Forecast Attainment¶

An aggregate planning ratio comparing realized shipments, demand, or supply for a period with the forecast snapshot selected at a policy-defined lag, used to expose overall under- or over-attainment without measuring item-level forecast accuracy.

Version: v1 · 2026-09-28 · History
Domain-specific #: 7646
Domain group: Professional & Organizational Practice
Origin domain: Organizational & Management Science
Subdomain: Demand Planning → Organizational & Management Science
Aliases: Forecast Achievement Ratio, Plan Attainment Ratio

Core Idea¶

Forecast Attainment is an aggregate planning metric that compares what was realized during a defined period with the forecast that had been committed for that period at a defined earlier snapshot.^[1] A common form is the sum of actual shipments divided by the sum of lagged forecast quantities.^[2] Multiplying by one hundred expresses the result as a percentage.

The lagged snapshot is essential. A forecast changes as new orders, observations, promotions, and constraints arrive. Comparing actual March shipments with a forecast revised on March 30 would not test what operations had time to prepare for. If replenishment or production lead time is three months, a planner may compare March actuals with the December snapshot designated “lag 3.”^[3]

The numerator must be defined. It may be shipments, customer demand, orders, sales, production, receipts, or another realized measure. These are not interchangeable. Shipments can fall below true demand because inventory was unavailable; orders can include cancellations; sales value mixes price with volume. A metric labeled attainment is interpretable only when the realized variable and unit are explicit.

The denominator is the corresponding planned quantity in the selected frozen forecast.^[4] The periods, products, locations, customers, units, and inclusion rules must align. Comparing unit shipments with revenue forecasts or a four-week actual period with a calendar-month plan produces a ratio but not a meaningful attainment measure.

An attainment of 1.00 or 100% means aggregate realization equals aggregate forecast for the selected scope. Greater than one indicates aggregate realization exceeded the forecast; less than one indicates it fell short. “Good” does not automatically mean as high as possible. Persistent over-attainment can signal underforecasting, capacity stress, shortages, or exceptional demand; persistent under-attainment can signal overforecasting, excess inventory, weak demand, or execution problems.

Attainment is not forecast accuracy. Item-level positive and negative errors can cancel in the aggregate. Suppose one product is forecast at 100 and realizes 150 while another is forecast at 100 and realizes 50. Aggregate attainment is exactly 100 percent, yet both item forecasts are wrong by 50 percent in absolute volume. Accuracy metrics such as mean absolute error or weighted absolute percentage error expose the miss that aggregation hides.^[5]

It is also not identical to forecast bias, although repeated departures from one can reveal top-level directional imbalance. Bias is ordinarily calculated from signed forecast errors across items or periods under a stated convention. A one-period attainment ratio can be high because of a promotion, supply release, timing shift, or denominator error. Persistent patterns and drill-down evidence are needed before diagnosing forecasting behavior.

The metric can conflate demand and execution. If demand exceeds forecast but supply constraints prevent shipment, shipment attainment may appear near or below one. If a backlog from the prior period ships now, attainment may exceed one even when current demand is not unusually strong.^[6] Organizations should pair shipment-based attainment with unconstrained demand, backlog, service, and capacity measures when those distinctions matter.

Aggregation weights the result implicitly by volume or value because totals are divided. Large products dominate; small products with severe errors can disappear. That can be appropriate for total capacity planning but inappropriate for customer-level reliability or assortment health. Separate segment, item, location, and value/volume views reveal different risks.

The denominator can be zero. If both plan and actual are zero, attainment is undefined rather than automatically perfect. If plan is zero and actual is positive, the ordinary ratio is infinite or undefined, signaling an unplanned event rather than a usable percentage. Reporting systems need an explicit zero-denominator policy and should count these cases separately.

Negative values also require policy. Returns, credits, cancellations, and netted demand can produce negative actuals or forecasts in some datasets. A ratio may then reverse intuitive interpretation. Many operational applications restrict the metric to nonnegative quantities and handle returns separately; others use signed financial plans with documented rules.

