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What distinguishes the fourth industrial revolution from previous ones is the convergence and interaction of several technological trends at the same time. Here are some of the main technological trends that that will permanently change our businesses and our lives.

Trend 1: Everything connected and smart

You’re no doubt familiar with the Internet of Things (IoT). IoT refers to the growing number of smart, connected devices and objects capable of collecting and transmitting data.

In the future, everything that can be connected will be connected. Not just devices and products – although this is certainly a key factor for businesses – but also the spaces in which we live and work. From smart, connected factories and offices to entire smart cities, the spaces around us will be increasingly equipped to track what is happening and act accordingly.

Trend 2: The datification of the world

Ubiquitous computing and IoT are bringing huge amounts of data being generated every day. But along with this machine-generated data, we humans also generate an enormous amount of data during our daily activities, a process that shows no signs of slowing down.

The good news is that companies can use this data to develop better products and services, improve business processes, make decisions and even create new revenue streams. However, companies also need to be aware of the risks associated with data, particularly data privacy and security.

Trend 3: Artificial Intelligence (AI)

All the data generated is an important tool for artificial intelligence, which has taken an incredible leap forward in recent years.

The result for businesses is that, as our interactions with machines become increasingly intelligent, customers will expect all types of products and services to have some form of AI capability.

Now, following all of these trends, we understand that we are entering an era of rapid and continuous evolution, where multiple technology trends combine and feed into each other to produce major changes. For businesses, this means that the days of incremental technology upgrades are gone forever. Continuous change is the way of the future.

If you want to know more about how technologies can improve your business processes, contact us and our best experts will inform you.

What is Web 3.0?

Although Web 2.0 may seem like an advanced approach to the Internet, it still has many drawbacks. What about the security of your personal data? With Web 2.0, trusted institutions take control of user data, especially because of the need for trusted intermediaries. If two parties want to make a transaction but do not know and trust each other, they will have to resort to the services of trusted intermediaries. However, the intermediary has control over the storage and management of the data, thereby increasing its power over the users. In addition, centralized power has never led to success in times of crisis, which requires decentralization.

Web 3.0 is a promising improvement over Web 2.0, especially given the major transformations in terms of infrastructure. The third generation of the Web, also called the semantic Web, uses an improved metadata system. The metadata system helps structure and organize all types of data, making them readable for humans and machines. The main benefit associated with Web 3.0 is almost the best twist on the differences between Web 2.0 and Web 3.0. Web 3.0 eliminates the need for centralized intermediaries and introduces the universality of information.

How revolutionary is Web 3.0?

Understanding the comparison between Web 2.0 and Web 3.0 should also focus on the uniqueness of Web 3.0. The third iteration of the Web is a formidable response to Web 2.0’s shortcomings with an emphasis on innovative technology. Web 3.0 uses artificial intelligence to enable machine-to-machine interaction as well as advanced analytics. In addition, Web 3.0 uses a decentralized network to bring data under the control of the owners. As a result, users are able to own their data as well as determine how it is distributed. In addition, the differences between Web 2.0 and Web 3.0 are improvements in user privacy and security. Web 3.0 uses encryption and distributed ledger technology to address trust issues that were evident in Web 2.0.

Notable features of Web 3.0

You can build a stronger foundation for understanding “What’s the difference between Web 2.0 and Web 3.0?” by focusing on Web 3.0 features. Here are some of the most important features of Web 3.0 that will help distinguish it from Web 2.0.

• Web 3.0 uses artificial intelligence to deliver the right results at a faster pace along with access to real-time information.
• Web 3.0 also allows users to tap into the potential of 3D images and graphics.
• Web 3.0 has a Semantic Web functionality. This means that Web 3.0 can support understanding the meaning of words. As a result, machines and people can easily find, share and analyze information on Web 3.0.
• Web 3.0 also offers important features such as improved privacy and security.
• Web 3.0 uses improved authorization mechanisms using distributed ledger technology and encryption to protect user identity and data.

If you want to learn all about how Web 3.0 and related technologies can improve your business processes, contact us and our top experts will inform you!

DEFINE A CLEAR DIGITAL STRATEGY
Planning a company’s digital transformation from the outset involves being able to see the big picture. You also need to be clear about your desired outcomes. Define your objectives from the outset. Decide what you want to achieve and make sure you understand why the organization needs to transform. Depending on your current and planned methods of operation, the following may be decisive factors:

• Reduced supply costs
• Increased sales volume
• Reducing waste or duplication
• Increasing profits
• Attracting new customers
• Catching up with or surpassing competitors
• Implement new technologies

In summary, start by assessing the current state of your business. Consider the requirements of your employees and systems, as well as potential areas for improvement.

