FDA Recall Supplier Exposure Analyzer

Turn FDA recall datasets into recalling-firm concentration, Class I exposure, distribution breadth and supplier-risk signals. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI agents. Returns structured.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "recallNumber": "R1",
      "recallingFirm": "Supplier A",
      "product": "Drug X",
      "classification": "Class I",
      "distributionPattern": "Nationwide"
    },
    {
      "recallNumber": "R2",
      "recallingFirm": "Supplier A",
      "product": "Device Y",
      "classification": "Class II",
      "distributionPattern": "CA TX NY"
    },
    {
      "recallNumber": "R3",
      "recallingFirm": "Supplier B",
      "product": "Food Z",
      "classification": "Class II",
      "distributionPattern": "Regional"
    }
  ],
  "previousItems": [
    {
      "recallNumber": "P1",
      "recallingFirm": "Supplier A",
      "product": "Drug Q",
      "classification": "Class II",
      "distributionPattern": "Regional"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.012000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Turn FDA recall datasets into recalling-firm concentration, Class I exposure, distribution breadth and supplier-risk signals.

Why use this Actor

FDA recall feeds report individual enforcement actions, but supply-chain and risk teams need recurring exposure intelligence across recalling firms, products, classes and distribution patterns. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Supplier and recalling-firm exposure instead of recall rows only.

• Snapshot comparison for new high-severity recalls and worsening firms.

• Recall-class and distribution-breadth analytics.

• Supplier exposure actions for recurring review.

• Low-compute 256 MB data-first architecture.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Supply-chain risk.
• Pharma operations.
• Food safety.
• Medical-device risk.
• Insurance analytics.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems inline or select currentDatasetId from Apify storage. For period-over-period analysis, add previousItems or previousDatasetId. The Actor uses read-only Dataset permission and accepts up to 50,000 records per run. Stable source identifiers improve exact new/removed record detection, while common aliases are normalized for entity, value, date and secondary dimensions.

Output

The default Dataset receives one compact intelligence report containing current and previous record counts, newly observed and removed records, an aggregate market signal score, ranked entity-level signals, secondary-dimension concentration and a deterministic agentAction. The same report is also saved as INTELLIGENCE_REPORT in key-value storage for downstream Tasks, Schedules, webhooks and agentic workflows.

Gen2 decision intelligence

This Actor preserves its original analysis and adds a decision layer with baseline awareness, regression detection, confidence, GO/WARN/BLOCK executive output, and an optional economic-impact estimate. Economic estimates are produced only when the user supplies valuePerImpactUnitUsd; the result states the calculation basis instead of inventing monetary value.