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.

In this article, we present the modeling of cutting performances in turning of 2017A aluminum alloy under four turning parameters: cutting speed, feed rate, depth of cut, and nose radius. The modeled performances include surface roughness, cutting forces, cutting temperature, material removal rate, cutting power, and specific cutting pressure. The experimental data were collected by conducting turning experiments on a computer numerically controlled lathe and by measuring the cutting performances with forces measuring chain, an infrared camera, and a roughness tester. The collected data were used to develop an artificial neural network that models the pre-cited cutting performances by following a specific methodology. The adequate network architecture was selected using three performance criteria: correlation coefficient (R 2), mean squared error (MSE), and average percentage error (APE). It was clearly seen that the selected network estimates the cutting performances in turning process with high accuracy: R 2 > 99%, MSE < 0.3%, and APE < 6%.

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In this article, we present a contribution to modeling, evaluation, and analysis of the inventory management systems performance and more generally stochastic discrete event systems with a batch behavior. For this contribution, we combine two models: the Supply Chain Operations Reference model, proposed by the Supply Chain Council, with the Batch Deterministic and Stochastic Petri Nets, which constitutes a very powerful dynamic modeling tool. To do this, we applied these tools on a typical model of inventory management system in order to show how the combination of these two tools can help us to model and analyze the performance of the inventory management system and to provide information on their behavior and the effects of their parameters. A resolution of the stochastic process associated with the warehouse management system will allow us to calculate the following performance indicators: average stock, average cost of stock, probability of an empty stock, and average supply and frequency. These indicators will help to monitor the activity of our stock management system, and therefore make the right decisions for the development of the organization.

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