In the last 10 years, the amount of data generated by industries is the largest in history. Despite the growing digitalization of the sector, there is still a great need to contextualize this information to be effectively useful to the industry.
In the contextualization process, three questions are fundamental for data processing. What is it? What is it for? and Who is it for?
From this, a filtering process begins, the goal of which is to classify data as useful or useless. In addition, the contextualization process is also responsible for formatting the data into usable formats and establishing relationships between different sources so that it can be effectively submitted to a specific context.
In this post, we will talk about how data contextualization is a fundamental technique for industrial processes’ operation and maintenance phase.
The PLM Phases
First of all, it is worth exemplifying the different phases of PLM (project lifecycle management)
PLM refers to the management of an asset throughout the many stages of its useful life. The concept ranges from the initial phases of a project to the final phases of its life cycle. Adequate PLM management needs the direct involvement of various departments throughout the organizational levels of a company.
The life cycle phases of an asset can be defined as
- Basic Project Design and Planning;
- Engineering and Design;
- Construction, Installation, and Commissioning;
- Operational and Maintenance;
- Decommissioning;
- Ending of the asset life cycle.
In order to analyze the advantages of contextualizing data for the operation and maintenance phase within a plant, let’s briefly describe the characteristics of the operation and maintenance phase.
The commonly called O&M phase refers to a product stage in which it is already performing its activities and that also involves maintenance journeys in order for this asset to have its operational life cycle extended through the extension of its performance.
This phase is one of the most important in the PLM cycle and is fundamental for any industry. However, it is here that we often encounter some of the biggest challenges in the process
Operation and Maintenance
This phase involves numerous systems, many teams, and above all, a lot of data. One of the biggest challenges faced by managers is in relation to communication with their teams and decision-making.
They in turn must follow heavy business rules, read countless manuals and procedures, and consult the information available in many different systems to carry out their activities.
This management method opens the door to inefficiencies, information mismatches, and difficulty in having a complete operational view.
In addition, those responsible for the teams will hardly know how to allocate their available resources efficiently, and will also have great problems in identifying which are the priority tasks for their teams.
In this context, data contextualization becomes a powerful tool for the organization, qualification, and disposition of the many data generated in this operation.
Having data that delivers real value to operators and decision-makers, activities can be executed more quickly, assertively, and with less demand on the field workforce.
Besides making inspection and maintenance journeys more efficient, contextualization supports a holistic view of everything that happens in a plant so that managers can take the necessary actions
How to contextualize data
Contextualization is not a simple technique to perform, but through digital twin technology and the management by a team with good engineering and asset integrity expertise in the operation and maintenance phase, it can be implemented in just a few weeks in an entire industrial plant.
Digital twin enables integration between multiple different systems and its database can be fed with historical data, sensors, and any kind of data available from the operation. From the contextualization, the tool can be parameterized in a high range of activities within the operation and maintenance phase.
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About the Author: Andre Andrade
