Predicting Risk in Supply Chain

Global Supply Chains

Auto Industry-quote

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.

 

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?

 The 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

AllegroGraph turns complex data into actionable business insights