A major US auto manufacturer needs to uncover and predict potential supply chain risks across the globe that can be precipitated by local, regional or global events such as a tropical storm, a regional labor shortage, a geo-political crisis or where vendors may be in financial distress or primary suppliers may unknowingly have common/shared sub-contractors.

With ever tightening just-in-time logistics and suppliers across the globe, we needed a supply chain “early warning system” to uncover and predict unseen vendor risk.

Program Director, Research and Innovation, $45 billion auto manifacturer

Their Goal

Answer highly complex questions and provide real-time alerts for:

  • Which parts produced by a (sub-sub-) vendor will be less available due to a flood in China?
  • Which of our cars will be affected by political unrest in Thailand?
  • How can our competitors disrupt our supply chain by buying up all producers of this chip?
  • Did one of our (sub-sub-) vendors start selling to our competition and what does that mean for us?
  • What happened historically with the price of this sub part when the prices for crude oil or any other raw material went up?
  • Is one of the (sub-sub-sub-) vendors in our chain in financial distress and how would that affect us?

Their Challenges

  • Three primary sources of data to integrate
    • Corporate internal
      • Bill-of-materials for components
        • Complex multiple levels deep, nested relationships
        • Identification of vendor supplier(s) for each assembly, sub-assembly and parts
        • Parts inventory if available
        • Vendor information
          • Primary and secondary suppliers
          • Sub-contractor suppliers to these vendors
      • External
        • Complex, real-time, largely unstructured or free form external data sources
          • News feeds, blogs, websites, tweets
          • Commodity/analyst reports
          • Weather for the specific vendor locations
          • Vendor and competition specific information

The Solution

Main components:

  • Ingested of data into the AllegroGraph semantic graph platform:
    • Bill-of-materials and supplier information
    • Vendor information
      • Facilities, operational, financial information (meta data)
      • Sub-contractor information to allow AllegroGraph to automatically resolved inter-relationships of (sub-sub-sub-)suppliers
      • Vendor geographic information is pulled from public data sources that are stored in Linked Open Data format and easily accessed by AllegroGraph

The Benefits

  • Predictive insights uncover hidden risk that traditional methods and analytics can’t see
  • Actionable intelligence to support better decisions
  • Highly flexible to handle huge variety of data types, sources and conceptual rules
    Enables processing of unstructured or free form text to extract insights
    Real time for time critical decision making