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
Propose a modeling framework that jointly forecasts demand and optimizes dynamic pricing in a two-sided marketplace while accounting for seller reactions, cross-elasticities across SKUs, and inventory constraints. Explain estimation strategy, computational approach (decomposition, approximate dynamic programming), and a pragmatic rollout/experiment plan.
EasyTechnical
0 practiced
Given a sales table with schema: sales(sale_id PK, seller_id int, sku_id int, quantity int, amount decimal, sold_at timestamp), write a SQL query that returns the top 10 sellers by total quantity sold in the last 90 days, along with total revenue, distinct SKUs sold, and average order size. Describe indexing or partitioning strategies to make this query fast on a large dataset.
MediumTechnical
0 practiced
What short-term tactical and long-term strategic metrics and models would you use to forecast and plan warehouse and driver capacity for a peak season expected to be 3x normal demand? Explain scenario modeling, surge staffing, and how you'd set safety margins and SLAs.
HardTechnical
0 practiced
Design an algorithmic approach to detect significant shifts in demand patterns caused by competitors or macroeconomic events in near real-time. Describe features and aggregations to monitor, statistical tests or drift detectors to use (CUSUM, KL divergence, permutation tests), use of SKU embeddings to propagate alerts across similar items, and an operational alerting policy.
HardTechnical
0 practiced
Design a robust backtesting framework for demand forecasting that handles promotions, rolling retraining, data leakage, and seasonality. Specify how you'd simulate production scoring (including feature staleness), the evaluation metrics (coverage, calibration, decision-relevant losses), and pitfalls to watch for when comparing models across SKUs and segments.
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