When policy conditions turn unstable, most planning teams respond the same way: more forecast updates, more scenario discussions, and more pressure to revise the numbers.
But the main challenge in 2026 is not a lack of data. It is that some of the data no longer means what teams think it means.
Demand may appear to be rising in parts of the market. But a closer look suggests that some of that apparent growth reflects something else: customers pulling orders forward, distributors ordering defensively, and companies repositioning inventory because of tariff uncertainty. Recent Federal Reserve, Walmart, and Logistics Managers’ Index materials all point to this mix of front-loading, shifting inventory posture, and planning difficulty.
That distinction matters. Once those behaviors get mixed into the demand signal, the hardest task is not spotting the distortion. It is normalizing it correctly without creating inventory, supply, or service problems in the cycles that follow.
The Distortion Is Already Visible in the Data
One reason cost-reduction programs go wrong is that companies often treat all buffers as if they serve the same purpose. They do not.
The pattern is visible in both market data and operating behavior.
U.S. imports rose sharply in early 2025 as many businesses accelerated shipments ahead of expected tariff increases. The Federal Reserve said real imports of goods and services surged at a historically high 43% annualized rate in the first quarter and that the surge “arguably reflects” businesses pulling imports forward ahead of higher tariffs. It also noted that goods imports fell sharply in April after many tariffs were raised.
A similar pattern appeared in industrial activity. S&P Global reported that manufacturing conditions got support from tariff front-running and inventory building, then later showed signs that the benefit was fading as that behavior normalized. Ryder’s March 2026 transportation report likewise said the earlier positive impact of inventory pull-forward had tailed off, with U.S. import container volumes remaining below prior-year levels.
At the company level, large retailers have described tariffs as a moving target and noted that inventory decisions are being recalculated as assumptions change. In Walmart’s Q1 FY26 earnings call, an analyst explicitly described tariffs as a “moving target” and asked how Walmart was thinking about inventory planning in that backdrop. Walmart’s management discussion also framed the near-term environment as unusually wide and difficult to predict.
Inventory behavior has also moved in different directions at the same time. The Logistics Managers’ Index said February 2025 was marked by significant inventory buildup to stay ahead of tariffs, while February 2026 reflected the opposite approach, with firms keeping inventory lean to avoid existing tariff costs.
Taken together, these patterns point to a simple problem: ordering behavior is being shaped by policy timing and risk response, not just by true end demand.
Why Volatility Is Not the Full Story
Most commentary describes the current environment as volatile. That is true, but it does not fully explain the planning problem.
Volatility suggests that demand is moving up and down. Distortion means something more specific. It means the signal itself is becoming less reliable because it contains a mix of different behaviors.
In practical terms, demand signals today often combine:
- Real end-customer consumption
- Forward buying ahead of tariff
- Defensive ordering to protect availability
- Inventory repositioning across channels
Those behaviors do not mean the same thing. But in many planning environments, they are still rolled into one number and treated as if they do.
That is where planning quality starts to erode.
How Distortion Turn Into Planning Difficulty
When distorted signals enter the planning process, the main problem is usually not that teams miss what is happening. In many cases, they know an order surge is linked to tariff timing or defensive buying.
The harder problem is deciding what to do with that signal.
A distributor may place larger orders ahead of a tariff change. The planning team usually understands that the increase is temporary. But it still has to decide how much of that order should be supported, how much demand has simply been pulled forward, and what the demand baseline should look like once that spike passes.
That is where normalization becomes difficult.
If teams strip too much out of the signal too early, they risk missing real short-term demand and creating service problems. If they normalize too slowly, production, procurement, and inventory plans stay elevated after the pull-forward effect has already passed.
The risk is not limited to a bad forecast. It affects purchase timing, production loading, inventory posture, and how quickly the business can reset once order patterns normalize.
The challenge is not spotting the distortion. The challenge is separating it, sizing it, and unwinding it correctly.