CALCULATING ROI BEFORE STARTING IOT PROJECTS
A digital transformation should include an assessment of how much the proposed technology will drive revenue growth. A return on investment (ROI) calculation provides a feasibility check prior to

SELECTIVE TECHNOLOGY INVESTMENT
Choosing the right tools and technology is critical. There are many digital transformation trends being discussed in the marketplace, but technology must serve the business, not the other way around. Choosing the best strategy for the IoT journey remains a challenge. Before deciding on an expensive IoT implementation and choosing the direction of your journey, examine your options and seek advice on choosing the best technology and systems for your business.

IT/OT INTEGRATION FOR IOT SOLUTIONS
Integrating information technology (IT) and operational technology (OT) is no easy task, but with the right approach, it can deliver unprecedented growth. By implementing the right IoT solutions, you can successfully bridge the gap and create an intelligent, interconnected foundation to improve business processes and add value to your data. By integrating IT and OT systems, you can unlock the power of your data to generate accurate predictions and make informed decisions.

To ensure that your digital transformation efforts do not stall, it is important to have a trusted partner to guide you on your digital transformation journey. Each digital journey is individual and should be tailored to the client’s needs.

By checking off the different aspects of this list, you will be on your way to a successful digital transformation. If you would like to discuss digital transformation readiness in more detail, please contact us and we will put you in touch with our experts.

David Vilasack, IDboxRT manager at CIC, participated last month, September 28, in the conference “Cantabria in the Digital Transformation” organized by Ascentic and inaugurated by the president of Cantabria, Miguel Angel Revilla.

The manager’s intervention was aimed at talking about the experience of the IDboxRT monitoring platform in Industry 4.0, focusing on one of its main success stories: Bosch, whose project with the tool involves the monitoring of thousands of signals between its Spanish plants in Madrid, Barcelona and Aranjuez. Among the benefits obtained by this customer, we can highlight the creation of calculations and subsequent consumption reports to evaluate energy losses, or even the acquisition of data from CO2 sensors for personnel control (COVID-19) and reports for HR and Medical Service on the status of the different rooms of the plant for CO2 control (COVID-19).

In addition, Vilasack spoke about the key technological pillars in Industry 4.0, on which the strategy and operations of any company in its digital transformation process must be based. Among them, he highlighted augmented reality, cybersecurity, Big Data, the cloud, also known as cloud computing, an essential axis since 90% of companies will partially or totally migrate to cloud environments in the next three years.

Artificial intelligence was another of the elements analyzed, as business environments will become much more intelligent, forming a clear cohesion between machines and humans. They will be more efficient, effective and competitive spaces due to the automation of processes, so companies will increase their production and productivity.

With integrated functionality, controls, sensors, and connected energy management platform capabilities, hotels can reduce energy consumption by 20-50% (depending on facility and location) and lower operating costs.

Hotel energy efficiency is a journey. A detailed understanding of a hotel’s energy consumption patterns, its carbon footprint and its overall compliance with established goals and objectives can be the key to achieving the right balance of efficiency and guest satisfaction.

It is very important to establish a historical baseline for the facility to understand in detail the patterns affecting energy consumption versus the hotel’s bottom line.

There are a few general tips that energy management experts say hoteliers can implement to achieve efficiency savings. The Internet of Things allows for smart connectivity, monitoring, control, asset tracking and more at relatively low cost.

1. Install an energy-saving thermostat based on occupancy to reduce the temperature in unoccupied rooms. Make sure the system can interface with the hotel’s property management system. Being able to manage an aggressive energy-saving profile in a room when it is not sold out can have a significant impact on savings, while preventing temperature drifts that may be unwanted by guests when the room is sold out.

2. Use a real-time occupancy signal from the energy management thermostat to turn off lights in empty guest rooms.

3. Ensure that the guest room energy management system can communicate with the building management system. Furthermore, the ability to view data through a single dashboard can be very useful in monitoring and measuring the energy efficiency of the entire facility.

4. Use run-time data from an energy management system to identify heating, ventilation, and air conditioning units that are operating unusually. Moreover, apply preventive maintenance to ensure that problems are rectified quickly.

5. Embrace the Internet of Things and its future development. Moreover, hotels need to keep their avenues open in the area of connectivity and management – an area that will grow strongly in the next five to seven years. In addition, this space is very fluid, and hoteliers need the flexibility to be able to pivot and embrace the changes that have happened and are coming.

