By Sagar Shankaran, Founder of CallSphere
A 12% miss on the treated buy costs a three-branch dealer up to $59,000. How 2026 demand models handle messy, seasonal building materials history.
Key takeaways
How many units of 5/4x6 ground-contact treated, sixteen foot, are you buying in February?
Not "roughly what we did last year." The actual number, committed to a treating plant that is already allocating, for a deck season that will not tell you what it is until the first genuinely warm Saturday in May. Every buyer in building materials distribution makes that call, and most of them make it the same way: last season's total, adjusted by a feeling about the housing market and whatever the outside sales reps said in the December meeting.
Because the material has to be treated, and treating takes a schedule. Plants run their cycles and allocate capacity in the first quarter, lead times stretch as the season approaches, and by the time the weather turns, the decking that is available is the decking somebody ordered in January. Add a northern yard's second constraint — spring thaw weight restrictions that cut what your trucks can legally haul on secondary roads for six to eight weeks — and your ability to react in-season is close to zero.
Meanwhile the demand you are buying for is genuinely lumpy. A deck season is not a curve; it is a switch. Two rainy weekends in May move a fifth of the season into June. A hail event in the wrong week turns your roofing department upside down and your deck department idle because every crew in the county is on a roof. Hurricane season starting the first of June moves plywood, OSB and fasteners on the Gulf coast in a pattern that has nothing to do with your five-year average.
Demand forecasting for a building materials distributor means predicting, item by item and week by week, how much of a seasonal product a specific branch will sell, early enough to commit a purchase order to a mill or a treating plant that cannot deliver on short notice.
This is now one of the most-adopted uses of AI in industry — around 48% adoption in manufacturing for demand forecasting, and about 72% in retail and e-commerce for pricing — and the reason is not that the math got smarter in some abstract way. It is that modern models handle messy, sparse, seasonal history that classical methods choked on.
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Your history is exactly that kind of mess. Five years of weekly sales where one year had a commodity price spike that distorted volume, one had a mill outage that created false stockouts, one branch opened in March, and half your treated items were renumbered when you changed suppliers. A classical seasonal model reads those as real signal. A 2026 model can be told what happened and work around it, and it can weigh things a spreadsheet has no column for: the actual start-of-season date each year rather than the calendar month, permit and lot counts inside your delivery radius, how many of your deck-building accounts are still buying, and the sell-through curve from the first three weeks of the season.
The last one is the quiet winner. A weekly re-forecast that reruns against actual pulls will tell you by the third week of May whether the season is running hot or ten days late — in time to take the top-up truck the plant offers, or to pass on it.
flowchart TD
A["Weekly sell-through history by branch and item"] --> B["Model splits the season into three buys"]
B --> C["January: ground contact 5/4x6 deck board, 16 ft"]
B --> D["February: 4x4 and 6x6 posts, joist material"]
B --> E["March: rail kits, balusters, hidden fasteners"]
C --> F["Buyer edits the split against plant allocation"]
D --> F
E --> F
F --> G["Reforecast every Friday against actual pulls"]
Both directions hurt, and they hurt differently. Assume a season treated-lumber buy of $1.9 million at a three-branch dealer. These figures are illustrations to show the shape of the arithmetic, not measured results.
| Scenario | Calculation | Cost |
|---|---|---|
| Over-buy by 12% | $228,000 sitting past Labor Day | — |
| Carrying cost, 9% for 5 months | $228,000 × 9% × 5/12 | $8,550 |
| Fall markdown to clear it, 6 points of margin | $228,000 × 6% | $13,680 |
| Degrade, checking, cull on weathered stock, 3% | $228,000 × 3% | $6,840 |
| Over-buy total | $29,070 | |
| Under-buy by 12% | $228,000 of sales you cannot fill in May and June | — |
| Lost gross margin at 22% | $228,000 × 22% | $50,160 |
| Air freight and premium in-season buys to partially cover | estimate | $9,000 |
| Under-buy total | $59,160 |
Two things fall out of that table. Under-buying costs roughly twice what over-buying costs, which is why experienced buyers lean long — and why your yard is full in September. And the swing between the two outcomes on a single category is most of a branch manager's annual salary, decided in a January meeting on a feeling. If a model narrows the miss from twelve percent to six, it paid for itself before the first truck ships.
Yes, and this is where most dealers find faster money. Pricing optimisation runs near 72% adoption in retail and e-commerce for a reason: the margin is not where you are defending it.
Every contractor in your market knows the number on about forty items — the stud, 7/16 OSB, half-inch sheathing, 5/8 board, the common treated sizes. You fight over those to the penny, and you should. The other eighteen thousand line items — connectors, sealants, fasteners, flashing, shims, roof cement, adhesives, hardware — nobody is shopping, and at most yards nobody has looked at those prices in three years. Meanwhile the price matrix has five customer levels, reps have override authority, and the discount that a rep gave a builder for one job in 2023 is still attached to that account.
A model that reads your invoice history line by line will show you three lists inside a week: items where you are consistently under the market and giving away margin nobody asked for, items where you are visibly high on something contractors price-check and are losing whole tickets over, and accounts whose realized margin has drifted well below their assigned level because of layered overrides. That third list is usually the uncomfortable one, and it is usually worth the most.
On anything the history cannot contain. A model cannot see that your biggest deck builder just lost his line of credit, that a developer's hundred-lot subdivision is stuck on a sewer approval, or that a treating plant two states over is down. Your outside sales reps and your buyer know those things, and their edits should go in on top of the forecast, with a note saying why — so next year you can look back and see who was right.
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Allocation is a relationship, not a number. When a plant can only give you sixty percent of what you asked for, what you get depends on the buyer's history with that plant and on how you treated them in the slow year. No model negotiates that.
And commodity timing stays human. A model can tell you how much dimensional lumber you will sell in June. It cannot tell you where the market is going, and any tool that claims to should be treated the way you would treat a guy at a trade show with a system for the futures market.
No, and that is genuinely new this year. Handling gappy, renumbered, distorted history is the specific thing that improved. You do have to tell it what happened: which weeks were stockouts rather than low demand, which year had the price spike, when the branch opened. Budget a week of somebody's time to write that history down once.
Far enough to make a February commitment with a defensible number, which is the practical bar. The forecast for June written in January will be wrong; the point is that it is wrong by a smaller and more measurable amount, and it gets re-run every Friday against what actually pulled.
Sometimes, and when it does that is a real answer. The value shows up in the split rather than the total — which lengths, which grades, which branch, and which weeks. Most yards are closer on the total than on the mix, and the mix is where the fall markdown comes from.
No, and you should not. Take the fifty largest margin-leak items, move them, and watch the units for four weeks. If volume holds, keep going. If a contractor calls to complain about a caulk price, you have learned something the model could not tell you.
Pull five years of weekly sales for one seasonal category — treated decking is the right one — with the stockout weeks marked, and have a model forecast last season from the data available in January. Then compare it to what you actually bought and what you actually sold. One category, one afternoon, and you will know whether it beats the feeling in the room.
A last practical note. Every one of these swings shows up first on the phone: the run on decking in the second week of May, the roofing calls after a hail event, the builder who wants to know today whether you have twelve more units. CallSphere builds AI voice and chat agents that answer the counter line and the website chat 24/7 and capture what people are asking for, which over a season becomes a running record of demand you asked about and could not fill — useful evidence next January, and orders you would otherwise have missed this year.
Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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