AI-Powered Track and Trace for Manufacturing

The Pressure to Act Is Real

The global track and trace market is projected to reach $12 billion by 2030. Three regulatory deadlines are forcing manufacturers to move:

• US DSCSA: Requires item-level drug serialization to trace every prescription drug through the supply chain.

• EU Battery Passport: Requires manufacturers to document the full lifecycle of EV and industrial batteries by February 2027.

• EU Digital Product Passport: Requires a verifiable digital record for all products sold in the EU by 2030.

Each of these mandates shares the same requirement: manufacturers must prove, at any point in time, exactly where a product has been and what happened to it. The cost of falling short is high. For example, medical device recalls rose 8.6% year over year in 2024, and a single pharma recall can cost up to $600 million.

The Data Exists: The Problem Is Accessing It

Meeting these requirements depends on data, and most manufacturers already generate it. The problem is that 90% of it never gets used. It sits in fragmented silos: free-text operator logs, inconsistent timestamps, and mixed measurement units that traditional ETL pipelines cannot handle without rigid schemas and brittle parsing rules. By the time data gets cleaned and structured, the window to prevent a recall or meet a compliance deadline has already closed.

How This Solution Works

The gap between fragmented factory data and compliance-ready records is an engineering problem. This solution closes it by connecting raw factory floor events to a structured, queryable product history using MongoDB Atlas and AWS Bedrock.

Raw factory floor events land in MongoDB as they arrive, then move through an AI cleaning pipeline powered by AWS Bedrock. The pipeline normalizes each event into a structured, EPCIS 2.0-compliant record. The complete product journey lives in a single document, so a compliance auditor or supply chain manager retrieves the full chain of custody in one read, with no joins and no separate system to query.

Regulated industries already run this architecture at scale:

• McKesson traces 1.2 billion pharmaceutical serial numbers per year and met its DSCSA federal deadline on MongoDB Atlas.

• GE HealthCare cuts data retrieval time by 83% using Change Streams and Atlas Search.

• Bosch tracks 6 million fastening events per aircraft with a full FAA-compliant audit trail.

The Raw Event

Factory floor events arrive exactly as operators write them without validation and preprocessing.

{
   "text": "ALERT | BATCH-GM005-037 | ShenZhn WH | 06:15:00Z - rcvd \n  raw mat'ls from Tianhe Biosci. Qty: 1000u glucose oxidase. \n  Temp: 4.2C avg. Purity: 99.3%. 12 units MISSING — QA hold \n  ref#QH-2024-001.",
   "stage": "Raw Materials Sourcing",
   "_timestamp": "2025-01-15T06:15:00.000Z"
}

The Product Document

After processing, the cleaned event updates the product document. Every manufacturing stage appends a new entry to the journey array. The complete chain of custody lives in one place, as shown below.

{
   "_id": "GM-005",
   "productName": "Continuous Glucose Monitor",
   "status": "in_production",
   "currentStage": "Enzyme Coating",
   "journey": [
      {
         "stage": "Raw Materials Sourcing",
         "status": "completed",
         "location": "Shenzhen, CN",
         "startTime": "2025-01-15T06:15:00.000Z",
         "eventCount": 3
      },
      {
         "stage": "Electrode Fabrication",
         "status": "completed",
         "location": "Penang, MY",
         "startTime": "2025-01-16T07:00:00.000Z",
         "eventCount": 4
      },
      {
         "stage": "Enzyme Coating",
         "status": "in_progress",
         "location": "Penang, MY",
         "startTime": "2025-01-17T08:00:00.000Z",
         "eventCount": 1
      }
   ]
}