Five ways traceability helps food manufacturers reduce costs

Food manufacturing is vastly complex. In addition to the complexity of the processes themselves, food manufacturing relies heavily on interdependencies between multiple systems and stakeholders: regulatory agencies, quality ingredient sources, reliable distributors, and countless other relationships. These complexities put pressure on your internal supply chains and the teams working at each site. 

Product traceability can help. Here are five ways that traceability can help food manufacturers address their biggest challenges.

What is product traceability?

Product traceability means going beyond understanding what a product is made of. It also includes understanding the intricate details of how a product was made. What vendor provided the source ingredients? How long was it in the fryer? What was the temperature of the fryer? Did this batch pass quality inspection?

This depth of product knowledge is often aided by advanced Industry 4.0 technologies, particularly Digital Twins. Teams can use Digital Twin data to make a wide array of continuous improvements, optimizations, and adaptations. 

Traceability leads to greater production agility, which is vastly beneficial for a competitive landscape like food manufacturing. In fact, when Cargill started using Braincube’s Digital Twin and Advanced Business Intelligence (BI) Applications, they increased their new recipe production by 400% a week.

With enhanced traceability insights, food manufacturers can address key challenges they face every day. This article discusses five of these challenges, including how traceability helps food manufacturers overcome these challenges.

Adhere to safety regulations

Safety regulations related to food manufacturing vary significantly from country to country. Different regulatory agencies, different package label requirements, and different allergen or ingredient limitations all come into play based on where food is being produced and consumed. 

Managing all these regulations—plus ensuring that your products meet regulatory safety standards—can give food manufacturers a headache. 

Many food-manufacturing-specific ERPs and MESs can help with ingredient and batch traceability. This fundamental level of product traceability makes it easier to adhere to regulatory requirements by providing food manufacturers with better information about what goes into their products. It can be especially beneficial if you are producing food products in multiple countries or exporting between countries. 

But what if you could get even more information? 

Advanced Digital Twins, like those powered by Braincube, take traceability a step further. Braincube’s Digital Twin contextualizes data from different systems (including ERPs and MESs you may already use), sensors, and processes. In the case of Braincube, Digital Twins can add in lag calculations and flow data to link process variation to the inputs associated with the cause of variation.

This provides teams with a comprehensive lens into the exact manufacturing conditions that went into producing each batch coming off the line. Everything from how long a dough was mixed, to the exact temperature of the oven, to the final weight of the end product is captured during the production process. 

A Braincube-powered Digital Twin is a dynamic replica of your exact production conditions. Teams can leverage this data to create alerts when outside of safety specifications, optimize quality checks, and set up protocols around regulatory obligations. 

Centralizing data in an IIoT platform, with added context from Digital Twins, makes both real-time and historical data available. For example, product temperatures or process settings are available in real-time to make quick, on-the-fly decisions. Historical data makes it easier to track trends related to alerts, making it easier to avoid (or at least minimize the impact) of unsafe conditions. All this data can be provided via reports, dashboards, or other visual elements in charts, PowerPoint, or graphs across the organization.

This data can also be shared with regulatory agencies as verification that products were produced safely every step of the way. This is particularly important if you need to track down any type of recall or impacted product on demand.

Improve product quality

Quality is at the forefront of food manufacturing, both for safety compliance and to meet customer expectations. With such a wide array of food products being produced, “quality” can mean something different to every food manufacturer. However, the underlying question of all quality issues is this: 

“Is the product I planned to produce the same as what is being produced? “

In other words, does the product coming off the line right now align with the product you set out to make? Is the color, taste, texture, and size the same as you expected? If not, there is likely a quality issue in some capacity.

Is the product you planned to produce the same as what is being produced? If not, you may have a quality issue.

Enhanced traceability of your products makes it easier to track down the source of quality variation (and, in turn, minimize the chances that they happen again). When you can track a product through its entire production cycle, you can better understand the parameters and production conditions that went into making finished products. For example, you can see if any pieces of equipment were operating out of spec, if a certain task took longer or shorter than expected, or if an incorrect amount of ingredients were used. 

While it’s obvious that these production variations can all impact the final quality of your product, you might not know how they impact your overall product quality. This is where enhanced traceability can make a major impact in diagnosing and resolving quality issues, particularly when paired with advanced AI. 

