Most middle-market manufacturers treat the launch forecast as a planning exercise handed to a demand planner or sales dictating inventory. It’s actually a capital allocation decision that establishes production capacity, purchases of raw materials, minimums to suppliers, marketing expenses, and the revenue number in the plan.
The hard part is that the evidence is often very thin. According to research by the Institute of Business Forecasting and Planning, approximately 43% of businesses use only judgment to forecast items with little or no history, and around 17% use statistical methods through supersession or like-item forecasting. That is, 60 percent of launch forecasts are based on an opinion or a comparison.
In middle-market manufacturing, a new product forecast is not a prediction: it is a supply commitment, and the decision governs how much working capital to invest in a number that’s difficult for the team to commit to.
Why does new product forecasting fail before the launch even starts?
It breaks down as soon as someone is pressed for a comparison. A planner is under time pressure, which forces them to find the closest match in the existing item list and adjust that item’s history accordingly. From that point on, all discussion centers on that number — not actual demand.
In our experience, we call this the Analog Anchor Trap: the first comparable named becomes the launch number, and for the next eight weeks the team works in percentages, all based on a number that was never validated.
The trap becomes difficult to see through because it comes with analytics. There is a curve, a spreadsheet, and a percentage adjustment. What is missing is any documentation of why that item was selected, what was unique about its launch, or whether its history includes a one-off promotion that will never be repeated. In a mid-market item master, all of that context typically lives in one person’s head — and when that person is on PTO during the commitment window, it’s gone.
Meanwhile, the rest of the business holds information that never makes it into the forecast: prior launch performance, quoting activity, customer commitments, channel mix, pricing decisions, and the lead-time realities from the contract manufacturer. The launch number is being built from one data point when the company has 40 others available.
What happens when the launch number reaches the S&OP meeting?
The disagreement escalates to a wider audience at this stage. S&OP is where the number should get stress-tested, and for a new product, it is where the number should get negotiated.
You are the COO of a $120M food and beverage manufacturer. The slide shows 180,000 units. The co-packer needs a firm order 10 weeks out for a minimum of 60,000. You are not making a forecast in this meeting — you are making a purchase order. Sales says national account interest was strong. Marketing has already allocated spend against the volume, so cutting the number means losing spend that cannot be recovered. Your CFO asks a quiet question: “What happens if half of this is still on hand at 90 days?” You look to your demand planner — who also handles customer service two days a week — and she tells you the analog item was launched in 2023 at a different price point.
There are no wrong people in this room, but no one can explain the number. You have to respond to the co-packer by end of Friday.
[INSERT: anonymized client example — confirm with Rob]
The inability to explain is the real problem, and it compounds. When decision-makers cannot see what is driving a forecast, they stop assessing risk and start assessing credibility. The meeting shifts from a supply decision to a credibility contest. The loudest function wins, and the number that leaves the room goes into the quarterly plan — where finance will defend it even as early orders tell a different story.
If you agree to the number now, you preserve the launch window and the promotional schedule — but you risk a 40% overshoot that gets discounted and written off in a quarter already committed to another initiative.
Stage the commitment, and you preserve working capital and retain options — but you are exposed to expedite premiums, partial fill on initial orders, a national account that runs out of stock in week three, and a sales team that will reference it for two years.
How do you know your launch forecast is built on air?
By the time the write-off arrives or the expedite invoice lands, it has been three months. There is only one point at which to intervene: upstream, in the process that created the number — not in the number itself. Run that process through a diagnostic before the next launch.
| Signal | Risk | What it indicates |
|---|---|---|
| No one can name who selected the like-item | The anchor is undocumented and unchallengeable | The forecast is a judgment call wearing analytics |
| The launch number equals the business case number | Forecast bias is built in, not measured | You have a sales target, not a demand forecast |
| Post-launch accuracy is never measured by SKU | The same error repeats every cycle | No learning loop exists between launches |
| Supplier minimums are discovered after the forecast is set | Commitment is driven by the co-packer, not the plan | Lead time is not an input to the launch decision |
| Launch inventory aging is not reviewed at 60 and 90 days | Trapped cash surfaces in a lender conversation | Working capital exposure is invisible until it is a problem |
Two questions separate good companies from great ones on this. First: why did the final number change from the initial number? Second: do you measure new product forecast error separately from base business forecast error? If the answer to either is no, the next launch will produce the same outcome for different SKUs.
