Artificial Intelligence, Reusable Packaging, and the Next Phase of Supply Chain Decision-Making

Artificial Intelligence, Reusable Packaging, and the Next Phase of Supply Chain Decision-Making

by Rick LeBlanc, Reusable Packaging News

(Reposted with permission)

When Tim Debus, President and CEO of the Reusable Packaging Association (RPA), took the stage at Pack Expo Las Vegas in late September, his message was both ambitious yet grounded. Artificial intelligence, he argued, is advancing at a pace few industries fully appreciate, and supply chains will be among the most deeply affected. What makes the moment especially significant for packaging and logistics professionals is the role reusable transport packaging can play in supplying the data AI systems need to function effectively.

Debus framed artificial intelligence not as a distant concept, but as a practical force already reshaping how organizations forecast demand, plan supply, and optimize logistics. Citing recent examples from finance, retail, and technology, he noted that AI systems are rapidly closing the gap between human judgment and machine-driven decision-making. In some cases, they are already surpassing it. For supply chains facing geopolitical uncertainty, climate-related disruptions, and increasingly complex market demand, the ability to act on real-time, data-driven insight is becoming a competitive requirement rather than a future aspiration.

Why AI Changes the Supply Chain Equation

Artificial intelligence, at its core, refers to systems that process data to perform tasks that once relied primarily on human experience and intuition. Over the past several decades, AI has evolved from early conceptual questions about whether machines can think into practical tools built on machine learning, deep learning, and large language models.

More recently, generative AI has brought these capabilities into mainstream awareness, allowing users to create summaries, analyze information, and generate content based on prompts. Beyond that, newer forms of “agentic AI” are emerging, where systems move beyond responding to commands and begin to act autonomously. These systems can collect data, reason through possible actions, interact with other software tools, and continuously learn from outcomes.

In supply chain environments, this shift opens the door to more dynamic decision-making. AI models can analyze historical sales, seasonality, pricing changes, weather patterns, social media signals, and operational constraints to improve demand forecasting. They can also support supply planning by factoring in raw material availability, production capacity, maintenance schedules, labor constraints, and transportation conditions. Logistics optimization, already visible in route planning and last-mile delivery, is expected to scale further as predictive analytics become more deeply embedded in daily operations.

Despite growing adoption, Debus acknowledged that many organizations have yet to see full returns from AI investments. Studies show a gap between experimentation and measurable impact, often due to challenges related to data quality, integration, and organizational readiness. Still, investment levels and executive attention suggest that AI’s role in supply chains will continue to expand.

Artificial intelligence is increasingly being embedded into core supply chain technology —from demand forecasting and supply planning to logistics optimization and risk management.

The Data Challenge and the Role of Reusable Packaging

One theme Debus returned to repeatedly was data. AI systems depend on large volumes of accurate, timely, and trustworthy data. Without it, even the most advanced algorithms fall victim to the familiar problem of “garbage in, garbage out.”

This is where reusable transport packaging enters the conversation. Pallets, bulk bins, containers, and trays move the vast majority of commercial goods. In the United States alone, more than 80 percent of goods travel on pallets. These assets already form the physical backbone of supply chains. When equipped with sensors, RFID, or other tracking technologies, they also become consistent sources of high-quality operational data.

Reusable packaging assets can transmit information about location, movement, dwell time, condition, and even temperature. Because they circulate repeatedly through supply chains, they generate structured, repeatable data streams that AI systems can analyze over time. This makes them particularly well suited to supporting predictive analytics, loss prevention, asset utilization analysis, and regulatory traceability.

Debus shared examples from the reusable packaging ecosystem where technology-enabled assets are already delivering measurable benefits. In one case, a bakery reduced losses of plastic bread trays by gaining visibility into asset flows and identifying route- and customer-level inefficiencies. In another, RFID-enabled plant trays allowed operators to capture data on thousands of containers simultaneously, improving operational insight and supporting reuse at scale. He also pointed to food traceability requirements as an area where automated data capture through reusable packaging could dramatically reduce compliance costs and response times during recalls.

What’s Changed Since Pack Expo Las Vegas

While the Pack Expo presentation focused on the opportunity ahead, developments since late September suggest the conversation is beginning to move from awareness toward coordinated action.

In a recent follow-up, Debus noted that the Reusable Packaging Association has increased its activity around artificial intelligence through its Technology Working Group. The group is now positioned to lead industry-wide discussions and collaborative efforts focused on education and practical use cases for AI in reusable packaging systems.

According to Debus, the RPA sees significant potential in how data captured from reusable packaging assets can support AI models that deliver real-time, accurate decision-making capabilities for supply chains. Rather than approaching AI as a standalone technology project, the association is emphasizing the role of shared learning, common challenges, and cross-company collaboration in helping the industry understand what is possible and how to implement it responsibly.

The RPA is also beginning to integrate AI into its own operations, with the goal of improving efficiency and enhancing the value it delivers to members. This internal adoption reflects a broader recognition that AI is not only a subject for discussion, but a tool that will increasingly shape how industry organizations operate and serve their constituencies.

From Potential to Practice

Taken together, the Pack Expo presentation and subsequent updates illustrate how quickly the landscape is evolving. Artificial intelligence is advancing rapidly, investment is accelerating, and supply chain leaders are under growing pressure to turn data into actionable insight. Reusable transport packaging, long valued for durability, efficiency, and sustainability, is emerging as a critical enabler in this transition because of its ability to generate reliable, repeatable data at scale.

While many questions remain around standards, integration, and organizational readiness, the direction is becoming clearer. As AI capabilities mature, the companies best positioned to benefit will be those that understand their data sources, invest in quality information flows, and view reusable packaging assets not only as physical tools, but as digital contributors to smarter supply chains.

For the reusable packaging industry, the opportunity goes beyond sustainability or cost reduction. It extends into the realm of intelligence, where physical assets help power, the analytical systems shaping the next generation of logistics and supply chain decision-making. Watch Tim’s full Pack Expo presentation here.