Timing alignment matters near period boundaries. A customer order forecast for March can ship in April due to operational delay, making both months look poorly attained even if total quarterly volume matches. Rolling windows can reduce boundary noise but also mask short-term problems. Calendar, fiscal period, working days, and cutoff time should be recorded.

Version governance makes the measure auditable. Forecast systems should preserve historical snapshots with creation time, scenario, approval status, unit, and hierarchy. Reconstructing an old forecast from current data invites hindsight bias. A “lag 3” value should mean the same temporal rule across reports or explicitly document exceptions.

The metric is most useful in a balanced scorecard. Forecast attainment reveals overall plan realization; forecast accuracy reveals magnitude of disaggregated errors; forecast bias reveals systematic direction; service and inventory measures show operational consequences. No single metric determines whether planning, supply execution, or market response caused the result.

Targets should be asymmetric when consequences differ. Over-attainment may be more costly in a capacity-constrained network; under-attainment may be more costly where excess inventory expires. A tolerance band around one can be more meaningful than a rank that rewards closeness without regard to business loss. The band should be justified by lead time and decision sensitivity.

Forecast attainment can be computed over units, weight, hours, or currency. Currency-weighted attainment can change with prices and exchange rates even if physical volume is stable.^[7] Volume metrics better describe capacity, while value metrics can align with financial planning. Publishing both can expose mix effects.

The name also appears in sales contexts where quota attainment or forecast attainment may compare booked revenue to a salesperson's submitted forecast. That usage shares the ratio structure but has different incentives and data. The present node centers operations and demand planning; extensions should name the carrier to avoid mixing quota, consensus plan, and shipment forecasts.

Structural Signature¶

Sig role-phrases:

the planned carrier — demand, shipments, supply, sales, or another quantity is specified in a declared unit.
the realized carrier — an actual measure of the same kind is observed for the target period.
the aligned scope — products, locations, customers, calendar boundaries, units, and inclusion rules match across actual and plan.
the preserved forecast snapshot — one historically saved forecast version supplies the denominator without hindsight revision.
the lag policy — lead time or governance selects how far before realization that forecast had to be available.
the aggregate numerator — aligned actual quantities are summed under a stated volume, value, or other weighting convention.^[8]
the aggregate denominator — the corresponding quantities from the lagged snapshot are summed over the identical scope.
the attainment ratio — actual total divided by forecast total measures realized scale relative to the actionable plan.
the directional interpretation — values above or below one indicate over- or under-attainment under the declared nonnegative convention, not automatic success or failure.
the cancellation limit — offsetting item errors and large-item dominance allow perfect aggregate attainment alongside poor mix accuracy.
the companion diagnostics — absolute error, bias, demand, service, backlog, inventory, and capacity separate hidden error from execution effects.
the exception policy — zero denominators, negative values, returns, missing data, and timing shifts route to explicit branches rather than an ordinary percentage.

What It Is Not¶

Not item-level forecast accuracy. Opposing misses can cancel in the aggregate, so 100 percent attainment can coexist with large product-, location-, or period-level absolute errors.^[9]
Not an absolute-error metric or forecast bias. The ratio reports aggregate realized scale relative to a frozen plan; magnitude and persistent signed direction require companion calculations under their own conventions.
Not service level, fill rate, or unqualified quota attainment. Those metrics use different numerators, denominators, obligations, and decision questions even when they also produce percentages.
Not meaningful against a hindsight-revised forecast. The denominator must come from a preserved snapshot available at the declared planning lag, rather than a version updated after realization became known.
Not valid when numerator and denominator are misaligned. Carrier, units, products, locations, customers, calendar boundaries, exclusions, and aggregation weights must match before the ratio is interpretable.
Not proof that higher is always better. Persistent over-attainment may reflect underforecasting, backlog release, or capacity stress, while under-attainment may reflect supply constraints rather than weak demand.
Not a diagnosis of forecasting health by itself. Demand, shipments, inventory, backlog, service, capacity, zero denominators, and returns must be separated before assigning cause to the attainment result.