6. Investment in staff and quality products are key. With high staff turnover, hotels may not invest as much in staff training, but it’s worth it for the energy management savings. They should study the data and understand what problems you are having. The analysts will recommend how to solve the problem and how to work more efficiently.

A better product may cost more, but hoteliers have to base it on the value the product brings. The link to that value includes guest comfort, staff convenience, guest feedback, etc. Invest in the products and technology they provide.

7. Give guests the opportunity to understand their habits and how they use energy in the guest room. Give them the opportunity to make a difference in their environment. Motivate them to save energy by educating them about their carbon footprint.

In conclusion, integrating advanced controls, sensors, and energy management platforms can greatly reduce a hotel’s energy consumption and operating costs. Key steps include using energy-saving thermostats, real-time occupancy signals, and ensuring communication between systems. Preventive maintenance, staff training, and quality investments further enhance efficiency. Engaging guests in energy-saving initiatives also contributes to sustainability. This approach balances efficiency, cost savings, and guest satisfaction, making energy efficiency a continuous and adaptive journey.

INTRO

Although Artificial Intelligence (AI) technologies began to be developed several decades ago, several definitions currently coexist that include concepts such as Big Data, data science, machine learning or IoT.

AI is defined as a set of tools and methodologies that allow machines to simulate processes and develop tasks that require applying human intelligence such as decision making, object recognition or speech understanding.

The industrial sector has been immersed for years in its digital transformation in what has been called the fourth industrial revolution or Industry 4.0. There is no doubt that this sector has a high potential to lead the application of AI technologies in the monitoring and optimization of its processes, providing an appropriate scenario to test its benefits, from design and manufacturing processes to the management of the value chain. Thus, large companies such as GE, Siemens, Intel, Funac, Kuka, Bosch, ABB, NVIDIA and Microsoft are investing heavily in the development of new systems to improve their production.

At this juncture, the concept of Industrial Artificial Intelligence (IAI) has recently emerged, which can be defined as the application of AI to the operations, processes and physical systems of a company, so that the behavior of these operations, processes and systems can be monitored, optimized or controlled to improve their efficiency and performance, providing them with greater autonomy. Thus, this concept includes applications related to the manufacture of physical products, production lines and warehouses, or operations related to different processes.

It is an end-to-end system, in which sensors generate data, which are sent, managed and analyzed by means of different algorithms and models that generate decisions in real time and whose results are returned for actual implementation in the actuators.

USE CASES

There are several different use cases related to three categories of applications depending on the degree of automation involved: monitoring, optimization and control.

Monitoring

Industrial processes need to monitor the performance of their systems and products to identify or predict failures and other situations that lead to unsatisfactory results. Some examples that will benefit from IAI technologies are as follows:

• Quality Control

Companies find it difficult to maintain high levels of quality and comply with regulations and standards due to the current short time-to-market for new products and services and their increasing complexity. On the other hand, consumers expect defect-free products, so companies must avoid the damage that complaints and defective products can do to the brand.

In this context, AI algorithms enable a new form of quality control. On the one hand, algorithms based on image recognition technologies warn production teams in real time about faults in production systems that can reduce product quality (e.g., recipe deviation, changes in raw materials). On the other hand, the implementation of these algorithms allows the prediction of maintenance and planning tasks that minimize the associated risks. Finally, the integration of such algorithms with IoT platforms allows the collection of data on the use and behavior of products during their useful life, information that can be very valuable when making strategic and design decisions.

• Predictive maintenance

The continuous maintenance of production machinery represents a major expense in manufacturing processes, so it is important to implement solutions based on AI algorithms that allow predicting future failures in a part, machine or system in order to drastically reduce unplanned downtime and increase the useful life of production systems.

Optimization

IAI-based decision making and planning systems allow users to design plans to optimize a set of business metrics.

• Process planning

Many industrial scenarios include complex work sequences whose order of execution can significantly impact factors such as cost, time, quality, workloads, supplies or waste. The application of AI-based optimization algorithms allows such sequences to be defined dynamically in real time.

• Generative design

AI is changing the way products and services are designed. Generative design uses AI algorithms to design new products based on their description, including parameters such as type of material, means of production, budget or time to market. These algorithms analyze different configurations before proposing the best solutions.

• New market adaptation strategies

The application of AI is not limited to the production plant; its algorithms make it possible to optimize supply chains or help companies anticipate market changes. All this is a great advantage for business management, moving from a reactive approach to a strategic one. Thus, estimates of market demand can be formulated by searching for patterns that relate location, socioeconomic and macroeconomic factors, weather patterns, political status or consumer behavior so that companies can optimize their resources or control inventory.

