As the global population grows, so does the demand for food, and food loss and waste remain major challenges. Indeed, roughly 13% of the world’s food is lost between harvest and retail – before it even reaches the consumer.
This percentage is expected to be even higher for perishable, temperature-sensitive goods like fruits and vegetables, which are particularly vulnerable to spoilage and damage. Since food quality is a key driver of value from farm to shelf, preserving it throughout the supply chain is essential.
Preventing food loss requires a shift towards a more efficient, circular food system – one that proactively minimizes waste rather than merely reacting to it. Traceability – the ability to monitor food products and ingredients throughout every stage of the value chain – is a key enabler of this transformation.
Today, emerging technologies like non-destructive inspections, artificial intelligence (AI) and the internet of things (IoT) are advancing traceability. By collecting real-time data and generating actionable insights, these tools can help monitor food, rapidly identify inefficiencies and enable advanced analytics to predict spoilage before it occurs.
The convergence of traceability and technology is transforming how we manage food from farm to fork. By equipping every player in the supply chain with greater visibility and smarter decision-making tools, we can more effectively prevent food loss – and accelerate the shift toward a more resilient, sustainable food system.
AI assisted food quality inspections
Traditionally, food quality assessments have been largely subjective, labour-intensive and often destructive. However, recent innovations in data-driven, non-destructive technologies – such as hyperspectral imaging spectroscopy and electronic sensors – now enable rapid, reliable and objective measurement of food quality.
The data generated by these technologies can be analysed using AI, which enhances the accuracy and scalability of quality assessments and shelf-life predictions. This advancement improves food safety, reduces waste and optimizes supply chain efficiency.
Agritech start-up Neolithics automates quality control of fresh produce using artificial intelligence (AI) assisted non-destructive technology. The food quality inspection technology combines hyperspectral optical sensing with regular colour imaging. It analyses internal and external features such as nutrition, Brix sweetness, dry matter, maturity, anomalies and distribution of organic compounds. With an integrated software it enables more accurate grading, shelf-life prediction and comprehensive data management.
By digitizing quality control processes, Neolithics reduces manual inspection time by up to 90% and improves accuracy by over 15%. This innovation also cuts inventory loss and waste management costs by approximately 65%, making a significant impact on efficiency and sustainability in fresh produce supply chains.
IoT and AI assisted logistics monitoring in cold chain logistics
Beyond food quality inspections, innovations such as IoT and AI in cold chain logistics are helping stakeholders maintain consistent food quality and reduce food loss. IoT technologies are used to monitor transport by tracking temperature fluctuations, humidity, ethylene levels and other critical factors that directly impact product quality and shelf life.
The real-time visibility enabled by these technologies allows for more agile, responsive supply chain management – facilitating dynamic adjustments to transportation routes and storage conditions to preserve freshness and reduce waste.
Tech startup Dockflow offers a logistics enablement platform that provides real-time container tracking by aggregating data from geographic locations and IoT sensors monitoring temperature and humidity during transit.
Since inefficient cold chain logistics drives significant food loss, their AI-powered system sends instant alerts for temperature deviations, enabling prompt corrective actions. With an aim to create a seamless, intelligent and sustainable logistics ecosystem, Dockflow enhances decision-making and supply chain responsiveness, helping partners prevent food loss.
Food quality traceability pilot in the fresh produce supply chain
Fresh produce is particularly vulnerable to losses in the supply chain due its highly perishable nature. According to the International Fresh Produce Association (IFPA)’s recent Supply Chain Project, 78% of stakeholders found food loss as a major challenge caused by inefficient and non-transparent data flows, underscoring the need for collaboration and seamless data exchange.
To address these challenges, the Circulars Accelerator Network – a partnership between Accenture, UpLink and the World Economic Forum – collaborated with Neolithics and Dockflow, winners of the Traceability for Circularity Challenge, on a pilot project focused on improving food quality traceability and enabling real-time data sharing across the supply chain.
The pilot combined Neolithics’ AI-powered inspection with Dockflow’s real-time container tracking and IoT monitoring (e.g., temperature, humidity), with data integrated and visible via Dockflow’s shared platform. The goal was to enhance food quality monitoring of avocados and reduce food shrinkage and loss. In this context, food shrinkage refers to the reduction in the quantity or quality of avocados available for retail, while food loss refers to avocados diverted to composting or anaerobic digestion.
Guided by an expert advisory board – including the International Fresh Produce Association, World Resources Institute, Avery Dennison, The Consumer Goods Forum, Sensitech, Foodcareplus and Food Waste Free United – the technologies were tested in the avocado supply chain with global fresh fruit supplier Westfalia Fruit and its supplier Granot Fresh.
Though this was an early-stage trial tracking only a single container of avocados and comparing it to a week of shipments from the previous year, the pilot yielded promising results. Automating and digitizing inspections significantly reduced the need to manually cut open avocados, lowering shrinkage by up to 100% at Granot and 67% at Westfalia, and reducing inspection time by 15% at Westfalia. Overall, Westfalia achieved a 17% reduction in food loss and associated GHG emissions. The reduced food loss also resulted in a 1.15% revenue increase, with more avocados reaching the retailer.
However, it must be noted that these results apply to one single container and are not broadly generalizable; longer trials across multiple containers are needed to account for variability. Nonetheless, the findings show the potential of AI- and IoT-enabled technologies to reduce food loss and improve quality management.
Traceability and responsive supply chain management key to reducing food loss
Food loss remains a persistent challenge in the supply chain, particularly for perishable products, and addressing it requires a more ambitious, systems-level approach. This means moving beyond siloed efforts toward integrated, data-driven strategies that improve efficiency and enable more responsive supply chain management.
Traceability and digital technologies are already helping stakeholders monitor food and its quality more effectively. To scale these solutions and maximize impact, cross-sector collaboration between producers, retailers, logistics providers and technology innovators is essential.
Transitioning to a food system where loss and waste are proactively prevented depends on close value chain partnerships and seamless data exchange – enabling faster responses to issues, corrective action, and the preservation of product value.
The Circulars Accelerator Network will soon publish a playbook sharing insights from the Food Quality Traceability Pilot to help prevent food loss and enhance traceability.
The Food Innovators Network, part of the World Economic Forum’s Food Innovation Hubs Global Initiative, connects more than 200 global innovators, including experts, entrepreneurs and farmers. It fosters a dynamic space for networking and knowledge exchange, focusing currently on protein innovation and data readiness for food systems.