Simon Drake is a data science executive and executive vice president of data science solutions at Ever.Ag. He is known for integrating advanced analytical techniques into the global food supply chain. Previously, Simon was chief executive officer of Austin Data Labs, an AI-based SaaS company that delivers data science solutions crucial for the commodity agriculture, and food and beverage sectors globally.

Drake’s career spans more than 15 years, marked by significant contributions in data analysis and leadership in retail and manufacturing. Before Austin Data Labs, he worked at SignalDemand from 2006 to 2013, where he helped implement optimization solutions aligned with clients’ strategic goals as a solution architect and director of services. Prior to SignalDemand, Drake served at KhiMetrics, leading a team to develop pricing optimization strategies, which contributed to its acquisition by SAP. He holds a bachelor’s degree in theoretical physics from York University.

In 2024, under Drake’s leadership, Ever.Ag Data Labs launched a yield optimization feature, which feeds the data being created within dairy processing plants into an AI model to predict and improve the production outcomes.

What is Cheese Yield Optimization?

Cheese Yield Optimization (CYO) refers to the practice of maximizing the amount of cheese produced from a given amount of milk input. Essentially, it is about achieving the greatest possible cheese output using the least amount of resources. CYO employs advanced data science, including machine learning and AI algorithms, to analyze the vast array of data collected in dairy plants. By identifying patterns and insights, it provides actionable recommendations to enhance cheese yield. Thus, CYO can be considered a best practice towards sustainability, increasing revenue and profitability, while at the same time reducing the material costs and waste, essentially doing more with less.

How does CYO benefit cheese processors?

CYO benefits cheese processors by transforming raw data into actionable insights, which can significantly boost productivity and efficiency. By leveraging data science to optimize cheese yield, processors can significantly increase their revenue, even with marginal improvements. A tiny enhancement in yield can translate into significant additional revenue, potentially reaching millions of dollars for large processing plants. Additionally, CYO not only enhances yield, but also helps in managing input costs and improving the quality of the cheese produced, leading to a more profitable and efficient production process.

How does CYO affect margin yields?

CYO positively impacts margin yields by reducing the input costs required to produce cheese. By optimizing the formulation based on market conditions (like cream multiples). In addition, the ability to hit a target with more accuracy means that the processor can increase its average moisture level while avoiding the creation of undergrade cheese. Moreover, by reducing the amount of undergrade cheese, processors can enhance profitability further. Bringing together yield optimization with quality management can create substantial economic benefits, significantly impacting margin yields.

What considerations need to be made before implementing this type of technology?

Before implementing CYO technology, processors need to evaluate their current data collection processes and ensure they have the necessary historical data available. Running an initial assessment is crucial to determine whether there is sufficient data to conduct an accurate yield prediction and control which parameters might be adjusted. Integration will likely involve digitizing manual or outdated processes, using expert guidance to navigate this transition smoothly. A partnership approach in which subject matter experts help assess and implement the technology can ensure maximum benefit from CYO.

What role does CYO play in reducing waste?

CYO plays a vital role in reducing waste by optimizing the entire cheese production process. By analyzing data, it can highlight inefficient practices or parameters that lead to waste, allowing for adjustments that reduce losses. Optimizing yield and quality simultaneously ensures that the maximum amount of milk is transformed into sellable cheese, decreasing the overall waste generated. Furthermore, by improving accuracy in production steps, CYO helps in minimizing undergrade cheese, thus enhancing the sustainable operation of dairy plants.

How does this technology pair with other solutions in Ever.Ag’s portfolio?

Yield optimization is a complement to production planning and S&OP. This helps to drive greater visibility into the accuracy of critical factors such as actual observed and predicted yield vs. theoretical annual averaged yield. How do yields change your long-term planning? It also integrates with Orbis, the Ever.Ag MES system.