Many food and beverage manufacturers believe they already know what leads to food waste, spoilage and shrinkage. But the number of variables in a food manufacturing environment are plenty: employees, ingredients, steps in a process, order of steps involved, temperature and more. Over the course of thousands of production orders, you accumulate a lot of data, which, with the right technology, can reveal trends and correlations hard if not impossible to uncover without AI.
Machine learning is changing the game in food and beverage manufacturing.
Machine learning identifies issues – known or unknown – in your production line and solves for them. You can see which factors, when they converge, tend to correlate with higher nonconformance or higher quality issues.
An example would be uncovering that using two particular ingredients together (rice and saffron, for example) leads to greater shrinkage and lower yields than another combination. Another example: identifying that a vendor’s batches tend to produce lower yields because their product is lower quality. Correlating that back to the vendor, you can then negotiate better contracts and hold them to higher standards. And, finally, you may find that quality issues increase when Stephen the operator and Curt the night manager are working together.
Any number of factors have an impact:
- Which employees are working
- Combination of employees working together
- The suppliers those ingredients are sourced from
- Lots those ingredients are sourced from
- The machines being used
- The order products are being prepared in
- Which shift the production order was run on (night/day)
- And more
Machine learning can process and identify opportunities for improvement much faster than a human could. With a computer, because of its speed, you can literally find that needle in a haystack.
With machine learning, you can make small changes that over time can yield big results. Over thousands of orders or multiple lines or facilities, the return can become significant very quickly. The money you save could easily justify an upgrade to your equipment.
Implementing Machine Learning
It’s important to understand that
Mapping a data journey provides awareness and understanding of where your organization is to ultimately get where you want to go, with enablement and adoption of the technology throughout. Without the clarity provided by a data journey, your organization won’t be positioned to successfully deploy the latest technology. At Columbus, we can guide you along the process.