Lebanese industries are growing so fast and expanding their businesses while managing their time, inventory, labor, quality and other production factors in order to increase productivity and output while reducing waste. For that, lean manufacturing aims to reduce the inventory and gain more profits while getting a better quality of the goods delivered to the customers. The aim of this Article is to assess and explain, through a survey, to which extent lean tools are implemented in a Lebanese pharmaceutical industry and to find out if there is any relationship between the application of these tools (Kaizen, JIT, TPM and standardization) and the effectiveness of lean on the productivity.

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Most exchange rates are volatile and mainly rely on the principle of supply and demand. Millions of people around the world are influenced, one way or another, by the variation in exchange rates. In this research we demonstrate that the Artificial Intelligence, specifically Artificial Neural Networks (ANN), can improve the accuracy of forecasting exchange rates compared to statistical techniques such as regression. When we compared the results from regression and artificial neural network, it was clear that the ANN outperformed regression in forecasting exchange rates. Moreover, it became clear that using ANNs instead of regression for forecasting exchange rates is rewarding and necessary because the average error given by an ANN is smaller than the average error given by regression. Accuracy in forecasting became a major issue and not a minor detail. It was the combination between Artificial Intelligence and Macro Economics that made these two models come into reality, making it possible to use computer sciences and engineering fields in the service of an economical problem.

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