Manufacturing Business Outlook Survey: Inventories: Future (increase vs decrease; diffusion index; percentage points; seasonally adjusted) - United States - Philly FED - Monthly
This series is part of the dataset: Manufacturing Business Outlook Survey (Philly FED)
Download Full Dataset (.xlsx)Latest updates. According to survey data about manufacturers collected by the Federal Reserve in Pennsylvania, New Jersey and Delaware, the seasonally adjusted diffusion index for inventories (share of CEOs seeing a future increase minus share forecasting a decrease) was 11.6 in September 2026, compared to 17.4 in August 2026.
Sample. In the monthly series displayed in the chart, there are 701 observations overall. The series covers the time range stretching from May 1968 to September 2026.
History. Here's a peek at a few statistics we computed on the whole sample: the diffusion index had a mean of -0.9; it reached its minimum of -41.8 in December 2008; it attained a maximum of 41.0 in September 1983.
Latest values
| Date | Value - Percentage points |
|---|---|
| 2026-07-31 | 12.5000 |
| 2026-08-31 | 17.4000 |
| 2026-09-30 | 11.6000 |
Suggestion. For easier exploration, we group indicators into worksheets and datasets. When you look below, you will find how we structured further material linked to the statistics published here.
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Series Metadata
| Field | Value |
|---|---|
| Description | Diffusion index for the question: Will inventories increase or decrease over the next six months? |
| Country | United States |
| Economic concept | Business survey response |
| Data type | Leading indicator of the business cycle |
| Deflation method | Not applicable |
| Seasonally adjusted | Yes |
| Rescaling | None |
| Frequency | Monthly |
| Unit | Percentage points |
| Source | Federal Reserve Bank of Philadelphia |
| Source type | Central bank |
| Data licence | Not copyrightable under U.S. law |
| Measure type | Diffusion index (share of respondents reporting an increase minus share reporting a decrease) |
Series in the same data set
Discover the other time series included in this data set.