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AGENTIC Data Mining
● Public TOOL dla.demand_forecast LAST SYNC —

DLA published demand forecast

DLA's own published forward demand per NIIN, month by month — the catalog surface that speaks about the future rather than the past. Keyed on NIIN (the source file has no FSC column).

A forecast is an expectation, not a commitment. The file name says “24 Month Forecast” but the published horizon is much longer — always read the named window. Negative forecast quantities are stored verbatim. Most open-board NSNs have no forecast at all.

Source & conduct: Source: DLA FOIA Electronic Reading Room demand-forecast bulk file. Owner browser download. Export-control carve-out via cross-referenced supply class.

There is a portal for this: DIBIQ puts DLA award and item context in front of a bidder as pages rather than tool calls. Same subscription, same refusals.

Records

—

One row per NIIN × forecast month

Coverage

—

Populates from /api/dataset-coverage when a row exists

Scope

Forecast rows

FOIA Electronic Reading Room · export-control carve-out

Freshness

Monthly

Human drop + ingest · JSON · Public · included in subscription

Coverage figures populate from the ingestion pipeline after bin/refresh-coverage.py writes a dataset_coverage row for dla.demand_forecast.

Query parameters

nsnstring

National Stock Number, dashed (5340-01-678-8659) or bare 13-digit -- both work, you never have to reformat. Reduced to its NIIN, because that is what the source file is keyed on.

niinstring

National Item Identification Number, bare or dashed 9-digit. The source file's own key.

month_fromstring

ISO calendar date (YYYY-MM-DD), inclusive lower bound on the forecast month. Forecast months are stored as the FIRST of the month, so this bound is snapped DOWN to the start of the month you name -- 2026-09-15 includes all of September 2026 rather than silently excluding it.

month_tostring

ISO calendar date (YYYY-MM-DD), inclusive upper bound on the forecast month, snapped UP to the end of the month you name. Use this to bound the long tail -- the file reaches 2035.

limitinteger

Max records to return (default 25, max 50)

offsetinteger

Rows to skip before the page starts (0-based, default 0). Read `has_more` to know whether another page exists.

The full input schema is served by the MCP server at connection time.

Example call

Request · dla.demand_forecast 
{
  "tool": "dla.demand_forecast",
  "params": {
    "niin": "000000001",
    "month_from": "2026-09-01",
    "month_to": "2027-08-31",
    "limit": 10
  }
}

What you can ask

  • Is DLA publishing recurring forward demand for this NIIN, or is the buy a one-off?
  • Size a bid against DLA's stated expected demand over a named window
  • Find items whose forecast demand is rising across coming months
Full tool reference →

Schema

Fields from the live catalog entry for dla.demand_forecast. Sample values below are synthetic schema shapes — not live FOIA rows.

Field Type Description
niinstringNational Item Identification Number, bare 9-digit — the source file's own key, and the join key to dla.flis item data. This file carries NO FSC column.
forecast_monthdateFirst day of the forecast month (the source publishes YYYYMM). Spans 2026-07 to 2035-04 — read the month, never assume a 24-month horizon.
forecast_qtynumberDLA's forecast quantity for that month. SIGNED: negative values are DLA's own published figures and are stored verbatim.
file_stampdatePublication stamp of the monthly file the row came from, taken from the FILENAME (not the zip entry mtime, which a re-zip would silently change). This is also the record timestamp — the forecast month is a horizon, no…
source_filestring'<zip>::<member>' — the exact published file this row was read from.

Sample record

Sample · dla.demand_forecast · synthetic 
{
  "niin": "000000001",
  "forecast_month": "2026-09-01",
  "forecast_qty": 100.0,
  "file_stamp": "2026-08-17",
  "source_file": "0000-00-00-0000_24monfcst.zip::0000-00-00-0000_24monfcst.txt"
}

Illustrative synthetic record from catalog.json — identifiers are fabricated so a real FOIA row is never reproduced on this public page.

Use it for

  • Tell a recurring DLA buy from a one-off before investing in tooling or stock
  • Size a bid against DLA's own stated expected demand over a named window
  • Find items whose forecast demand is rising across the next several months

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