Methods for Forecasting Seasonal Items with Intermittent Demand

Explore forecasting methods for seasonal items with intermittent demand, focusing on managing inventory and meeting sporadic demands efficiently. Learn about assumptions, policies, simulations, and strategies to minimize overstock while maximizing customer demand fulfillment.

• Forecasting
• Seasonal Items
• Intermittent Demand
• Inventory Management
• Demand Fulfillment

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1. Methods for Forecasting Seasonal Methods for Forecasting Seasonal Items With Intermittent Demand Items With Intermittent Demand Chris Harvey University of Portland

2. Overview Overview What are seasonal items? Assumptions The ( ,p,P) policy Software Architecture Simulation Results Further work

3. Seasonal Items Seasonal Items Many items are not demanded year round Christmas ornaments Flip flop sandals Demand is sporadic Intermittent Evaluate policies that minimize overstock, while maximizing the ability to meet demand.

4. Demand Quantity of a Representative Seasonal Item Demand Quantity of a Representative Seasonal Item

5. Assumptions Assumptions Time till demand event is r.v. T, has Geometric distribution T ~ Geometric(pi) where pi = Pr(demand event in season) T ~ Geometric(po) where po= Pr(demand out of season) Geometric distribution defined for n = 0,1,2,3 where r.v. X is defined as the number (n) of Bernoulli trials until a success. pmf http://en.wikipedia.org/wiki/Geometric_distribution

6. Assumptions Assumptions Size of demand event is r.v. D, has a shifted Poisson distribution D ~ Poisson( i)+1 where i+ 1 = E(demand size in season) D ~ Poisson( o)+1 where o+1 = E(demand out of season) Poisson distribution defined as Where r.v. X is number of successes (n) in a time period. Pmf http://en.wikipedia.org/wiki/Poisson_distribution

7. Histogram and Distribution Fitting of Histogram and Distribution Fitting of Non Non- -Zero Demand Quantities Zero Demand Quantities

8. The ( The ( , , p p, P , P) policy ) policy Order When Pr T Pr t and D IP p Order Quantity Q F = ( ) 1 , P IP ( ) = 1 inverse cumulative demand distribution function inventory position " " " " O Off season , F IP = = + OH OO BO In season I =

9. New Simulation Structure New Simulation Structure Organization Modular Interchangeable Bottom up debugging Global Data Structure Very fast runtime [[lists]] nested in [lists] Lists may contain many types: vectors, strings, floats, functions Generic call args Specific call args Director for Each Method: Data Structure ignorant Main simulation: Data structure aware Generic Function definitions Generic return args Specifc return args

10. Performance Performance

11. ROII for ROII for =.9 =.9 P p

12. Future Work Future Work Bayesian Updating Geometric and Poisson parameters are not fixed Parameters have a probability distribution based on observed data Parameters are continuously updated with new information Modular nature of new simulation allows fast testing of new updating methods

13. Giving Thanks Giving Thanks Dr. Meike Niederhausen Dr. Gary Mitchell R