Bayesian probability

Demand forecasting

Autoregressive integrated moving average

Mathematics

Operations research

Statistics

Machine learning

Time series

Akaike information criterion

Engineering

Autoregressive model

Econometrics

Artificial intelligence

Economics

Bayesian information criterion

Moving average

Information Criteria

Computer science

Model selection

Box–Jenkins

International Journal of Engineering Business Management

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.

Forecasting of demand using ARIMA model