What actually improves launch forecasting in the middle market?
First, not a larger model. Most mid-market teams don’t have the bandwidth to build and maintain one, and new products are typically small assets in the portfolio — a planner can spend several weeks on a SKU that never moves the needle.
Three changes do most of the work. If you skip them, you pay for it on each launch.
Expand the evidence base beyond one comparison. There is signal in prior launches, pricing, promotions, channel mix, customer concentration, and quote activity. Every subsequent launch inherits the distortion from the one analog you chose — which may have had a one-time promotional lift that will never be repeated.
Separate the demand forecast from the business case. Build an unconstrained view of demand first, then compare it to the revenue goal. Until you do this, forecast bias cannot be measured — and an error baked into a goal cannot be corrected.
Time supply commitments to actual decision points. Tie quantities to supplier lead cycles and cancellation windows so you can buy in tranches rather than all at once. How much working capital goes into the launch is determined by those windows and the co-packer’s minimum order quantity — not by the forecast number alone.
[INSERT: typical staged-commitment result from a Waypost engagement — confirm with Rob]
What is shifting here is driven by better evidence and structured decision points — not by AI or new forecasting models, though those tools can help when analyzing an entire portfolio rather than one comparison. A better model will not save a launch plan that was changed without informing the planner, or a process where no one owns the number.
Key takeaways
- The new product forecast is not a modeling problem — it is a supply commitment decision.
- According to IBF research from September 2026, about 43% of businesses forecast low-history items primarily on judgment.
- The Analog Anchor Trap locks in a launch number before it can be validated, based on a comparison that may not hold.
- Explainability matters more than accuracy in the S&OP room. A number no one can explain gets negotiated, not evaluated.
- Staging supply commitments ahead of the supplier’s lead time turns one large irreversible bet into a series of smaller, manageable ones.
Frequently asked questions
How would you forecast sales for a product with no sales history?
Apply patterns from across your portfolio — not just one similar item. There is signal in prior launches, pricing, channel mix, customer concentration, and quote activity. Then express the output as a range with an explicit commitment plan, rather than a single number.
Are like-item forecasts inherently flawed?
Not inherently — but they are often used poorly. The method assumes you selected a genuinely comparable item, that pricing is similar, and that the market will behave the same way. Document those assumptions explicitly, because they can be challenged, and use more than one analog when possible.
Who should own the new product forecast?
The number should be the responsibility of Demand Planning, while the underlying assumptions should belong to Sales, Marketing, Finance, and Operations. The failure mode in mid-market companies is murky ownership — the forecast migrates to whoever has the most organizational weight.
What is the right initial inventory commitment for a launch?
Don’t build to the full launch forecast — build to the first firm decision point. Estimate supplier lead times, minimum order quantities, and cancellation windows, then make commitments in tranches based on actual demand signals rather than the plan number.
How do we know if our launch forecasting process is working?
Measure new product forecast error separately from base business error, and review launch inventory aging at 60 and 90 days. If neither is monitored, you have no feedback loop — and the next launch will repeat the same outcome for different SKUs.
Next steps
Solve the process problem and you still face one more decision: how much capital to commit to a launch number, and whether to commit it all at once. Commit fully, and you are paying for the opportunity window — which may take three quarters to pay back. Stage the commitment, and you preserve cash but accept expedite premiums and the first-order fill rate your best customer will remember. Neither is inherently wrong. But only the second is intentional — and being intentional requires knowing what the number is based on and who owns that assumption. The companies that launch well are not the ones with the best forecasting software. They are the ones with a clear number owner, documented assumptions, and supply commitments tied to real decision points rather than a single spreadsheet.
Waypost Advisors works with mid-market manufacturing and distribution companies to build planning processes that hold up under launch pressure — by designing S&OP cycles, cleaning the data those forecasts rely on, and staying hands-on through execution. In most cases where recent launches produced trapped inventory or expedite spend, the forecast was not the problem. The process that created it was.
