What is a backlog?
A backlog is work that has been ordered or started but not yet finished. In business, it commonly refers to sales orders waiting to be delivered, applications or paperwork pending processing, or future contractual performance that will be fulfilled over time. Backlog measures work demand that currently exceeds (or will be carried into) a company’s completed output.
Key definitions
– Backlog: accumulated work or orders that remain unfulfilled at a given point in time.
– Capacity: the maximum amount of output a company or production line can deliver in a given period.
– Order intake (or incoming orders): new work or sales received during a period.
– Revenue recognition: the accounting principle that determines when a company can record sales as revenue (usually when goods/services are delivered).
– SaaS (software-as-a-service): a subscription model in which delivery of services is spread over future periods; therefore some contracted service time is inevitably “in the backlog” until performed.
Why backlogs matter
– Demand signal: A growing backlog can indicate strong demand for a product or service.
– Operational strain: A backlog may also reveal capacity constraints, bottlenecks, or process inefficiency.
– Forecast impact: Unexpected or persistent backlogs change delivery timing and can alter revenue timing and guidance.
– Accounting distinction: Backlog is not the same as booked revenue — revenue is recognized when the company satisfies performance obligations under accounting rules.
Common contexts
– Manufacturing and construction: orders exceed the current production rate, creating a queue of work.
– Service and finance operations: loan or claims processing piles up when staffing or throughput can’t keep pace.
– Subscription businesses (e.g., SaaS): contracted future service periods create a scheduled backlog rather than an operational shortfall.
– Distressed markets: large-scale events (e.g., housing crisis) can produce administrative or legal backlogs that delay resolutions.
Simple checklist to evaluate a backlog (for managers or investors)
1. Define what’s included: Are these firm customer orders, unbilled contracts, or internal tasks?
2. Age profile: What portion is new versus aged (e.g., 0–30 days, 31–90, >90)?
3. Convertibility: Historical conversion rate from backlog to shipped goods or recognized revenue.
4. Capacity vs. demand: Current production/processing capacity and planned changes.
5. Lead times: Typical time to fulfill an order now versus historically.
6. Cancellation and churn risk: Likelihood that orders will be canceled or renegotiated.
7. Accounting treatment: How and when the company recognizes revenue from backlog items.
8. Seasonality and one-offs: Are backlogs seasonal or driven by temporary events?
9. Communication and expectations: Is the company providing guidance on backlog management?
10. Financial impact: Expected timing of cash flows and effects on future earnings.
A compact formula
– Change in backlog over a period = Incoming orders − Completed output
– Backlog_t = Backlog_{t−1} + Orders_t − Production_t
– Time
= Example calculation (worked numeric example)
Assume a manufacturer reports:
– Beginning backlog (Backlog0) = $500 million
– New orders received during the quarter (Orders) = $300 million
– Production/shipped goods during the quarter (Completed) = $400 million
Ending backlog (Backlog1) = Backlog0 + Orders − Completed
Backlog1 = $500m + $300m − $400m = $400 million
Two simple ratios analysts use:
– Backlog-to-revenue ratio = Backlog1 / Last 12 months revenue
If LTM revenue = $1,200m, backlog-to-revenue = 400 / 1,200 = 0.333 (33.3%).
– Backlog days = Backlog1 / (LTM revenue / 365)
Average daily revenue = 1,200 / 365 ≈ $3.29m → Backlog days ≈ 400 / 3.29 ≈ 122 days.
Interpretation: 122 backlog days implies current backlog, if converted to revenue at historic daily run-rate, would cover about four months of revenue. That is an orientation, not a forecast (see limitations below).
