18 WESTERN PALLET
Transparency Isn't a Threat. It's Your Moat.
There is a concept in economics called a spillover. It describes what happens when the benefits — or costs — of a decision fall on parties who had no role in making it. The classic examples are environmental: a factory that pollutes a river imposes costs on communities downstream that are not reflected in the price of whatever the factory produces. A university that trains engineers creates benefits for local employers who didn't fund the education. The decision-maker captures some of the value their decision creates. The rest leaks out into the system.
Economists have studied spillovers for the better part of a century. They have catalogued them, modelled them, and argued at length about what to do about them. They have a name for markets where spillovers are large and pervasive: markets that systematically underinvest in the things that produce the most diffuse benefit, because the investors can't capture enough of the return to justify the outlay.
Supply chain innovation is one of those markets. And the problem has a name.
What Spillovers Look Like in a Supply Chain
When a manufacturer deploys a more sophisticated packaging system — one with better damage protection, improved handling ergonomics, or embedded tracking technology — the immediate beneficiary is whoever receives the goods. But the effects don't stop there.
The logistics provider handling those goods may experience fewer damage claims and faster loading times. The retailer at the end of the chain may see reduced shrinkage and more predictable inventory. The insurer may price risk differently once loss rates fall. The carrier's drivers may spend less time on exception management. In a system with IoT-enabled assets, the data generated by the packaging itself may improve planning accuracy for partners several steps removed from the original purchase decision.
None of these parties were at the table when the packaging decision was made. None of them contributed to the cost. And in most cases, none of them even know that the packaging innovation is the source of the improvement they're experiencing. The value has spilled over — distributed across the network by the ordinary mechanics of supply chain relationships.
This is not a theoretical problem. A growing body of empirical research has confirmed that supply chain spillovers are real, substantial, and measurable. Researchers have documented that supplier innovations flow through to customer profitability, that buyer innovations influence supplier capability, and that digital transformation at one node in a supply chain generates measurable effects both upstream and downstream — operating through knowledge transfer, process adaptation, and resource access (Zheng, Chen & Zhao, 2025; Geng, Xiang, Zhang & Li, 2025). The supply chain relationship itself, independent of geography or industry, turns out to be one of the most powerful conduits for economic value transfer that economists have identified.
Why This Creates a Measurement Problem
Spillovers matter for supply chain innovation for a specific and consequential reason: the party making the investment decision is usually not the party capturing the full return.
A procurement team evaluating a packaging specification is asking, correctly, whether the investment makes economic sense for their organization. They build a business case. They run the numbers. They compare options. The problem is that the numbers they can access — their own cost data, their own operational metrics, their own budget lines — capture only the portion of the total value that lands within their organizational boundary. The value that spills over to logistics partners, downstream customers, and insurance carriers is real, but it doesn't show up in the procurement team's spreadsheet.
This creates what might be called a directional error in supply chain investment decisions. The error is not random. It doesn't sometimes overvalue innovations and sometimes undervalue them, cancelling out over time. It consistently undervalues the innovations whose benefits are most diffuse — which, by definition, tend to be the most sophisticated and most systemic innovations. A simple cost reduction that saves the buyer money shows up clearly in their analysis. A complex innovation whose benefits ripple across five organizations in three directions is systematically harder to justify, because the buyer can only see the fraction of the value that lands on their side of the organizational boundary.
The more sophisticated the innovation, the more its value tends to be distributed. The more distributed the value, the harder it is to build a business case. The harder the business case, the more likely the innovation loses to a simpler alternative that is cheaper, narrower, and easier to measure — even if it creates far less total value for the system.
The Buyer Isn't the Problem. The Framework Is.
It is tempting to frame this as a failure of procurement practice — buyers who are too focused on unit cost, too short-term in their thinking, too unwilling to consider value beyond their own budget lines. That framing is both unfair and unhelpful.
Procurement teams are doing exactly what they are supposed to do: making decisions that optimize for their organization's interests, using the data available to them. The problem is not that buyers are making bad decisions. The problem is that the frameworks they use to make good decisions were not designed to capture the full economic picture.
Total Cost of Ownership — the most widely used framework for moving procurement decisions beyond purchase price — is, at its core, a single-actor tool. It asks what a decision truly costs or saves the organization making it. Even the most sophisticated TCO models don't attempt to measure the economic impact on other actors in the supply chain who are not party to the transaction. The same is true of the ROI calculators and business case templates that vendors and trade associations have developed to support procurement decisions. They are genuinely useful tools. They represent real progress over purely intuitive justification. And they faithfully reproduce the single-actor assumption of the academic frameworks they are built on. The system boundary is drawn at the firm — because that is where the decision is being made, and because nobody has yet provided a practical method for drawing it anywhere else.
Naming the Problem Is the First Step to Solving It
The economics literature on spillovers doesn't just describe the problem. It also points toward the conditions under which spillovers can be internalized — brought back within the decision-making framework so that investors can capture more of the return their investment creates.
Two conditions matter most. The first is measurement: you can only internalize a spillover you can see. If the value flowing to logistics partners and downstream customers is invisible — not tracked, not attributed, not connected to the innovation that produced it — then it cannot be the basis for any commercial arrangement. The second is commercial structure: even when spillovers can be measured, capturing them requires mechanisms that allow the innovating party to be compensated by the parties who benefit. This might take the form of shared savings arrangements, multi-party contracts, or service models that bundle the innovation's value across stakeholders rather than selling it to a single buyer at a single price.
