Keeping a lid on costs is a top priority today as companies contend with high material costs, fluctuating energy prices and economic uncertainty. One area that’s ripe for improvement and that can deliver significant savings is yield optimization.

Sub-optimal yield can happen for a variety of reasons. However, businesses can only pinpoint where and why giveaway is happening if they can collect and correlate the right data. This is where many food producers fall short.

Getting a grasp on elusive yield issues and addressing them in real time is now an achievable reality. Producers can gain immediate visibility – not days or weeks after the fact – by unifying disparate data, creating actionable insights from that data and using digital tools to address yield issues at their source.

A Deeper Understanding of Yield Losses

Overall equipment effectiveness (OEE) is the gold standard for measuring how assets and the people operating them perform against target. And it can reveal the culprits of yield loss, like machine stoppages, performance anomalies and quality variances.

But OEE alone doesn’t tell the whole story of yield losses. It only articulates what’s happening at the machine level. It doesn’t provide insights into what’s happening deeper at the material level.

By tracking the journey of materials – from initial allocation and various transactions, all the way to packaging – you can gain insight into where losses are occurring. This material loss analysis, combined with OEE, can give a complete picture of both yield losses and the production issues causing them.

With these insights, you can create focused efforts to address the losses. You may even find you can shift to a zero-based analysis approach, where giveaways are no longer simply accepted as a cost of doing business.

Historically, a key barrier to material loss analysis was that it took too much time and effort. But now, you can get these insights quickly and easily by folding material loss analysis into a digitalization strategy.

Digital Tools Target Yield-Loss Issues

A manufacturing execution system (MES) enables material loss analysis. It can collect real-time data from food manufacturing control systems to track food products from raw materials to finished products.

In the past, deploying an MES for material loss analysis was a complex, highly customized process that could take well over a year to complete in one plant. Modern MES platforms delivered as a software as a service (SaaS) standardize much of the process, including what’s being collected from control systems, interface designs and transactions with business systems.

This approach is easier, less expensive and can cut project times down to three to six months.

Once you have access to your material loss analysis from your MES, you can deploy targeted solutions to address the root-cause issues. AI in particular holds great potential to mitigate yield losses while also increasing production efficiency. 

Opportunities for using AI to optimize yield include:

Closed-loop control of single operating units: A good place to start an optimization effort is with AI-based technologies that can improve the performance of a single machine.

For instance, optimizing fillers to minimize giveaways is a common challenge. An AI-based filling solution can use operating condition variables like speed, pressure and temperature to predict the weight of product being put into a container. The solution can then use this information to make real-time control setpoint changes and optimize the process without any manual intervention.

This ability to achieve accurate fills on every product can help you realize big savings over time. One mayonnaise maker used an AI-based filling solution to fill packets with more than 99 percent accuracy. This helped the brand reduce overfill giveaway by more than 50 percent while also reducing product lost to underfilling.

Synchronized control of multiple operating units: AI can also help optimize yield across a larger production line or process.

A model predictive control (MPC) solution, for instance, can monitor what’s happening at various points in production and make control adjustments upstream or downstream to optimize yield. It constantly assesses current and predicted operational data and adjusts control targets when needed to reduce process variability. 

One global dairy producer used MPC to optimize control of a drying process to ease regulatory compliance. Using sensor data, the MPC made predictive control adjustments throughout the line to reduce moisture variability and raise dryer throughput. This helped the dairy producer reduce quality variability by up to 42%.

Enhanced motor control: Drives learn a lot about the motors they control, including when they’re experiencing anomalies. Knowing what your drives know can help you identify and address issues in their early stages. However, the sensors needed to get these insights have traditionally been cost prohibitive. 

AI-based solutions for virtual frequency drives create soft or virtual sensors that monitor the electrical signals for assets like pumps, fans and blowers. This can help food producers detect and identify anomalies ranging from a loose or misaligned blade on a fan, to a viscosity change on a pump, to a ball-bearing fault in a motor. 

With these insights, you can quickly address production anomalies, possibly before they impact yield.

Other Opportunities

In addition to AI, several other digitalization tools and tactics can help you optimize yield.

For starters, your MES can do more than enable material loss analysis. It can also improve how operators work. For example, it can provide exception-based reporting so workers can quickly identify yield issues without needing to sift through large volumes of data. And it can give staff quick access to files like CAD images to quickly troubleshoot machine issues.

A digital twin, or virtual replica, of a machine, process or entire plant can also help you optimize production and reduce yield losses. Because a digital twin can realistically model and simulate how production operations run, it can help you identify and remove inefficiencies and waste from new production lines, process changes and product recipes. 

A digital twin can also accelerate simulations of operations to uncover yield issues that happen infrequently. For example, an issue that only happens once a month in the real world can happen several times in mere seconds in a virtually simulated environment. This can make it easier to identify the root cause of issues.

Additionally, all these technologies capturing crucial knowledge about your operations help create an insurance policy against worker retirements. Now, if an experienced worker leaves, crucial knowledge about production, maintenance or training won’t leave with them. Instead, it will be preserved in your MES, digital twin or AI agents.

Don’t Let Losses Linger

Every day that yield issues go unaddressed contributes to a bigger loss on the balance sheet. Even by taking a small step, whether it’s proving the value of an MES with a small deployment or using AI to reduce giveaway on one machine, you can start realizing savings and generate momentum for minimizing yield losses across your enterprise.