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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteUse purchasing managers’ indexes (PMIs) and other leading indicators as timely context for a forecast—not as a direct prediction of your company’s sales. Match each indicator to your business’s sector and geography, examine relevant sub-indices, then translate plausible signals into assumptions about company-level drivers such as orders, conversion, pricing, capacity, and costs. Test those relationships against your own history.
What PMI can—and cannot—tell you
A PMI is a monthly, survey-based diffusion measure. Business executives report whether selected conditions rose, fell, or stayed unchanged compared with the previous month; the index summarizes the breadth of those responses. In S&P Global’s interpretation, 50 indicates no net change, a reading above 50 signals improvement or expansion, and a reading below 50 signals deterioration or contraction relative to the previous month. It is not a percentage change in output, nor a forecast of your company’s sales growth. S&P Global explains its PMI product and methodology.
Choose the series that best reflects the business exposure: manufacturing PMI for manufacturing, a services business activity index for services, or a suitable composite when the company spans both. A national reading may still be a poor fit for a niche industry or a company whose customers and suppliers are concentrated elsewhere.
Use the components to understand the signal
The headline compresses several measures that can point in different directions. New orders or new business may help frame demand; output or activity describes current volume; backlogs and employment can add context about capacity; prices may inform cost or selling-price scenarios; delivery times and inventories may flag supply conditions. These are hypotheses to check against company data, not automatic cause-and-effect rules. S&P Global’s PMI FAQ describes the indices and their interpretation.
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For S&P Global’s manufacturing PMI, the FAQ lists weights of 30% for new orders, 25% for output, 20% for employment, 15% for supplier delivery times (inverted), and 10% for stocks of purchases. Those are weights in that index’s calculation, not suggested coefficients for a company forecast. For services, the headline is the Services Business Activity Index, based on a business-activity question.
How to build the forecast
- Define the decision and forecast horizon. Specify the outcome (for example, revenue, staffing, inventory, or cash needs), the geography and business segment, and how often the forecast will be updated. Decide what the indicator is meant to inform: demand, price or cost, staffing, investment, or downside risk.
- Map the company’s exposure. Identify the sectors and geographies that generate revenue or determine costs. Select manufacturing, services, construction, or composite PMI series accordingly. Treat a national aggregate as context, not a precise signal for a niche segment.
- Read sub-indices, not just the headline. Note which components support or contradict the headline and connect each relevant component to a possible business mechanism. For example, an external demand signal might inform a pipeline or order assumption; an input-price signal might prompt a cost sensitivity.
- Cross-check with independent indicators. For a US business-cycle comparison, The Conference Board’s Leading Economic Index (LEI) is designed to signal turning points, while its Coincident Economic Index (CEI) tracks current conditions. The Conference Board describes an approximate seven-month lead for the US LEI; that estimate is specific to this index and geography, not a forecast horizon for every company. See The Conference Board’s US Leading Indicators page.
- Record the release vintage and timing. For every observation, save the survey or reference month, publication date, and whether it is a flash or final estimate. Preserve the data available when each forecast was made so later comparisons are meaningful. PMI can arrive ahead of many comparable official statistics, but early publication does not remove sampling or interpretation uncertainty. S&P Global’s US Flash PMI methodology page says the flash estimate represents around 85% of that month’s total US PMI survey responses; this describes that release’s response coverage, not every PMI series.
- Translate signals into company assumptions. Keep direct company drivers—actual orders, customer retention, pipeline conversion, pricing, capacity, staffing, and costs—at the center of the forecast. Use external indicators to adjust an assumption only when you can state a plausible mechanism and label the adjustment as an assumption.
- Build scenarios and test them. Create a base case and reasonable upside and downside sensitivities. Compare past indicator observations with company outcomes at the horizon you are forecasting. Check whether the relationship is stable, differs by segment, or breaks during unusual periods. Correlation alone does not establish causation.
- Update on a fixed cadence. When releases arrive, record which assumptions changed and why, then compare forecasts with actual outcomes. Keep an audit trail of series, transformations, assumptions, responsible owners, and decision dates.
How to choose and combine indicators
More indicators do not necessarily make a better forecast. Select measures that match a decision or forecast driver, and compare them on the attributes below before combining them.
- What they measure: survey direction, actual activity, financial conditions, orders, employment, prices, or a composite.
- Timing and lead or lag: when the measure is released and any stated horizon. Do not assume an index’s lead time applies to another series, sector, or company.
- Geography and industry: whether coverage matches the company’s customers, suppliers, and operations.
- Frequency and publication lag: early monthly surveys can offer a timely signal; slower official data may provide a more complete but later reading.
- Preliminary status and revisions: distinguish flash estimates from later updates and retain the data vintage used in the forecast.
- Overlap and independence: a composite index may include PMI or components that overlap with another measure. Do not treat correlated or shared inputs as independent confirmation.
- Actionability: favor measures linked to an assumption or decision over a large dashboard with no defined use.
Common interpretation mistakes
- Turning a PMI reading into a growth rate. The index indicates the breadth and direction of monthly change, not the exact size of output growth.
- Equating an economic signal with company performance. Market share, product mix, execution, customer concentration, and contracts can make a firm outperform or lag its sector.
- Applying a universal PMI-to-sales conversion. The cited indicator sources do not establish an equation that converts a PMI reading into company revenue, profit, or cash flow.
- Reading only the headline. A steady headline can mask divergent movement in orders, employment, inventories, delivery times, or prices.
- Assuming a lead time is guaranteed. The Conference Board’s approximate seven-month estimate applies to the US LEI’s anticipation of business-cycle turning points, not to every business or forecast target.
- Mixing geographies and sectors. A US manufacturing reading does not automatically describe a non-US services company.
- Confusing published data with a planning judgment. The indicator is an observation; the company-specific adjustment is an analyst assumption that should be identified and tested.
What a timely PMI signal is useful for
Because PMIs are released ahead of many comparable official statistics, they can support timely monitoring of economic activity and economy-wide GDP nowcasting. An economy-level nowcast is not a forecast for an individual company. S&P Global provides context on the survey in its PMI FAQ and discusses the output index in its PMI output index research.
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