Some Alternatives to Exponential Smoothing in Demand Forecasting

Abstract

The report contains a study devoted to a comparison of exponential smoothing with other alternatives to demand forecasting. Special attention is paid to the stock-out risks assumed whenever reorder levels are set using the various methods being compared. Models presently used by NavSup are employed in order that the results be applicable to the system in use. Simulation techniques are used for drawing comparisons. For constant mean, normal demand, it is shown that exponential smoothing does not produce as accurate results as ordinary maximum likelihood techniques. For the case of a linear mean changing with time, it is shown that the two methods are about comparable. Finally, a sequential Bayes forecasting method is defined and found to compare quite favorably with exponential smoothing. The need for additional study of Bayesian methods is established.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1972
Accession Number
AD0745876

Entities

People

  • Peter W. Zehna

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Networks
  • Classification
  • Computational Science
  • Computations
  • Delphi Method
  • Errors
  • Estimators
  • Maximum Likelihood Estimation
  • Models
  • Observation
  • Operations Research
  • Pilot Studies
  • Probability
  • Probability Distributions
  • Random Variables
  • Simulations
  • Standards

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Logistics and Supply Chain Management.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms