What is the best way to successfully define, scale and lead Digital Transformation?

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.

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

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.

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.

Cantabria in Digital Transformation

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.

7 Tips for Hotel Energy Management

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. 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. Apply preventive maintenance to ensure that problems are rectified quickly.

5. Embrace the Internet of Things and its future development. 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. 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.



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.


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


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.


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.


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.


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.


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.

3 ways in which industrial AI is revolutionizing manufacturing

Artificial Intelligence (AI) is most often used in manufacturing 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. 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 don’t know how to get started, and often attribute their caution in implementing AI to cost, IT requirements and/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

Some of the biggest downtime in manufacturing can be caused by a major piece of equipment not working due to a mechanical or electrical failure. Breakdowns can usually be easily prevented by following the recommended preventive maintenance schedule for equipment. Often, preventive maintenance is overlooked or not optimized in terms of optimal 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. Maintenance schedules can be optimized prior to predicted breakdowns to keep machines in perfect condition and keep the production floor running smoothly.

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. AI is becoming an essential tool for the rapid delivery of products from production to the consumer. With machine learning algorithms, manufacturers can determine the optimal supply chain solution for all their products.

Managing internal inventory can be a major challenge in itself. A production line relies heavily on inventory to keep the lines running and produce products. Each step in the manufacturing process requires a certain amount of components to operate; once they are used up, they need to be replenished in time to continue the process. Making sure the factory floor has all the supplies it needs is a task that artificial intelligence can help with. The AI can learn the quantities of components, expiration dates, and optimize their distribution 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 more and more large companies are developing devices for this market, it’s only a matter of time before the manufacturing industry fully embraces it. Virtual reality can help better train product assemblers to perform assembly or preventive maintenance tasks. 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. Manufacturing AR/VR applications are endless and can play an important role in solving today’s problems.

What are the features and benefits of Smart Building?

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 is a building that uses technology to use resources efficiently and economically, while creating a safe and comfortable environment for occupants. Intelligent buildings can use a wide range of existing technologies and are designed or retrofitted to integrate future technological developments. Internet of Things (IoT) sensors, building management systems, artificial intelligence (AI) and augmented reality are some of the mechanisms and robotics that can be used in a smart building to control and optimize its performance.

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.
    Providing a space that promotes good indoor air quality, physical comfort, safety, sanitation, lighting, efficient processes, and space that employees need at an optimal level will allow them to perform well. Therefore, identifying and understanding how people use and move around your building is integral to improving the physical layout toward optimizing frequently used space while minimizing 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. 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 to reduce unnecessary energy consumption that these aspects emit.
  • 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. At the heart of smart building systems is the data that determines how a facility is used. Once you have this information, you can determine where improvements can be made, 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.

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

EDF Fenice inaugurates its Energy Efficiency Control Center in Madrid

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.

Synoptic: the best data visualization tool

We often find ourselves in the situation of possessing or having access to information, a lot of information, but which, without any kind of organization, is of little use. What is the point of having thousands of data if I am not able to exploit them? Organized data becomes information, very valuable at times, and that is precisely what operational intelligence platforms such as IDboxRT offer me.

This ability to squeeze the most out of my data to obtain valuable information is presented to me in many different ways: the ability to graph and compare historical and real-time data, performing calculations to obtain new data, positioning this data on maps, reports…, but we also offer a more visual way to work with this information: the synoptic.

A synoptic is that “which presents the main parts of a subject in a clear, quick and summarized way”, as dictated by the dictionary. In our case, we use the synoptics to capture data in a visual way, being a schematic representation of reality, which allows us, at a glance, to obtain that valuable information and to know what is happening at the present moment.

Throughout all these years working with synoptics we have used many different approaches based on the nature and needs of each project and client, but experience has made us adopt certain techniques to improve these representations that are often repeated. This is the case of the drill-down approach, in which we start from a generic representation, a high-level visualization, where at first glance it is interesting to know general data, KPIs, and get an idea of where everything is located. From this first level we would navigate to the next one in which we could go into the detail of the selected area, and even obtain data of this in comparison with the rest of the analogous areas, and so, we would continue descending in level to go more and more into detail and get to the specific data.

A real example that we have developed with this approach could be: A first representation of a map of the world, dealing with a client that operates globally, where the top management can get at a first glance information on how their manufacturing plants are operating, and compare to make sure everything is working correctly. With a single click we could navigate to a second, more country-specific level to see the detail of the country’s plants, and in turn navigate to a plant, either because we want more detail or because something has caught our attention when reviewing the KPIs. Once we are visualizing the representation of the plant, we can consider the navigation to specific areas of the plant, and even continue down to the level of machinery or specific parts of each of the machines.

There are infinite approaches for this type of representation, although it must always be in accordance with the need for information. The synoptic representation loses its usefulness if the data shown are not understood or provide unnecessary information.

It is also important that the design of these graphic representations is carried out in accordance with the client, since usually each company has its own way of representing its assets (diagrams, maps, drawings, plans…) and it would be a contradiction to propose something that is unfamiliar to them and would involve a work of adaptation. In addition, we work to maintain the corporate identity of each client so that they perceive these representations as their own and feel “at home”. In this way, they can also make use of these representations at company level on information panels or videowalls. All this entails a previous work to the realization of the synoptic based on the study of the brand and its uses.

Following this approach we have worked on the representation of water treatment plants, road construction projects, industrial production monitoring, refining plants, energy efficiency in buildings, Smart Cities projects and so on.

On the other hand, it is important to highlight that all our synoptic representations use the standard SVG (Scalable Vector Graphics) format, which makes the representations scalable ensuring that there is no loss of quality, and allows the customer to make use of their own representations by importing them directly into the platform.

In addition, from IDboxRT we have an extensive library of pre-made elements of all kinds, so the user can make use of these forms with a simple drag and drop, and even add their own forms to this library.

Synoptics are undoubtedly one of the most powerful tools for data analysis within IDboxRT and are widely used by our clients, regardless of the sector, as they greatly facilitate the understanding of the data and are visually very attractive.

What is the difference between Operational Intelligence (OI) and Business Intelligence (BI)?

Understanding the differences between operational intelligence (OI) and business intelligence (BI) is crucial to contextualizing and taking action on the information and insights provided by your analytics toolset. While both operational and business intelligence are used to drive action and inform decision making, there are key differences that distinguish these two areas of analysis.

Business intelligence maintains a relatively narrow focus with an emphasis on finding efficiencies that optimize revenue or profitability. BI typically means taking a snapshot of data over a defined period of time in the past and reviewing it to understand how the organization might achieve better success in the future.

In contrast, operational intelligence focuses on systems, rather than profits. OI uses real-time data collection and analysis to reveal trends or problems that could affect the operation of IT systems and to help front-line workers make the best decisions about how to address those problems.

The differences between operational intelligence and business intelligence can be summarized as follows:

Business intelligence focuses on finding efficiencies that increase or protect profits, while operational intelligence focuses on maintaining the health of IT systems.

Business intelligence leverages more historical data, while operational intelligence relies on real-time data collection and analysis. Operational intelligence has been described as immediate business intelligence gained from ongoing operational functions, a definition that speaks to the real-time nature of data collection and focuses on the operational functions that characterize operational intelligence in an enterprise environment. While business intelligence typically runs within a specific data silo, operational intelligence helps organizations break down data silos to uncover trends and patterns of activity within complex and disparate systems.