AI in Supply Chain Optimization Quiz

Answers and explanations appear as you take the quiz.

1. What is the main goal of AI-driven demand forecasting in supply chains?
   • To predict how much of each product will sell, where, and when
   • To design new product packaging
   • To write marketing copy for promotions
   • To replace all warehouse workers
2. Which type of problem are routing and inventory decisions typically framed as?
   • Image classification problems
   • Mathematical optimization problems (e.g., mixed-integer programs)
   • Speech recognition problems
   • Random guessing problems
3. What type of demand output does the cited Zalando replenishment study integrate into inventory optimization?
   • A guaranteed exact unit count for every future day
   • An unqualified social-media popularity score
   • Probabilistic forecasts that represent uncertainty in future demand
   • A fixed rule that ignores the forecast distribution
4. What does a digital twin represent in a supply-network scenario?
   • A guarantee that every sensor is live and synchronized at all times
   • A second physical warehouse that automatically executes every decision
   • A model that proves a disruption cannot occur
   • A virtual representation of a physical system that can be linked with data for modeling or simulation
5. What can make an AI-enabled supply chain more resilient during a disruption?
   • Visibility, scenario planning, and the ability to adjust decisions as conditions change
   • A guarantee that demand will never change
   • Removing all human operators
   • Using only historical averages
6. How should observed supply-chain outcomes be used in a forecast feedback loop?
   • Assume every new observation automatically improves the model
   • Evaluate forecast errors and consider validated updates using appropriate outcome data
   • Retrain continuously without checking data quality or performance
   • Treat each forecast as a confirmed future outcome
7. Which replenishment policy did the cited e-commerce study extend?
   • A proportional-derivative controller for motor speed
   • A shortest-path algorithm for vehicle routing
   • The (R, s, Q) policy, adding variables for initial replenishment and a policy horizon
   • A fixed moving-average demand forecast
8. Why combine forecasts with an optimization model?
   • They are identical tasks
   • Optimization removes all uncertainty
   • Forecasting automatically places orders
   • A forecast estimates future conditions, while optimization chooses actions under constraints