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When large project orders become sporadic, do not extend the last project peak into the future or treat a quiet period as proof that demand has disappeared. Build a forecast from three visibly separate signals—actual shipments, confirmed open orders and an adjustable statistical baseline—then classify each product’s demand pattern. For intermittent or lumpy items, estimate both the timing and the size of possible orders. Review that baseline with sales, operations, procurement and finance, record every material override, and connect the approved plan to purchasing, production, inventory, backlog and capacity decisions.

The result is decision support, not a promise that another project will arrive. A useful forecast tells the business what to prepare, what to defer and what risk it is accepting.

Why project-driven demand defeats ordinary forecasting

A project can create months of shipments in a few weeks, followed by a long interval with no orders. If a planner averages the peak and the silence together, the rate may look stable even though the next requirement is an event with a specific date and quantity. If the planner simply repeats the last project, the model can trigger speculative purchases after the customer has changed design, delayed the program or selected another supplier.

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Project demand also mixes different kinds of information. A shipment is evidence that product moved. A confirmed order is a commitment with a delivery requirement. A sales forecast is an assumption about an opportunity. A cancellation removes previously expected demand. Keep those quantities in separate fields and reconcile them before calculating a baseline. The U.S. Census Bureau’s M3 survey illustrates the principle by publishing shipments, new orders net of cancellations, unfilled orders and inventories as distinct measures. Its coverage is limited to reporting establishments with at least $500 million in annual shipments, so it is sector context rather than a benchmark for a small or midsize manufacturer’s own order book.

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1. Establish a clean demand picture

Start with a consistent time grain—weekly for short supplier or production decisions, monthly for longer project cycles—and create a snapshot that can be reproduced later. Pull the following fields for each product, customer and project where the relationships are known:

  • Historical shipments, including returns or credits handled consistently.
  • Booked orders, requested dates, promised dates and confirmed changes.
  • Cancellations and the date they became known.
  • Unfilled orders or backlog, separated from uncommitted opportunities.
  • Inventory on hand, open purchase orders, work in process and scheduled production.
  • Project milestones such as bid, award, design freeze, release to production and expected site delivery.
  • Supplier lead times, minimum buys, substitution options and component shelf-life limits.

Do not count a confirmed order once as “actual” and again as a sales forecast. Keep the order in the committed-demand view and remove the same quantity from the uncommitted baseline unless your planning policy explicitly models a separate incremental risk. Preserve the product–customer–project links: they reveal whether a spike came from a repeatable application or a one-off design.

Separate evidence from assumptions

Use distinct columns or measures for shipped quantity, open-order quantity, cancellation quantity and forecast quantity. Give every assumption an owner and a date. For example, “customer expects award decision in November” is a dated commercial signal; it is not the same as an order. This separation prevents a large but uncertain bid from inflating material requirements as if it were production-ready demand.

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2. Segment products before choosing a method

One algorithm should not govern every item. First classify the series by behavior and by whether the demand still exists:

Steady repeat demand

Items with frequent, reasonably consistent consumption can use ordinary time-series methods or a simple moving baseline. Their history contains enough observations to estimate level and trend without relying on a single project.

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Intermittent or lumpy project demand

These items have many zero periods and occasional non-zero quantities, often with large variation in order size. An intermittent method estimates two components: the non-zero demand quantity and the interval between non-zero events. SAP’s documented intermittent approach also applies outlier correction before forecasting. That is a vendor-documented method, not a universal prescription; compare it with transparent baselines and document the assumptions.

Structurally discontinued demand

A zero period may mean more than timing. A customer may have changed a design, ended a program or moved to another supplier. Treat evidence of discontinuation as a lifecycle or commercial decision, not as another zero to average into an indefinitely recurring series. Route the item for an explicit end-of-life or redesign review.

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Check whether the series has enough independent demand events to support a local model. If it does not, pool information only when products genuinely share drivers, lead times and customer behavior. A global model can reuse data across many series, but a 2025 review of make-to-order (MTO) forecasting notes that highly different series may not behave alike; interpretability and maintenance also become harder as models grow more complex.

