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In the pharmaceutical industry, serialization is fundamentally about compliance—are your products meeting government-mandated measures for traceability ? In our last blog,
 “Can Serialization Improve Supply Chain Agility?,” we pointed to how industries of all stripes are beginning to adopt serialization to gain better visibility into their supply chains—often in concert with machine learning and AI tools and solutions.

Forward-thinking organizations are using serialization today not just to track supply chain activities, but to understand them, predict them, and, when appropriate, influence them. Consumer-focused companies especially are beginning to use serialization in unique ways, turning what started as a way to track goods at the item level into a tool for turning consumer engagement into intelligent, trackable, and interactive data.

Case in point: Westrock, one of the largest packaging companies in the world, has embarked on a journey to produce smart packaging through their connected packaging solutions. Using serialization data gathered from unique identifers, Westrock eliminates one of the significant challenges consumer brands face in the implementation of track and trace solutions: how to identify discrete items during manufacturing or packing to make those items more intelligent, more interactive, more trackable, and more valuable. Companies can now apply data intelligence on an item-by-item basis to build a foundation for end-to-end traceability and better customer engagement. And they can do it without adding costly infrastructure to their manufacturing and distribution facilities.

Real Supply Chain Intelligence

Serialization is capturing the attention of supply chain professionals beyond the pharmaceutical industry who want better—and deeper—visibility into their supply chains. But serialization offers another benefit for industries eager to drive more value from their data. According to Gartner, by 2023, at least 50% of large global companies will be using AI, advanced analytics, and IoT in supply chain operations. To do so successfully, those companies will need more data, captured in a realiable way across the supply chain, to build a viable foundation for analytics at scale. By implementing a track and trace solution, industries can now use unique identifiers to gather critical data from every point in the supply chain—and use that data to begin their AI journeys—what we see as the next step in supply chain evolution.

That journey will begin with machine learning, which is already helping supply chain planners improve demand forecasting, product planning, and even untangle supplier management, among other use cases. (See the chart below from Gartner for the top use cases of machine learning in supply chain planning).

Using machine learning to assist with planning and forecasting is incredibly valuable. But, as we have learned painfully in 2020, disruptions don’t occur predictably. Deliveries can be delayed; suppliers can shut down, and goods can be contaminated. As machine learning and  AI mature, the data we can collect from across an increasingly interconnected supply chain will be more prescriptive—more able, in other words, to make intelligent decisions about what actions will produce the most value.

A true AI-driven supply chain is in our near future. The challenge, of course, is making sure we have the kind of high quality, granular data that algorithms need to drive real intelligence. For AI to work, organizations need to capture a much richer set of data from across the supply chain. That means working more collaboratively with partners to track goods more diligently across a multi-tier supply chain from raw material suppliers, through manufacturing, to the end consumer.

Serialization provides a singularly efficient way to gather the kind of product-specific and granular data that AI needs to deliver real insight. What might the future look like? Digital identities linked across your supply chain for deeper insight, faster decision making, and traceable interactions with partners and consumers. Serialization 2.0.