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.

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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!

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.

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​

Aspect	Training	Inference
Objective	Learn from labeled data	Make predictions
Complexity	High (needs GPU/TPU)	Lower
Time	Hours/days	Milliseconds
Environment	Data centers	Cloud/edge devices

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

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

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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.

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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 labeled data (e.g., classifying emails as spam or not). 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 training datasets to build predictive models. 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.

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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.

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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).

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Main Differences:​

Term	Field	Description	Example
AI	General	Simulates human intelligence for complex tasks.	Siri, autonomous systems
ML	Subset of AI	Focuses on learning from data to improve performance.	Fraud detection, clustering
LLM	Specialization in NLP	Advanced models for understanding and generating natural language.	GPT, BERT
Generative AI	Creation of original content	Generates new content such as texts, images, videos, or music.	DALL·E, ChatGPT, MidJourney

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​

Period	Credit
From 1/1 to 12/31/2022 until 11/30/2023 with reservation by 12/31/2022	- 40% up to 2.5 million, - 20% between 2.5 and 10 million, - 10% beyond 10 and up to 20 million
From 1/1/2023 to 12/31/2025 until 6/30/2026 with reservation by 12/31/2025	- 20% up to 2.5 million, - 10% between 2.5 and 10 million, - 5% beyond 10 and up to 20 million, 5% between 10 and 50 million for PNRR investments.

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​

Period	Credit
From 1/1/2023 to 12/31/2023 until 6/30/2024 with reservation by 12/31/2023	20% up to 1 million euros
From 1/1 to 12/31/2024 until 6/30/2025 with reservation by 12/31/2024	15% up to 1 million euros
From 1/1 to 12/31/2025 until 6/30/2026 with reservation by 12/31/2025	10% up to 1 million euros

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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 easily 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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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​

This prediction has a direct impact on data management in digital platforms: while the data flow can overwhelm non-optimized information systems, the insights derived can be accurate and valuable for all stakeholders involved. 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.

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.

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 the Turkish capital could see other cities across Europe adopt similar initiatives.