Supply Chain Optimization

For every global enterprise, effective supply chain management is critical to successful operations. Cyc’s Supply Chain Optimization solution combines detailed supply chain modeling with intelligent data source integration and natural language explanations to provide insights that go beyond more traditional systems.

Often, the task of identifying supply chain stressors and vulnerabilities is dependent on manual data processing, hand-crafted issue trees, and black-box machine-learning techniques. While each of these techniques can shed insight into one or more critical aspects of the supply chain, collectively they form a brittle system with ample blind spots. In the absence of an overarching model of sufficient breadth and detail, complex interactions among supply chain inputs become difficult to understand, making disruptions to the supply chain hard to predict, and, by extension, difficult to remediate. 

The foundation of the Cyc solution is a rich and detailed model of the client supply chain in the Cyc knowledge base. Cyc already has deep semantic models of the everyday world, common sense, financial transactions, and specialized areas of computer component production.

To implement our Supply Chain Optimization solution, we configure Cyc by “explaining” internal and relevant external client data sources to it – mapping their schemas to Cyc’s knowledge base.  Cyc’s Semantic Knowledge Source Integration (SKSI) technology allows Cyc to connect to any number of disparate structured knowledge sources and to query them using their native query format (such as HTTP GET, or SQL or SPARQL), without any need for data migration. Knowledge of client’s data sources and systems is complemented by capturing and codifying the specialized knowledge and expertise of client analysts and other subject matter experts, who help us teach Cyc the “Client Way” to reason about the data to identify supply chain disruption markers.

Finally, we configure Cyc’s natural language generation and query dialogue capabilities, to more naturally and efficiently interface with your analysts directly.  To ensure the output is actionable, Cyc will surface only relevant insights for specific analysts in context using their nomenclature, approach, acronyms, and relevant domain-specific language.

Once connected to client data sources, Cyc begins its unblinking, vigiliant situationally- and contextually-aware conditions monitoring, looking out for any potential supply chain disruption markers. Once identified, Cyc uses its high-fidelity models of client tribal knowledge to suggest mitigation strategies for those risks and recommends risk-mitigation actions to take in response to predicted disruptions, along with clear English explanations of why those actions are warranted.

Further, Cyc enables “What if?” reasoning over a variety of permutations of scenarios. For example:

  • What would the impact be on all extant and forecast sales if we substituted component 1 for component 2 in client X’s order? 
  • What would the impact be if we prioritized client X’s order over client Y’s order? 
  • What would the impact be of a 2-week delay on customer Z’s order?
  • If we implement risk-mitigation action Q, will that itself risk any further SC disruption?

Supply Chain disruption insights relating to both risks and mitigations are returned to the analysts in easy to understand natural language and, most importantly, to engender trust, each insight is accompanied by complete pro- and con- justifications explaining each step in Cyc’s logical reasoning (also in natural language) along with citations pointing to the provenance of the data and knowledge used in the reasoning. 

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