Today, the processing facility is a full-fledged operation supporting Sunsweet’s ongoing growth. Given its complexity and the company’s investment in cutting-edge features, the plant also serves as a “learning lab” where Sunsweet can test ideas and experiment with different processing efficiencies that will be applied to its future facilities.
When Sunsweet decided to expand their existing facility in Chile and needed design help, they turned to Stellar for a partner to help them not only design the facility but guide them through the entire process. From selecting the right site, to understanding sanitary design principles which ensure food safety, to vetting of local subcontractors, choosing the right firm to support your project is one of the most important decisions you face.
Big data comprises the large volume of data that businesses collect on a day-to-day basis. The question is: Are you taking advantage of it?
Data and analytics tools can be customized to meet your facility’s unique needs and goals — whether you simply want to gain insights to resolve certain pain points or install system-wide automation technology that takes your efficiency to the next level.
Let’s take a look at three applications for big data in a food processing facility:
Today’s big data tools and technology can create significant cost savings in modern food and beverage plants — and the return on investment (ROI) can come in the form of reducing losses or improving production.
Every company has a different outlook when it comes to capital spending, though, with some focused on short-term investing and others taking a long-term approach. Let’s look at some short-term and long-term options when it comes to big data and analytics tools.
Among all these moving parts, it can be easy for a plant owner’s original vision or goals to be lost or not fully realized. That’s why commissioning is becoming a critical part of the design-build process. A commissioning partner works with the owner throughout the design-build process to ensure their goals are achieved.
So you want to incorporate artificial intelligence and machine learning into your food processing facility — but where do you start? These tools have grown increasingly popular, and you’ve likely heard people discussing different platforms like Amazon Web Services (AWS), Google Cloud and Microsoft Azure. But how do you get access to these tools and what can they do?
Three layers of cloud computing
When it comes to introducing machine learning to your processing, think of it as a three-tiered ecosystem:
1. Service providers (AWS, Google Cloud, Microsoft Azure)
2. IoT solutions partners (system integrators, data experts, etc.)
3. End users (Facility operator, plant owner, etc.)
Recent construction growth is forecasted to continue in the coming years, and when it comes to their next food processing facility, many companies are retrofitting existing buildings rather than building new. Speed to market is a top priority in today’s rapidly changing food industry, so this often seems like a quick solution for a growing business.
However, food and beverage facilities have unique and specific infrastructure needs, so if you’re converting a non-food facility into one, there are four main factors you’ll have to address.
Managing total package oxygen (TPO) can be a challenge for beer producers looking to grow their output. TPO is the total concentration of oxygen (O2) present in packaged beer at the time of packaging. When beer comes into contact with air, it begins to oxidize — and too much oxygen can negatively affect the beer’s flavor.
The ultimate goal is to reduce the amount of oxygen allowed in during packaging to prevent oxidation and maintain product quality and taste. However, this can be easier said than done, especially if you’re transitioning from a smaller-scale production with manual processes to greater throughput with increased automation.
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