, measure the gains, and factor in all costs. Savings rarely come from a single source. They accumulate through avoided downtime, better-planned maintenance, reduced travel, optimized energy use, simplified compliance, and new services.
Eziwan helps drive this ROI by connecting field equipment, securing remote access, centralizing data, generating alerts, and producing actionable reports. A profitable industrial IoT project isn’t the one that collects the most data, but the one that transforms the right data into measurable actions.
Further Reading
- Calculate Your ROI — use our calculator to estimate the return on investment for your IoT project
- Industrial Remote Diagnostics — one of the key drivers of ROI: fewer trips, less downtime
- IIoT Predictive Maintenance — reduce corrective maintenance costs through proactive planning
- Industrial Solutions — explore all use cases with concrete ROI examples
- Pricing — compare offers to build your IoT business case