Checklist — What to verify when you see a reported backlog
1. Disclosure source: Where is backlog quantified? (10‑K, 10‑Q, MD&A, earnings slides.)
2. Definition: Is backlog firm orders only or does it include options/LOIs (letters of intent)?
3. Accounting recognition: When will backlog items become revenue under relevant standards (ASC 606 / IFRS 15)?
4. Cancellability: What percentage of backlog is cancellable without penalty?
5. Lead times: Typical fulfillment lag for backlog items today vs. historical.
6. Capacity constraints: Is the company capacity-limited or supply-constrained?
7. Price/margin mix: Are backlog orders at the same margins as historical sales?
8. Customer concentration: Is backlog driven by one or few large contracts?
9. Geography/regulatory risk: Any export controls, tariffs, or approvals required?
10. Cash flow timing: Expected billing schedule and working capital impact.
Step-by-step approach to analyze a company’s backlog
1. Collect data: Get beginning and ending backlog, orders, completed shipments, LTM revenue, and disclosure notes.
2. Reconcile definitions: Confirm the company’s definition of “backlog” and whether it matches what you are comparing it to.
3. Compute change components: Break down change into orders received, shipments/recognitions, cancellations (if disclosed).
4. Calculate coverage metrics: Backlog-to-revenue, backlog days, and backlog turnover (Completed / Average backlog).
5. Adjust for quality: Reduce reported backlog for cancellable orders or orders requiring significant future capital expenditure.
6. Look for trend divergence: Compare orders vs. completions over several periods to spot demand shifts or capacity issues.
7. Read MD&A and management comments: Seek management’s view on backlog drivers, seasonality, and risks.
8. Stress-test: Model scenarios where a share of backlog is delayed, renegotiated, or cancelled to see cash/earnings impact.
Common red flags
– Backlog grows while new orders fall — may indicate opportunistic recognition or lumpy one-off deals.
– Large portion of backlog labeled “subject to contract” or “non‑binding” — low quality.
– Backlog far exceeds reported capacity with no disclosed capacity expansion plan.
– Frequent restatements or inconsistent definitions across filings.
– Backlog concentrated in a single customer or geography with elevated political or credit risk.
Limitations and caveats
– Not standardized: “Backlog” is a management-defined metric; companies vary in scope and measurement.
– Not equal to revenue: Backlog represents future production/delivery obligations or optional orders, not immediate cash or earnings.
– Subject to churn:
– Subject to churn: customers can cancel, defer, or renegotiate orders; management may also reclassify or write down backlog when orders become unlikely. Historical cancellation rates and contract terms materially affect how much of reported backlog is economically real.
– Timing mismatch: backlog measures future obligations or optional orders; it does not tell you when revenue or cash will arrive. Seasonal businesses or long project cycles create lumpy recognition patterns that can make a large backlog feel less valuable in the short run.
– Accounting and disclosure variation: companies differ in whether backlog includes only signed, firm contracts versus letters of intent, purchase orders, or “subject to contract”
That variation reduces comparability across companies and industries. A reported backlog number is only as useful as the definitions, assumptions, and disclosures that support it. Analysts need to translate a headline backlog figure into an economically meaningful stream of future revenue and cash by adjusting for cancellation risk, timing, margin, and contract terms.
Practical checklist for analyzing backlog
1. Confirm the definition
– Does the company include only signed, firm contracts, or also letters of intent, verbal orders, and “subject to contract” items? Prefer concrete, signed obligations.
2. Check historical conversion and cancellation rates
– Ask: of past backlog, what percentage became recognized revenue and when? Use multi‑year averages if available.
3. Map timing of recognition
– Find or estimate the schedule: what portion of backlog is expected to be recognized in the next 12 months, 12–24 months, etc. Seasonality and long project cycles matter.
4. Adjust for margins
– Backlog is an order dollar amount; profit contribution depends on margins and estimated contract costs. High‑value backlog with low margins is less valuable than moderate backlog with healthy margins.
5. Identify concentration and dependencies
– Is backlog concentrated in a few customers, geographies, or a single program (e.g., a defense contract)? Large customers can amplify risk.
6. Review contract terms that affect cash
– Payment milestones, retainage, performance bonds, and progress‑billing impact timing and certainty of cash flows.
7. Compare related balance‑sheet items
– Look at contract liabilities/deferred revenue and receivables to reconcile reported backlog with recognized revenue.
8. Watch for one‑time reclassifications
– Management may reclassify old orders into backlog during quarters with weak revenue — check for accounting policy changes or adjustments.
