Quantitative Estimation of Demand for Conveyor Belt Supplies

Carlos Nuñez-Uribe, Alexis Olmedo, John Ríos, Jairo R. Coronado-Hernández, Daniel Morillo, Gustavo Gatica, Samir F. Umaña-Ibáñez, Guillermo Cabrera

Research output: Contribution to journalConference articlepeer-review

Abstract

Demand forecasts provides quantitative data to estimate, with a reasonable degree of certainty, customers' requirements of a company. Applying this tool in manufacturing companies allows them to generate predictions for decision making. Forecasts have a transverse impact on finances, human resources, inventories, and production, among others. In Chile, qualitative models are used to make these estimates based on information from the sales force, customers, or group of experts. This article incorporates three exponential smoothing models into these estimates. Data is available from a manufacturing company (2016 to 2019); it is used to make comparisons and adjustments to select, the best model for each product. Also, a correlation and covariance analysis is carried out between the inputs, to determine the degree of relationship between the products and thus project their demand.

Keywords

  • Correlation
  • Covariance
  • Demand
  • Exponential Smoothing
  • Forecast
  • Time Series

ASJC Scopus subject areas

  • General Computer Science

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