Neither condition is easy to meet. But both are more achievable than they were a decade ago. The data infrastructure required to measure diffuse supply chain benefits — sensor networks, digital platforms, supply chain visibility systems — has improved dramatically. What has been missing is a systematic analytical framework for identifying which benefits exist, where they land, and how to connect them to observable data. Without that framework, even organizations motivated to capture spillover value don't know where to look.
What Comes Next
The white paper this article series accompanies — Beyond the Buyer: A Multi-Stakeholder Framework for Evaluating the True Economic Impact of Supply Chain Innovation — proposes exactly that framework. The Multi-Stakeholder Benefit Attribution Model provides a structured methodology for mapping the full population of actors who experience an economic effect from a supply chain innovation, identifying the benefit streams that flow to each, and connecting those streams to the data sources and KPIs that make measurement possible.
It is, in economic terms, a practical approach to spillover internalization — applied not at the level of regulatory policy, but at the level of the individual investment decision.
Subsequent articles in this series will work through specific dimensions of the problem: why Total Cost of Ownership isn't enough, what the pallet market reveals about how diffuse value gets systematically discounted, and what it actually looks like to measure benefits that current frameworks can't see.
The economists figured out a long time ago that markets underinvest in things whose benefits are hard to capture. Supply chain innovation is one of those things. The question now is whether the industry can build the tools to do something about it.
Beyond the Buyer: A Multi-Stakeholder Framework for Evaluating the True Economic Impact of Supply Chain Innovation is available at packagingrevolution.net.
A few notes on the draft. It comes in around 1,350 words — comfortably in the target range. The two citations appear in the third section where they earn their place without feeling like a literature review. The TCO critique is brief and structural — consistent with the white paper's treatment but not repeating it at length, since Article 3 is dedicated to that topic. The closing section teases Articles 3, 4, and 5 by name without giving away their content.
Let me know what you'd like to adjust.
Hey Industry Trailblazers! Last month, we talked about how your shop floor machines are the "eyes and ears" of your future AI. We shifted the perspective from automation as a labor-replacement cost to automation as a data foundation.
But let’s get real. If you’ve spent the last 60 days digitizing your back office and mapping your data leaks, you’re likely asking, “Okay, Kat, where’s the actual ROI?”
In the pallet world, we are experts at measuring the ROI of a new nailing line or a more efficient grinder. We can see the boards per minute. But the ROI of an AI-optimized business process is often invisible until it’s massive.
I like to think of it as the "Friction Tax." Every time an invoice gets stuck, every time a customer service rep has to hunt for a BOL, or every time a yard manager makes a "gut-feel" lumber order based on an incomplete spreadsheet, you are paying a tax on your own efficiency. AI in your business processes is how you stop paying that tax.
When we look for technology that actually pays off, we should look at how it’s being applied in other "heavy" industries where the margins are just as tight as ours. Take Samsara, for example. They aren't just selling dash cams; they’ve transformed fleet safety. By using AI to analyze thousands of hours of driving footage in real-time, they don’t just record accidents—they prevent them by coaching drivers on the fly.
The "payoff" isn't the camera; it’s the dramatic drop in insurance premiums and litigation costs. That is a business process transformation that hits the bottom line immediately.
Or look at Flexport. They’ve taken the incredibly messy process of global freight forwarding and layered an AI agent over it to handle "exception management." Instead of a human being sifting through thousands of emails to find the one container that’s delayed, the AI identifies the outlier and alerts the team only when a human decision is actually needed. They aren't just "moving freight" faster; they’ve eliminated the administrative drag that kills profitability.
Sound familiar? We spend so much of our day "managing the exceptions"—the broken order, the late core delivery, the billing discrepancy. We’ve been taught that this grit is just "part of the job." Here’s the exciting part: It doesn’t have to be. When your software and your shop floor data are connected,
AI can begin to automate the boring stuff. Imagine an AI agent that doesn't just track your inventory but automatically adjusts your procurement strategy based on a shift in lumber futures and your actual real-time yield. The payoff isn't just "saved time"—it's the captured margin that you were previously leaving on the table.
To make this technology actually pay off, you have to look for the "High-Volume, Low-Complexity" tasks. If you have a team member spending four hours a day manually reconciling pallet counts, that is a process begging for a "bridge." That is twenty hours a week of high-level human brainpower being used for $15-an-hour data entry.
A company in China that took this concept to the ultimate extreme: every single employee is required to automate one task using AI every single day, or they face termination. It’s an intense, high-pressure approach that sounds worlds away from our family-owned yards.
Now, I’m not suggesting we start handing out pink slips at the repair line for not using a chatbot, but there is a powerful lesson in that level of commitment to progress. What if we challenged our teams to automate just one task a week? Imagine the compound
Samsara isn't just selling dash cams; it has transformed fleet safety.
interest of a culture where every person is looking for ways to let technology handle the friction.
Your roadmap for May is to conduct a "Friction Audit." Sit with your team and ask: "What is the most annoying, repetitive task you do every day that involves moving data from one place to another?"
Don't look for the biggest problem; look for the most frequent one. Once you find it, look for a "low-code" AI tool or a simple integration that can handle the heavy lifting. The payoff will be immediate, not just in dollars, but in the collective sigh of relief from your team. Stop looking for the "magic pill" tech that solves everything. Start looking for the "bridge" tech that fixes the friction in your daily grind.
Until next time, keep automating the friction, keep leading with precision, and keep bridging the gap!
WPM