A Comparison of Demand Forecasting Techniques

Abstract

For periodic review inventory models with stochastic demand, the idea of stock-out risk is defined, from which the importance of accurate prediction of demand is deduced. Methods of demand model parameter estimation are investigated and several methods compared on the basis of theoretical soundness, ease of application, and accuracy of estimates based upon the results of extensive computer simulation. The theoretical development of maximum likelihood and exponential smoothing estimators as applied to prediction is presented along with the development of a new Bayesian approach to the problem of demand forecasting.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1971
Accession Number
AD0721235

Entities

People

  • John A. Coventry

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Counter IED
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Bayesian Networks
  • Coefficients
  • Computational Science
  • Computations
  • Computer Simulations
  • Estimators
  • Inventory
  • Mathematical Analysis
  • Mathematical Models
  • Maximum Likelihood Estimation
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Statistical Algorithms

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Logistics and Supply Chain Management.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference