Introduction​

The "Investimenti Sostenibili 4.0" grant opens on 6 October 2026, offering southern Italian SMEs up to 75% funding—35% as a non-repayable grant and 40% as a zero-interest loan—for projects that combine Industry 4.0 technologies with measurable energy savings. The call prioritises applications that demonstrate a minimum 5% annual energy reduction and allocate the majority of costs to 4.0-enabling technologies, such as IoT sensors, real-time monitoring, and data analytics.

This article explains how to design the IoT component of your project so it meets both the technical requirements of the grant and the audit criteria of the technical verification. We focus on the data infrastructure that turns raw telemetry into documented savings, using Mative’s Things Connector, thingsboard-gateway, and Synapsis Analysis.

Why the grant demands IoT—and what “preponderant” really means​

The decree (DM 18 March 2026) states that “the amount of expenditure related to 4.0-enabling technologies must be preponderant compared to the total eligible costs of the programme.” Preponderant means more than 50%.

The same decree lists the mandatory 5% annual energy saving (measured against the previous year’s consumption at the same production unit). Without IoT, you cannot collect, store, or certify the data that prove the saving. Therefore, the IoT layer is not an optional add-on; it is the core of the grant application.

1. Mapping the IoT requirements to the grant’s technical criteria​

The grant’s Allegato n. 3 (energy efficiency measures) and Allegato n. 1 (4.0 technologies) define the eligible technologies. Below is a checklist that translates them into IoT components:

• Sensors and edge devices: temperature, pressure, flow, power (kWh), vibration, and production counters.
• Gateways: industrial-grade devices that collect, buffer, and forward data to the cloud.
• Connectivity: secure, low-latency protocols (MQTT, HTTPS, TLS).
• Cloud ingestion: scalable, timestamped data ingestion.
• Time-series database: storage optimised for high-frequency telemetry.
• Analytics engine: real-time dashboards, anomaly detection, and energy KPIs.
• ERP integration: bidirectional data flow between shop-floor and enterprise systems.

Each of these components must be budgeted, described in the application, and traceable during the audit.

2. The data journey: from machine to grant compliance​

Below is the reference architecture that Mative implements for grant-funded projects.

┌─────────────┐    ┌─────────────┐    ┌───────────────────────┐
│             │    │             │    │                       │
│  Machine    ├───►│  Things     ├───►│  Mative Cloud         │
│  (PLC,      │    │  Connector  │    │  (Synapsis IoT +      │
│  OPC-UA,    │    │  (Synapsis  │    │  Synapsis Analysis)   │
│  Modbus)    │    │  ISBC GW)   │    │                       │
└─────────────┘    └─────────────┘    └──────────┬────────────┘
                                                   │
                                                   ▼
┌───────────────────────────────────────────────────────────┐
│                                                           │
│  1. Data Collection: 100 Hz vibration, 1 Hz power,        │
│     1/min production counters                             │
│  2. Data Transport: MQTT over TLS, 99.9% uptime SLA       │
│  3. Data Processing: payload conversion, timestamping     │
│  4. Data Storage: time-series DB, 13 months retention     │
│  5. Data Analysis: real-time dashboards, anomaly alerts   │
│  6. ERP Integration: bidirectional OPC-UA ↔ ERP           │
│                                                           │
└───────────────────────────────────────────────────────────┘

3. Step-by-step: designing the IoT layer for the grant​

3.1. Define the KPIs that prove the 5% saving​

The grant requires “a saving of at least 5% compared to the previous year’s consumption.” You must:

• Baseline: Measure the same production unit for 12 months before the project starts.
• Target: Set a 5% reduction in kWh per unit produced (or per hour of operation).
• Metric: Use Synapsis Analysis to calculate specific energy consumption (SEC) = kWh / unit produced.

3.2. Select the sensors and protocols​

• Power meters: Class 1 MID-certified meters (IEC 62053-21) for each machine or line.
• Production counters: Optical or magnetic sensors on conveyors.
• Environmental: Temperature, humidity, and pressure (if relevant to energy).
• Protocols: OPC-UA for PLCs, Modbus TCP for meters, CAN Bus for automotive lines.

3.3. Deploy the gateway​

• Things Connector (Synapsis ISBC Gateway) collects data from all sensors.
• Buffering: Stores data locally for 7 days in case of connectivity loss.
• Protocol conversion: Converts OPC-UA/Modbus to MQTT payloads.
• Security: TLS 1.3 encryption, device authentication via X.509 certificates.

3.4. Connect to Mative Cloud​

• Synapsis IoT provides secure endpoints for MQTT ingestion.
• Uptime: 99.9% SLA, monitored 24/7.
• Scalability: Handles up to 10,000 devices per tenant.

3.5. Store and analyse the data​

• Synapsis Analysis stores telemetry in a time-series database optimised for high-frequency data.
• Retention: 13 months minimum (required for the 5% year-on-year comparison).
• Dashboards: Real-time energy KPIs, production counters, and anomaly alerts.
• Anomaly detection: Machine learning models flag deviations from expected consumption.

3.6. Integrate with the ERP​

• Synapsis IoT exposes REST APIs that push production and energy data to the ERP.
• Bidirectional flow: The ERP can send production orders back to the shop-floor.

3.7. Document the chain of custody​

The grant’s technical verification will audit:

• Sensor calibration certificates (ISO 17025 or equivalent).
• Gateway logs showing data continuity.
• Cloud timestamps proving data integrity.
• Dashboards showing the 5% saving calculation.

4. Budgeting the IoT layer: how to stay “preponderant”​

Below is a sample budget breakdown for a project.

The IoT layer is 53% of the total, meeting the “preponderant” requirement.

5. Energy analytics: turning data into the 5% saving​

The grant’s Allegato n. 3 lists the eligible energy efficiency measures. Below are the most common ones and how Synapsis Analysis supports them:

• Variable speed drives (VSD): Monitor power draw before/after VSD installation.
• LED lighting: Measure kWh reduction in lighting circuits.
• Heat recovery: Track recovered kWh from waste heat.
• Predictive maintenance: Reduce unplanned downtime, which increases energy waste.

Synapsis Analysis provides:

• Real-time dashboards showing kWh per machine, per shift, per product.
• Anomaly detection that flags energy spikes caused by misaligned machines.
• Automated reports that document the 5% saving for the technical verification.

6. Common pitfalls and how to avoid them​

• Pitfall 1: IoT layer below 50%
  • Solution: Budget sensors, gateways, and cloud services first; then add machinery.
• Pitfall 2: No baseline measurement
  • Solution: Start collecting data 12 months before the project starts.
• Pitfall 3: Data gaps during the audit period
  • Solution: Use Things Connector’s local buffering and Mative Cloud’s 99.9% uptime SLA.
• Pitfall 4: No ERP integration
  • Solution: Use Synapsis IoT’s bidirectional APIs to link shop-floor and enterprise data.

7. Timeline: from application to audit​

8. Next steps: preparing your application​

1. Start the baseline measurement now; you need 12 months of data.
2. Select the sensors and gateways that match your machines.
3. Contact Mative to size the Things Connector and Mative Cloud subscription.
4. Build the dashboards in Synapsis Analysis that will prove the 5% saving.
5. Compile the application with the IoT architecture diagram and budget.

Conclusion​

The "Investimenti Sostenibili 4.0" grant is not just about buying new machinery; it is about building a data infrastructure that proves energy savings and meets the 4.0 criteria. The IoT layer—sensors, gateways, connectivity, cloud processing, and analytics—must be preponderant (over 50% of costs) and auditable.

By using Mative’s Things Connector, thingsboard-gateway, and Synapsis Analysis, you transform raw telemetry into documented savings, strengthening your application and ensuring compliance during the technical verification.

The window opens on 6 October 2026; funds are allocated on a first-come, first-served basis. Start designing your IoT layer today to secure your share of the €447.6 million budget.

📩 Ready to build your grant-compliant IoT project? Contact Mative for a tailored architecture review.

Learn more​

• Decreto attuativo Transizione 5.0
• Guide to Industry 4.0 Bonuses
• Industrial IoT and solutions by Mative
• What is Synapsis Analysis
• Mative energy and sustainability solutions

SOS Panic Button: Technology Protecting Drivers, Operators and Citizens​

Safety in Public Transportation Has Become a Priority​

Over the last decade, public transportation operators have faced increasing challenges related to staff and passenger safety.

Driver assaults, onboard emergencies, medical incidents and security threats require faster response times and more effective coordination between vehicles, control centers and emergency services.

Modern IoT and AIoT technologies provide new opportunities to improve safety and operational resilience.

Among these solutions, the SOS Panic Button developed by Mative offers an advanced emergency management platform designed for public transportation companies, mobility operators, public administrations and organizations requiring reliable emergency response systems.

What is the SOS Panic Button?​

The SOS Panic Button is an intelligent emergency system installed onboard vehicles that allows operators to request immediate assistance through a dedicated emergency button.

Once activated, the system can:

• automatically call predefined emergency contacts;
• transmit real-time GPS location;
• identify the involved vehicle;
• notify the operations center;
• trigger emergency management workflows.

The objective is to reduce response times and provide emergency coordinators with immediate situational awareness.

Integration with the European Emergency Number 112​

One of the most valuable features of the platform is its ability to interoperate with emergency response infrastructures associated with the European Emergency Number (112).

Through integration with "Where ARE U" services and authorized emergency communication interfaces, the platform can securely share real-time location information during critical events.

This enables:

• improved location accuracy;
• faster emergency response;
• real-time situational updates;
• better coordination between transportation operators and emergency services.

Lone Worker Protection Devices​

The SOS Panic Button ecosystem can also be extended through wearable Lone Worker protection devices.

These devices are particularly suitable for:

• field technicians;
• maintenance personnel;
• security operators;
• utility workers;
• isolated workers.

Equipped with GPS positioning, rechargeable batteries and intelligent motion sensors, they can automatically detect potentially dangerous situations.