Scope of Application¶

Forecast Attainment is a precondition-bounded planning measure: it applies only when an aligned realized aggregate is compared with a preserved forecast snapshot selected before realization at a policy-defined decision lag.^[10] Every habitat must state formula, carrier, units, scope, weighting, version, lag, calendar, zero and returns rules, tolerance, and companion metrics; current-plan comparison or quota attainment without a historical forecast is outside scope.

Demand planning. Realized demand is compared with the forecast available at the replenishment or decision lead time, with unconstrained demand kept distinct from fulfilled shipments.
Supply planning. Produced, received, or supplied quantities can be compared with a lagged supply forecast when constraints, substitutions, and backlog treatment are explicit.
Sales and operations planning. Aggregate product-family or regional attainment exposes whether realized scale matched the consensus demand or supply plan while forecast error, service, capacity, and inventory retain separate diagnostics.
Integrated business planning. Unit, value, and margin-oriented views remain literal when they use the same versioned-plan principle and disclose how prices, exchange rates, or mix change the ratio.
Production and capacity planning. Shipment or output attainment tests whether actual aggregate load matched the forecast used for labor, materials, or equipment decisions at the relevant lead time.
Inventory management. The measure supports analysis of over- or under-planning only when paired with stock, shortage, service, backlog, obsolescence, and execution evidence.
Product, location, and customer hierarchies. Ratios can be computed at multiple aggregation levels, but large-item dominance and cancellation require disaggregated checks before interpreting a top-line result.
Rolling and fixed planning calendars. Monthly, fiscal, weekly, or rolling windows qualify when cutoff dates and period alignment are preserved, especially where late shipments can shift attainment between adjacent periods.
Executive performance review. A summary ratio can signal plan-realization imbalance, yet it cannot assign causal responsibility or make closeness to one inherently favorable without consequence-based tolerances.
Forecast-process governance. Historical snapshots, approval state, scenario, creation time, and lag definitions are audited to prevent hindsight revision and keep repeated reports comparable.
Exception reporting. Zero forecasts with positive actuals, zero-over-zero cases, negative or returned quantities, missing values, and unplanned events are routed under an explicit policy rather than forced into an ordinary percentage.^[11]
Sales forecasting outside quota measurement. Bookings or revenue can instantiate Forecast Attainment when compared with a salesperson's or organization's time-stamped forecast at a declared lag; a fixed quota or target without that forecast provenance is a different metric.

Clarity¶

Naming forecast attainment makes legible whether the total realized load matched the plan that was actually available at the decision-relevant lag. It dissolves the confusion between aggregate plan realization and item-level forecast accuracy: offsetting product errors can yield 100% attainment even when the mix forecast is poor. It also forces a bare percentage to reveal its carrier—shipments, demand, supply, or sales—and the frozen snapshot, unit, scope, and period used in the denominator.

The direction of the ratio is descriptive before it is evaluative. 120% means the realized aggregate was 1.2 times the selected plan; whether that is favorable depends on service, backlog, capacity, inventory, and margin, and the ratio alone cannot assign cause. The better question is: Which aligned actual and lagged forecast totals produced this value, what errors may have cancelled inside the aggregate, and which companion measures distinguish forecast miss from execution constraint?

Manages Complexity¶

Forecast attainment compresses a product–location–period plan into one comparison between an aligned realized total and the forecast snapshot that operations actually had at the policy-defined lag. The analyst tracks the numerator's carrier and unit, the denominator's frozen version, the aggregation scope, and the lag rule. Once those are fixed, the ratio shows whether the realized load matched the planned scale without revisiting every item forecast: 1 is aggregate equality, a value above 1 is over-attainment, and a value below 1 is under-attainment under the ordinary nonnegative convention.

