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ISEN 315 Spring 2011 Dr. Gary Gaukler

ISEN 315 Spring 2011 Dr. Gary Gaukler. Newsvendor Model - Assumptions. Assumptions: One short selling season No re-supply within selling season Single procurement at start of season Known costs, known demand distribution. Newsvendor Model – Continuous Demand. Demand: pdf f(x)

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ISEN 315 Spring 2011 Dr. Gary Gaukler

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  1. ISEN 315Spring 2011Dr. Gary Gaukler

  2. Newsvendor Model - Assumptions • Assumptions: • One short selling season • No re-supply within selling season • Single procurement at start of season • Known costs, known demand distribution

  3. Newsvendor Model – Continuous Demand • Demand: • pdf f(x) • Cdf F(x) • Cost parameters: • “overage” co: cost per unit of inventory remaining at end of season • “underage” cu: cost per unit of unsatisfied demand • Total cost over season: G(Q, D)

  4. Review the Newsvendor Solution Safety Stock • Amount of inventory held to hedge against demand uncertainty

  5. Extension – initial inventory • Assume we have initial inventory of y units

  6. Extension – initial inventory and setup cost • Assume we have initial inventory of y units, and there is a setup cost K when we order

  7. When to Use Newsvendor Models • Short selling season, no replenishment • Buying seasonal goods • Fashion products • Making “last-run” decisions • Product end of life

  8. A Behavioral Issue • Consider you are a buyer for a store that sells DVDs. You can return unsold DVDs to the wholesaler for a small restocking fee, say 20% of the wholesale cost of $5. Your profit margin on each DVD is high: $10.

  9. Service Level of the Newsvendor What is service level? A naïve proxy: probability that demand will be less than what we stock = =

  10. Service Level of the Newsvendor What is wrong with this proxy definition of service level?

  11. Service Level of the Newsvendor Instead, use expected fill rate as service level measure:

  12. Demand Uncertainty How do we come up with our random variable of demand? Recall naïve method:

  13. Demand Uncertainty

  14. Demand Uncertainty and Forecasting Using the standard deviation of forecast error:

  15. Example

  16. Example

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