Tying the production conditions to the final outcome makes it easier for teams to understand the impact of certain parameters on quality issues. Bringing in AI to crunch through your production data and quickly identify parameter optimizations makes it even easier for teams to know where they should focus their improvement efforts, saving them both time and effort.

Better inventory control and reduced costs

With such tight margins, cutting costs is always a leading KPI for food manufacturers. 

Leveraging an IIoT Platform with Digital Twin data and advanced AI technology can help teams find process optimizations that save money. For example, Braincube’s Advanced Analysis App utilizes our exclusive CrossRank AI to quickly crunch through historical data and isolate the key process inputs that create specific outcomes. The app can also provide recommended settings for important processes. 

For example, the app may help teams discover that the fryers can run at a lower temperature: something unexpected in most situations. AI can uncover settings that are surprising and often lead to saving energy costs. On the other hand, it can also be used to drive new optimizations, such as new fryer temperature ranges that enable the fryers to maintain a more consistent temperature, reducing variation that results in lower overall energy usage.

The same data can be used to find ingredient optimizations for minimizing costs. Instead of defaulting to the most expensive ingredients in a response to customer trends, you can identify where to source materials from and how to maintain quality at the lowest cost. Braincube’s Apps and Digital Twins work together and enable teams to test these substitution results digitally to ensure they generate quality outputs.

Instead of defaulting to the most expensive ingredients in a response to customer trends, you can identify where to source materials from and how to maintain quality at the lowest cost.

Traceability also helps you keep better track of ingredient inventory. As ingredients are moved into production, you can readily see how much of each ingredient you have in stock. These insights make it easier to manage purchasing and ingredient sourcing, ensuring that you always have ingredients available and maintain uptime.  

React quickly to product recalls

We’ve covered why adherence to regulations is important when you’re producing a food product. But what about if you have to respond to a customer complaint or product recall for a source ingredient used to make your products? 

Improved product traceability makes it easy to identify products that may have been impacted by a product recall or issue with one of your ingredients. Remember that production data is accessible in Braincube Digital Twins, with end-to-end information from source ingredient to final product. This means teams can quickly determine which batches might be impacted by a recall. 

From here, teams can investigate what was impacted and take the necessary steps to manage the recalled product. They can also work to resolve any internal issues, such as altering current production or cleaning impacted equipment. 

Manage time-sensitive supply chains

Packaged foods aren’t made to last forever. Food manufacturers are always operating on strict, sensitive timelines. From specialized storage facilities and shipment schedules to inventory management and maintaining a safe working environment, every part of food manufacturing is on a tight schedule.

Sticking to these schedules can be difficult if teams have questions they can’t easily answer. Which batch of inventory ingredients should be used first? Were products in the freezer long enough? Traceability can help answer these questions and more, easing the stress of certain tasks so employees can work towards other optimizations.

These time-sensitive supply chains mean that downtime is extremely costly. Downtime doesn’t just jeopardize your output yield. Downtime can impact inventory ingredients that may go bad or half-completed goods sitting on conveyor belts midway through production. 

While not directly related to product traceability, the data gathered in your Digital Twins can also be used to improve your predictive maintenance strategy. Braincube’s Advanced Apps, such as Machine Status or Group Benchmarking, utilize Digital Twin data to help teams understand how to better optimize equipment. This can help them stay on top of unexpected downtime. These automated condition monitoring tools make it easy to track machine statuses and understand when something is possibly going wrong. 

The same technology tracking your data can also be used to understand what production conditions look like just before, during, and after an issue occurs. As teams learn what these early indicators look like, they can set up automatic notifications (using the Alerts App) to notify maintenance teams early. Minimizing downtime is a key way to stick to your production schedule—and, in time, improve overall output.

Summary

This level of product traceability is not science fiction. Food manufacturing teams can make remarkable progress on their biggest challenges when systems, data, and outcomes come together in a seamless digital environment for improved traceability. Advancements in Digital Twin and IIoT Platform technologies mean product traceability is more attainable than ever before. 

Whether you are looking to improve costs or better adhere to regulations around the globe, Industry 4.0 tools can help.