## 2 Related Works
### Studies about Demand Forecasting Models
It was seen that the forecasts made on a weekly and daily basis were better than [PERSON]'s model. In their study, they also evaluated the results by combining the model they developed with the genetic algorithm [12]. In their study, [PERSON] and [PERSON] (2016) compared expert estimates with the best estimation method selection and combination of estimations and investigated the effect of this on the inventory management performance. Forecast selection and combination improved both the inventory and forecast performance over other models or expert forecasts. On the other hand, it was found to be the best strategy for minimizing the total cost and maximizing the estimation accuracy measured by the MAE [13]. In another study on the combining of methods, [PERSON] and [PERSON] (2018) proposed a model for the combination of the ARFIMA and LRNN methods. Running the model on NNPC's estimation of gasoline production, the authors concluded that the proposed model outperformed the prediction success of the two models alone [14]. [PERSON] et al. (2019) compared the forecasting performances of six different time series models (MA, ARMA, MARS, GM, SVM, and ANN) for container forecasting. As a result of the analysis, they found that the support vector machine method gave the best results in all cases. The result of the low prediction success of artificial neural networks in the related problem is one of the prominent results [15]. [PERSON] et al. (2020) performed a performance analysis by comparing machine learning models, which have been widely used in estimations in recent years, with statistical models such as logistic regression in assumed risk assessment estimations. The results of the analysis show that machine learning models are more successful when there is a limited set of information, but this advantage decreases when there is precise information such as credit behavior. The researchers used the financial ratios of 300,000 non-financial Italian companies between 2011 and 2017 as a huge dataset in their studies [16]. According to [PERSON] and [PERSON] (2020), traditional data mining technology cannot successfully exploit enormous data in the electrical supply, because it relies on time-consuming and labor-intensive characteristic engineering. They claim that convolutional neural networks can efficiently utilize a vast quantity of data and can automatically extract effective features from the original data with increased availability. In their study, a convolutional neural network was utilized to mine e-commerce data to anticipate commodities sales, while weight attenuation technology and transfer learning technology were employed to increase the accuracy of commodity sales forecasting. To validate the effectiveness of their suggested technique, they compared the following algorithms: data analysis autoregressive integral moving average (ARIMA), convolutional neural network (CNN), single CNN, and photonic deep neural network (PDNN). Experiments on large-scale datasets revealed that their model may significantly increase the accuracy of sales forecasting [17]. The findings of the literature indicate a distinct trend in the use of ML and DL methods for manufacturing demand forecasting. This pattern illustrates how certain techniques--like neural networks and long short-term memory--can manage complicated demand patterns better than more traditional statistical techniques [18]. The accuracy of demand forecasting methods is considerably increased by using AI techniques alone or in combination with statistical techniques. In their literature review article, [PERSON] et al. (2022) analyzed 23 different methods that were successfully applied in demand forecasting between 2017 and 2021, clearly demonstrating the use of deep learning techniques [19].