A journey through production

A journey through production

Imagine if every product that reaches your hands could explain its story: where it comes from, what materials it was made with, what processes it went through, how its quality was guaranteed and under what conditions it was transported to its destination.

We live in an era in where information is everything. However, in the industrial world we still let valuable data get lost in siloed systems and time-sensistive decisiones. What if we could make that data visible, useful and connected?

Today, thanks to technologies such as Industry 4.0 and real-time capture systems, production plants generate more information than ever before. But having dat is not enough. The key is structuring, interpreting and connecting it. Turning disparate data into useful knowledge is the first step toward truly intelligent digital labeling.

This is precisely what the European project bi0space is seeking: to develop a digital labeling system for bio-based products that allows each batch to be traced from origin to delivery. This system not only collect technical information ont raw materials, processes and quality controls, but will also include logisitcs data, transportation conditions and environmental KPIs.


In today’s industrial processes, much of the key information about a product’s manufacturing is scattered across different platforms or unstructured. This makes complete traceability of what happens in the plant difficult and, consequently, complicates operational decision-making, continuous improvement, and the justification of sustainability and quality standards. In the context of bio-based production, where materials can vary depending on the supplier, harvest, or process, having control over each stage of the product’s lifecycle becomes especially important. Hence the need to establish a system that allows all this information to be collected and accessed in a unified and accessible manner.


The digital labeling system being designed at biOSpace includes five essential blocks of information:

All this data is linked by a unique digital identifier that accompanies the product throughout its entire journey, from entry into the factory to exit. This label is progressively completed, adding information as the product goes through different stages of the process: raw material reception, processing, quality control, packaging, transportation, etc.

This modular identifier structure allows for precise tracing of the product´s journey and condition at each stage, ensuring all relevant information is connected in a clear and structured manner.


The value of this information lies not only in its storage, but also in its practical use, tailored to each need. Therefore one of the goals is to enable the system to be accessed from internal dashboards that help plant staff make decisions in real-time, and that at the same time to be integrated into broader digital environments, such as management systems or digital twin platforms.

Furthermore, the same digital label can offer different levels of information depending on the profile of the user consulting it. An operator can view technical data on the process or quality controls, while a sutainability manager can acces environmental KPIs, and an end consumer can view an accesible summary of the product´s origin, characteristics, and traceability.

This detailed traceability will also contribute to what is now becoming known as the digital product passport, a tool that is gaining importance within the framework of European policies toward a more transparent and circular economy.





Although this solution is still in the design phase, it´s based on a simple but important question: what are we doing with all the information already generated in our factories?

In several cases, data exists, but it´s not connected, shared, or simply not used. This project seeks precisely that: to make sense of it, organize it, and make it availabke to those who need it, from the operator who manages a batch to the strategic decision-maker or the persona who, at the end of the chain, consumes the product.

It´s not about incorporating technology as a trend, but rather about using it with criteria. It´s about building tools that allow us to better understand what we produce, how we do it and what impact it has, at a time when traceability, sustainability and transparency are no longer options, but rather conditions for continued progress.

Managing industrial data: prevention is better than cure

Managing industrial data: prevention is better than cure

In the field of health, it is known that is more effective prevent illnesses than treat them once they have manifested themselves. In a similar way, it can be apply in the context of industrial data, its continuous and proactive maintenance helps to avoid the need of an extensive pre-treatment before using advance data analytic techniques for decision-making and knowledge generation.

Pre-treatment data implies doing several tasks as: (1) data cleaning, (2) correction of errors, (3) elimination of atypical values and (4) the standardisation of formats, among others. These activities are necessary to assure quality and data consistency before using it in analysis, decision-making or specific applications.

Fuente: Storyset en FreePik

However, if robust data maintenance can be implemented from the outset, many of these errors and irregularities can be prevent. By establishing proper data entry processes, applying validations and quality checks, and keeping up-to-date records, it is possible to reduce the amount of pre-treatment need later, identifying and addressing potential problems before they become major obstacles. This includes early detection of errors such as inaccurate data, correction of inconsistencies and updating of outdated information. It is true that companies currently store large amounts of data but it is important to highlight that not all of this data is necessarily valid or useful, for example, for use in an artificial intelligence project. Indeed, many organisations face the challenge of mantaining and managing data that lacks relevance or quality. This management aims to ensure te integrity, quality and availability of data over time.

Efficient data maintenance is crucial to ensure that data are relaible, up-to-date and accurate, but this involves continuous monitoring and management by company staff, ensuring that they remain accurate, consistent, complete and up to date. The most common activities related to data maintenance include:

  1. Regular monitoring: Is carried out a periodic data tracking to detect possible problems, such as errors, inconsistencies, loses or atypical values. This can involves the revision of reports, tendance analysis or the implementation of authomatized alerts to detect anomalies.
  2. Updating and correction: If errors or inconsistencies in data are identified, maintenance staff will ensure that theyr are corrected and updated appropriately. This may involve reviewing records, checking external sources or communicating with those responsible for data collection.
  3. Backup and recovery: Procedures and systems are established to back up data and ensure its recovery in the event of failure or loss. This may include implementing regular backup policies and conducting periodic data recovery tests.
  4. Access management and security: Data maintenance staff ensure that data is protected and only accessible by authorised users. This may involve implementing security measures such as access control, data encryption or monitoring audit trails.
  5. Documentation and metadata update: Dara-related documentation, including field descriptions, database structure and associated metadat, is kept up to date. This facilitate the understanding and proper use of the data by users.

In summary, data maintenance involves: (1) regularly monitoring, (2) correcting errors, (3) backing up, and (4) securing the data to ensure that it is in good condition and reliable. These actions are fundamental to mantaining the quality and security of stored information.

At CARTIF, we face this type of problems in different projects related to the optimisation of manufacturing processes for different companies and industries. We are aware of the amount of time consumed in staff hours due to the problems explained, so we are working on providing certain automatic mechanisms that make life easier for those responsible for the aforementioned “data maintenance”. One example is s-X-AIPI project focused on the development of AI solutions with auto capabilities that require special attention to data quality starting with data ingestion.


CO-authors

Mireya de Diego. Researcher at de Industrial and Digital Systems Division

Aníbal Reñones. Head of Unit Industry 4.0 at the Industrial and Digital Systems Division