Logistics & Marketplace Dynamics Fundamentals Questions
Foundational concepts and practices for understanding and optimizing logistics within marketplace ecosystems, including order fulfillment, inventory management, routing and transportation planning, demand forecasting, capacity planning, and the economic dynamics of seller and buyer behavior, pricing strategies, incentives, and platform governance.
HardTechnical
0 practiced
Design a multi-objective executive dashboard that helps leadership balance GMV growth, profitability, and delivery SLAs. Specify the core metrics, how you would present trade-offs (visualization techniques), how to allow interactive scenario modeling (e.g., increase marketing spend vs tighten SLA), and how to compute a composite score if requested.
EasyBehavioral
0 practiced
Tell me about a time you had to explain complex logistics KPIs to a non-technical business stakeholder. Use the STAR method: Situation, Task, Action, Result. Focus on how you simplified the metrics, the visualizations you used, and the outcome for the business decision.
EasyTechnical
0 practiced
Explain the difference between Gross Merchandise Value (GMV), marketplace take rate, and net revenue for a marketplace. Provide formulas for each, discuss common reporting pitfalls (refunds, cancellations, taxes), and describe how BI should present these to finance and operations audiences.
HardTechnical
0 practiced
You discover that a delivery ETA ML model systematically underestimates times in certain low-income neighborhoods, causing poorer service for those customers. As the BI lead, describe how you would surface this issue in BI reports, propose remediation steps (short-term and long-term), and lead cross-functional action with product, legal, and ops to address fairness and compliance risks.
HardTechnical
0 practiced
Design an anomaly detection system for order cancellations that accounts for seasonality and is explainable to operations. Describe architecture (streaming vs batch), candidate algorithms (statistical control charts, seasonal decomposition, supervised ML), features, how to surface the top contributing factors for each alert, and a monitoring plan to control false positives.
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