Worked numeric example: converting backlog to a revenue forecast
– Reported backlog: $500 million
– Company disclosure: 60% of backlog expected to be recognized within 12 months, 30% in months 13–24, 10% thereafter
– Historical cancellation/write‑down rate: 10% (i.e., on average 10% of backlog never becomes revenue)
– Average gross margin on completed contracts: 25%
Step 1 — adjust for cancellations:
Adjusted backlog = $500m × (1 − 0.10) = $450m
Step 2 — allocate by timing:
– Year 1 expected revenue = $450m × 0.60 = $270m
– Year 2 expected revenue = $450m × 0.30 = $135m
– Year 3+ expected revenue = $450m × 0.10 = $45m
Step 3 — estimate gross profit contribution:
– Year 1 expected gross profit = $270m × 0.25 = $67.5m
(Repeat for other years as needed.)
This exercise shows how a headline backlog converts to a phased revenue and profit stream after applying realistic assumptions. Document your assumptions and stress test them (e.g., use 20% cancellation to see sensitivity).
Common related metrics and formulas
– Book‑to‑bill ratio = Orders received in period ÷ Revenue billed in period
– Interpretation: >1 implies expanding orders relative to current billings; <1 suggests declining demand.
– Backlog turnover (simple) = Revenue recognized over period ÷ Average backlog during period
– Interpretation: how quickly backlog is being converted to revenue.
– Adjusted backlog = Reported backlog × (1 − estimated cancellation rate)
– Use historical cancellation/write‑down data if available.
– Expected revenue in period = Adjusted backlog × percent scheduled for that period
Red flags in backlog disclosures
– Ambiguous definitions (no clear statement of what’s included)
– Rapid, unexplained backlog growth without corresponding operating metrics (bookings, margins)
– Large backlog concentrated in one or two customers without contingency plans
– Frequent restatements or reclassifications to backlog
– Disclosed backlog that is inconsistent with contract liabilities, revenue recognition policies (ASC 606 / IFRS 15), or cash‑flow trends
How companies present backlog (what to look for)
– A breakdown by time bucket (e.g., 24 months)
– A reconciliation from opening backlog + bookings − recognized revenue − cancellations = closing backlog
– Discussion of contract types (firm fixed price vs. cost‑plus), payment terms, and any performance guarantees
– Disclosure of assumptions used to estimate future recognition and cancellations
Caveats and best practices
– Backlog is a forward indicator, not a guarantee. Convert it into multiple scenarios (base, downside, upside).
– Use backlog alongside bookings, deferred revenue, cash flow, and margin forecasts for a fuller picture.
– For long‑cycle industries (aerospace, construction, defense), prioritize contract terms and progress‑billing detail.
– For recurring‑revenue businesses, backlog for
– For recurring‑revenue businesses, backlog for subscription or SaaS models is often small or misleading because customers are billed and revenue is recognized on short, recurring cycles. In those cases prefer metrics such as Annual Recurring Revenue (ARR), Remaining Performance Obligations (RPO), deferred revenue, churn rates, and contract term length rather than headline backlog alone.
Practical checklist — what to verify in filings
– Time buckets: split of backlog by 24 months. This shows pace of conversion to revenue.
– Reconciliation: opening backlog + bookings − recognized revenue − cancellations = closing backlog. Verify the math.
– Booking definitions: how does management define a “booking”? (signed contract, purchase order, or merely committed intent?)
– Contract type and risk: firm‑fixed price vs. cost‑plus, presence of change‑order exposure, performance guarantees, and penalties.
– Payment terms and credit risk: upfront payment, milestones, progress billing, retention, or long receivable terms.
– Deferred revenue vs. backlog: ensure items aren’t double counted (deferred revenue is collected but not yet recognized; backlog is future contracted work).
– Margin and cost recognition: whether backlog margins are fixed or likely to erode with cost inflation or change orders.
– Cancellation and renewal assumptions: management’s historical cancellation rate and renewal probability.
– Concentration: share of backlog from top customers and single large contracts.
