Accaving 1 million litres of wine per year with AI

What if artificial intelligence could stop wine from being lost before it ever leaves the bottle line? At Accolade Wines, technology and tradition came together to save over a million litres of wine each year.

Catch up quickly
Accolade Wines partnered with ML6 to further reduce wine loss during bottling. By implementing real-time sensors, dashboards, and predictive machine learning alerts on Google Cloud, they gained insights into live wine flow and prevented waste before it occurred. The solution helped cut losses by more than half across three bottling lines, saving 1 million litres of wine annually the equivalent consumption of 50,000 people and delivering a 200% ROI.
About this client
Park, the largest wine bottling facility in Great Britain. Bottling over 250 million bottles of wine eachyear, The Park is a Carbon Neutral facility, and 4 timeswinner of the UK’s Sustainable Manufacturer of theYear.
Impact
ML6 helped Accolade Wines to implement a ML process to capture real time insights during the manufacturing process and prevent wine loss from happening with predictive analytics, enabling all 3 bottling lines to further reduce losses.
Saving 1 million litres of wine per year.
This solution resulted in a 200% ROI for Accolade Wines.
Saving the consumption of 50 000 people per year.
Challenge
Accolade Wines with an impressively low margin of waste, still had ambitions to reduce wine loss even further but had no way of tracking live wine flow during the bottling process. Therefore, they decided to seek a strategic partner in machine learning to investigate how a self learning and adaptive wine management system could be used to monitor and improve wine yield to unprecedented levels
Solution
Together with Accolade Wines, ML6 built a solution for wine loss detection that combines sensors, dashboards, and machine learning to prevent waste in real time.
Sensors and Real-Time Dashboard
01Together with Accolade Wines, ML6 built a solution for wine loss detection by implementing sensors in place for the process & having a realtime dashboard for individual components. The idea was to prevent wine loss from happening on time using rule based and ML alerts. We created a real-time Grafana dashboard that visualizes the end-to-end process and helps identify when the wine loss occurs so operators can directly act on it.
Built on Google Cloud
02The solution was built on Google Cloud to iterate quickly and scale up the solutions to multiple lines and/or countries.
Rule-Based Alert System
03The new rule-based alert system shown in the main dashboard monitors for big discrepancies in volume during the bottling process. So when there is a sizable difference in the amount of wine going in and coming out of any part of the manufacturing line, an alert is fired so that operators can investigate what’s causing the loss and make changes instantly.
Macro and Micro Insights
04We then mapped out information collected on a macro and a micro level. Operators can see the total wine volume in the system over time and the difference in the amount of wine that comes in and out of the facility. For digging into the details, there are also graphs for flow rates that measure the amount of liters per second that flow at certain times during the bottling process.
Results
Beverage packing businesses typically quote a wine process loss of 2%. Accolade wines had already driven the average loss down significantly below this level by using standard process improvement techniques. However the extra process insights provided by ML6, enabled all 3 bottling lines to further reduce losses by over half of that. Resulting in saving another 1 million litres of wine per year.
Inspired?
Let’s connect and make it happen!
Ready to elevate your AI game? Schedule a meeting with us today and let’s craft a winning strategy together!
Cupcake ipsum dolor sit amet apple pie.
Frequently Asked Questions
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.