A sequence of such ratios makes persistent direction visible, while drill-down branches locate what the top line cannot. Segmenting by product, location, or period reveals mix and timing effects; comparing shipment attainment with unconstrained demand, backlog, service, inventory, and capacity separates a forecasting imbalance from an execution constraint. Unit- and value-weighted versions can further distinguish physical load from price or mix effects. Zero forecasts, returns, negative quantities, and shifted period boundaries remain explicit exception regimes rather than being forced into an ordinary percentage.

The compression ends at aggregate plan realization. Large items dominate the totals, and positive and negative item errors can cancel completely, so 100% attainment is not item-level accuracy or absence of bias. The ratio also cannot assign causation or say whether over- or under-attainment is favorable; those judgments require the disaggregated error pattern, operational consequences, and business-specific tolerance policy.

Abstract Reasoning¶

Calculation moves from an auditable planning question to two aligned totals. The analyst fixes the realized carrier—such as shipments or unconstrained demand—the product/location/period scope, unit, lead-time lag, and preserved forecast version, then divides the actual aggregate by that lagged plan. A value above one establishes over-attainment of planned scale under those definitions; a value below one establishes under-attainment. It does not yet say whether either direction was favorable or why it occurred.

Diagnostic reasoning compares the ratio with disaggregated and operational evidence. Item-level signed errors reveal cancellation, absolute-error measures reveal mix miss, and segment or period views locate concentrated deviation. Comparing shipment attainment with demand, backlog, inventory, service, and capacity separates a forecast imbalance from an execution constraint or timing shift. For example, one high and one low item can yield exactly one hundred percent attainment while both forecasts are materially wrong; a backlog release can raise current shipments without indicating current-demand growth.

Interventionist governance tests the measurement itself. Moving the snapshot closer to realization should improve apparent attainment if late information is being incorporated, but no longer evaluates the plan available at the action-relevant lead time. Switching from units to currency can expose price or mix effects while changing the question. Zero forecasts with positive actuals belong in an unplanned-demand branch rather than an ordinary ratio, and returns or negative values require an explicit policy. The inference is therefore bounded to the frozen information set and aggregation rule: it predicts how realized total load compared with the actionable plan, not individual accuracy, causal responsibility, or the business value of the deviation.

Knowledge Transfer¶

Within demand and supply planning, Forecast Attainment transfers literally across products, locations, planning hierarchies, unit- and value-weighted views, monthly or rolling calendars, shipment- and demand-based numerators, and sales-and-operations planning forums when realized totals are compared with the aligned forecast snapshot chosen at a policy-defined lag. The carried instrument fixes scope, unit, calendar, numerator, historical forecast version, and zero or returns policy before taking the ratio. Its diagnostics disaggregate the total, compare shipments with unconstrained demand and backlog, and pair attainment with absolute error, bias, service, inventory, and capacity; its interventions change only one carrier or aggregation rule at a time. Forecast snapshot, lag, under- or over-attainment, cancellation, and unplanned-demand exception remain literal planning vocabulary.

Beyond demand planning, the honest reach is chiefly (C) instrument/measure, with a limited (B) shared mechanism through Ratio and an (A) analogy boundary. Sales forecasts, fundraising forecasts, staffing demand, or other forward plans can use Forecast Attainment literally if they preserve a time-stamped prediction, decision-relevant lag, aligned actual, and aggregation policy; ordinary budget or quota attainment without a forecast snapshot is only a related ratio. What travels is the versioned plan-versus-realization comparison and the warning that aggregation can hide cancelling errors. What remains home-bound is shipment or demand semantics, SKU and location hierarchies, replenishment lead time, backlog, inventory, service level, and S&OP governance. The stopping boundary is loss of a preserved forecast selected before realization: beyond that point the measure may be generic plan attainment or adherence, but it is not this Forecast Attainment metric.

Examples¶

Canonical¶

A production process with a three-month decision lead time evaluates June unit shipments against the forecast frozen at the end of March.^[12] The saved snapshot contains 1,000 units for the same products, locations, and June calendar window; June records show 1,100 shipped units. Forecast attainment is therefore (1{,}100 / 1{,}000 = 1.10), or 110%. The result means aggregate shipments exceeded the actionable plan by 10%. It does not say whether the forecast was accurate by item, whether unconstrained demand was also 1,100, or whether the extra volume caused shortages or capacity stress.

