• White Papers
2min July 5, 2022 2min Jul 5, 2022
July 5, 2022 2 minute read

IIoT opportunities in spirit beverage manufacturing

By 2025, the global alcoholic beverage market is expected to reach $2.3 trillion USD—with spirits accounting for about 25% of the overall market. Despite the growth, there are challenges challenging spirit manufacturers. Quality can be fickle. Raw material costs are cutting into your bottom line. And energy conservation is a goal among many.

Consistent taste and the utmost quality are essential to spirit manufacturers, master distillers, and their loyal customers. Data can help the talented people working within your facility make better decisions, know when something is off, and build upon their intrinsic knowledge.

Today’s Industrial Internet of Things (IIoT) tools and technologies put valuable data into the hands of your employees so they can work their magic.

Here are some of the key opportunities that IIoT provides to beverage manufacturing teams. Complete the form to download the white paper now.

Fill out this form to download the white paper.

Do beverage manufacturers need IIoT for quality control?

For beverage manufacturers, quality can also be a fickle thing to maintain and perfect. Luckily, today’s IIoT tools can help. Learn more.

Read more

Quality control case study: ofi

Since implementing Braincube, ofi improved its yield by 6.5% and achieved a 100% quality score on a key product line by implementing new AI-recommended rules

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Different types of Digital Twins in manufacturing

Digital Twins can be built for any part of your process or any physical entity. In this article, we unpack a few different types of Digital Twins found in manufacturing, including the pros and cons of each type.

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The manufacturer’s guide to AI Readiness

This white paper outlines a three-stage model to help manufacturers overcome common challenges in AI adoption, such as data quality and system integration, and achieve AI readiness to unlock significant operational benefits. Manufacturers can enhance efficiency, quality, and decision-making capabilities by progressing through these AI Readiness stages.

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