– Disclosure of estimation methods: how the company converts backlog into expected revenue (assumptions, timelines, and cancellation rates).
Key formulas (definition and use)
– Closing backlog = Opening backlog + Bookings − Recognized revenue − Cancellations.
– Define bookings: new contracts signed during the period (monetary value).
– Recognized revenue: amount recorded in the income statement for work/performance completed.
– Cancellations: value of contracts cancelled and removed from backlog.
– Backlog‑to‑revenue ratio = Closing backlog / Trailing 12‑month revenue.
– Interpretation: >1 implies more than a year’s revenue already contracted at current run rate; <1 implies less than a year.
– Forecasted revenue from backlog (simple conversion) = sum over time buckets (backlog_i × conversion_rate_i), where conversion_rate_i is the fraction expected to be recognized in the forecast horizon.
Worked numeric example — reconciliation and conversion
– Given (in $ millions):
– Opening backlog = 100.0
– Bookings during period = 60.0
– Revenue recognized during period = 70.0
– Cancellations = 5.0
– Compute closing backlog:
– Closing backlog = 100 + 60 − 70 − 5 = 85.0
– Backlog composition (closing): 24 months = 10 (total = 85).
– Assume management’s conversion assumptions for the next 12 months:
– 24 months: 10% converts in the next 12 months
– Forecasted revenue from backlog next 12 months:
– = 50×0.80 + 25×0.30 + 10×0.10
– = 40.0 + 7.5 + 1.0 = 48.5
– Backlog‑to‑revenue ratio (if trailing 12‑month revenue = 120):
– = 85 / 120 = 0.708 → about 0.71 years of revenue contracted at current run rate.
Sensitivity check (simple)
– If cancellation risk is higher than management
assumes, run lower-conversion scenarios and compare. Scenario analysis highlights how sensitive the near‑term revenue runway is to changes in conversion and cancellation rates.
– Practical sensitivity example (step‑by‑step)
1. Start with the reported backlog buckets (closing): 24 months = 10 (total backlog = 85).
2. Choose an alternative, more conservative set of conversion assumptions. Example (conservative):
– 24 months: 0% (instead of 10%)
3. Compute forecasted revenue from backlog over the next 12 months:
– = 50 × 0.70 + 25 × 0.20 + 10 × 0.00
– = 35.0 + 5.0 + 0.0 = 40.0
4. Compare to the management‑base forecast (48.5 in the baseline). Change = 40.0 / 48.5 ≈ 0.825 → about a 17.5% reduction in expected backlog‑derived revenue for the next 12 months.
5. Recompute the backlog‑to‑revenue ratio (if trailing 12‑month revenue = 120). The total backlog is unchanged at 85, so:
– backlog‑to‑revenue = 85 / 120 ≈ 0.71 years of revenue contracted at the current run rate.
6. Interpretation: the headline ratio (0.71) may mask the drop in near‑term realizable revenue when conversion assumptions are adjusted downward. Use both measures: total backlog size and converted revenue estimate.
– Quick checklist when stress‑testing backlog figures
– Verify the definition: is “backlog” gross contract value, undiscounted future revenue, or something else? Companies may differ.
– Check revenue recognition policy (ASC 606 / IFRS 15 principles): timing of recognition depends on performance obligations, not simply billing.
– Review historical conversion/realization: what percent of backlog actually converted to recognized revenue in prior periods?
– Inspect age profile: older backlog is typically less likely to convert quickly; track backlog aging.
– Examine cancellation and amendment terms: are contracts cancellable, subject to customer approval, or long‑term firm commitments?
– Watch for concentration risk: a few large contracts can make backlog fragile.
– Compare booked backlog to deferred revenue (billings collected but not yet recognized) — they are related but not identical.
– Ask management for the assumptions behind their conversion rates and the evidence supporting them.
– Other useful calculations and mental models
– Revenue from backlog (next 12 months) = Σ (backlog_bucket × assumed_conversion_rate_bucket).
– Implied backlog duration = backlog / trailing 12‑month revenue (years