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The output is decision support, not a promise that another project will arrive. A sound plan shows what is known, what is plausible and what action is justified at each level of uncertainty.
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Why project-driven demand defeats ordinary forecasting
Project business produces long gaps followed by unusually large orders. A simple average can flatten those spikes, while extrapolating the last spike can leave a manufacturer with excess components and finished goods. The first problem is not choosing a sophisticated algorithm; it is defining which quantities represent reality.
Keep different demand signals separate
Maintain separate fields for actual shipments, customer-confirmed open orders, new orders net of cancellations, unfilled orders, inventory and forecast assumptions. The U.S. Census Bureau’s M3 survey uses these as distinct measures for large manufacturing establishments, illustrating why they should not be collapsed into one “demand” number. M3 coverage is limited to reporting establishments with at least $500 million in annual shipments, so its sector statistics are context rather than a benchmark for a small or midsize manufacturer’s own order book.
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Do not count a confirmed order both as booked demand and as an unchanged statistical forecast. Mark the order’s project, customer, product and component relationships where they explain why the demand occurred and whether the same use case can recur.
Distinguish a pause from structural loss
A gap may mean a project is between phases, a customer has delayed an award, or the product is no longer needed. Review design changes, program discontinuation, supplier replacement and customer schedule changes before assuming that the historical pattern will return. A discontinued demand stream should be removed or separately modeled; otherwise it can keep generating false reorder signals.
Start with a decision-ready demand picture
Use a consistent time grain—weekly or monthly, depending on supplier and project lead times—and create a snapshot of what was known at each planning date. Pull the following data into one reconciled view:
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- Booked orders, requested dates, promised dates and cancellation history.
- Backlog or unfilled orders, including status and probability of release.
- Inventory on hand, open purchase orders, work in process and open supply.
- Project milestones such as bid, design approval, award, production release and completion.
- Supplier lead times, minimum buys, substitution options and component shelf life.
- Available production hours, critical work centers, setup requirements and staffing limits.
Reconcile units, dates, revisions and customer identifiers before modeling. A forecast cannot repair duplicate orders, inconsistent project codes or a shipment history that includes returns as positive demand.
Segment products before selecting a method
One method rarely fits a catalogue that mixes repeat items, engineered products and obsolete programs. Segment at the product-family or item level according to demand behavior and business status.
Steady repeat items
Items with frequent, relatively stable consumption can use ordinary time-series baselines. Keep their forecast separate from project items so a large one-time order does not distort replenishment for routine demand.
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Intermittent or lumpy items
Intermittent demand has many zero periods and occasional non-zero quantities; lumpy demand also varies substantially in the size of those non-zero events. SAP’s documented intermittent method estimates non-zero demand quantities and the intervals between demand periods, with outlier correction before forecasting. That is a documented vendor approach, not a universal prescription. Compare it with simple baselines such as a recent non-zero quantity and interval calculation before deployment, and record the assumptions that make the method credible for your data.
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Separate items affected by a customer redesign, discontinued program or lost supplier position. Their historical orders are evidence of what happened, not evidence that the event will repeat. Use a zero, retirement or replacement plan when business facts support it, rather than allowing a statistical model to revive a dead series.
Forecast both when an order may arrive and how large it may be
For a make-to-order manufacturer, an average demand rate alone may not answer the operating question. Planners need to know whether a project could land inside a supplier lead time and what quantity it might require. A 2025 academic review of make-to-order decision support explains why event timing and event size should be modeled together and why a conventional aggregate or per-period error can miss an important failure.
Use scenarios when evidence is thin
Build explicit scenarios rather than presenting an unsupported point estimate as certainty:
- No-reorder case: no new project is assumed during the decision horizon; buy only for confirmed requirements and essential continuity.
- Likely-repeat case: a repeat is included only when customer history, account information or a credible schedule supports it.
- Identifiable-project case: a named bid, award or market signal supplies a bounded quantity and timing range.
These are planning cases, not probabilities unless the underlying data supports probability estimates. State the horizon, quantity range, timing assumptions and trigger that would move the plan from one case to another.
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Evaluate the forecast over the time needed to place materials, schedule production and recover capacity. A supplier with a 20-week lead time needs a different view from a work center that can be rescheduled next week. Keep the forecast horizon, project lead time, production planning horizon and evaluation horizon visible rather than calling all of them “the forecast.”
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Build a statistical baseline, then apply governed judgment
A baseline gives the review team a consistent starting point. It should be generated from the cleansed history and selected segment, with non-zero quantities, intervals, outliers and known discontinuations handled explicitly.
Review the baseline with the people who know the pipeline
Sales and account teams can supply named project status, bid stage, customer schedule changes and credible cancellation or award information. Operations can identify bottlenecks, substitutions and feasible production windows. Procurement can add supplier lead times and minimum buys. Finance can expose working-capital limits and the cost of being wrong.
Classify every adjustment as fact or judgment. Record the owner, date, reason, affected item or project, old baseline and approved value. Later, compare overrides with outcomes; an override log that is never evaluated becomes anecdote rather than learning.
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Use internal and external signals carefully
Some demand-sensing systems can add internal order information and external signals to a short-term view. Such signals are useful only when their source, freshness, geographic scope and relationship to your customers are understood. Do not let an unverified market indicator silently replace a confirmed customer commitment.
