چکیده مقاله
Forecasting demand accurately is crucial for effective supply chain planning, budget control, and achieving sales goals Decision makers rely on this information to understand customer needs, the required quantity, and timing We researched how deep learning models and neural networks can predict pharmaceutical demand to improve supply chain performance in sales, marketing, and product development We assessed three univariate pharmaceutical time series and broke down each time series into trend, seasonal, and residual components Then, we created a data frame containing these components and the time series After dividing the data into 70% for training, 15% for validation, and 15% for testing, we analyzed the time series by using the Long Short Term Memory or LSTM model, Multilayer Perceptron or MLP model, and Autoregressive Integrated Moving Average or ARIMA model We used Bayesian Optimization to fine tune the hyperparameters in LSTM and MLP models and followed the Box Jenkins methodology to create seasonal ARIMA models with exogenous variables Our research found that the LSTM model slightly outperformed the MLP model and the ARIMA model in daily time series, with Root Mean Squared Error RMSE of 1 606, 1 135, and 1 125 compared to 1 650, 1 152, and 1 161 for the MLP model These findings suggest that the LSTM model can effectively identify complex time based dependencies within pharmaceutical demand data
کلیدواژهها
نویسندگان
Seyed Masoud Mousavi
Department of Industrial Engineering Faculty of Engineering, College of Farabi, University of Tehran Tehran, Iran
Shahrokh Asadi
Department of Industrial Engineering Faculty of Engineering, College of Farabi, University of Tehran Tehran, Iran
شیوه ارجاع
Mousavi, Seyed Masoud and Asadi, Shahrokh,1403,Demand forecasting based on deep learning methods for univariate time series,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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