Understanding Waves of Tech Change in the Wooden Pallet Industry
Why tech adoption looks different depending on your operation, and what to expect next.
Over time, the wooden pallet industry has undergone several waves of technological transformation, from the introduction of basic mechanization to the adoption of advanced AI-powered systems. These shifts have helped companies improve throughput, reduce labor intensity, and gain more predictable performance from their processes.
Not all businesses are riding the same wave, however. Understanding where your operation sits on the tech adoption curve and what comes next can be a key factor in long-term competitiveness.
(These waves are arbitrarily assigned and presented for illustrative purposes only. The introduction dates for various technologies are approximations.)
Wave 1: Mechanization (1940s–1960s)
This wave marked the move from manual-only labor to mechanized processes. The introduction of handheld pneumatic nail guns, chop saws, and forklifts made production faster and less physically demanding. These tools improved output without requiring high capital investment or digital skills, making them accessible to even the smallest shops.
The U.S. military experimented with box-making machines for pallet construction during World War 2. By the early 20th century, Morgan Machine Company had established itself as a prominent manufacturer of nailing machines for box construction, and it moved into pallet assembly machinery. Matthew Wylie & Co. Ltd., another box-making machine producer, filed a patent for a pallet nailing machine in 1958.
Wave 2: Basic Automation (1970s–1990s)
This period brought semi-automated equipment like pneumatic pallet nailers, as well as rotating-head and band dismantlers. Pallet dismantling machines emerged in the 1970s and became more popular over time. Viking Engineering produced its first nailing system in 1975.
Such machinery increased throughput and allowed operators to focus on quality and consistency. PDS Version 1.0 was introduced in 1985. The late 1970s and 1980s saw the introduction of thin-kerf sawing technology, with adoption expanding in the 1990s and beyond. We see leading pallet companies evolve from station-based workflows to inline material flows through facilities.
Wave 3: Digital Integration (2000s–2010s)
With ERP systems such as Palmate(™), first introduced in 2002, companies gained greater control over production, inventory, and customer service. Digital tracking allowed for faster quoting, better scheduling, and more accurate data, helping teams work smarter rather than harder. Production machinery 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.