Wrangling and cleaning your manufacturing data sounds worse than herding cats. It’s about as un-sexy as technology gets. When improvements cascade throughout the entire organization, however, cleaning your data may become a priority.

Robust data lakes, large repositories of raw information, draw from disparate sources of data spread across a plant or facility. The larger the breath of data gathered from across the manufacturing and supply chain processes, the better AI analyzes patterns and makes predictions.

Drawing data from separate sources naturally lends itself to creating silos for data. In order to close the gaps between those silos, glass manufacturer Vivix, based in Brazil, employed the low-code AI development software Mendix to create linkages between these varied data repositories.

The resulting improvements demonstrate why cleaning and connecting your data is worth the time and investment.

Change From the Top

When Aristoteles Neto, industrial transformation manager at Vivix, took over the department in 2021 he focused on overall strategy, not technology. In 2022, Vivix began using low-code AI software to unify and clean the company’s manufacturing data, a complicated, frustrating task at best.

“How can you build this data foundation, this foundation for using data, not only machine data, but production data, transaction data, documents and engineering data? The complexity of industry regarding data is very high,” says Neto.

Neto enjoyed two advantages when he began his campaign to improve how Vivix processed data.

The mandate to create the industrial transformation department came straight from the C-suite. Neto therefore had wide latitude to experiment and looked to build a portfolio of solutions, not just a single pilot project. Vivix even implemented a new position, value management officer, to report the impacts of the projects.

Neto also worked with plants that featured state-of-the-art automation. OT was not an impediment to creating the data foundation he wanted. Disconnected business layers created the problems.

“When I thought about how to build a digital solution…it’s difficult to use data from a machine and contextualize with different processes. If you use too many disconnected systems, you need a lot of different people to create your solution. Eight, sometimes more than 10 people from different departments like the AI expert, the generative AI expert, data engineers and automation engineers,” says Neto.

Improving Data Improves Everything

Neto rattles off a series of benefits Vivix enjoys from analyzing normalized, contextualized and unified data:

·       85% reduction in production issue resolution time.

·       Saved 6,000 work hours in a single year.

·       Increased energy efficiency of furnaces by 5%.

·       Avoided $1 million in maintenance costs in 2025.

“We increased our net promoter score (NPS) from our customers because [having a unified data architecture] allowed us to answer some complaints from our clients in two or three days at maximum. Before it was two or three weeks. Now we can understand our pain points from our clients and give better answers and make faster decisions, reduce the cost of operations and give some stability,” said Neto.

Getting Creative with AI

The unified data foundation created by Neto and his team supports experiments with agentic AI, digitalization, predictive maintenance and robotics. The more points of data, the easier to draw conclusions as to what innovations warrant further explanation or not. Even failure enriches the data pool.

“We reuse this architecture, we reuse these foundations for projects in logistics, maintenance, production, security. … We use agile methodology. Sometimes we start a project thinking [there is value add], but when the project is [at the midway mark] and we understand that [the value add hasn’t] happened, we stop the project and put our effort into another initiative,” says Neto.

“Now we can use any data that’s available in an easy way. It opens the door to creativity.”