Mapped back: Unit shipments are both the planned carrier and the realized carrier, matched by the aligned scope. The March version is the preserved forecast snapshot, selected by the lag policy. The 1,100 and 1,000 totals are the aggregate numerator and the aggregate denominator; their quotient is the attainment ratio. Reading 110% as over-attainment but not automatic success applies the directional interpretation.

Applied / In Practice¶

In an S&OP review, two products were each forecast at 100 units at the governing lag. Product A realizes 150 and product B realizes 50. Aggregate attainment is ((150+50)/(100+100)=200/200=100\%), even though each item misses by 50 units and total absolute error is 100 units. On this small example, weighted absolute percentage error is (100/200=50\%). The top-line ratio says the total planned scale was attained; it conceals that the product mix was wrong and cannot reveal whether backlog, supply constraints, or demand timing caused either miss.

Mapped back: The equal aggregate totals satisfy the attainment ratio, while the opposing item errors demonstrate the cancellation limit. Item-level absolute error, backlog, demand, service, inventory, and capacity belong to the companion diagnostics needed to interpret the result. If one forecast were zero and actual demand positive, the exception policy would route it as an unplanned event rather than force an ordinary percentage into this calculation.

Structural Tensions¶

T1: Aggregate balance versus item fidelity. A single ratio makes total plan realization legible, but aggregation lets over- and under-attainment cancel and lets high-volume items dominate. The same compression that supports capacity-level review can therefore conceal a badly forecast product or location mix.

Diagnostic: Does the decision concern total planned load, or does it require item-, location-, or period-level error to remain visible?

T2: Shipment realization versus demand realization. Shipments are operationally observable and relevant to supply execution, yet stockouts, backlog releases, and delayed fulfillment can move them away from the demand that the forecast purported to anticipate. Substituting one carrier for the other can change an apparent forecast miss into an execution effect.

Diagnostic: Is the numerator the realized phenomenon being forecast, or an execution measure constrained by inventory, capacity, and timing?

T3: Actionable lag versus current information. A snapshot frozen at the decision-relevant lead time honestly tests the plan available when action was possible, while a later snapshot can incorporate useful new information. Moving the lag forward may improve apparent fit precisely by weakening the historical planning test.

Diagnostic: Was the denominator fixed at the time operational commitments had to be made, or selected later because it better matches realization?

T4: Physical volume versus financial value. Unit- or weight-based attainment preserves the physical load relevant to capacity, whereas currency-weighted attainment aligns with financial planning but mixes price, exchange-rate, and product-mix effects into the comparison. Neither weighting answers the other's question.

Diagnostic: Does a change in attainment reflect a change in physical realization, or only the valuation and mix used to aggregate it?

T5: Period accountability versus boundary noise. Fixed weekly or monthly windows support accountable recurring review, but late shipments and backlog movement can depress one period and inflate the next even when a longer-window total matches plan. Rolling windows reduce cutoff noise while potentially masking short-lived failures.

Diagnostic: Would the same underlying demand and fulfillment pattern reverse the conclusion if adjacent periods or a rolling window were combined?

T6: Closeness to plan versus favorable consequence. A value near one indicates aggregate equality to plan, not operational success, and deviations on opposite sides can carry sharply different inventory, service, or capacity costs. A neutral mathematical center therefore coexists with asymmetric business tolerances.

Diagnostic: What consequence-based tolerance makes under-attainment, equality, or over-attainment preferable in this planning setting?

T7: Uniform ratio versus exception integrity. A common percentage makes scopes comparable, but zero forecasts, positive unplanned demand, returns, negative quantities, and missing data do not all admit the ordinary quotient or its usual direction. Forcing them into the headline value improves apparent completeness at the cost of interpretability.

Diagnostic: Which observations satisfy the ordinary nonnegative ratio convention, and which must remain visible as separately governed exceptions?

