20 WESTERN PALLET
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
There are three takeaways we can consider from looking back at the waves of technological change:
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.
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.
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.
WPM
One great way to find out about the latest technology advancements is to touch base with event sponsors at the WPA Annual Meeting.