Man Down Detection​

The device detects falls or abnormal body positions and automatically generates an emergency alert.

No Movement Detection​

If prolonged inactivity is detected, the system can identify potential medical emergencies or incapacitation and initiate an automatic alarm procedure.

AMAT Palermo Case Study​

A significant reference project is represented by AMAT Palermo, the public transportation operator serving the city of Palermo, Italy.

Over the years, AMAT has demonstrated a strong commitment to employee safety through investments in:

• onboard video surveillance systems;
• anti-aggression driver protection solutions;
• fleet monitoring technologies;
• advanced emergency management tools.

The adoption of the SOS Panic Button platform developed by Mative further strengthens this strategy.

The project demonstrates how digital technologies can provide tangible benefits in protecting transportation personnel while supporting service continuity.

Safety as a Strategic Business Asset​

Today, safety should not be viewed merely as a regulatory requirement.

For transportation companies, municipalities and public service organizations, safety has become a strategic asset capable of improving:

• operational continuity;
• service quality;
• workforce protection;
• risk management;
• corporate reputation;
• organizational resilience.

Real-time monitoring, IoT technologies and emergency integration platforms help organizations create safer environments for both workers and citizens.

The Mative Approach​

Mative develops advanced AIoT solutions for safety, monitoring and intelligent asset management.

The SOS Panic Button ecosystem combines IoT devices, cloud platforms, telemetry, GPS tracking and emergency response technologies into a scalable and interoperable architecture.

Our mission is clear: leverage technology to protect people, accelerate emergency response and improve safety across public and private services.

Learn more at www.mative.ai

Learn more​

• IoT & ML: Energy and Economic Benefits in a Public Transport Company
• AI Accident Reconstruction
• GPS Trackers
• Mative solutions for public transport

AI Accident Reconstruction​

In the high-stakes sectors of car rental and commercial fleet management, there is a critical moment that every operator dreads: receiving an accident alert. In that exact millisecond, complex questions arise: What is the extent of the damage? Who is at fault? Can the vehicle safely continue its journey?

Until recently, the answers relied on rushed paperwork, conflicting driver statements, or complex, raw telemetry logs that took days for specialized engineers to interpret.

Today, Mative is redefining industry standards. We have developed an intelligent architecture capable of transforming invisible vehicle vibrations into a crystal-clear, objective impact report available in real time.

Behind the Scenes: How Mative Technology Works​

Traditional telematics systems rely purely on fixed deceleration thresholds (G-force). This approach frequently generates false positives—hitting a deep pothole or speeding over a speed bump can trigger false alarms, overwhelming fleet managers.

The Mative Cloud ecosystem operates on a radically superior level, structured across three key phases:

1. The Challenge: Decoding Accelerometer Noise​

Every GPS tracking device installed inside a vehicle is equipped with a triaxial accelerometer. This sensor constantly records acceleration forces along three vector axes:

• X-Axis: Longitudinal acceleration (sudden braking and harsh acceleration).
• Y-Axis: Lateral acceleration (sharp cornering, swerving, or sideways impacts).
• Z-Axis: Vertical acceleration (potholes, speed bumps, or road surface changes).

When a collision occurs, the device sends an "impact alert" to the cloud platform, accompanied by a string of raw G-force data. To the human eye, this data is just an unreadable jumble of numbers and erratic graphs. Mative Cloud intervenes by isolating a targeted ±5-minute time window around the event to capture the exact pre- and post-impact kinematics.

2. The Mathematical Brain: Machine Learning for Localization​

This is where our Machine Learning model comes into play: a Random Forest Classifier built with 100 parallel estimators and trained on vast datasets of crash kinematics. The core strength of the model lies in its Feature Engineering stage. Instead of just looking at an isolated G-force peak, Mative's pipeline extracts advanced mathematical indicators from the signal: net magnitude, continuous power (RMS), wave integrals, longitudinal-to-lateral force ratios, and the estimated angle of impact.

By analyzing this structural "energy signature", the algorithm recognizes the physical dynamics of the event within milliseconds, precisely classifying the direction of the blow and mapping the car into physical quadrants (Front, Rear, Left Side, Right Side) with an accuracy exceeding 95%.

3. The Human Voice: LLMs Translate Numbers into Narrative​

Identifying the point of impact with mathematical precision is a major breakthrough, but a raw JSON string or a vector chart isn't immediately helpful to an insurance adjuster or a busy desk operator at a car rental station. It requires an accessible, easy-to-understand explanation.

To bridge this gap, we integrated an LLM (Large Language Model) directly into our reporting pipeline. The LLM acts as the ultimate translator, turning the objective output of the Machine Learning classifier into a natural, logical, and professional narrative. Because it is strictly anchored to the verified mathematical calculations validated by the Random Forest, it eliminates any risk of narrative "hallucinations."

Example of an Automated Mative Report Summary: "Mative Cloud detected a high-energy structural anomaly. Our predictive model classified a severe impact localized on the front-right side of the vehicle. The force vector distribution suggests an oblique collision with a partially static obstacle during a turning maneuver."

A Game-Changer for Car Rental and Fleet Operations​

Why is this technology developed by Mative so vital for vehicle operators?

• Eliminate Hidden Damage: Renters often return vehicles without mentioning minor collisions or structural impacts to lower components (like suspension arms or underbody shielding). With Mative, car rental operators know exactly if and where a vehicle suffered a blow during the rental period, enabling them to address damages with objective, digital proof at check-out.
• Minimize Vehicle Downtime: Knowing the exact side and severity of the impact immediately allows operators to order spare parts and schedule workshop repairs before the car even returns to the yard, optimizing fleet logistics and reducing lost revenue days.
• Fraud Prevention & Legal Protection: In disputed insurance cases, the Mative report provides an unalterable, scientific reconstruction of the crash physics. It is impossible to claim rear-end damage when sensors certify a front-end impact. This accelerates claims resolution and safeguards assets against fraudulent demands.

Telematics is no longer just about tracking physical locations; with Mative, it becomes a contextual understanding of asset health. The future of intelligent vehicle monitoring is here, and it is driven by data.

Learn more​

• Driver Insurance
• Car Rental and Software Solutions
• Anomalies detection
• What are the Large Language Models
• Mative Artificial Intelligence

Mative On-Board Load Weight Measurement Solution: IoT and Angular Sensors for Advanced Logistics​

In modern logistics, transport, and industrial fleet management, accurate measurement of transported payload has evolved from a simple operational choice to a critical business function. Lacking real-time visibility into vehicle load leads to severe operational risks: overloading penalties, premature wear of tires and mechanical components, exponential increases in fuel consumption, compromised road safety, and logistical inefficiencies like under-utilized trips.

Traditionally, the market has addressed this need by installing load cells directly into the vehicle suspension or by relying exclusively on external static weighbridges. Although effective, these solutions involve high structural costs, prolonged vehicle downtime during installation, and significant vulnerability to the mechanical and environmental shocks typical of construction sites or quarries.

However, an alternative approach engineered by Mative proves to be highly pragmatic, robust, and cost-effective: transforming the vehicle's own structure into a natural sensor by leveraging the mechanical micro-deformation of the chassis and suspension system.

The Physical Principle: The Chassis as a Natural Sensor​

From a materials science perspective, every industrial vehicle obeys the laws of linear elasticity (Hooke's Law). When a load is placed on a truck bed, the chassis and suspension system (whether mechanical leaf springs or pneumatic systems) undergo compression and elastic deformation directly proportional to the applied mass.

This vertical deformation translates into a macroscopic alteration of the alignment and relative distance between the fixed chassis (upper frame rails) and the rear axles (unsprung mass).

While traditional systems try to measure vertical force directly (compression), the Mative solution interprets this geometric variation as a relative angular rotation. The heavier the payload, the greater the vertical deflection of the suspension system, and consequently, the wider the geometric angle measured by the mechanical linkage connected to the sensor.

The Technological Solution: Angular Sensor + Mechanical Linkage​

The hardware system developed by Mative stands out for its structural simplicity, minimizing potential mechanical and electrical failure points. It consists of two key components:

1. Industrial-Grade Analog Angular Sensor​

A high-precision rotary Hall-effect or potentiometric sensor is used, enclosed in a rugged, waterproof housing with an IP67/IP69K protection rating (resistant to water, mud, and fine dust). Installed on the vehicle's fixed chassis, it outputs a standardized analog signal (0–5V or 0–10V) featuring high immunity to electromagnetic interference.

2. Linkage and Mechanical Rod Structure​

An articulated metal rod equipped with spherical joints (uniball) connects the wheel axle to the sensor lever. This linkage plays a crucial role:

• Axles (Input): Experience vertical movement due to loading operations and road roughness.
• Connecting Rod (Conversion): Transforms linear vertical displacement into pure rotational movement.
• Sensor (Output): Reads the angular variation in degrees or radians, instantly translating it into electrical voltage.

On-Board Intelligence: Signal Processing and Edge Computing​

The raw analog signal generated by the sensor would be useless without on-board processing and digitization. The electrical voltage is captured by an industrial telematics tracker (IoT Gateway) installed inside the cabin.

The tracker does not merely digitize the signal (via a 10 or 12-bit Analog-to-Digital conversion), but performs complex Edge Computing tasks:

• Dynamic Filtering: On-board software applies moving average algorithms or low-pass filters to eliminate "noise" and sudden oscillations caused by potholes, speed bumps, or braking while driving.
• Data Enrichment: The weight value (or current angle) is sampled and instantly cross-referenced with GPS coordinates, vehicle speed, engine status (ON/OFF), and timestamps.
• Smart Transmission: Data packets are optimized and transmitted to the Cloud infrastructure via cellular networks (4G/5G/LTE-M), ensuring connectivity even in low-coverage areas.

