Technical practice
Designing Agentic AI Workflows for Supply Chain
What to coverExplain how you would design an agentic workflow to handle supply chain exceptions, such as a sudden capacity bottleneck. Discuss how you define the agent's scope, the necessary business guardrails, and the criteria for human-in-the-loop intervention.
Integrating AI into S&OE and IBP Cycles
What to coverHow do you transition planning from static reporting to AI-enabled decision orchestration? Describe the technical and process considerations for embedding predictive models and conversational assistants into existing S&OE and IBP routines.
Data Product Management for Decision Intelligence
What to coverWhat is your approach to managing the lifecycle of a decision intelligence data product? Address how you ensure interoperability, maintain semantic model governance, and manage access controls in a complex, multi-regional environment.
Balancing Supply Chain Trade-offs with AI
What to coverWhen using optimization models to balance service, cost, cash, and inventory, how do you handle conflicting objectives? Explain how you would configure an AI system to provide actionable recommendations that align with strategic business priorities.
Kinaxis and ERP Integration Strategy
What to coverDiscuss the challenges of integrating AI-driven decision intelligence layers with established systems like Kinaxis and ERP. How do you ensure the data remains consistent and reliable for automated decision-making?
Scenario Simulation and Predictive Modeling
What to coverDescribe your experience in building scenario simulation models for supply chain planning. How do you validate the accuracy of these models and ensure they provide meaningful insights for executive decision support?
Ensuring Auditability and Responsible AI
What to coverIn the context of automated supply chain workflows, how do you ensure the auditability of agentic decisions? Suggest strategies for maintaining transparency and compliance when AI agents trigger corrective actions.
Defining KPIs for Decision Intelligence
What to coverHow do you measure the success of a decision intelligence initiative? Suggest specific outcome KPIs beyond simple adoption metrics that demonstrate value in areas like forecast accuracy, inventory health, and plan attainment.
Behavioural practice
Managing Cross-Functional Stakeholder Alignment
What to coverDescribe a situation where you had to align diverse stakeholders—such as Finance, Manufacturing, and Logistics—on a new data product roadmap. How did you manage competing priorities and ensure the final solution met the needs of all parties?
Driving Adoption of New Technology
What to coverTell me about a time you introduced a new AI or analytics tool to a team accustomed to manual processes. What specific change management strategies did you use to overcome resistance and drive adoption?
Leading from POC to Scaled Deployment
What to coverProvide an example of a project where you took an AI or analytics solution from a Proof of Concept (POC) to full-scale production. What were the biggest hurdles in scaling, and how did you resolve them?
Handling Ambiguity in Strategic Planning
What to coverDescribe a time when you had to define a product vision for a complex, ambiguous problem space. How did you break down the requirements and build a roadmap that provided clear direction for your engineering and data science teams?