Innovative applications of AI/ML in food safety & manufacturing

Innovative applications of AI/ML in food safety & manufacturing

By Mia Vujovic

As the food and beverage (F&B) industry faces new challenges related to food safety and manufacturing, the fourth industrial revolution is providing new solutions through the integration of advanced technologies such as AI/ML, Internet of Things (IoT), and Big Data. AI/ML in food safety & manufacturing is transforming the industry by enabling businesses to overcome significant challenges. F&B companies are under increasing pressure to enhance supply chain efficiency, reduce waste, and improve product quality while meeting food safety regulations and standards.

AI and Machine Learning (ML) are game-changing technologies that are revolutionizing the F&B industry. They offer businesses new ways to tackle food safety and manufacturing obstacles while gaining a competitive edge, driving growth, and spurring innovation.

This article delves into how AI and ML are being used in food safety and manufacturing and how F&B and Consumer Packaged Goods (CPG) companies can benefit. As a decision-maker in the F&B industry, it is essential to understand how these technologies can confer a competitive advantage to your business in the constantly evolving food industry.

Why use AI/ML in food safety & manufacturing

AI and ML have the potential to provide numerous advantages to F&B and CPG companies in terms of food safety and manufacturing.

These technologies empower F&B businesses to examine and leverage vast amounts of data, enabling them to recognize possible hazards in the food supply chain from the procurement of raw materials to delivery. 

Through AI’s ability to monitor and detect anomalies in food production, companies can swiftly respond to possible food safety issues, decreasing the risk of foodborne diseases and product recalls.

Moreover, AI and ML can improve manufacturing efficiency and reduce waste. For example, by analyzing production data, AI can identify areas of inefficiency in the manufacturing process and suggest changes to optimize output. 

This optimization not only results in substantial cost savings for F&B companies but also promotes a more sustainable approach to food production.

By leveraging AI, F&B companies can gain a competitive advantage in the marketplace and drive growth and innovation in the industry.

Use cases of AI/ML in food safety & manufacturing

Below are several examples of how AI and ML are being used in different F&B sectors:

  • Agriculture | 7.5% yield increase: AI is being used to optimize crop yields and reduce waste by predicting weather patterns, soil moisture levels, and pest outbreaks. For example, companies like Taranis and Blue River Technology use AI-powered drones to monitor crops and identify potential issues. Taranis claims to have helped farmers achieve yield increases of up to 7.5%, while also reducing water usage and fertilizer application. A case study by Blue River Technology showed that using their AI-powered “See & Spray” system can reduce herbicide use by 80%, resulting in cost savings and reduced environmental impact.
  • Food processing | 15% waste reduction & 60% efficiency gain: AI and ML are being used to improve the efficiency of food processing by automating quality control and monitoring production lines for defects. For instance, Nestle has implemented an AI-powered system to detect defects in its production lines and minimize waste. Nestle has reported a 15% reduction in waste and a 60% increase in production efficiency since implementing their AI-powered quality control system.
  • Supply chain | Zero waste by 2025: AI is being used to optimize supply chain efficiency by predicting demand, identifying potential bottlenecks, and streamlining logistics. For example, Walmart uses AI to optimize its inventory management and reduce food waste by predicting which products will sell best at each store. Their goal to achieve zero waste in the operations by 2025 is largely based on the implementation of AI-powered initiatives.
  • Retail | Personalization: AI and ML are being used to improve customer experience by providing personalized recommendations and enabling predictive maintenance. For instance, Starbucks uses AI-powered predictive analytics to optimize its menu offerings and improve customer engagement. According to the Cheetah Digital 2022 Digital Consumer Trends Index survey, 74% of global consumers want brands to treat them as individuals, and 71% have a favorite brand that has developed a customer relationship strategy.
  • Quality control | Zero defects: AI and ML are being used to improve quality control processes by detecting and identifying potential issues in food products. For example, Cognex uses AI-powered vision systems to inspect and identify defects in food products during production. Its vision system helped Knorr achieve their zero defects goal by inspecting 100% of the seals on sachets produced.
  • Packaging | 50% time reduction: AI is being used to improve packaging design and reduce waste by predicting how different packaging materials will perform under different conditions. For instance, the packaging company Tetra Pak uses AI to simulate and test the performance of its packaging materials, reducing the need for physical testing, which results in a 50% reduction in the time and costs needed to develop and validate new packaging materials.
  • Food safety | $150 billion savings potential: AI and ML are being used to improve food safety by predicting and detecting potential contamination issues. For example, IBM has developed an AI-powered system called IBM Food Trust, which tracks food products through the supply chain, enabling faster and more accurate identification of contamination sources. Implementing a tracking system that monitors product waste, loss, and expiration dates has the potential to save up to $150 billion per year in food waste.
  • Sensory analysis | Predict sensory attributes: AI and ML are being used to improve the sensory analysis of food products, enabling more precise and consistent evaluation of flavor, texture, and aroma. For instance, the company Gastrograph uses AI-powered software to analyze and predict sensory attributes of food products based on their chemical composition and production processes.

As these technologies continue to advance, we can expect to see even more innovative applications emerge, transforming the F&B industry in new and exciting ways.

Challenges of implementing AI/ML in food safety & manufacturing

While AI and ML offer tremendous potential for the F&B industry, there are also challenges associated with their implementation. 

One of the primary challenges is the need for a large amount of high-quality and effectively organized data to train the algorithms adequately. F&B companies must ensure that the data used for training the AI models are accurate, representative, and unbiased. This can be challenging, given the complexity and variability of the food supply chain.

Another challenge is the cost and expertise required to implement AI and ML systems effectively. Developing AI models and integrating them into existing production systems can be expensive, requiring significant investment in hardware, software, and personnel. F&B businesses must also have the necessary expertise to manage these systems and interpret the data generated. 

Additionally, there is no one-size-fits-all solution, and each company must carefully review its unique needs and goals before investing in technology. Customized solutions and a thoughtful strategy are necessary for successful implementation.

F&B and CPG companies must carefully consider the challenges and develop strategies to overcome them in order to realize the full potential of AI in the F&B industry.

The F&B and CPG industries can gain a competitive edge and drive growth by leveraging Industry 4.0 technologies, big data analytics, and advanced algorithms to enhance product quality, reduce waste, streamline the supply chain, and improve food safety and customer experience. 

Despite the challenges of data privacy, cost, and skilled personnel, investing in AI and ML in food safety and manufacturing offers significant benefits and returns. As the industry evolves, more transformative applications of AI and ML in food safety and manufacturing can be expected, and businesses that embrace these new technologies can position themselves for success in this rapidly changing industry.

If you have any questions or would like to know if we can help your business with its innovation challenges, please contact us here or email us at

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