Forecasting of demand using ARIMA model

By J. Fattah, L. Ezzine, Zineb Aman, Haj El Moussami, A. Lachhab. 2018

425 citations recorded by the source corpus.

The work presented in this article constitutes a contribution to modeling and forecasting the demand in a food company, by using time series approach. Our work demonstrates how the historical demand data could be utilized to forecast future demand and how these forecasts affect the supply chain. The historical demand information was used to develop several autoregressive integrated moving average (ARIMA) models by using Box–Jenkins time series procedure and the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 0, 1) and it was validated by another historical demand information under the same conditions. The results obtained prove that the model could be utilized to model and forecast the future demand in this food manufacturing. These results will provide to managers of this manufacturing reliable guidelines in making decisions.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b950c9d322db494d38facab63ce01b91c234ba56

Citations in the corpus

• Application of the ARIMA model on the COVID-2019 epidemic dataset (2020, neutral): “The ARIMA model includes autoregressive (AR) model, moving average (MA) model, and seasonal autoregressive integratedmoving average (SARIMA)model [2].”