May 2025
Waves of technology. Not all businesses are riding the same wave at the same time, however. Understanding where your operation sits on the tech adoption curve and what comes next can be a key factor in long-term competitiveness.
continues to evolve, providing greater throughput and reliability, and production systems become increasingly integrated.
Wave 4: Smart Systems & Robotics (2010s–2020s)
The 2010s saw a shift from individual machines to integrated systems. Robotic dismantlers, such as those from Alliance Automation, enabled high-throughput, low-fatigue board recovery. This wave introduced robotics, vision systems, and real-time data monitoring.
Robotic dismantlers, camera-assisted repair stations, and automated conveyors helped reduce variability and labor dependence. Smart conveyors with built-in sensors and variable-speed drives allowed for smoother transitions between machinery and improved uptime.
Pallet technology is increasingly becoming global, with European nailing providers such as Cape and Storti gaining traction in the U.S. Plants began to operate more like systems, with feedback loops and automated optimization. Pallet companies adopted cloud-based software solutions for a range of back-office and operational functions.
The greater adoption of barcoding and other tracking technologies began allowing for serialized asset tracking in more sophisticated logistics environments. “Connected” systems gained momentum, whether through PLC-controlled nailing lines or cloud-connected machinery providing real-time diagnostics. Data was no longer just collected; it was used to monitor throughput, flag bottlenecks, and predict maintenance needs.
Wave 5: AI & Predictive Intelligence (2020s–)
Today’s frontier technologies include AI vision platforms, real-time analytics, and predictive maintenance. Tools like Zira, introduced to the
pallet industry in the early 2020s, use vision cameras and machine learning to track cycle times, alert operators to delays, and measure lumber yield.
These systems don’t just monitor—they guide decision-making by highlighting anomalies, recommending responses, and integrating with other plant systems. AI is also being applied in quality assurance, identifying nail pops, cracks, or incorrect board lengths without requiring manual inspection.
Some companies are experimenting with AI-driven labor scheduling, energy consumption tracking, and load optimization—all signs that software is becoming as important as steel in building competitive operations.
Key Learnings from the Waves
1. Each wave builds on the foundation of the last. You can’t run predictive AI without reliable data collection, and you can’t collect good data if basic workflows are disorganized.
2. Technology adoption is uneven across the industry. Some small repair shops still operate largely in Wave 1 or 2, while larger multi-site operations have embraced Wave 4 or 5.
3. It’s possible to skip a wave, but risky. A company might move from basic automation directly into smart systems, but if core processes are unstable, the ROI may fall short. Aligning improvements with operational readiness is critical.
The wooden pallet industry continues to evolve. For decision makers, the key is not chasing the newest tools but understanding which wave they’re in—and what investment will deliver the best outcomes at their current stage of development. By learning from past waves, pallet manufacturers and recyclers can better chart their path forward.