Blockchain and big data can help grocers deliver fresher produce

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The world’s grocery market is poised to grow by one third over the next five years, with a large portion of the increase coming from developing nations. But competition is increasing too — from expanding supermarket chains and other major players such as Amazon and Walmart. To compete, food retailers will have to cater to changing consumer preferences.

When it comes to produce, after years of competing primarily on price and quality, freshness has become the new battleground. In a recent A.T. Kearney study, 93 percent of consumers said freshness was one of the most important attributes they looked for, way ahead of the next-highest attribute, quality, which was selected by 58 percent.

The availability of fresh produce will depend more and more on the use of emerging blockchain and big data solutions.

A new toolset

Up to now, food retailers have relied on supply chain solutions developed for less perishable goods, which do nothing to safeguard freshness.

Zest Labs found, for example, that delivered strawberries had shelf-life variability of up to 12 days, representing 86 percent of their total freshness capacity. But today’s fresh food supply chains can’t account for this variability, relying instead on date labels to assess remaining freshness.

As a result, spoilage from unaccounted-for shelf-life variability contributes to a significant portion of the $161 billion fresh food waste problem in the U.S. But emerging technologies aimed at ensuring freshness and eliminating the waste of perishables are already in use, with more on the way.

Blockchain will play a vital role. By assigning tokens, it allows individual batches of produce to be traced throughout shipment and delivery to the retailer’s shelf. Retailers will be able to track the geographic origin — down to a specific orchard — of an apple, as well as how long it sat in a distribution center, what truck it was loaded onto and when it arrived at the store.

With blockchain ensuring data integrity via permanent entries, timestamping, and audit trails, retailers can determine product quality and answer questions from consumers about where their produce came from.

Walmart, Nestlé’, Unilever and Dole are teaming up with IBM to explore how to apply this technology to their supply chains; and start-up has partnered with salad franchise Sweetgreen to prove that blockchain can be used to track individual crops, giving detailed information to purveyors from farmers to restaurateurs.

End-to-end traceability provided by blockchain will also identify the source of contamination from foodborne illnesses, telling retailers exactly which items to take from their shelves instead of throwing out entire shipments.

AI and predictive analytics

The increasing quantities of produce-related data gathered via blockchain and other cutting edge technologies will be tied together by artificial intelligence applications that recognize patterns and draw inferences essential for retailers in the fresh space. For more details, see the A.T. Kearney article, A Fresh Look: Perishable Supply Chains Go Digital.

Much of the data retailers require will be gathered via sensors in growing fields, on packaging, in warehouses and stores, or on vehicles transporting merchandise. Zest Fresh uses IoT-enabled temperature sensors to monitor time and temperature of pallets, combining this information with product and field data to yield a real-time freshness metric that helps retailers decide how to route produce.

Infratab’s Freshtime SP sensor platform for perishables uses data from sensor tags, a computer or smartphone app, and cloud-based analytics to defend against tampering and counterfeiting.

Japanese food giant Kewpie Corporation uses Google’s TensorFlow machine learning to spot anomalies in its diced potatoes. A test at one plant concluded that it worked with almost perfect accuracy—in contrast to manual inspections.

Improved forecasting will be one of the most important AI applications for retailers. AI-enabled systems can comb through vast troves of disparate and complex data, from weather reports and local event schedules to social media feeds and historical trend information, to yield better forecasts than traditional methods.

This allows retailers to continuously optimize activities at multiple points along their supply chains, including sourcing, capacity planning, and even staffing at the local store level. For example, German grocer Kaufland leveraged Blue Yonder’s machine learning technology to help automate central planning of daily orders, improve product availability, optimize stock levels and minimize waste.

Using AI-based predictive analytic capabilities, retailers can anticipate problems before they occur. For instance, a system might use weather data to forecast a bad crop yield for a particular supplier, leading a retailer to change suppliers and avoid shortages in the coming season.

To ensure a fresh supply chain, companies need to develop a road map that identifies the capabilities they need, outlines which emerging technologies will be a vital part of their solution, and defines how these will be linked to their existing IT infrastructure. Companies that harness solutions to increase the agility, speed, and efficiency of their fresh supply chains will be able to raise margins, improve customer satisfaction, and achieve competitive advantage.

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