End-to-End IoT Architecture​

The data flow of the Mative solution follows a structured 5-layer IoT architecture, ensuring raw data is successfully turned into actionable insights for the Fleet Manager:

1. Angular Sensor (Edge Hardware): Detects mechanical deformation and generates voltage variation (0-5V / 0-10V).
2. Telematics Tracker (Edge Computing): Acquires the signal, applies stabilization filters, appends geolocational data, and digitizes the packet.
3. Cellular Network (Connectivity): Safely transports data, encrypting it via industrial IoT protocols (e.g., MQTT, CoAP, or HTTPS).
4. Synapsis IoT by Mative (Cloud Data Bridge): Backend layer responsible for massive data ingestion, real-time buffering, and applying the mathematical conversion models (calibration).
5. Mative Cloud (Dashboard & Analytics): The application front-end where the end user views data through intuitive charts, receives real-time alerts on overload attempts, and generates advanced fleet efficiency reports.

The Calibration Process and Linear Accuracy​

Because every vehicle possesses unique structural rigidity and elastic responses, the system's accuracy relies on an initial calibration phase, managed entirely through software algorithms within the Mative cloud:

1. Tare Point (Empty Vehicle): The minimum voltage emitted by the sensor when the vehicle is completely empty is recorded.
2. Known Load Points: Intermediate and full-load weighings are carried out at a certified public weighbridge, storing the corresponding voltage values.
3. Angle-to-Weight Conversion Curve: The cloud algorithm processes these reference points to generate a custom mathematical regression curve for that specific vehicle.

Typical Accuracy: The solution guarantees an accuracy range between 3% and 5% of the maximum payload. This margin of error is ideal for remote logistics monitoring, delivering excellent visibility without the prohibitive costs of commercial-grade certified load cells.

Ideal Application Fields​

This technology delivers its highest value in demanding operational environments where traditional weighing systems would suffer frequent breakdowns:

• Concrete Mixers: Monitoring concrete payload and preventing solid buildup inside the mixing drum.
• Tipper Trucks and Dumpers: Immediate verification during loading operations in quarries to prevent road fines.
• Aggregates and Bulk Transport: Trip optimization to maximize fleet productivity without exceeding legal limits.
• Construction and Municipal Fleets: Tracking the volume of materials or waste handled per job site or operational route.

Competitive Advantages of the Mative Solution​

• Non-Invasive, Quick Installation: Requires no structural modifications to the chassis or suspension (unlike load cells). Installation takes only a few hours, eliminating workshop downtime.
• Reduced Total Cost of Ownership (TCO): Costs a fraction of traditional load cell systems, making large-scale adoption across an entire corporate fleet highly scalable.
• Mechanical Robustness and Resilience: Located outside the direct load path, the sensor is protected from violent shocks caused by heavy materials dropping into the truck bed.
• Native IoT Integration: Data is not isolated on an in-cab display; it is natively shared and integrated with company ERP and management software through Mative Cloud.
• Minimal Maintenance: No hydraulic fluids or components under constant load stress, drastically reducing the need for recalibration or repairs.

Conclusion​

The Mative on-board load weight measurement solution proves that the Industrial Internet of Things (IIoT) must be driven by pragmatism. Instead of overloading vehicles with complex, expensive, and delicate hardware, the smarter approach is to exploit the intrinsic physical properties of the vehicle, shifting accuracy and intelligence to cloud-based software data processing.

Optimizing logistics, protecting corporate assets, and ensuring regulatory compliance has never been this straightforward and efficient.

Learn more​

• Depth Measurement System for Excavators by Mative
• IoT & ML: Energy and economic benefits in a Construction Company
• How RFID Monitoring Works in Garbage Collection Trucks
• Sensors
• Mative solutions for construction and heavy equipment

Revolutionizing Excavation with Data Intelligence: Depth Measurement System for Excavators​

In the earthmoving sector, precision, execution speed, and safety are three essential elements for the competitiveness of any construction site. Even today, many excavation activities still rely on operator experience, manual checks, or external tools that slow down operations and increase margins of error.

From this need comes our new project: an intelligent real-time excavation depth measurement system for excavators, designed to bring practical innovation to the world of heavy machinery.

Why is a depth measurement system needed?​

During excavation, knowing the exact depth reached is essential to:

• build trenches and foundations according to design;
• avoid over-excavation and related extra costs;
• reduce manual verification time;
• improve operator productivity;
• increase site safety;
• digitally record completed work.

Our goal is to transform the excavator into a connected and aware machine, capable of knowing at any moment where the bucket is relative to the working plane.

How the system works​

The solution is based on continuous detection of the position of the main moving elements of the excavator arm. Through a proprietary geometric calculation algorithm, the system reconstructs the arm configuration in real time and determines:

• excavation depth with centimeter-level precision;
• height relative to the machine reference plane;
• bucket operating position;
• operations history;
• machine productivity.

All of this without modifying the machine structure and with fast installation.

Real-time data and cloud telematics​

One of the most innovative aspects of the project is its digital component.

Information collected onboard the machine is automatically sent to the cloud platform, where it can be:

• monitored in real time;
• historically archived;
• displayed through intuitive dashboards;
• exported into reports;
• integrated with management systems or ERP;
• analyzed through productivity KPIs.

This enables site managers, fleet managers, and technical directors to maintain full control over operations.

Concrete benefits for companies and job sites​

1. Greater operational precision​

The operator immediately knows the depth reached, reducing errors and rework.

2. Reduced downtime​

Fewer manual checks and fewer machine stoppages.

3. Improved safety​

Reduced need for personnel on the ground for depth verification.

4. Construction site digitalization​

Excavation becomes a traceable and measurable process.

5. Maintenance and advanced analytics​

Data can be used to evaluate machine usage, work cycles, and predictive maintenance.

A scalable solution​

The system is designed to be adopted on different types of earthmoving machines and can evolve with future features such as:

• 3D excavation maps;
• volumetric calculation of moved material;
• georeferencing of works;
• integration with BIM and digital construction models;
• artificial intelligence for optimization of operating cycles.

The future of excavators is connected​

The construction sector is undergoing a profound digital transformation. Machines are no longer just mechanical equipment, but intelligent nodes in a data network capable of generating value.

With this project, we aim to bring to market a concrete, accessible, and immediately useful technology: a tool capable of improving profitability, control, and precision in every excavation project.

Want to test it on your fleet?​

We are launching pilot programs and collaborations with contractors, rental companies, and operators in the earthmoving sector.

📩 Contact us for a demo or to evaluate integration on your fleet.

Learn more​

• Mative’s solution for on-board load weight measurement
• IoT & ML: Energy and economic benefits in a Construction Company
• Guide to Industry 4.0 Bonuses
• What is Mative Cloud
• Mative solutions for construction and heavy equipment

Introduction​

Electric mobility is expanding at an unprecedented pace. According to the International Energy Agency, by 2030 over 300 million electric vehicles will be in circulation worldwide. This growth demands charging infrastructure that is capillary, reliable — and above all, interoperable.

This is where the Open Charge Point Protocol (OCPP) plays a decisive role: it is the common language that enables charging stations, distribution networks, and management systems to communicate regardless of manufacturer. At Mative, we integrate and manage OCPP within our IoT platform, enabling intelligent monitoring, control, and analytics for EV charging infrastructure.

Origins and Historical Context​

Before OCPP, the EV charging market was fragmented into a patchwork of proprietary, closed ecosystems. Each charging station manufacturer used its own communication protocols, making centralized management of mixed-brand stations virtually impossible. An operator managing a heterogeneous network was forced to run multiple parallel software systems, driving up operational costs and delivering an inconsistent user experience.

In 2009, the Open Charge Alliance (OCA) — an international consortium of operators, manufacturers, and service providers in the EV sector — responded to this fragmentation by introducing OCPP as an open standard. The goal was clear: standardize communication between charging stations and Central System Management Systems (CSMS), breaking down barriers between heterogeneous technologies and fostering a truly competitive market.

The initiative proved remarkably successful: OCPP is now the de facto standard in European and North American markets, adopted by hundreds of manufacturers and thousands of network operators.

How OCPP Works: The Core Architecture​

OCPP governs communication between two main actors:

• The Charging Station (CS) — the physical unit installed in the field.
• The Central System (CSMS) — the software platform that remotely manages, monitors, and controls the stations.

Communication happens over WebSocket (in more recent versions) and is based on an asynchronous request/response model: each message is uniquely identified, which reduces the impact of latency and ensures reliability even over unstable connections. The protocol natively supports temporary disconnection scenarios, with automatic retry mechanisms, local transaction buffering, and timestamp-based synchronization.

This architectural robustness makes OCPP well-suited for geographically distributed deployments — highways, industrial areas, rural zones — where connectivity cannot always be guaranteed.

The Complementary Protocol Ecosystem​

OCPP does not operate in isolation. It is part of a broader ecosystem of standards that together cover the entire lifecycle of electric charging.

OCPI — Open Charge Point Interface​

While OCPP handles vertical communication between the charging station and the central system, OCPI manages horizontal communication between Charge Point Operators (CPOs) and e-Mobility Service Providers (eMSPs). It enables roaming between different networks: a user can charge on any compatible network using a single account or RFID card — much like international roaming on mobile networks.

ISO 15118 — Vehicle-to-Infrastructure Communication​

ISO 15118 defines the direct communication protocol between the vehicle and the charging station, without intermediaries. It enables two strategically important features:

• Plug & Charge: the vehicle automatically authenticates to the network as soon as the cable is inserted, eliminating the need for apps, RFID cards, or PIN codes.
• Vehicle-to-Grid (V2G): the vehicle becomes an active participant in the power grid, capable of feeding energy back into the network during peak demand. This transforms the electric car from a simple consumer into a true distributed energy resource.

OpenADR — Demand Response Management​

OpenADR (Open Automated Demand Response) operates at the electricity distribution network level. It allows grid operators to send automatic signals to charging stations to modulate consumption in real time — for example, reducing delivered power during demand peaks or taking advantage of surplus renewable energy. The integration of OCPP with OpenADR is a cornerstone for next-generation smart grid development.

