TY - JOUR
T1 - Quantitative Estimation of Demand for Conveyor Belt Supplies
AU - Nuñez-Uribe, Carlos
AU - Olmedo, Alexis
AU - Ríos, John
AU - Coronado-Hernández, Jairo R.
AU - Morillo, Daniel
AU - Gatica, Gustavo
AU - Umaña-Ibáñez, Samir F.
AU - Cabrera, Guillermo
N1 - Publisher Copyright:
© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Conference Program Chairs.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Correlation
KW - Covariance
KW - Demand
KW - Exponential Smoothing
KW - Forecast
KW - Time Series
UR - http://www.scopus.com/inward/record.url?scp=85141723461&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2022.07.087
DO - 10.1016/j.procs.2022.07.087
M3 - Conference article
AN - SCOPUS:85141723461
SN - 1877-0509
VL - 203
SP - 605
EP - 609
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 17th International Conference on Future Networks and Communications / 19th International Conference on Mobile Systems and Pervasive Computing / 12th International Conference on Sustainable Energy Information Technology, FNC/MobiSPC/SEIT 2022
Y2 - 9 August 2022 through 11 August 2022
ER -