T8: Forecast Attainment autonomy versus reduction to Ratio (Ratio). The parent Prime carries the portable numerator-to-denominator relation. Every Forecast Attainment value is a strict kind of Ratio because it divides an aligned realized aggregate by its forecast aggregate, but the child additionally fixes a preserved forecast snapshot and policy-defined lag within operations planning. Reduction loses the historical information set and the distinction between aggregate plan realization and item-level forecast quality; total autonomy hides the general quotient structure.

Diagnostic: Does the proposed instance preserve the lagged forecast-versus-realization roles that make it Forecast Attainment, or only the quotient carried by Ratio?

Structural–Framed Character¶

Forecast Attainment is framed-leaning. Its vocab_travels is moderate because numerator, denominator, and quotient are general, while lagged snapshot, actuals, attainment, mix accuracy, and exception policy belong to operations planning. Its evaluative_weight is high: the chosen lag, scope, aggregation weights, and interpretation of over- or under-attainment reflect governance and decision purpose. Its institutional_origin lies in forecasting and performance-management practice. Its human_practice_bound is decisive because the metric requires a preserved plan, policy-defined snapshot, aligned reporting scope, and exception rules. On import_vs_recognize, realized quantities are observed, but the comparison baseline and aggregation frame are deliberately imposed.

The smallest reviewed portable skeleton is Ratio: an ordered focal quantity divided by a nonzero reference yields a scope- and unit-sensitive quotient invariant under common scaling. Portable and cross-domain reach belongs to that Prime. Forecast Attainment fills the roles with an aggregate actual numerator, preserved lagged-forecast denominator, aligned products and periods, and explicit zero or negative-value branches. Snapshot governance and cancellation across items distinguish it from a generic ratio and from item-level forecast accuracy.

Its character: framed-leaning because ordered division is portable and exact, while planning policy determines the denominator, aggregation, exception handling, and meaning of the result.

Structural Core vs. Domain Accent¶

Forecast Attainment is domain-specific rather than a Prime because it fixes a planning-governed numerator, historical forecast denominator, and interpretation rather than expressing ordered division wherever it occurs.

What is skeletal (could lift toward a cross-domain prime). A focal quantity and a nonzero reference are aligned in carrier, unit, scope, and period, then ordered division yields how much of the numerator obtains per unit denominator. Reversing roles changes the claim, common scaling preserves the quotient, and changing the denominator can change interpretation without changing the focal amount. Forecast Attainment is therefore a strict specialization of Ratio: realized aggregate is numerator, the frozen forecast aggregate is denominator, and zero forecasts trigger a separate exception rather than an ordinary percentage.

What is domain-bound. The planned and realized carriers may be demand, shipments, supply, sales, or production, but must use identical units, products, locations, customers, and calendar rules. A preserved forecast snapshot and policy-defined lag bind the denominator to information available when action was possible; volume or value aggregation creates cancellation and dominance limits; and values above or below one indicate directional attainment without automatically being good or bad. Item error, bias, unconstrained demand, backlog, service, inventory, and capacity remain companion diagnostics, while zero forecasts, returns, negative quantities, missing data, and period shifts require explicit exception policies.

Why this does not clear the prime bar. The complete lagged-forecast-snapshot, aligned-realized-carrier, policy-lag, matched-aggregation, attainment-direction, cancellation-limit, companion-diagnostic, and operational-exception signature does not recur literally across at least three unrelated domains under the same recognition and failure conditions. Knowledge Transfer keeps the calculation literal across demand, supply, sales, and production planning when historical forecast provenance remains intact; generic plan attainment without that frozen information set is related Ratio use, not this metric. Removing forecast versioning, planning lag, operational carriers, and cancellation interpretation leaves a Ratio but not Forecast Attainment, while removing the ordered numerator, nonzero denominator, aligned scope, division, units, and common-scale invariance leaves two totals without the Ratio skeleton that makes their attainment comparison meaningful.

