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Dutch HAI Blog

HAI wins SAS Hackaton 2023 with Team Notilyze

HAI, together with data science specialist Notilyze, won the SAS Hackathon competition of 2023 in the Manufacturing category.
The winning case is a smart model that focuses on further optimizing the industrial production of cheese.
For the data analysis, a whopping two years’ worth of historical data from hai-app, HAI’s cloud platform, was utilized.

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Kanarie in de Kolenmijn

Environmental monitoring: the canary in the coal mine at a milk powder factory

Operations Manager Arie van Aalst had everything running smoothly in his brand new dairy factory. Or so he thought. Until that one day in spring, when everything was turned upside down by an outbreak of Salmonella. Months later, when the factory was allowed to restart, he had learned a valuable lesson. The canary in the coal mine had definitely sent a signal… So why did this remain undetected?

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zo gescant zo gedaan engels

Barcode scanning in (Food) Factories

Scanning in the factory proves to be a fantastic solution for reliable factory information
The photo is not about a box being held at gunpoint. You see an operator scanning barcodes on the box. In the past, operators had to record all codes manually on lists. That was time consuming with a high risk of errors. At this plant, they are glad they made the move to scanning. “Nobody wants to go back to the forms of the past,” says the team leader. “It’s important that I can now quickly find out exactly what happened in production. And that I can rely on it,” adds the Quality Manager.

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batchraport HAI lights engels

I wish I had a DIGITAL Batch report!

Digital batch reports provide significant benefits: it’s faster, simpler, more complete and more reliable. But how do you make this happen?
Batch reports become more and more important in the food industry. After all, it includes an overview of all quality control records, production checks, and production data of a batch.
Many food manufacturers still work with paper-based records or pdf files to record their quality tests and production checks. This means that batch release and complaint investigations require a lot of manual work.

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Manage Digital Factory from Home

A smart way to manage your digital factory from home

The Covid-19 measures have created a new reality for the way factories are organized. For many factories, there is now a minimum of factory personnel on-site. Management and supporting plant personnel work predominantly from home. People working remotely often struggle to stay well-informed on exactly what’s happening in the factory.
Thus, the new reality of factory personnel working from home has intensified the need for plant information systems in the cloud: with a digital factory we have all the information we need, anytime, anywhere.

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CIP Reiniging ENG   HAI lights


A small example of smart use of factory data: a Food manufacturer recently discovered that the CIP-tank cleanings (cleaning-in-place) took 5 minutes longer than before. After investigation, they solved the issue and they won 4 hours of production time per week!

CIP duration trending reports in the HAI smart4industries software triggered this investigation.

What happened?

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inline measurement of product quality

Super smart: in-line measurement of product quality

In-line measurement of product quality versus of off-line quality tests. It helps.

Why? Because you can immediately notice if the production process is not performing optimally: it allows you to timely adjust your production process, in order to prevent rejected batches and ensure product quality consistency.
So, is the introduction of in-line measurement always a no-brainer? I’d say yes, provided you take the right approach.

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data hard to find

“Data – I mean the right data – is hard to find”

“Data – I mean the right data – is hard to find”. This observation comes from a Lean 6Sigma Manager in the Food Industry. He understands the value of CORRECT data. Indeed, it appears that food manufacturers often seem not to have the right data in place to solve production and quality issues.

However, a professional plant information system enables fact-based decision-making. Decisions about: What are the reasons for a Rejected batch? Can we improve quality? Can we increase output?

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