This is why the issue can persist even inside capable planning organizations. The signal is not invisible. It is mixed. And once temporary behavior enters the system, every downstream decision has to account for how much of that signal is real, how much is temporary, and how quickly conditions are likely to reset.
A More Useful Way to Read Demand
If the problem is not lack of data but mixed signals, the answer is not simply more forecasting rounds. The better approach is to separate what the signal is actually telling you.
Instead of asking, “What is demand?”, planning teams need to ask, “What is driving this demand signal?”
A practical way to do that is to break the signal into three categories.
- Consumption-driven demand
This is the part closest to true end-market activity. It reflects what customers are actually buying or using.
How to identify it:
Look for patterns supported by sell-through, point-of-sale data, usage rates, or reorder behavior that remains reasonably consistent over time.
Example:
A retailer’s sell-through rises steadily for several weeks across multiple regions, and replenishment orders follow that pattern. That is more likely to reflect real demand.
- Channel behavior
This is demand created by the actions of distributors, retailers, or intermediaries rather than by a clear change in end consumption.
How to identify it:
Look for sudden changes in order size, order frequency, order timing, or customer mix that are not matched by downstream sell-through.
Example:
A distributor that usually orders weekly places a much larger order near month-end, but store-level sell-through has not changed much. That is more likely channel behavior than true market growth.
- Policy-driven behavior
This is demand shaped by tariff timing, cost avoidance, expected shortages, or other external triggers.
How to identify it:
Look for order spikes tied to announced policy dates, import timing shifts, or unusual inventory builds just before a cost change takes effect.
Example:
A customer increases purchases in March ahead of an April tariff change, then reduces purchases over the next two cycles. That spike should not be treated as a lasting step-up in demand.
The point is not to create complexity for its own sake. It is to avoid treating all volume as if it reflects the same logic.
When teams do not separate these categories, several problems follow:
- Forecasts stay inflated longer than they should
- Production plans react too broadly
- Inventory buffer gets set at the wrong level
- S&OP discussions spend more time debating the number than interpreting the drivers behind it
What Planning Teams Should Do Differently
Planning teams do not need perfect visibility to improve this. But they do need a more disciplined way to test what the signal is really saying.
A few practical checks help:
- Compare order growth to sell-through growth, not just to prior orders
- Flag volume spikes that line up with tariff announcements or policy deadlines
- Review whether larger orders are broad-based across customers or concentrated in a few accounts
- Separate one-time inventory positioning from repeat consumption
- Define how quickly pull-forward demand should be normalized once the event passes
That last point matters more than it may seem. In many cases, the real planning difficulty is not whether a pull-forward happened. It is deciding how much of that volume should stay in the near-term plan and how much should be removed from the baseline.
These are practical planning disciplines. They help teams avoid carrying temporary behavior too far into future decisions.
Conclusion
The key question in 2026 is not whether demand is rising or falling.
It is whether the signal being used for planning still reflects real consumption, or whether it also contains a meaningful amount of timing, channel, and policy-driven behavior.
Right now, some of what looks like demand is actually timing, protection, and risk response showing up in the order stream.
In this environment, the planning problem is not just demand variability. It is deciding what part of the signal belongs in the plan and what part should be treated as temporary distortion.
If this sounds familiar, a structured review can help identify where temporary demand is getting mixed into your planning process.
Further Readings
- Federal Reserve, Monetary Policy Report, June 2025. Import surge, pull-forward, and post-tariff drop.
- Federal Reserve, Beige Book, April 2025. District-level evidence of significant import pull-forward ahead of tariffs.
- Walmart, Q1 FY26 Earnings Call Transcript. Tariffs described as a moving target and inventory planning discussed in that context
- Logistics Managers’ Index, February 2025 and February 2026. Shift from inventory buildup ahead of tariffs to leaner inventory posture to avoid tariff costs.
- S&P Global, manufacturing / PMI commentary, mid-2025. Tariff front-running and later fading effects on activity and inventories.
- Ryder, State of the Industry, March 2026. Import container volumes tailed off after earlier pull-forward.