Core Features of OCPP​

Interoperability​

OCPP frees operators from dependence on a single vendor. A charging station from any certified manufacturer can communicate with any compatible CSMS. This dramatically reduces technological lock-in risk and fosters market competition, with direct benefits on deployment and maintenance costs.

Scalability​

The protocol was designed to support charging networks of any size — from a small private parking lot to national networks with thousands of distributed points. The asynchronous architecture ensures stable performance even with a large number of stations connected simultaneously.

Security​

OCPP includes robust security mechanisms: data encryption via TLS (Transport Layer Security), mutual client-server authentication, and — in version 2.0.1 — a multi-layered security model with granular management of digital certificates. In a sector where charging stations are critical infrastructure, cybersecurity is not optional.

Smart Charging​

One of the most operationally relevant features: OCPP enables intelligent charging, dynamically adjusting the power delivered by each station based on local grid availability, real-time energy costs, and operator-defined priorities. In a context of growing renewable energy penetration — with its inherent variability — this modulation capability is essential to prevent grid congestion.

Offline Management​

OCPP ensures operational continuity even when the connection to the central system is lost. Transactions are buffered locally and synchronized once connectivity is restored. This is particularly relevant for stations installed in areas with limited network coverage.

Protocol Versions​

OCPP 1.6​

Currently the most widely deployed version in the market. Compared to earlier releases, it introduced native support for JSON format (in addition to the more verbose SOAP/XML), reducing communication overhead and simplifying integration with modern technology stacks. It offers comprehensive functional coverage for the vast majority of operational scenarios and is characterized by high stability, the result of years of real-world deployment.

OCPP 2.0.1​

The latest version represents a significant evolutionary leap. Key innovations include:

• Advanced security: a layered security model with X.509 digital certificate support and centralized credential management.
• Enhanced smart charging: support for more granular charging profiles and dynamic tariff models, with real-time negotiation of available power.
• Component-based architecture: the charging station is modeled as a set of independent logical components (EVSE, connectors, etc.), offering greater flexibility in remote configuration.
• Deeper ISO 15118 integration: essential for enabling Plug & Charge and V2G at scale.

The main barrier to widespread OCPP 2.0.1 adoption is its lack of backward compatibility with version 1.6, which requires significant hardware and software upgrades to existing infrastructure. Adoption is growing but will take time to become dominant.

Strategic Role in the EV Market and IoT Integration​

OCPP today is far more than a technical protocol: it is a business enabler. The standardization it introduces allows new operators to enter the market without developing proprietary technologies from scratch, lowering barriers to entry and stimulating innovation.

From the perspective of the Mative platform, OCPP represents a strategic gateway for collecting operational data from charging stations: connection status, energy consumption, charging sessions, anomalies, and alarms. Once acquired, this data can be processed with artificial intelligence models to optimize network management, predict failures, and maximize infrastructure availability.

Integration with renewable energy sources — photovoltaic, wind — and with corporate energy management systems is another high-value development area: OCPP, combined with OpenADR, makes it possible to build charging systems that maximize the use of locally produced energy, reducing both costs and environmental impact.

Future Evolution: Toward Smart Grids​

OCPP's evolution over the coming years will be driven by three converging trends:

Widespread Plug & Charge adoption. As ISO 15118 is deployed at scale, RFID and app-based authentication will become obsolete. Vehicles will autonomously identify themselves to the network, making the charging experience as frictionless as traditional refueling — no apps, no cards, no friction.

Vehicle-to-Grid expansion. V2G will transform fleets of electric vehicles into distributed storage systems, capable of contributing to grid stability during critical hours. OCPP, integrated with ISO 15118, is the connective tissue that makes this scenario possible.

Integration into next-generation smart grids. In the medium term, OCPP will become a core infrastructure component of intelligent electricity networks, contributing to distributed energy management at both local and national levels. AI-powered automation, fed by data collected via IoT, will be the differentiating factor for operators able to fully leverage the protocol's potential.

Conclusions​

OCPP is not simply a technical standard: it is the backbone of tomorrow's charging infrastructure. Its ability to ensure interoperability, scalability, and openness makes it indispensable for anyone operating in the electric mobility sector.

At Mative, we integrate OCPP as part of a broader IoT architecture, where data collected from charging stations becomes input for artificial intelligence models capable of optimizing operations, reducing costs, and improving the end-user experience. The energy transition is not won with better hardware alone: it is won with data, intelligence, and open protocols.

Learn more about how Mative integrates OCPP and IoT in charging infrastructure: mative.ai

Learn more​

• Electric Vehicles
• Fuel and Energy
• MQTT Advanced
• What is Mative Cloud
• Mative energy and sustainability solutions

How RFID Monitoring Works in Waste Collection Trucks: The Smart & Eco-Sustainable Revolution by Mative​

Introduction​

In the framework of modern Smart City management, IoT (Internet of Things) and RFID (Radio Frequency Identification) technologies represent some of the most effective and revolutionary tools available to optimize waste collection processes, maximize traceability, and elevate urban service quality.

Mative has engineered a comprehensive, state-of-the-art ecosystem for integrating and managing advanced waste tracking workflows. By deploying custom, heavy-duty RFID hardware directly onto collection trucks, municipalities and waste management utilities can automatically identify containers, categorize collected waste streams, and stream data in real-time to the back-office. This eliminates human error and digitizes the entire environmental supply chain. Below, we break down the architecture, operational workflow, and strategic advantages of this technology.

System Architecture: The Hardware and Software Infrastructure​

An enterprise-grade RFID waste collection system relies on the seamless synergy between four primary hardware components, orchestrated by Mative’s central cloud software. This modular, highly scalable architecture manages the entire lifecycle of data:

1. Passive UHF RFID Tags on Waste Containers​

Every bin, dumpster, or residential tote is outfitted with an ultra-rugged Ultra-High Frequency (UHF) passive RFID tag. These specialized transponders are built to survive extreme weather conditions, high-pressure washing, and intense mechanical stress. Each tag holds:

• Global Unique Identifier (UID): A non-duplicable, factory-locked serial number.
• Account Association: A direct link in the central database to a specific household, commercial user, or geographic zone.
• Waste Type Setting: Pre-configured classification of the material stream (e.g., residual waste, organic, paper, glass/plastic).

Because these tags are passive, they do not require internal batteries. Instead, they are temporarily powered by the electromagnetic field generated by the truck’s antenna during the tilt cycle, ensuring a virtually maintenance-free lifespan of over 10 years.

2. Long-Range Rugged RFID Antennas on the Vehicle​

Collection vehicles are equipped with directional RFID antennas featuring high ingress protection ratings (IP67/IP69K). These antennas are strategically positioned near the mechanical lifting mechanism (rear-loaders, side-loaders, or front-loaders).

Their primary function is twofold:

• Electromagnetic Field Generation: Activating the passive tag the moment the bin enters the designated reading zone.
• Contactless Reading Accuracy: Capturing data within milliseconds, even in high-vibration environments, high operational speeds, or when covered in dirt and grime.

3. Industrial RFID Reader and Onboard Computer Unit​

The antenna connects directly to the computational heart of the truck: an industrial RFID Reader paired with a ruggedized Onboard Computer running a real-time operating system.

This module executes several critical edge computing functions:

• False-Positive Filtering: Advanced algorithms prevent cross-reading adjacent bins left on the sidewalk that are not being serviced.
• Edge Validation & Sensor Fusion: The system validates the collection event by cross-referencing the RFID read with mechanical telemetry (such as hydraulic pressure or arm inclination sensors). This certifies that the bin was actually emptied, rather than just driven past.

4. High-Precision GPS and Cellular Communication Modules​

The onboard telematics unit integrates robust global connectivity:

• High-Precision GPS/GNSS Receiver: Records the exact geographic coordinates (latitude and longitude) down to the millisecond of the collection event.
• 4G/5G/LTE Communication Module (with Satellite Failover): Establishes an encrypted, secure channel to transmit telemetry packets directly to Mative's cloud servers. In cellular dead zones (e.g., rural areas or mountainous terrain), the system stores the data locally in secure flash memory and auto-syncs as soon as connectivity is restored.

Real-Time Operational Workflow: From Street to Cloud​

The tracking process is entirely automated, requiring zero manual inputs or extra steps from the collection crew:

[ Vehicle Approaches Container ] 
               │
               ▼
[ Mechanical Lifter Engages Bin ] ──► (Arm Telemetry Sensor Triggers)
               │
               ▼
[ Antenna Field Activates ] ──► (Powers Passive RFID Tag)
               │
               ▼
[ Instant UID Capture & Edge Validation ]
               │
               ▼
[ Data Enrichment ] ──► (Injects: Exact Timestamp + GPS Coordinates + Weight if applicable)
               │
               ▼
[ Encrypted Transmission via 4G/5G Telemetry ]
               │
               ▼
[ Cloud Ingestion & Real-Time Back-Office Dashboard Update ]

1. Approach and Engagement: The truck aligns with the container, and the mechanical lift grabs the bin.
2. Reading Trigger: Lifting sensors detect the mechanical motion and instantly power up the RFID antenna's reading field.
3. Capture and Edge Processing: The antenna captures the tag's UID. The onboard software processes the signal and generates a secure ledger packet known as a "Collection Event."
4. Data Enrichment: The system injects real-time parameters: exact timestamp, sub-meter GPS coordinates, vehicle ID, and any driver-reported operational codes (entered via the in-cabin touchscreen).
5. Cloud Synchronization: The complete data package is dispatched over the cellular network and logged into the central database instantaneously.

