Supply-chain traceability: Produce grower Ocean Mist Farms encodes traceability data, such as which crew picked the produce, the farm location, and packaging methods, in barcodes on case labels to enhance inventory management and order optimization. The grower calculates that barcodes, digital tools, and data standards help them achieve up to 35% in time savings compared to their former system.
Safety and quality assurance: Fast-food restauranteur Subway relies on barcodes for product data, which identify product expiration dates, best-before guidelines, and sell-by data. Traceability means faster and more accurate inventory management, and cuts down on human error. Data standards and technology empower them to quickly apply safety practices to protect consumers.
Improved sustainability posture: Consumers and investors increasingly want to see environmental, social, and governance (ESG) data. Companies can build trust by increasing availability of ESG data, providing accountability. Phygital ESG data can include such things has product origin, ingredients, biodegradability, production processes, and energy use.
More connected consumers: When customers scan object identifiers, they establish a phygital connection. This can provide customers with information such as how an item is made, ingredients, or geographical origin. Customers are interested: McKinsey 2022 data says customers who buy using omnichannel methods (a combination of physical, digital, and other experiences) shop 1.7 times more than consumers who don’t—and they also spend more.
Building for phygital success
Organizations can benefit—using standards, technology, and data—by putting their data to work more broadly, says Nuce-Hilton. She suggests the following:
Deploy the right technology tools: Advanced data carriers hold a large amount of information, so it’s critical to use appropriate analytics tools and IT resources to analyze and convert data into business insights. Throughout the supply chain, these tools can enhance inventory management, streamline logistics, and support traceability and sustainability.
Look to AI for speed: As phygital systems make it easier to collect product data, innovative technologies such as generative AI promise to up the ante by accelerating tasks, such as creating code and analyzing supply chain data to detect anomalies and recommend corrections.
Shape behavior around standards and collaboration: Increasingly complex supply chain ecosystems make collaboration and communication critical. “You can deploy any technology you want and call it what you want, but until the behaviors associated between trading partners change, you won’t be successful,” says Nuce-Hilton. Processes and underlying data structures are a common language for supply-chain partners.
Supply chain resilience is needed to meet fluctuating consumer demands, respond to unanticipated roadblocks, and satisfy ESG goals. Today’s business environment makes that a challenge, says Nuce-Hilton. “But we could change that, if we empower organizations with the data to make better decisions further upstream in the supply chain,” she says.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.