Control

Finally, control systems are at the heart of industrial process operations and are essential to reap the full benefits of automation. Examples of applications that benefit from AI include the following:

• Robotics

Traditionally, industrial robots have been explicitly programmed to move between a series of 2D or 3D points and perform specific actions at those points. New approaches such as collaborative robots or co-robots simplify programming by allowing the capture of these points based on the robot’s physical position. However, in both cases, the robot does not detect changes in the environment or in the position of the parts it is manipulating. Therefore, computer vision allows robots not to interfere with people or other robots, and to interact independently.

• AGV

Autonomous mobile robots (AGVs) are used in warehouses and companies to transport and collect materials thanks to the use of image-based AI algorithms that enable them to understand, map and navigate these environments more efficiently.

CHALLENGES OF INDUSTRIAL ARTIFICIAL INTELLIGENCE

Industrial AI presents a number of challenges that set it apart from other consumer-facing AI applications.

• Data acquisition and storage

IAI systems rely on data captured by sensors that seek to digitally represent the real world. The implementation of IoT platforms and cyber-physical systems (CPS) has enabled large volumes of data in industrial processes whose acquisition, management and storage has led to different architectures and storage systems.

However, it should not be forgotten that large volumes of data are often captured with a lot of noise, which makes it difficult to acquire and store the data for subsequent analysis. Therefore, advanced simulation techniques including digital twins are used to generate training data under different conditions.

• Data hybridization

A challenge of IAI is the generation of common formats for heterogeneous data coming from diverse sources (images, videos, dwg plans, …). For this, it will be necessary to develop semi-automatic models that help to structure (e.g. extraction of information from a plan, extraction of tags from an image), homogenize (e.g. spatial/temporal interpolation techniques to move from data in one unit to another) and/or harmonize data (e.g. product matching/record linkage techniques to associate data from different sources).

• Training

The correct application of IA algorithms is associated with the availability of annotated training data. Capturing this data can be complex in industrial environments, as it is often difficult to detect and reproduce some of the failures or lack of quality of products and services.

• Regulated environments

Industrial environments must comply with certain standards and regulations that impact their operations, such as product safety, public health, environmental impact or occupational safety. In some cases, regulatory controls can make it difficult to implement AI technologies.

As stated in the European Data Strategy1 published by the European Commission in February 2020, Europe must become a model of a data-empowered society for better decision making in the public sector and private enterprise. To this end, the European Union will promote a legal framework in relation to data protection, fundamental rights, security and cybersecurity. The aim is to generate an ecosystem of trust thanks to a regulatory framework for AI2.

SITUATION IN SPAIN

According to a study by EY for Microsoft, the majority of companies surveyed in Spain (65%) have plans, pilot projects or proofs of concept around AI. However, only 20% have solutions in operation, 12 points below the European average.

The “Strategy for Artificial Intelligence in R&D&I in Spain” includes a section oriented to the application of AI in the Connected Industry. As the document states, Spanish industry represents 13% of the country’s added value and employs 11% of the employed population, so the social and economic impact of AI technologies is essential.

Manufacturers most often use Artificial Intelligence (AI) to improve overall equipment effectiveness (OEE) and first-cycle output. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, enabling better forecasting.

Like many other components of digitalization, implementing AI can seem like an overwhelming task. Additionally, concerns about how to effectively use and manage the billions of data points generated by AI computing power and connected machines are common among manufacturers. Many people don’t know how to start; moreover, they often attribute their caution in implementing AI to costs, IT requirements, or fear of not being ready for Industry 4.0.

To stay competitive, it is important for manufacturers to adapt to a more data-driven business model. This often involves reorganizing personnel, upgrading equipment and software.

AI, a concept often associated with the future, is now a reality and can be applied to your facility today. Here are 3 ways in which industrial AI is revolutionizing manufacturing and tips on how to implement it:

1. Predictive and preventive maintenance

Major equipment failures, whether mechanical or electrical, cause significant manufacturing downtime. Following recommended preventive maintenance schedules can prevent these breakdowns. However, preventive maintenance is often overlooked or not optimized for quick turnaround times. Thanks to the capabilities of IoT devices, sensors, MES data and machine learning algorithms, manufacturers can use multiple points of machine data to predict breakdowns. Optimize maintenance schedules before predicting breakdowns to maintain machines and ensure smooth production floor operations.

