3 Ways AI Solves Food and Agriculture’s Key Operational Challenges
Food and agri-businesses across the globe are facing operational challenges such as aging assets, rising energy costs and increases in raw material costs due to food losses during production. AI and machine learning-based predictive analytics can help transform legacy operations and help answer key questions such as: how can we improve OEE, how do we improve batch consistency, how do we optimize raw material consumption and how do we minimize waste?
At Canvass, we are helping leading food and agri processing businesses to utilize AI/machine learning to solve these challenges and more. Some examples include:
1: Optimizing processes to increase yield
Using Canvass AI, a Fortune 100 food production company is improving the profitability of its animal feed line. The company is utilizing machine-learning based predictive analytics to optimize the fermentation process on a time basis to reduce batch cycle duration and improve batch consistency. As a result, the business unit has increased asset utilization, allowing the operations team to produce more animal additive feed without additional capital expenditure.
2: Improving energy efficiency and reducing carbon emissions
A North American food ingredient manufacturer introduced AI to optimize their plant's energy production - with the aim to forecast the optimal power and steam generation required, while minimizing gas usage. By optimizing multiple gas turbines, the company has lowered fuel costs and considerably reduced the plant’s greenhouse-gas emissions.
3: Optimizing processes to increase quality and reduce waste
Another food plant is using AI to improve the quality and shelf life of one of its grain products by ensuring consistent drying and optimizing the moisture level composition of the product. Previously, the operations team would need to perform manual checks on the moisture level every two hours. However, with Canvass AI, the operations team now predicts the moisture content across different time intervals and can adjust the various temperature and pressure set points required to accomplish the desired moisture content. As a result, the production line has reduced moisture level variance and improved the production line’s overall quality.
In the food and agriculture processing industry, the lack of real-time visibility into production processes has left plants consuming more raw ingredients and energy than necessary, while producing inconsistent batches. With AI, operators now have the data, processing power and speed to predict, detect and rectify costly operational challenges across highly complex and dynamic production processes.
To learn more about how AI and machine learning accelerates operational improvements in the food and agriculture processing industry, download the whitepaper here.