Back-Office Platform and Business Intelligence​

Once telemetry flows into Mative's central platform, the back-office web application (accessible via browser or REST APIs) turns raw data into actionable business intelligence for utility operators and city officials:

• Pay-As-You-Throw (PAYT) Support: This is the primary regulatory and economic driver. By knowing precisely how many times a specific user has discharged their residual (non-recyclable) waste, utilities can generate fair, volume-based waste bills instead of relying on generic property-tax estimates. This rewards eco-conscious citizens.
• Dynamic Route Optimization (Smart Routing): By tracking historical collection patterns and container fill histories, routing algorithms simulate and deploy optimal collection paths. This prevents trucks from servicing half-empty dumpsters, cutting $CO_2$ emissions, fuel consumption, and fleet wear-and-tear.
• SLA Compliance & Operations Monitoring: Real-time fleet overview via interactive live maps with Geofencing. Fleet managers can evaluate route completion percentages and instantly audit operator performance against contractual service level agreements.
• Anomalies and Exception Handling: Automated alerts for un-registered bins (cloned or unknown tags), damaged containers, or blocked access due to illegally parked vehicles (logged instantly by the driver).

Strategic Advantages of the Mative Ecosystem​

Implementing Mative's end-to-end framework delivers significant tangible benefits across the entire municipal ecosystem:

Future Horizons: Integrating Smart City Capabilities​

Mative's flexible, API-first architecture is natively ready to integrate with next-generation smart infrastructure:

• Predictive AI Models: Machine learning models that combine historical RFID collection trends with weather forecasts, seasonal public events, and demographic data to forecast bin fullness with high accuracy.
• Edge Computer Vision: Onboard cameras backed by deep-learning models that inspect the quality of the waste stream during the dumping cycle. The system detects contamination (e.g., plastic in organic bins) in real-time and links the infraction directly to the scanned RFID tag.
• Fleet Digital Twins: A real-time virtual replica of the city’s entire waste logistics ecosystem, allowing planners to simulate route variations and policy updates before live deployment.

Conclusion​

RFID and IoT monitoring in waste management is no longer a futuristic concept—it is an operational imperative for modern, eco-conscious municipalities. Mative’s unified ecosystem demonstrates how combining heavy-duty hardware, intelligent edge computers, and cloud-based analytics transforms traditional municipal workflows into an industrial-grade, data-driven utility. Adopting this framework empowers organizations to minimize operating costs while accelerating progress toward global circular economy goals and climate neutrality.

Learn more​

• Routes optimizations
• Smart City: turning traffic into clean energy
• Mative’s solution for on-board load weight measurement
• What is Mative Cloud Fleet Management
• Mative solutions for waste and recycling

Noticeboard of Calls, Vouchers, and State Incentives through which Mative technologies and services can be financed:

logIN Business CALL​

From 1 to 15 September 2025

The Ministry of Infrastructure and Transport has published the ‘LogINBusiness’ call, a strategic measure worth 157 million euros provided by the PNRR to support the digital transition of freight transport and logistics companies. Development of the National Logistics Platform (PLN) through the digitalization of the logistics chain – also at the European level (eFTI).

• Incentive​

Co-financing: 40% non-repayable grant for sustainable multimodal projects under the “De Minimis” regime, 100% non-repayable grant.

• Timeline​

Platform opening: 01/09/2025
Application deadline: 15/09/2025
Publication of provisional ranking: after 15 September
Project implementation: by 30/06/2026

• Eligible Expenses​

Digital platforms and tools for e-CMR and load optimization
Systems for computerized dialogue with public authorities and loaders
Technologies for route planning, monitoring, e-learning, and AI

• How to apply​

Via the LogIN Business platform (through MIT and RAM)
Applications are evaluated in chronological order until resources are exhausted

Voucher for Double Digital and Ecological Transition - Year 2025​

From 16 to 30 September 2025

As part of the strategic initiatives promoted by Unioncamere and in line with the objectives of the Transition 5.0 Plan.
The measure aims to support micro, small, and medium-sized enterprises (MSMEs) by providing non-repayable vouchers for projects that promote the adoption of innovative solutions geared towards digital and/or ecological transition. Funded interventions include the purchase of capital goods, specialist consulting services, and enabling 4.0 technologies.

• Financial allocation and expected contribution​

  • Fund allocated: €150,000.00
  • Maximum contribution per company: €5,000.00
  • Contribution rate: 70% of eligible expenses
  • Bonus: +10% for companies that have completed the PIDNEXT program

• Areas of intervention and eligible expenses​

  1. Digital Transition: purchase of instrumental goods/services and expenses for consulting aimed at implementing one or more 4.0 digital technologies.
  2. Ecological Transition: expenses for consulting activities and, if necessary, for the purchase of 4.0 technological goods and services.

• Beneficiaries​

  • MSMEs with an operational headquarters in the province of Avellino or Benevento
  • Active, registered in the Business Register, compliant with annual fees
  • Have not already benefited from the same call in 2024

• Application deadlines​

Applications may be submitted from 12:00 on 16 September to 23:59 on 30 September 2025 and must be sent exclusively online via the ReStart platform, with a digital signature of the company’s legal representative or a delegated intermediary.

Ask your Chamber of Commerce or contact us for more information.

Learn more​

• Decreto attuativo Transizione 5.0
• Guide to Industry 4.0 Bonuses
• Investimenti Sostenibili 4.0 grant: how IoT turns 75% funding into measurable savings
• What is Mative Cloud Fleet Management
• Mative compliance solutions

In the world of modern agriculture, precision and process control have become key elements to improve productivity, sustainability, and profitability. In this rapidly evolving scenario, technology plays a crucial role, especially when it comes to monitoring agricultural vehicles and optimizing field activities. This is where Synapsis FMX Smart CAN Control comes into play—our solution based on the FMB140 device from Teltonika, designed to bring agriculture into the IoT era.

A Common Challenge: Monitoring and Managing Agricultural Vehicles​

Farming companies often have to manage fleets of tractors, harvesters, and other operational vehicles in complex and geographically dispersed contexts. The lack of real-time visibility into vehicle behavior and the absence of data on fuel consumption, working hours, or performed operations can lead to inefficiencies, excessive fuel use, and difficulties in ensuring proper maintenance.

Our Answer: Synapsis FMX Smart CAN Control​

Synapsis FMX Smart CAN Control is the system designed by Mative to collect, analyze, and transform data from agricultural vehicles into strategic information, leveraging the capabilities of the FMB140. Thanks to access to CAN bus data and high-precision GPS localization, the device enables you to:

• Monitor vehicle position and activity in real time
• Detect operational data such as speed, engine RPM, fuel level, and operating hours
• Receive alerts in case of anomalies or non-compliant behavior
• Optimize routes and resource usage

A Flexible, Integrable, and Customizable System​

Our device is compatible with a wide range of agricultural vehicles and easily integrates with the Mative Synapsis IoT platform, which offers intuitive dashboards, smart notifications, and detailed reports. Additionally, you can enable extra features such as:

• Dynamic geofencing, to always know if a vehicle enters or leaves a specific area
• AI analysis of collected data, to identify usage patterns and suggest improvements
• Maintenance scheduling, based on actual vehicle usage

Concrete Impacts in the Field​

The adoption of Synapsis FMX Smart CAN Control has already proven to deliver tangible benefits:

• Reduction of operating costs thanks to consumption monitoring and breakdown prevention
• Increased efficiency of agricultural operations through smart task planning
• Better traceability of activities, also useful for certifications and quality controls
• Greater safety for operators and vehicles

A Step Towards the Agriculture of the Future​

At Mative, we believe that technological innovation should be accessible and functional. With Synapsis FMX Smart CAN Control, we support farmers, cooperatives, and agri-food companies in their digitalization journey, providing concrete tools to face sector challenges in a sustainable, efficient, and data-driven way.

To find out how to integrate Synapsis FMX Smart CAN Control into your farming business, contact us: we will be happy to guide you on your path towards Agriculture 4.0.

Learn more​

• Agriculture: Smart Farming
• IoT & ML: Energy and economic benefits in an Agricultural Company
• Autonomous tractors on market
• Teltonika
• Mative solutions for agriculture

Figure 1. A custom Dashboard, created by Mative experts, to monitor the "Productivity" of a client's machines.

In the highly competitive landscape of the metalworking industry, small and medium-sized enterprises (SMEs) face a series of crucial challenges. Among these, monitoring and optimizing production emerges as one of the most pressing. Industrial IoT presents itself as a revolutionary solution, capable of radically transforming the way these companies operate and compete in the global market.

What is the situation of Metalworking SMEs?​

SMEs in the metalworking sector are the beating heart of the Italian manufacturing industry. These companies face growing challenges: inflation, rising operating costs (especially energy), increasing competitiveness of foreign markets, and reduced operating margins (on average less than 10%). In this context, the need to reduce waste and optimize processes becomes crucial.

A modern and interconnected production monitoring system is essential to overcome these challenges and maintain competitiveness. The lack of real-time visibility into the status of machinery, production times, and overall plant efficiency represents a significant obstacle to optimizing production processes.

The adoption of industrial IoT solutions is the most effective response to these challenges. Advanced solutions such as the Mative Cloud and Mative Synapsis Industrial Edge platforms, specific for IoT and equipped with integrated AI, allow metalworking SMEs to overcome technological barriers, providing the necessary tools for a true digital revolution.

Identifying and reducing bottlenecks​

One of the most significant advantages offered by an Industrial IoT system is the ability to quickly identify bottlenecks in the production process. Through data analysis, the Mative platform can highlight the stages that slow down the entire production cycle.

• Mative Cloud: remote monitoring, in the Cloud, accessible also through a mobile app.
• Mative Synapsis Industrial Edge: production chain monitoring, facilitating communication between departments, material consumption monitoring, integrated HMI.
• Mative Synapsis ML: the main core of Mative, a module based on Artificial Intelligence tools, available on all Mative platforms, integrates AI Agent, RAG, and Machine Learning;
• Mative Synapsis Analysis: ETL, Graphs, Smart Reports, and much more for a complete data analysis software suite;

These platforms, directly acting on the data collected from the machines, allow companies to intervene in a targeted manner, implementing specific solutions to increase overall efficiency.

