7 posts tagged with "smart_industry"

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.

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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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.

Introduction​

Technological innovation plays a key role in enhancing efficiency and sustainability in manufacturing companies. Mative, a leader in providing advanced technological solutions, offers Internet of Things (IoT) and Machine Learning (ML) services that can revolutionize operations in the window, door, and frame manufacturing sector. This report explores the energy and economic benefits derived from adopting these technologies in a manufacturing company.

Internet of Things (IoT) in Manufacturing​

Definition and Operation​

IoT involves the interconnection of smart devices via the internet, capable of collecting, exchanging, and analyzing data in real-time. In manufacturing, IoT sensors can monitor various parameters such as machinery performance, energy consumption, production quality, and supply chain conditions.

Energy Benefits​

1. Optimization of Machinery Energy Use: IoT sensors can monitor the energy consumption of machinery and equipment, identifying inefficiencies and suggesting improvements to reduce energy use.
2. Efficient Facility Management: Sensors can control heating, ventilation, and air conditioning (HVAC) systems based on real-time data, reducing unnecessary energy consumption.
3. Improved Production Processes: Monitoring equipment conditions in real-time helps optimize production processes, reducing energy waste associated with machinery downtime or suboptimal performance.

Economic Benefits​

1. Reduction of Operating Costs: Efficient management of energy consumption and machinery performance leads to significant reductions in operating costs.
2. Enhanced Production Efficiency: Real-time monitoring and optimization improve production efficiency, leading to higher output and reduced waste.
3. Predictive Maintenance: Data from IoT sensors enable predictive maintenance, reducing downtime and extending the lifespan of machinery, thereby saving on repair and replacement costs.

Machine Learning (ML) in Manufacturing​

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 manufacturing, ML can analyze data from IoT devices to identify patterns, forecast trends, and optimize processes.

Energy Benefits​

1. Predictive Energy Management: ML algorithms can analyze historical and real-time data to forecast future energy needs, allowing for more precise energy management and reducing overall consumption.
2. Optimization of Production Schedules: ML can optimize production schedules based on data analysis, leading to better energy utilization and reduced peak demand.

Economic Benefits​

1. Process Optimization: ML helps in identifying the most efficient production processes, leading to cost savings and increased productivity.
2. Demand Forecasting: ML algorithms can predict market demand more accurately, allowing for better inventory and production planning, reducing excess inventory and associated costs.
3. Quality Improvement: ML can analyze data to identify quality issues early in the production process, reducing defects and waste, and improving overall product quality.

IoT & ML: Implementation in a Window, Door, and Frame Manufacturing Company​

Consider a manufacturing company specializing in windows, doors, and frames 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. 20% Reduction in Energy Consumption: Through optimized machinery use and efficient facility management based on real-time data.
2. 15% Decrease in Operating Costs: Due to enhanced production efficiency and reduced waste.
3. 25% Improvement in Production Efficiency: Resulting from optimized production processes and better use of resources.
4. 10% Savings in Maintenance Costs: Thanks to predictive maintenance and extended machinery lifespan.

Conclusion​

The adoption of IoT and ML technologies proposed by Mative Srl represents a significant advancement for manufacturing companies specializing in windows, doors, and frames. The benefits from implementing these technologies not only contribute to energy and economic efficiency but also enhance competitiveness and profitability, positioning the company to effectively meet future industry challenges.

Predictive Maintenance​

Predictive maintenance uses IoT sensors to collect data on the operating parameters of industrial machines. This data is analyzed by machine learning software to identify correlations and predict maintenance needs or failure risks. Over time and with more data, the software improves its predictions. This approach changes the traditional method of periodic maintenance, preventing sudden failures and production stops. Additionally, machine learning can be used for monitoring and controlling the production process, recognizing products and defects with almost absolute precision.

Logistics and Supply Chain​

Machine learning is widely used in risk management in logistics and industrial supply chains. Continuous data analysis of transport and product movements optimizes transport plans considering various parameters such as costs, distances, and sales time flexibility. Logistics 4.0, thanks to advanced data analysis enabled by machine learning, allows quick and precise decisions to meet customer demand timely and economically, promoting the creation of a 'global warehouse' through data cross-referencing from different operational centers. Integrating machine learning with Digital Twins, digital models of the production reality, allows efficient testing of products and services, reducing errors and improving the production chain.

Process Automation​

Machine Learning algorithms enable the automation of many industrial processes, increasing efficiency and reducing human errors.

Product Quality​

The analysis of data collected by sensors during production by machine learning models ensures more rigorous and immediate quality control.

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