Safer bites: Tech-led food safety practices

The app through which you track where your pizza’s at, the suggestions you get to buy an appetizer with your burger, the platform on which you can write a complaint and gain a refund, are all innovative solutions aimed at enhancing the food experience for consumers. Making the consumer feel closer to the food process fosters a sense of reliability, safety and transparency. In return, it propels brand loyalty.

But that’s from the point of view of an end-product/consumer-engagement.

The same commitment to reliability, safety and transparency holds importance in the fundamental manufacturing process; from sourcing raw ingredients to the final stages of production, the holistic process must undergo stringent quality control and real-time monitoring.

Regardless of how fast-paced the world is today, now more than ever before, technology too enables organizations to ensure quality, safety and satisfaction at a desirable rate. Where we fall short, consumers will hold us accountable – at a detrimental cost.

So, how can businesses risk-proof food safety and ensure promised quality? Optimizing technology might be the answer.

Blockchain technology has emerged as a formidable tool in the recent past because of its capability to leverage a “decentralized and tamper-resistant” ledger to track transactions across various networks, at each stage, and verify information in real-time. Gartner predicts that by 2025, 20% of the top 10 global grocers by revenue will be using blockchain for food safety and traceability to create visibility to production, quality and freshness.

In a supply chain where multiple stakeholders collaborate, from manufacturers to suppliers to retailers, blockchain supports the collating of data from farm to fork. For example:

• Manufacturers can leverage the ledger for end-to-end visibility on the supply chain.
• Suppliers can authenticate the origin and quality of raw materials.
• Distributors can keep track of products as they move through the distribution process.
• Retailers can combat food fraud by providing unaltered records.

While this ensures that every step of the process is recorded, this level of transparency also enables fast response times in the event of a food safety concern. Consequently, it helps in building trust amongst consumers who are demanding more information about the origins and handling of their food.

The means of analyzing historical and existing data to predict patterns or “what could happen next”, is essentially the function of predictive analytics in risk management. Statistical algorithms and machine learning can speculate areas of concern and shed light on high-risk areas such as potential sources of contamination, risks in the food supply chain journey and environmental conditions, and enable businesses to implement preventive measures. This proactive approach can empower businesses to maintain their competitive advantage by carrying out early preventive measures such as conducting inspections, improving safety protocols and allocating resources more efficiently.

• Early detection of contamination: By analyzing data and recognizing early warnings signs, food manufacturers can move from reactive measures to a proactive approach and ensure the safety of their products and avoid unnecessary damage-control costs.
• Supply chain monitoring: Enhanced monitoring of the supply chain, from suppliers to transportation to storage conditions can reduce the risk of contaminated products entering the market.
• Quality control optimization: Businesses can optimize testing schedules and resource allocation by applying predictive analysis to quality control data, thereby improving overall product safety.
• Weather and environmental impact: From temperature variations to extreme weather events, predictive analytics can help businesses examine conditions that could affect food safety and safeguard products during transportation, storage and processing.
• Consumer complaint prediction: By analyzing historical data on consumer complaints, businesses can address concerns preemptively, enhance customer satisfaction and maintain a positive brand image.
• Optimized recall management: Given that predictive analysis can assist businesses in identifying products with a higher likelihood of safety issues, it could help businesses reduce the scope of recalls, minimizes waste, and ensure that the right products are removed from the market in a timely manner.
• Resource allocation for inspections: With the visibility of high-risk areas, businesses can ensure that inspections are held where they are most needed, improving efficiency and effectiveness of food safety monitoring.

Read more about how data analytics and AI are enabling the Food and Beverage industry by clicking here.

Smart devices and the food industry are joining forces to produce innovative solutions to fundamental challenges, ranging from food safety protocols, efficient supply chain management, personalized customer experiences and sustainable practices.

In 2022, the market size for food sensors was USD 21.2 billion and was predicted to undergo a growth rate of 9.7% in 2023-2028 around the globe. It’s clear that the F&B industry shows a keen interest in integrating the Internet of Things (IoT) to monitor crucial variables such as temperature, humidity and storage conditions. However, beyond monitoring the products and keeping track of the supply chain, these sensors are also providing consumers with information about the manufacturing process and safety of the products. For example, a QR code on a package can be scanned to access information about the product’s journey from production to the store shelves.

Be it Dominos introducing the “Pizza Tracker” allowing customers to track their orders in real-time, or Nestle using RFID tags and IoT sensors to monitor the movements of its products through the supply chain, IoT products are facilitating the sharing of information to elevate both the manufacture and consumer experience.