Real-time monitoring: the key to efficiency​

Real-time production monitoring is one of the main opportunities arising from the adoption of an industrial IoT system. The Mative Cloud platform allows the collection and analysis of data from every stage of the production process, providing crucial information on:

• Operational status of machinery
• Production times
• Efficiency of individual processing stages
• Energy consumption

This immediate visibility allows managers to make informed and timely decisions, reducing downtime and optimizing resource allocation.

Predictive and conditional maintenance: how to avoid machine downtime​

Predictive and conditional maintenance represents a qualitative leap compared to traditional reactive or preventive approaches.

Thanks to Industrial IoT systems, metalworking SMEs can constantly monitor the health of their machinery, identifying potential problems before they turn into failures. This proactive approach not only reduces unplanned machine downtime but also optimizes maintenance costs, extending the useful life of the equipment.

Optimization of energy consumption​

In an era where sustainability and energy efficiency have become absolute priorities, industrial IoT offers valuable tools for monitoring and optimizing energy consumption.

The Mative platform allows detailed tracking of energy use for each machine and process, identifying areas of waste and opportunities for savings. This granular visibility enables companies to implement targeted energy efficiency strategies, reducing operating costs and environmental impact.

Complete visibility on production​

Real-time monitoring is just the first step towards a true digital revolution in the metalworking industry. To transform this visibility into a concrete competitive advantage, companies need advanced tools capable of analyzing, interpreting, and acting on the collected data.

Designed to meet the specific challenges of metalworking SMEs, the Synapsis Industrial Edge and Mative Cloud Platforms integrate industrial IoT with powerful artificial intelligence algorithms, offering a complete digital ecosystem for production monitoring and process optimization.

The Mative Cloud Platform not only provides real-time data but also transforms it into valuable insights that support the informational needs of companies.

What are the key features of the Mative platforms?​

The Mative platforms, Mative Cloud and Mative Synapsis Industrial Edge, offer a complete suite of features specifically designed for the needs of the metalworking industry:

• Customizable Dashboards: Intuitive visualizations of the most relevant KPIs for industrial plant monitoring.
• Advanced Analytics: data analysis tools to identify trends and improvement opportunities.
• Intelligent Alerting: real-time notifications for anomalies or critical situations.
• IoT Integration: simplified connection of new and legacy machinery to the digital platform.

Efficiency of metalworking SMEs with Mative: real cases​

The adoption of industrial IoT through the Mative platforms is not just a matter of technology, but of business transformation. Metalworking SMEs that have embarked on this path have achieved significant results:

• Reduction of machine downtime by up to 30%
• Increase in production efficiency by 15-20%
• Optimization of energy consumption with savings of up to 25%
• Improvement in product quality and reduction of waste

These results translate into a tangible competitive advantage, allowing companies to respond more quickly to market demands and offer superior quality products at lower costs.

Learn more​

• Industrial IoT and solutions by Mative
• IoT & ML: Energy and Economic Benefits in a Door and Frame Manufacturing Company
• Big Data in the Era of Smart Industry
• What is Synapsis Analysis
• Mative for manufacturing

Article available only in Italian

L’Industria 5.0 rappresenta un passo avanti fondamentale per le imprese, superando i limiti dell’automazione e dell’interconnessione per abbracciare una visione umanocentrica e sostenibile. Ora sono disponibili online tutte le normative per accedere agli incentivi! Con il recente decreto attuativo del Piano Transizione 5.0, le aziende italiane dispongono ora di un quadro normativo chiaro per accedere a incentivi fiscali e supporti economici mirati a favorire l’adozione di tecnologie innovative e sostenibili.

Il decreto attuativo Industria 5.0: quali sono le novità?​

Dopo mesi di attesa, è stato pubblicato il testo integrale e definitivo del Piano Transizione 5.0. Il decreto, come illustrato nell’articolo di Innovation Post, conferma due principali novità: l’ampliamento delle figure dei certificatori e l’ampliamento delle esclusioni dal divieto generale relativo al regolamento DNSH.

È stato eliminato, però, il comma che prevedeva la cumulabilità generale con altri finanziamenti dell’UE, mentre resta invariata la possibilità di cumulare la misura con altri incentivi finanziati con risorse nazionali, eccezione fatta per il credito d’imposta ZES e Transizione 4.0.

Synapsis ML di Mative: ottimizzazione e analisi dei Dati con l’Intelligenza Artificiale​

Nel contesto dell’Industria 5.0, Mative ha sviluppato gli Synapsis ML con strumenti di intelligenza artificiale integrati, per trasformare l’analisi dei dati aziendali in informazioni fruibili e intuitive per prendere decisioni strategiche. Queste statistiche intelligenti offrono una visione completa e in tempo reale delle operazioni aziendali, permettendo alle imprese di monitorare e ottimizzare i loro processi produttivi e di consumo energetico. Inoltre, per quanto riguarda la documentazione prevista dal piano 5.0, abilitano i certificatori alla compilazione delle certificazioni ex ante ed ex post per dimostrare l’efficientamento della produzione ed energetico.

Grazie a una sofisticata piattaforma di raccolta e analisi dei dati, gli Synapsis ML non solo aiuta a identificare inefficienze, ma fornisce anche raccomandazioni su come migliorare le performance aziendali.

Utilizzando una combinazione di sensori IoT e algoritmi di Intelligenza Artificiale, questo strumento è in grado di raccogliere una vasta gamma di dati, dal consumo energetico alla manutenzione predittiva. In questo modo, le aziende possono prendere decisioni informate basate su dati accurati e tempestivi, migliorando non solo l’efficienza operativa ma anche la sostenibilità ambientale.

Mative: Il Partner Ideale per la Transizione Digitale ed Energetica​

Mative si propone come soluzione ideale per le aziende che desiderano affrontare la sfida della Transizione 5.0. Dal design iniziale alla realizzazione e implementazione delle soluzioni, Mative offre un supporto completo, garantendo che ogni progetto risponda ai requisiti normativi e alle esigenze specifiche del cliente.

In particolare, Mative è in grado di aiutare le aziende a soddisfare i criteri necessari per accedere ai crediti d’imposta previsti dal nuovo decreto, fornendo soluzioni che garantiscono una riduzione significativa dei consumi energetici. Inoltre, con l’adozione di Synapsis ML, le aziende possono non solo monitorare i propri progressi ma anche dimostrare in modo documentato le migliorie ottenute, un elemento chiave per la rendicontazione e l’accesso agli incentivi.

Want to learn more about how Mative can help you achieve the benefits of Industry 5.0? Contact us today!

Learn more​

• Guide to Industry 4.0 Bonuses
• Investimenti Sostenibili 4.0 grant: how IoT turns 75% funding into measurable savings
• Incentives in Italy from September 2025
• What is Synapsis ML
• Mative energy and sustainability solutions

Digitization and IoT in the Smart Industry Era​

• Digital transformation in production processes: Digitization is revolutionizing production processes in the Smart Industry era, enabling companies to interconnect machinery, sensors, and management systems. This interconnection creates a continuous and real-time data flow that can be used to optimize operational efficiency and improve production. Every machine and asset becomes part of an intelligent network, capable of self-managing and responding to changing market conditions.

• IoT for operational efficiency: The Internet of Things (IoT) plays a central role in the Smart Industry, allowing real-time data collection from connected devices. Sensors installed on machines and plants provide crucial information to monitor performance, detect imminent failures, and optimize the production cycle. This approach reduces downtime, ensuring greater efficiency and timely maintenance.

• Integration and innovation: Digitization combined with IoT facilitates the implementation of new technologies and personalized services. Companies can integrate their production systems with cloud platforms and artificial intelligence solutions, enabling automation and remote control of operations. This represents a continuous evolution, capable of adapting to new market needs and fostering competitive growth.

Mative's Solutions for Industrial IoT​

• Mative Cloud for Smart Industry: Mative can manage new devices, handle their lifecycle, receive and store data from telematics devices and sensors in the Cloud, execute remote commands and firmware updates over-the-air (FOTA), analyze device data, and create rules for intelligent alerts. Mative's connectivity and data processing capabilities leverage widespread protocols like MQTT and can easily integrate with popular data management systems and databases, seamlessly fitting into your existing backend.

• Implementation of Industrial IoT: Mative Cloud is a widely used enterprise IoT platform as an industrial IoT (IIoT) solution, functioning as a cloud application manager for connected industrial production plants. A key feature of Mative is its independence from hardware and transport means, allowing easy integration with a wide range of sensors, controllers, machines, and device gateways, supporting any existing industrial infrastructure. The Mative Cloud platform offers a complete and integrated IIoT solution: we manage ModBus, OPC UA, Can Open protocols, and integrations with PLC plants.

• Development and integration: Mative's APIs simplify integration and DevOps tasks, enabling the rapid assembly of end-to-end IoT solutions for industrial system automation, predictive maintenance, and remote monitoring. Mative also has an intuitive web dashboard tool to configure data visualization widgets that perform production monitoring routines. Recent innovations such as IIoT, Big Data, and AI are ready to autonomize factories using industrial robots and smart devices. The Mative Cloud platform is at the forefront of making autonomous factories a reality.

Learn more​

• Overcoming Challenges in the Metalworking Industry with Industrial IoT
• Big Data in the Era of Smart Industry
• Machine Learning for Smart Industry
• What is Mative Cloud Things Connector
• Mative Cloud Things Connector platform

AI Inference is the process through which an artificial intelligence model applies what it has learned during training to make predictions, classifications, or decisions based on new input data.

How AI Inference Works​

1. Trained Model: An AI model is trained on a dataset. During training, it learns patterns and relationships from the data.
2. Inference: Once trained, the model is used to make predictions on previously unseen data.

Example:

• A computer vision model trained to recognize images of cats (training phase) receives a new image and determines whether it contains a cat or not (inference phase).