2. Supply chain automation

Today’s supply chains are ultra-complex networks that need to be managed, with thousands of parts and hundreds of locations. Moreover, AI is becoming an essential tool for the rapid delivery of products from production to the consumer. By leveraging machine learning algorithms, manufacturers can determine the optimal supply chain solution for all their products.

Furthermore, managing internal inventory can be a major challenge in itself. A production line heavily relies on inventory to maintain operations and meet production targets. Throughout the manufacturing process, operators must replenish a specific amount of components at each step to sustain efficiency. Ensuring the factory floor has all necessary supplies is critical, and artificial intelligence can play a pivotal role in optimizing this process. AI can learn component quantities, track expiration dates, and efficiently distribute them across the shop floor.

3. Production optimization

Optimizing a manufacturing process can be a data-intensive task that involves countless historical data sets. Determining which process parameters provide the highest quality products is no easy task. Production and quality engineers are constantly conducting dozens of experiments to optimize process parameters, but they can often be costly and time-consuming. By using artificial intelligence to process data quickly, engineers can find the best process recipe for different products. The AI will constantly learn from all points of production data to continuously improve process parameters.

Bonus: Augmented and virtual reality

As augmented and virtual reality technology improves every day, and with more and more large companies developing devices for this market, it’s only a matter of time before the manufacturing industry fully embraces it. Furthermore, virtual reality can help better train product assemblers to perform assembly or preventive maintenance tasks. Similarly, augmented reality provides real-time, machine-learning-based reporting in the factory or in the field, helping to quickly identify defective products and areas of improvement. The applications of AR/VR in manufacturing are endless and can play an important role in solving today’s problems.

The era of the smart building has arrived. Today, thanks to advances in technology, a building can not only provide all the services the occupants need. But, also, do it as efficiently as possible, minimizing costs and increasing energy savings over the life of the building. It’s a balance that will be key to business in the future.

What is a smart building?

A smart building uses technology to use resources efficiently and economically, while creating a safe and comfortable environment for occupants. Intelligent buildings not only use a wide range of existing technologies but also integrate future technological developments through design or retrofitting. Moreover, Internet of Things (IoT) sensors, building management systems, artificial intelligence (AI), and augmented reality are some of the mechanisms and robotics that control and optimize performance in a smart building.

The benefits of smart building analytics?

Smart buildings generate a lot of valuable data about how they are used. Analyzing this data can give you insight into usage patterns and trends, allowing you to make informed decisions to optimize building performance, providing the following benefits:

• Increased productivity.
  Creating a conducive environment with good indoor air quality, comfort, safety, sanitation, and efficient processes enhances employee performance. Therefore, understanding how people use and move within your building is crucial for optimizing space and reducing waste.Increasing the size of a cramped, high-traffic area can be a practical example of this.

• Reduced energy consumption.
  Smart buildings can improve energy efficiency and, in turn, reduce energy costs. For instance, by connecting IoT sensors that track occupancy to your building management system, you can automatically turn off lights or HVAC systems in unoccupied rooms or spaces. This reduces unnecessary energy consumption and the emissions that these aspects produce.

• Lower operating costs.
  Building overhead is a significant expense for any building owner/user. However, while they are a necessary expense for a business, the level of cost is often wasteful because it is not applied wisely. By identifying patterns of underutilization, you can reduce the footprint of a property to reduce costs.

There are many benefits to implementing smart systems in a building, from cost-effectiveness to making the structure more environmentally friendly. Today, smart buildings are relatively new. But, given the wide range of benefits they offer, they will soon become the norm.

How can you make your building smart with data?

The key to successfully turning your building into a smart and efficient space is understanding that only accurate and reliable data can help. Indeed, data is at the heart of smart building systems, determining how a facility is used. With this information in hand, you can identify areas for improvement. Either by integrating with other smart technologies and building systems that provide automation or by facilitating strategic decisions. The IDboxRT Operational Intelligence solution can help you monitor different types of usage data in real time.

Additionally, want to get started or learn more about how your building is being used? Request a Demo and talk to our experts.

One of our main customers, specialist in Energy Efficiency and Photovoltaic Self-consumption solutions for the industrial sector, EDF Fenice, has launched its Energy Efficiency Control Center, EnergyHub, thanks to the implementation of the IDboxRT tool. This installation is now operational and will enhance the performance of the energy assets of its customers’ production plants.

“From EDF Fenice’s new offices in Madrid, its energy efficiency engineers and technical support staff permanently monitor in real time all the energy vectors of its customers’ factories, which have already improved their energy efficiency, reduced their costs and minimized the environmental impact of their activity.” – Europa Press comments.