Fair Data Forecast Interview with Gregor Stühler of Scoutbee
Scoutbee’s CEO and founder, Gregor Stühler, who has a background in computer science and electrical engineering, first learned about the challenges of procurement and supply base management as a project engineer for a multinational medical device company. Scoutbee’s focus on solving supply base problems through hybrid knowledge graph and large language model (LLM) technologies reflects that understanding.
It’s not like supply chains are getting easier to manage. In this interview, Stühler states that ten years ago, companies like GE or Procter & Gamble, for example, could manage to track their top 1,000 or 2,000 suppliers with available technology. But given new regulatory requirements such as the German Supply Chain Due Diligence Act (which focuses in particular on supplier human rights compliance), the need to track multiple tiers of suppliers as well becomes necessary.
The result? Major consumer packaged goods (CPG) makers and retailers will now need to integrate and analyze millions of different data sources and data points.
Thus the growing demand for knowledge graphs (designed for ease of integration, massive scaling and ecosystem-level sharing) and contextualized data (data that is self-describing, so that one context can be easily related to others).
Scoutbee takes a practical approach to integration, one that’s based on the most important supply base management questions to answer. Each added dimension of context should provide answers to a key aspect of the supplier marketplace. The addition of a green supplier dimension, for example, suggests the need to add at least one additional data source. The data source(s) would need to answer these kinds of questions:
- How green is the supplier?
- How much money is the CPG manufacturer or retailer spending on each supplier?
- Which contracts are up for renewal?
- Which contracts should be discontinued?
- Which contracts should be initiated?
It’s interesting how Scoutbee has been acting as a mentor to customers who don’t yet have what Stühler calls “data muscle.” The company designs templates to share with customers that embody the essence of a smart, knowledge graph-enabled approach to sophisticated supply- base analytics.
Scoutbee uses AI and large language models (LLMs) to bring in and enrich the data in the knowledge graph. AI models analyze the relationships between the data points on the knowledge graph and draw conclusions. Users can query the data in the knowledge graph through Scoutbee’s new generative AI features, which gives them access to insights on their supply base in aggregate that was previously only accessible with assistance from data engineers. The combination of LLMs and knowledge graph technology helps companies unlock more contextual insights that help them drive resilience, make confident decisions, and advance their strategic priorities.
Hope you find the interview as illuminating as I have.