Key Features of AI Inference​

• Efficiency: Fast and optimized for real-time or resource-constrained environments
• Deployment: Runs on edge devices (smartphones, IoT sensors) or cloud environments
• Optimization: Uses techniques like quantization to improve performance

AI Inference vs Training​

Common Applications​

1. Speech Recognition: Virtual assistants like Alexa
2. Computer Vision: Self-driving cars, surveillance
3. Recommendations: Netflix, Amazon suggestions
4. Translation: Google Translate

Learn more​

• Differences between AI, ML, LLM, and Generative AI
• Machine Learning: Predictive Algorithms
• Synapsis ISBC Gateway
• Mative Artificial Intelligence

Here is an overview of the differences between AI, ML, LLM, and Generative AI:

1. AI (Artificial Intelligence)​

Artificial Intelligence is the broadest field that deals with creating machines or systems that can simulate human intelligence. It includes any technology or method that allows a system to perform tasks that normally require human intelligence, such as reasoning, natural language recognition, planning, and problem-solving.

Examples of AI:

• Recommendation systems (e.g., Netflix, Amazon).
• Virtual assistants like Siri and Alexa.
• Autonomous driving systems.

2. ML (Machine Learning)​

Machine Learning is a subset of AI that focuses on using algorithms to enable machines to learn from data without being explicitly programmed.
ML algorithms analyze data, identify patterns, and improve their performance over time.

Main types of ML:

• Unsupervised Learning: The algorithm is trained on unlabeled data and finds patterns or groups in it by itself. There are two types of analysis that can identify patterns and relationships in data without the need for training or human intervention: anomaly detection and outlier detection.
  • Anomaly Detection: This approach requires time series data. It builds a probabilistic model that continuously monitors the data to identify unusual events as they occur. The model evolves over time and can provide useful insights for predicting future behaviors.
  • Outlier Detection: Unlike anomaly detection, this technique does not require time series data. It is a type of data analysis that identifies unusual points in a dataset by evaluating the proximity of each point to others and the density of the group of points around it. This analysis is not continuous: it produces a copy of the dataset, where each point is annotated with an outlier score, indicating how different that point is from the others.
• Supervised Learning: Supervised Machine Learning uses labeled training datasets to build predictive models (e.g., classifying emails as spam or not). The main techniques are classification and regression. In both supervised machine learning techniques, the result is a dataset where each point is enriched with a prediction and a trained model. This model can then be applied to new data to make further predictions.
  • Classification: This type of analysis learns the relationships between data to predict discrete or categorical values. For example, it can be used to determine whether a DNS request comes from a malicious or benign domain.
  • Regression: This method focuses on predicting continuous numerical values. A typical example is estimating the response time for a web request based on available historical data.
• Reinforcement Learning: The system learns through trial and error (e.g., robotics, games).

Examples of ML:

• Anomaly detection.
• Predictive analysis.
• Image recognition.
• Weather forecasting.
• Fraud detection.

3. LLM (Large Language Models)​

Large Language Models are a specific category of AI models trained on large amounts of textual data to understand, generate, and interact in natural language. These models use deep learning architectures, such as Transformers (e.g., GPT, BERT), to analyze context and generate responses.

Characteristics of LLM:

• Trained on billions of parameters and enormous datasets.
• Capable of understanding complex linguistic nuances and responding realistically.
• Suitable for a variety of applications, such as creative writing, customer service, and text analysis.

Examples of LLM:

• GPT (like ChatGPT).
• BERT.
• LaMDA.

4. Generative AI​

Generative AI is a specific branch of AI that focuses on creating original content, such as images, texts, music, or videos. It relies on deep learning models, including GANs (Generative Adversarial Networks) and transformer-based models like GPT and DALL·E.

Main characteristics:

• Can create entirely new content based on input or prompts.
• Uses training data to understand underlying patterns and generate realistic outputs.

Examples of Generative AI:

• Image creation (e.g., DALL·E, MidJourney).
• Text generation (e.g., ChatGPT).
• Music or synthetic voice generation (e.g., OpenAI's Jukebox).

Main Differences:​

Learn more​

• AI Inference
• Machine Learning: Predictive Algorithms
• Anomalies detection
• What are the Large Language Models
• Mative Artificial Intelligence

Investments for the technological and digital transformation of companies in line with the Transition/Industry 4.0 perspective, as well as the purchase of related intangible assets (software, systems and system integration, platforms, and applications), remain incentivized until December 31, 2025, and under certain conditions, until June 30, 2026.

The incentives are available to all companies resident in the territory of the State, including permanent establishments of non-resident entities, regardless of legal nature, economic sector, size, accounting regime, and the system of determining income for tax purposes.

Incentives for 4.0 material assets​

The incentives for investments in new material assets, according to the "Industry 4.0" model (Annex A of Law 232/2016), are available until 2025. All healthy companies resident in Italy, including permanent establishments of non-resident entities, are eligible, provided they comply with workplace safety regulations and correctly pay worker contributions.

For investments until December 31, 2025 (or until June 30, 2026, if by December 31, 2025, the order is accepted and deposits of 20% have been paid):

• 20% of the cost, for the portion of investments up to 2.5 million,
• 10%, for the portion of investments over 2.5 and up to 10 million,
• 5%, for the portion over 10 million and up to the limit of 20 million.

Incentives for related intangible assets​

A three-year extension with a gradual reduction of the bonus for investments in intangible assets related to those in Industry 4.0 material assets (Annex B of Law 232/2016): software, systems and system integration, platforms, and applications, and cloud computing services, for the portion attributable by competence.

The 2023-2025 tax credit decreases by five percentage points each year:

• 20% for investments until December 31, 2023 (or June 30, 2024, if by 2023 the order is accepted and 20% deposits have been paid);
• 15% for investments until December 31, 2024 (or June 30, 2025, if by 2024 the order is accepted and 20% deposits have been paid);
• 10% for investments until December 31, 2025 (or June 30, 2026, if by 2025 the order is accepted and 20% deposits have been paid).

Industry 4.0 Bonus Calendar​

Below is the detail of the measures and incentives provided.

Investments in material assets​

The tax credit is recognized for investments until June 30, 2026, provided that by December 31, 2025, the order is accepted and deposits of 20% of the acquisition cost have been paid.

Investments in technologically advanced intangible assets​

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Learn more​

• Decreto attuativo Transizione 5.0
• Investimenti Sostenibili 4.0 grant: how IoT turns 75% funding into measurable savings
• Incentives in Italy from September 2025
• What is Mative Cloud Things Connector
• Mative compliance solutions

What autonomous tractors are already on the market?​

The majority of tractor manufacturers is working on the technology to let at least one of their tractor models work unmanned. A recap based on what we know now.

Just a handful of tractor types currently is available ex-factory as unmanned/autonomy ready. Backed by the OEM and not by a third party offering an autonomous retrofit kit. You can operate these tractors without a driver while an operator controls it from another tractor or other vehicle nearby, or even from a remote location. Japanese manufacturers Kubota and Yanmar were the first to commercially offer autonomous tractors in their country of origin, Japan. Followed by Monarch Tractor as well as by John Deere.

Upcoming developments​

Every tractor manufacturer, whether they admit it or not, is developing driver optional tractors. Out in the open or behind closed doors. Vehicles that resemble the current looks of a tractor and built with components you’d normally find in tractors as we now know them for decades. Recent trade shows such as last year’s Agritechnica in Hanover, Germany showcased a few upcoming autonomous tractor types. Such as the Claas Xerion 12.590 TerraTrac, Fendt e107 Vario and 900-series and the Kubota AgriRobo MR 1000 A.

Earlier on, Case IH showed an autonomous Magnum 400 in Brazil and demonstrated an autonomous Magnum 340 in Austria. But also Belarus has made public that they are working on their autonomous tractor model A3523i. Other manufacturers that are very likely developing autonomous tractors include Farmtrac, Mahindra and also Keestrack. The latter might not be a very familiar name for most farmers and contractors. The manufacturer however has a long track record in construction machinery, owns tractor specialist Goldoni and manufactures Swiss Rigitrac developed electric tractors in its Goldoni factory in Italy.

Most autonomous tractors still have a cab​

What commercially available autonomous tractors and those that are being developed have in common, is that most of the technology onboard can already be found in the latest tractor model series. Those are automated to such extent that they can automatically follow straight and curved AB-lines and contours, lower/lift and start/stop implements and machines and turn automatically on headlands due to sophisticated headland management systems. The only technology these tractors lack to work without a driver, are cameras and sensors to observe the surroundings, the machinery and the quality of the work being done.

Another aspect most available autonomous tractors and prototypes have in common, is the presence of a cab or at least a driver’s seat. This might be a result of a transition period between manned tractors and unmanned tractors similar to what we saw at Tesla and Beyond Meat for instance. The first Tesla cars still had cooler grills to resemble the familiar and generally accepted looks of existing cars and trucks. Beyond Meat and other vegan food producers first develop plant-based, vegan meat products that resemble looks, taste and texture of meat for initial acceptance, adoption and uptake. A further aspect comes down to the flexibility of driver optional tractors that include a cab, controls and an operator terminal. You can still use your tractor as usual, ánd you can drive it from one field to another on public roads. The latter might not be an issue on large outback farms, it is an issue in most other areas. Moving autonomous machinery from one field to another still requires a tractor or truck with a (flatbed) trailer.

Exceptions without a cab or driver’s seat by the way are the Belarus A3523i, the Kubota New Agri Concept and to some extent also the Krone/Lemken Combined Powers VTE.

Official statements​

We asked most major manufacturers for official statements on the introduction of autonomous tractors and these are the answers we got.

Agco (Fendt) says: “We have several different projects for autonomy/autonomous solutions running at the same time but none of them are commercially available at this time.”

Claas says: “Our autonomy connect will not be available in 2024 but in the mid-term, without giving a concrete date.”