Approve one version for planning
A practical governance sequence is: load historical data, generate the baseline, review and adjust it, approve a version, and publish that version into planning. Microsoft documents this baseline-review-adjust-approve flow in its forecasting workflow. Freeze the approved snapshot with a date and horizon so later changes do not rewrite the evidence used for a purchasing decision.
Turn forecast scenarios into operating choices
A forecast should answer what to do, not merely produce a number. Microsoft Learn states: “In a make-to-order model, the procurement and/or production lead times constrain the fulfillment lead time.” Use that constraint to test each scenario against actual decisions.
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Long-lead materials
For a component that cannot be recovered before a plausible project date, compare an early buy, a supplier reservation, a flexible purchase option and a no-buy case. Include minimum order quantities, price breaks, shelf life, redesign risk and the ability to substitute. An uncertain project forecast does not automatically justify a large speculative purchase.
Production and capacity
Translate timing ranges into work-center hours, setup sequences, specialist labor and overtime requirements. A likely project may justify a provisional slot or cross-training rather than finished-goods production. If capacity is constrained, identify which existing orders would move under each scenario and what customer promise would be affected.
Inventory posture
Finished goods may remain make-to-order while common, reusable components are stocked. Conversely, a component tied to one design may be safer to defer even when its forecast quantity is high. Weigh shortage, expedite and delayed-delivery costs against carrying cost, obsolescence, expiration and cash limits.
An academic case study simulates forecast-informed component-purchasing limits alongside obsolescence and expiration risk. Its findings are specific to that modeled purchasing context; they are not a guaranteed savings or inventory reduction for another plant.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review demand and supply together in an S&OP cycle
Run a cross-functional cycle that compares the approved forecast with open orders, backlog, inventory, planned supply and capacity. IFS documents S&OP for both make-to-order and make-to-stock families, including a forward planning horizon, forecast and order-book review, inventory and backlog plans, and rough-cut capacity analysis.
The cadence should match supplier lead times, project cycles and decision speed. A vendor’s documented cadence is not a universal rule. In every cycle, identify changes since the prior snapshot, decisions required before the next meeting, owners and due dates. Escalate conflicts such as a project that consumes the same constrained machine or component as confirmed customer orders.
Measure whether the forecast improves decisions
Save forecast snapshots so each horizon can be compared with what was actually known when a decision was made. Review both error and bias, and disaggregate by product family, customer or demand type when aggregation hides project spikes.
Statistical checks
Use an error measure appropriate to the series and horizon, and inspect systematic over- or under-forecasting. Microsoft gives MAPE as an example of a model test in its product workflow, but a single general-purpose metric is not enough for intermittent project demand. Zero-heavy series can make percentage measures unstable or misleading.
Operational checks
Pair model metrics with the outcome the forecast was meant to improve:
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- Material exposure, excess, obsolete or expired stock.
- Late orders, expedites and missed customer dates.
- Schedule changes, setup disruption and overtime.
- Utilization of constrained work centers and staffing readiness.
- Working-capital use and the value of flexibility retained.
The 2025 make-to-order review notes trade-offs among timing-and-size accuracy, interpretability, model complexity and maintenance. A transparent baseline that planners can challenge may be more useful than a more complex model that nobody can explain or maintain.
Compare methods and planning choices on the right axes
| Decision axis | Questions to answer |
|---|---|
| Demand pattern | Is demand steady, intermittent, lumpy or structurally discontinued? |
| Horizon | What are the supplier, project, production and recovery lead times? |
| Required output | Do planners need an average rate, event timing, event size, a point estimate or ranges? |
| Data and explainability | How much clean history exists, and can users explain the drivers and overrides? |
| Cost of error | Which is worse: excess and obsolescence, or shortage, expedite, delay and lost business? |
| Operating flexibility | Can suppliers flex, can materials be substituted, and can production be postponed? |
| Evaluation | Which error, bias and operational measures show that the decision improved? |
Global models can reuse information across many series, but highly different products may not behave alike. The 2025 review also highlights interpretability and maintenance concerns. Machine learning is therefore not automatically superior to a transparent statistical baseline.
Where enterprise software fits
Software is useful when it preserves the distinctions and governance described above and connects forecasts to ERP or MRP execution. SAP documents intermittent forecasting and demand sensing; Microsoft documents a Dynamics 365 workflow for baseline forecasting, review, approval and publication; and IFS documents S&OP across make-to-order and make-to-stock families.
When evaluating a system, check integration with order, project, inventory and capacity data; support for intermittent demand; scenario ranges; explainable overrides; versioned snapshots; bias and error reporting; and the effort required to maintain master data. Demonstrated product capability does not prove suitability for your industry, data quality or configuration.
Quick Recap
A practical implementation checklist
- Define the planning grain, horizon and decision owners.
- Reconcile shipments, orders, cancellations, backlog, inventory and open supply.
- Tag product, customer and project relationships and identify discontinued streams.
- Segment steady, intermittent, lumpy and structurally changed demand.
- Generate a baseline and compare it with simple alternatives.
- Model timing and size, using scenarios where evidence is weak.
- Collect named-project and market information with owner and date.
- Log every material override and approve a dated planning version.
- Translate each scenario into buy, defer, reserve, schedule and staffing actions.
- Review demand, supply, inventory, backlog and rough-cut capacity together.
- Measure error, bias and operational outcomes from frozen snapshots.
- Retire methods or signals that do not improve the decisions they support.
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