How might Interos refine its AI-powered risk scoring algorithm to better predict potential disruptions in the supply chain?

Introduction

To refine Interos' AI-powered risk scoring algorithm for better predicting potential supply chain disruptions, we need to delve deep into the current system's capabilities, limitations, and opportunities for improvement. I'll outline a strategic approach to enhance this critical feature, focusing on key areas such as data integration, machine learning model optimization, and user-centric refinements.

Step 1

Clarifying Questions (5 mins)

• Looking at the product context, I'm thinking Interos might be dealing with vast amounts of data from various sources. Could you help me understand the current data integration process and the types of data being used in the risk scoring algorithm?

Why it matters: Determines the breadth and depth of information available for risk prediction Expected answer: Multiple data sources including financial reports, news feeds, and supplier data Impact on approach: Would focus on improving data quality and expanding data sources if limited

• Considering the complexity of supply chains, I'm curious about the current accuracy and reliability of the risk predictions. What are the key performance metrics for the algorithm, and how have they evolved over time?

Why it matters: Helps identify specific areas for improvement in the algorithm Expected answer: Metrics like precision, recall, and F1 score with gradual improvements Impact on approach: Would prioritize areas with the lowest performance for refinement

• Given the critical nature of supply chain risk management, I'm wondering about the user feedback and adoption rates. How are customers currently using the risk scores, and what pain points have they reported?

Why it matters: Ensures improvements align with user needs and expectations Expected answer: High adoption but requests for more granular and real-time insights Impact on approach: Would focus on enhancing granularity and real-time capabilities

• Thinking about the competitive landscape, I'm interested in understanding Interos' unique value proposition. How does our risk scoring algorithm differentiate from competitors, and what areas do we believe have the most potential for further differentiation?

Why it matters: Guides the direction of improvements to maintain competitive advantage Expected answer: Strong in multi-tier visibility, opportunity in predictive analytics Impact on approach: Would emphasize enhancing predictive capabilities and expanding scope