CNH says: “Currently we don’t have commercially available tractors that leave one of our factories capable of unmanned operations. Our Case IH Farmall 75C Electric and New Holland T4 Electric Power are capable of doing so and their prototypes are currently part of homologation procedures. Together with Raven, we can retrofit existing Case IH Magnum tractors with our autonomous retrofit kit in those countries where autonomous operations are legally permitted. This excludes Europe.”

Deutz-Fahr says: “In 2024 we will not introduce an autonomous tractor commercially.”

John Deere says: “Our wheeled and four-track model year 2025 8 series and 9 series tractors will offer an autonomy-ready option that will allow farmers to make the switch quickly and easily to fully autonomous operation when it’s right for their own farm. The autonomy-ready package offers all the hardware, software, and safety features that we know today will be required for autonomous operation in the future. The autonomy-ready package is available in the United States and Canada to order through local John Deere dealers. The only additional item a farmer will need to add in the future to complete autonomous operations will be the perception system. The perception system consists of cameras and vision processing units needed for autonomous operation.”

Trusted solutions and suppliers preferred​

If you let your autonomous tractor work your fields, you trust your fields and your crops to something (rather) unknown. Lessons learned during last year’s experiments in the Netherlands with a common tractor retrofitted with an autonomous kit, include farmers’ clear opinions that they find ‘an unmanned autonomous tractor mainly interesting for soil preparation. This opinion primarily arises due to the need for supervision, both for safety and to ensure the machine operates effectively and maintains work quality. Crop health and yield determining operations such as planting and hoeing are hardly trusted to autonomous alternatives as sufficient monitoring solutions still lack. A blocked planter row or a hoeing knife dragging along a piece of wood or metal and thus destroying a crop row, are the least farmers want. John Deere and others have a clear reason why they initiate autonomous operations with soil preparation tasks.

Then, let us draw another parallel with the automotive industry, at least in Europe. It took Korean car manufacturers decades to gain trust in their products. Not only from a quality and reliability point of view, but also from a depreciation or resale / trade in value point of view. While currently, again at least in Europe, it’s the Koreans along with Tesla who are known for their innovations, reliability and resale value for electric cars. While currently, the countless Chinese electric car makers are dealing with the same challenges, yet much and much faster.

The same is going in in agriculture. If you buy a tractor from one of the established manufacturers mentioned above, you know what you get. Quality wise, reliability and service wise, and you can also (roughly) estimate your resale or trade in value and thus your costs per hour. Also if you fitted it with an autonomous retrofit kit. That is not the case for field robots who are costly and for the time being, quickly depreciate/amortise.

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Learn more​

• Advanced Telematics for Modern Agriculture
• Agriculture: Smart Farming
• Big Data in the Automotive Industry
• Telematics
• Mative solutions for agriculture

Data Estimates from Connected Vehicles​

According to Gartner's estimates, by 2025, automotive manufacturers will be able to collect 1GB of data monthly from connected vehicles. At the same time, autonomous vehicles classified as SAE Level 3-5 will generate 1TB of data every hour, though less than 1% will be transferred to the cloud. This prediction implies a direct impact on data management on digital platforms: while the data flow can overwhelm non-optimized information systems, the insights derived can be highly accurate and valuable for all involved stakeholders.

Mative's Solution to Big Data​

At Mative, we are prepared: currently, we process tens of gigabytes of real-time data every day, offering our clients solutions that can collect, analyze, and leverage data from connected vehicles. This enables rapid decision-making and facilitates the creation of innovative mobility services.

Learn more​

• Machine Learning and Automotive
• Big Data in the Era of Smart Industry
• Telematics
• Mative for the automotive industry

Digitization of Production Processes According to projections for Smart Industry, by 2025, industrial enterprises will be able to implement a highly interconnected and automated production network. This scenario will be characterized by a vast amount of data generated by connected devices in real time along the entire production chain. For example, it is estimated that industrial machines will be capable of generating several terabytes of data per hour, providing crucial information on equipment performance, product quality, and plant status.

Remote Monitoring Systems: Use Cases Many companies are already investing in innovative solutions to address the challenges of the new industrial era. For example, through the implementation of smart sensors and remote monitoring systems, detailed data on machine operational efficiency can be collected, and potential failures can be predicted in advance, enabling preventive maintenance interventions. In summary, the Smart Industry world offers enormous opportunities for industrial enterprises but requires advanced data management and cutting-edge technologies to maximize the value derived from the digitization of production processes.

Mative's Solution to Big Data The main challenge is not only in data collection but also in its effective management and analysis. Mative handles the massive data flow through robust and scalable information systems to process, store, and analyze information in real time. To extract value from data and make predictive and optimized decisions, try our Synapsis ML machine learning algorithm.

Learn more​

• Big Data in the Automotive Industry
• Machine Learning for Smart Industry
• Industrial IoT and solutions by Mative
• What is Mative Cloud Things Connector
• Mative for manufacturing

The breeze produced from passing cars might not seem like much, but ENLIL’s long, unobtrusive, upright blades are powerful enough to produce one kilowatt of energy an hour.

A single turbine fitted with an additional solar panel on top can seamlessly produce enough electricity to power two Turkish households for a day.

Modern, well-designed standard wind turbines have a life expectancy of 20 years, something ENLIL could one day exceed due to its simplicity and durability. Each turbine follows a simple design, making it easy to assemble and also to fix.

The apparatus is small enough to be placed next to moving vehicles without disruption and takes up minimal surface area no matter where it is. This allows for easy transportation and assembly in areas where traditional wind turbines may not otherwise be practical, such as city streets and buildings.

But ENLIL’s environmental benefits extend further. The turbines also harness a number of smart technologies that track the temperature, humidity, carbon footprint and earthquake activity of the surrounding area with IoT systems.

Each measurement provides valuable information that is passed on to authorities and environmental scientists in Istanbul.

A growing appetite for wind power in Turkey​

Wind energy consumption hit record highs in Turkey last year and there is a burgeoning appetite for clean innovation. As of 2020, over 8 per cent of the country’s entire energy network is produced by wind power.

Turkey’s future capacity for energy production could take many forms. There are plans to collaborate on an offshore wind farm with Denmark, an €85.2 million mega-investment from the European Bank for Reconstruction and Development, and continuous schemes to expand green energy manufacturing on Turkish soil.

Though ENLIL may still be in its nascent stages, the project was given the ‘ClimateLaunchpad Urban Transitions Award’, and won the Mercedes-Benz Turkish StartUP Competition before it had even exited its research and development phase.

A successful rollout of the device across Turkey's largest city could see other cities across Europe adopt similar initiatives.

Learn more​

• Fuel and Energy
• How RFID Monitoring Works in Garbage Collection Trucks
• What is Mative Cloud Things Connector
• Mative solutions for smart cities

Introduction​

Technological innovation is a fundamental lever for improving the efficiency and sustainability of agricultural companies. Mative, a leading company in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services capable of revolutionizing agricultural practices. This report explores the energy and economic benefits derived from adopting these technologies in an agricultural company.

Internet of Things (IoT) in Agriculture​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In agriculture, IoT sensors can monitor various parameters such as soil moisture, temperature, air quality, and crop conditions.

Energy Benefits​

1. Optimization of Irrigation: Soil moisture sensors allow for irrigation only when necessary, reducing water and energy waste used for pumping.
2. Intelligent Management of Heating and Ventilation Systems: Temperature and humidity sensors help maintain optimal conditions for crops, minimizing unnecessary heating and ventilation use.
3. Reduction of Energy Consumption of Agricultural Machinery: Tractors and other machines equipped with IoT sensors can operate more efficiently, reducing fuel consumption through optimized routes and operations.

Economic Benefits​

1. Increased Productivity: Continuous and precise monitoring of environmental conditions allows for timely interventions, improving crop health and yield.
2. Reduction of Operating Costs: Optimization of resources such as water and energy leads to significant reductions in operating costs.
3. Predictive Maintenance: Data collected from IoT sensors allows for predicting equipment failures, reducing downtime and repair costs.

Machine Learning (ML) in Agriculture​

Definition and Operation​

Machine Learning is a branch of artificial intelligence that uses algorithms to analyze large amounts of data and make intelligent predictions or decisions. In agriculture, ML can be used to analyze data collected from IoT devices, identifying patterns and trends.

Energy Benefits​

1. Advanced Weather Forecasting: ML algorithms can analyze weather data to predict climatic conditions, helping farmers plan irrigation and other activities more efficiently.
2. Resource Optimization: ML can analyze historical data to suggest optimal use of resources such as fertilizers and pesticides, reducing environmental impact and energy consumption.

Economic Benefits​

1. Crop Management: Predictive data analysis allows for timely identification of diseases or infestations, reducing crop losses and improving product quality.
2. Production Planning: ML can help predict market demand, optimizing production and reducing waste.
3. Marketing and Sales: Market data analysis allows for optimizing sales and pricing strategies, improving the company's competitiveness.

IoT & ML: Implementation in an Agricultural Company​

Consider a medium-sized agricultural company that decides to adopt Mative Srl's IoT and ML solutions. Our forecast analysis after one year of implementation and adoption of our technologies, shows the following benefits:

1. 30% Reduction in Water Consumption: Thanks to the optimization of irrigation based on real-time data.
2. 25% Decrease in Energy Costs: Through more efficient management of heating, ventilation, and agricultural machinery.
3. 20% Increase in Production: Due to better crop management and timely interventions against diseases and infestations.
4. 15% Savings in Operating Costs: Thanks to predictive maintenance and optimized resource use.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant turning point for agricultural companies seeking to improve their energy and economic efficiency. The benefits derived from implementing these technologies not only contribute to environmental sustainability but also increase the competitiveness and profitability of agricultural companies, positioning them well to face future industry challenges.

Learn more​

• IoT & ML: Energy and Economic Benefits in a Door and Frame Manufacturing Company
• Agriculture: Smart Farming
• Economic returns of machine learning algorithms
• What is Synapsis ML
• Mative solutions for agriculture