Manufacturing Data: Why Contextualization is the Missing Key

Data is king. Manufacturers are collecting massive amounts of data from sensors, machines, and other sources to gain insights into their processes and optimize production. However, data alone is not enough. This is where an important component comes in: contextualization.

What is contextualization? Contextualization is the process of analyzing data in its proper context to gain insights and make informed decisions. In the manufacturing industry, contextualization involves collecting data from various sources, finding its relations, and analyzing it to gain insights into production processes. By analyzing data in its proper context, manufacturers can identify patterns, trends, and anomalies that may not be apparent from raw data. Contextualization also allows you to identify the root causes of issues and implement corrective actions.

There are several ways and stages of maturation to ensure data contextualization. Manual input of the production context with a connection to ERP, planning or MRP systems is one of the simplest alternatives. If we want a higher level of automation, we can directly inform the equipment of information such as the manufacturing order, planned quantity or reference to be produced, for example. Other information like stoppages reasons, rejection motives, or operators' level of expertise can be also valued information to transform data into knowledge.

Consider this example: one machine has more downtime than any other machine. How can contextualization help to find a root cause?

When did the downtime happen?

When did it start?

What is the difference compared to the other machines?

What are the products you produced?

Who can know what happened?

What type of stoppages were reported?

What alarms went off at that time?

Responding to this information, we could, for example, conclude that one of the shifts had new elements that were not properly trained to carry out a correct setup procedure and that would lead to longer downtime. This could lead to a corrective action decision to train these new elements.

Quick answers to this type of question using contextualized data can make problem identification and resolution much easier and more efficient. However, without this contextualization, there is a risk of making decisions based on incomplete or inaccurate data, which can lead to costly errors and lost productivity.

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