3. Match the forecast to the operating question

Ask what decision the number must support before selecting a metric or model. A monthly average may be adequate for a long-term workforce budget, but it is insufficient for reserving a machine next week or deciding whether to buy a component with a 20-week lead time. In MTO settings, both event timing and event size matter. The 2025 academic review of MTO decision support explains why aggregate or per-period errors can miss one of those dimensions.

Planning question Useful output Typical evidence Primary exposure
Should a long-lead component be purchased? Scenario quantities by likely requirement window, with committed orders shown separately Open orders, project milestones, supplier lead time and minimum buy Excess, obsolescence or cash tied up before award
Should capacity be reserved? Possible event dates and size ranges by product family Historical intervals, customer schedule and available machine hours Idle capacity or an expedited, late delivery
How many people should be scheduled? Workload range over the staffing horizon Approved orders plus repeatable baseline demand Overtime and temporary labor versus underutilization
What should the sales and operations plan assume? Approved baseline and named upside/downside scenarios Statistical history, commercial signals and supply constraints Plans that appear precise but cannot be executed

Use scenarios when evidence is thin

For a sparse series, publish at least three clearly labeled cases: a no-reorder case, a likely-repeat case grounded in customer information, and a higher case tied to an identifiable project or market signal. These are planning scenarios, not probabilities unless the underlying data supports probability estimates. State the horizon and the trigger that would move the plan from one case to another—for example, a purchase order, a design freeze or a customer schedule confirmation.

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4. Build and review the statistical baseline

Generate a baseline from the segmented history before adding opinions. For intermittent items, calculate the non-zero quantity behavior and the intervals between events; inspect outliers rather than allowing one unusually large project to define every future period. Keep a simple benchmark, such as the last non-zero quantity and observed interval, so a more elaborate model has to demonstrate value.

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Then run a structured review:

  1. Prepare the history. Align calendars, remove duplicate transactions, label cancellations and preserve the date each fact became known.
  2. Produce the baseline. Generate the selected method at the same horizon used for purchasing, production and capacity decisions.
  3. Review commercial facts. Sales and account teams add named project status, bid stage, customer schedule changes and credible cancellation or award information.
  4. Review supply realities. Procurement adds supplier lead times, minimum buys and substitutions; operations adds constraints, setup times and available hours; finance identifies working-capital limits.
  5. Adjust transparently. Record the old baseline, the new quantity or date, the person approving it, the reason, the evidence date and an expiry or review date.
  6. Approve and publish. Lock a version for the planning cycle and send it to the system that drives material, production and capacity plans.

Microsoft Learn documents this baseline-review-adjust-approve flow for forecasting. Its documentation also describes optional internal and external signals for short-term demand sensing. Use such signals as governed inputs, not as an automatic replacement for order data.

5. Turn the forecast into operating choices

A forecast has value only when it changes a decision. For each material or capacity constraint, state the action, trigger and downside:

  • Buy now: appropriate when a project is highly credible, the component lead time threatens delivery and the part is reusable if the project slips.
  • Defer or split the purchase: useful when supplier flexibility allows a reservation or staged release and the demand signal is not yet firm.
  • Prepare a supplier option: secure quotes, capacity or an alternate source without taking full ownership of inventory.
  • Reserve production capacity: hold machine or labor slots only when the opportunity cost of losing them is lower than the cost of an expedited recovery.
  • Keep finished goods make-to-order: avoid speculative finished inventory when configurations are customer-specific or vulnerable to redesign.

Compare shortage and delay costs with carrying cost, shelf life, redesign risk, minimum order quantities, supplier flexibility, setup constraints and the time needed to recover capacity. Microsoft Learn states: “In a make-to-order model, the procurement and/or production lead times constrain the fulfillment lead time.” That constraint should appear explicitly in the decision record.

A published academic case study simulated forecast-informed component-purchasing limits alongside obsolescence and expiration risk. Its results apply to that modeled purchasing context; they are not a guaranteed saving or inventory reduction for another plant. Do not trigger a large speculative buy merely because an upside scenario exists.

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6. Run demand and supply together in an S&OP cycle

Review the forecast alongside the order book, backlog, inventory, planned supply and rough-cut capacity. IFS documentation describes sales and operations planning for both MTO and make-to-stock families, using a forward horizon to review forecast and orders, inventory and backlog plans, and capacity. The cadence should fit your supplier lead times, project cycles and decision speed; a vendor’s documented cadence is not a universal rule.

