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.

EasyTechnical

60 practiced

Discuss pros and cons of evaluation metrics MAPE, RMSE, MAE, and weighted quantile loss when forecasting SKU-level demand in presence of intermittent demand (many zeros) and wide scale differences across SKUs. Which metric would you recommend for inventory decision-making and why?

HardTechnical

67 practiced

A marketplace wants to introduce a guaranteed 2-hour delivery product with penalties for late deliveries. Build a financial and operational model to evaluate profitability and risk: estimate demand uplift, incremental fulfillment and capacity costs, expected penalties, necessary buffer capacity, SLA monitoring, and sensitivity analysis across demand and on-time performance scenarios.

MediumTechnical

91 practiced

You're missing recorded delivery timestamps for 20% of last-mile deliveries. Describe a reproducible approach to impute delivery durations for downstream modeling of average delivery time. Include features you would use, imputation methods (model-based vs rule-based), assumptions, and validation methods to ensure the imputed values do not bias models.

EasyTechnical

57 practiced

Explain the key marketplace and logistics metrics you would report to executives for a two-sided e-commerce platform. Include clear definitions and business interpretation for at least: GMV, take rate, on-time fulfillment rate, fill rate, inventory turnover, lead time, and active-seller liquidity. For each metric describe one data source and one major caveat that could bias the metric.

MediumTechnical

70 practiced

You must forecast demand for thousands of SKUs with intermittent (zero-inflated) daily sales. Provide a step-by-step modeling plan covering preprocessing, model families (e.g., Croston, zero-inflated Poisson, hierarchical Bayesian, machine-learning hybrids), cross-SKU pooling strategies, and how to create probabilistic forecasts useful for safety stock calculations.

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