Computers and Related products Unfilled Orders (advance estimate; seasonally adjusted; USD million) - United States - Census - Monthly
This series is part of the dataset: Advance manufacturing survey (U.S. Census)
Download Full Dataset (.xlsx)Latest updates. In the United States, unfilled orders of computers and related products were 2.01 USD billion (seasonally adjusted) in September 2026, compared to 1.93 in August. This represents a rise of 3.94 percent.
Sample. There are 417 observations overall in the monthly time series displayed in the chart above. The span of time covered by the series is from January 1992 to September 2026.
History. Here's a glimpse of a few simple statistics calculated on the full sample: unfilled orders had an average value of 3.98 billion US dollars; they recorded a bottom of 1.14 billion US dollars in December 2019; they recorded their maximum of 10.16 billion US dollars in November 1996.
Latest values
| Date | Value - US dollars (USD) million |
|---|---|
| 2026-07-31 | 1,874.00 |
| 2026-08-31 | 1,931.00 |
| 2026-09-30 | 2,007.00 |
Tip. To simplify exploration, we group series into data sets and worksheets. By scrolling down, you will find how we arranged further material linked to the statistics published here.
Not for investment purposes. Information disseminated on FetchSeries is not meant for investment purposes or as a basis for financial-decision making. Users should consult professional advice and perform their own independent due diligence before taking any financial risk.
Series Metadata
| Field | Value |
|---|---|
| Description | Computers and Related products Unfilled Orders |
| Country | United States |
| Economic concept | Stock |
| Data type | Nominal aggregate |
| Deflation method | Current prices |
| Seasonally adjusted | Yes |
| Rescaling | None |
| Frequency | Monthly |
| Unit | US dollars (USD) million |
| Source | U.S. Census Bureau |
| Source type | National statistical agency |
| Data licence | Open Data |
| Measure type | Level |
Series in the same data set
Discover the other time series included in this data set.