A practical meeting sequence

  1. Demand review: reconcile shipments, open orders, cancellations and the approved baseline; highlight changes since the prior snapshot.
  2. Supply review: test material availability, supplier commitments, production load, setup conflicts and substitute parts against each scenario.
  3. Reconciliation: quantify gaps, choose actions and assign owners and due dates.
  4. Executive decision: approve the plan, risk appetite and exceptions that exceed normal inventory or capacity policy.

Keep the horizon long enough to cover the slowest consequential supplier or production decision, but show near-term weeks in finer detail when a project could land suddenly. Preserve each cycle’s snapshot so later reviews distinguish what was known at the time from information learned afterward.

7. Measure whether the forecast supports better decisions

Track error and bias, but do not stop at one accuracy percentage. A model can have an acceptable average error while repeatedly missing the timing of project starts or overstating a component that expires. Disaggregate results by product family, customer or demand type when aggregation hides spikes.

  • Statistical checks: compare forecast with realized demand at the horizon used for each decision; review systematic over- or under-forecast bias.
  • Timing and size checks: record whether a non-zero event occurred in the expected window and whether its quantity range was useful.
  • Material outcomes: measure excess, obsolete or expired stock, emergency purchases and supplier cancellations.
  • Service outcomes: review late orders, missed promised dates, expedites and customer escalations.
  • Capacity outcomes: examine setup changes, overtime, utilization and the cost of holding or releasing capacity.
  • Governance outcomes: count overrides, identify which reasons were reliable and retire rules that consistently failed.

Microsoft gives MAPE as an example model test in its product workflow. Use it only alongside measures suited to the decision. The MTO review cautions that conventional summed or per-step metrics may not assess timing and size together. No broadly applicable statistic establishes a guaranteed improvement, savings percentage or inventory reduction when project orders dry up.

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8. Software capabilities to evaluate

Enterprise tools differ less by whether they display a forecast than by how they connect that forecast to execution. When evaluating a system, check whether it can:

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  • Keep shipments, orders, cancellations, backlog and forecast assumptions separate.
  • Support intermittent-demand methods and show the non-zero quantity and interval logic.
  • Accept internal and external signals with dated, attributable overrides.
  • Generate scenarios or ranges and preserve approved versions.
  • Pass the plan into ERP/MRP purchasing, production, inventory and capacity workflows.
  • Link demand to customer, project, product and component relationships.
  • Report bias, timing and size outcomes at the level where decisions are made.
  • Expose model assumptions so planners can explain a recommendation and maintain it.

SAP documentation describes intermittent forecasting and demand sensing; Microsoft documents a Dynamics 365 workflow with a statistical baseline, user review, approval and publication into planning; IFS documents S&OP across MTO and make-to-stock families. These are examples of product capabilities, not endorsements or proof that a particular configuration fits your data. Assess integration effort, implementation burden, maintenance and total cost as carefully as forecast features.

Common failure modes and recovery actions

Repeating the last project peak

Symptom: every future period inherits the largest historical order. Recovery: separate the project event, model the interval between events and require a current customer or market signal before adding upside.

Treating every zero as lost demand

Symptom: the plan steadily decays even though projects recur. Recovery: distinguish a normal inter-event interval from evidence of design change, program end or supplier loss.

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Counting orders twice

Symptom: material requirements include both a confirmed order and the same quantity in the sales forecast. Recovery: reconcile committed and uncommitted views before baseline generation.

Letting overrides become invisible

Symptom: planners cannot explain why the approved plan differs from the model. Recovery: log owner, date, reason, quantity, timing and expiry for every material adjustment.

Optimizing a metric instead of the decision

Symptom: a lower average error accompanies more obsolete stock or late deliveries. Recovery: evaluate the operational outcome—material exposure, service, capacity and working capital—at the horizon that drove the decision.

Bottom line

Forecasting after project orders dry up is an exercise in separating signals and managing uncertainty. Maintain distinct shipment, order and assumption data; segment intermittent, repeat and discontinued demand; estimate event timing as well as size; review the baseline with accountable people; and convert scenarios into explicit buy, build, staff and capacity choices. A forecast earns credibility when its assumptions are visible and when the decisions it supports produce fewer costly surprises.

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