This entry is a kind of Ratio.

Instantiates — Ratio (Ratio). The aggregate numerator is the realized total and the aggregate denominator is the nonzero total from the preserved forecast snapshot; ordered division yields the attainment ratio. The aligned scope fixes carrier, units, products, locations, customers, and period so the quotient answers how much realization occurred per unit forecast. Multiplying both aligned totals by the same unit-conversion factor preserves attainment, while changing the lagged denominator or its scope can change the conclusion without changing actuals. Zero forecasts route to the exception policy, preserving Ratio's nonzero-denominator boundary. Remove the ordered actual-to-forecast division and the metric ceases to be Forecast Attainment, while Ratio remains broader because it does not require forecast versioning, a lag policy, or planning interpretation.

Relationships to Other Abstractions¶

Current abstraction Forecast Attainment Domain-specific

Parents (1) — more general patterns this builds on

Forecast Attainment is a kind of Ratio Prime

The aggregate numerator is the realized total and the aggregate denominator is the nonzero total from the preserved forecast snapshot; ordered division yields the attainment ratio.

Hierarchy path (1) — routes to 1 parentless root

Forecast Attainment → Ratio → Comparison → Self Checking

Neighborhood in Abstraction Space¶

Forecast Attainment sits in a moderately populated region (50^th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.

Family — Supply Chain & Inventory Management (28 abstractions)

Nearest neighbors

Make-to-Stock — 0.87
Backorder — 0.86
Service Level — 0.86
Cone of Uncertainty — 0.86
Value at risk — 0.85

Computed from structural-signature embeddings · 2026-10-08

Not to Be Confused With¶

Forecast Accuracy. Forecast accuracy measures the magnitude of prediction errors under a declared loss function, whereas forecast attainment compares an aggregate realized total with the aggregate forecast over a horizon. Tell: item- or period-level error magnitudes identify accuracy; a realized-to-forecast aggregate ratio identifies attainment.
Forecast Bias. Forecast bias measures the systematic signed direction of errors across observations and can reveal persistent over- or underforecasting that an aggregate attainment ratio masks. Tell: the mean signed error across comparable units measures bias, while the ratio of summed actuals to summed forecasts measures attainment.
Quota Attainment. Quota attainment compares performance with an assigned organizational target, not with a predictive estimate. Tell: if the denominator is a normative sales or production goal it is quota attainment; if it is an ex ante forecast of the realized quantity it is forecast attainment.
Plan Adherence. Plan adherence measures whether execution followed scheduled actions or quantities, even when the forecast itself was wrong. Tell: compare actual execution with the operational plan for adherence and compare realized demand or output with the prediction for forecast attainment.
Service Level. Service level measures the probability or proportion of demand-service obligations met, which depends on inventory and operations as well as forecasting. Tell: whether demand was fulfilled to a promised standard is service level; whether total demand matched its forecast is attainment.
Fill Rate. Fill rate is the share of demand supplied immediately or completely and can be high despite a poor forecast if buffers are large. Tell: fulfilled units divided by demanded units is fill rate; realized units divided by forecast units is forecast attainment.
Mean Absolute Percentage Error. Mean absolute percentage error averages itemwise or periodwise relative error magnitudes and preserves no cancellation of signs. Tell: averaging |actual − forecast| / |actual| across observations is MAPE, whereas taking one aggregate actual-to-forecast ratio is attainment.
Make-to-Stock. Make-to-stock is a fulfillment strategy in which inventory is produced ahead of demand; it uses forecasts but is not a forecast-evaluation measure. Tell: the timing of production relative to orders identifies the strategy, while comparison of predicted and realized totals identifies attainment.

References¶

[1] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[2] Improving Supply Chain Performance by Implementing Weekly Demand Planning Processes in the Consumer Packaged Goods Industry registry ↩

[3] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[4] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[5] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[6] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[7] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[8] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[9] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[10] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[11] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩

[12] Unverified encyclopedia synthesis; claim-specific authoritative support was not established in this verification pass. ↩