2.2. Studies about Demand Forecasting Models
As stated by Ludwig et al. (2009), as one of the nonlinear optimization problems in their studies, the forecasting process for oil production was carried out with genetic algorithm and artificial neural network-based models. The authors argued that choosing from input variables would increase the success of the prediction model. This study is important in terms of revealing how important the selection of variables is for the estimation process [
8]. Chodak (2009) proposed a model for demand forecasting in internet markets. According to this suggestion, taking advantage of product features to increase the chances of success in demand forecasting can minimize the error level. The proposed model took into account variables such as the status of the product on the showcase, price, lead time, number of products running together, and order frequency. The study, which used 14-month data as a learning process, estimated the next 2 months with MAPE values with 20% and 10.8% absolute errors, respectively, in the model established by the genetic algorithm [
9].
Sayed et al. (2009) developed a hybrid genetic algorithm-based forecasting model. With this model, which they call the BAFI structure, MSE is the fit function, and the mean squared error is an objective function. They created a forecasting model that would work differently in different situations by using the cause–effect diagram. To investigate the performance of the proposed model, they evaluated the forecasting success according to individual statistical methods for different trends and seasonality conditions. The model they proposed provided the lowest error rate, at around 20% in all conditions. As a result of their studies, the authors emphasized that more models should be used, and at the same time, more successful results can be obtained by changing the genetic algorithm tuning parameters and initial conditions [
10]. In a study that argued that traditional methods are not effective in estimating financial time data, Huang (2011) modeled the raw input data financially with wavelet analysis in the first stage and transformed it into a time–scale surface area for forecasting and then divided this surface into various areas with the spectral clustering algorithm. In the second stage, as a result of these distinctions, predictions were made using artificial neural network support vector sets and traditional models. He used the RMSE (root mean square error) indicator while investigating the forecast performances. As a result of his studies, he concluded that the prediction success of the time–scale hybrid model that he proposed is higher than that of traditional methods [
11]. In another study, Hasin et al. (2011) also took into account some general variables to improve daily consumption forecasts in the retail sector. In the variables, they dealt with weekends or weekdays; holiday days; low, medium, and high price levels; brand categories; and seasons. It was seen that the forecasts made on a weekly and daily basis were better than Holt–Winter’s model. In their study, they also evaluated the results by combining the model they developed with the genetic algorithm [
12].
In their study, Wang and Petropoulos (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, Pwasong and Athasivam (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].
Chan 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].
Moscatelli 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 Pan and Zhou (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, Mediavilla 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].
The performance of five machine learning regression methods—random forest, extreme gradient boosting, gradient boosting, adaptive boosting, and artificial neural network algorithms—was examined in the study in comparison to a hybrid model—the RF-XGBoost-LR—that was suggested for sales forecasting of a retail chain, taking into account various forecasting accuracy parameters. A retail company’s weekly sales data were taken into account while analyzing projections, and the hybrid RF-XGBoost-LR model was found to perform better than the other models when evaluated using a variety of performance criteria [
20].
Gustriansyah et al. (2022) aimed to increase the success of sales forecasting by using the data mining technique k-means and the best–worst method model in the sales forecasting of retail products. The researchers, who said that they increased the quality of clustering products with the k-means algorithm, showed that, with this new model, which they named SalesKBR, the sales forecasts had a reasonable level of accuracy, and they predicted with a small error rate of 27.12%. The authors suggested that more successful results can be obtained in different sectors with different models, which were limited because their studies were in the retail sector and the dataset was relatively small [
21].
Fu and An (2022) presented a hybrid neural network sales forecasting model based on the voting–ensemble approach. They claimed that their experiments demonstrated the high accuracy of a hybrid model in sales forecasting, enabling businesses to optimize inventory management and enhance logistics benefits [
22]. They also mentioned that Shi (2013) asserted that the grey neural network based on a genetic algorithm can better forecast future changes in sales volumes compared to time series regression and a BP neural network model [
23].
To estimate product demand in an e-commerce firm, Chaudhuri and Alkan (2022) presented an optimized forecasting model: an extreme learning machine (ELM) model paired with the Harris Hawks optimization (HHO) algorithm. The suggested ELM-HHO model’s performance was also compared to that of classic ELM, ELM auto-tuned using Bayesian Optimization (ELM-BO), a Gated Recurrent Unit (GRU)-based recurrent neural network, and Long Short-Term Memory (LSTM) recurrent neural network models. The results showed that the suggested technique outperforms the existing product demand forecasting models in terms of prediction accuracy, and it can be used in real-time to estimate future product demand based on sales data from the previous week [
24].
The goal of the study of Neelakandan et al. (2023) was to create and test a model for forecasting online product sales over a wide range of online product types. A continuous Stochastic Fractal Search (SFS) approach for optimizing the parameters of a deep learning modified neural network (DLMNN) was described by the authors, along with an analysis of a time series dataset. A RMSE, a MAE, and a MBE comparison of the SFS-DLMNN methodology with other popular methods such as the PSO, WOA, BRNN, and GA models were carried out by the authors. The results showed that their machine-learning technique produced improved outcomes with lower values [
25].
Zhang and Kim (2023) offered a network model for enterprise sales forecasting based on a mix of enterprise sales forecasting from the standpoint of digital management and neural networks. To improve sales forecasting and supply chain management, the authors provided a hybrid learning method that combined seasonal mode and support vector regression analysis. This hybrid model can investigate time series features and gain more valuable information to create a network capable of extracting more discriminative features. The authors forecasted the sales of clothing product types that were sensitive to seasonal effects and had a long sales cycle and used seasonal factors to process the data. Their suggested model had better accuracy for predicting the future sales demand of garment enterprises and boosted the model training speed and reaction time [
26].
According to Ramos et al. (2023), forecasting methodologies are important in assisting decision-making in e-commerce. Deep learning approaches (namely deep neural networks) were utilized by the literature to measure the “learning” ability to extract important insights from data. When compared to other simpler neural networks (e.g., multilayer perceptron architectures), “long memory” neural networks (e.g., long short-term memory architectures) are offered as the best alternative by [
27].
According to Aguiar-Pérez and Pérez-Juárez (2023), deep learning methods have demonstrated better performance than conventional forecasting methods in energy demand. It is a fact that big data are essential to overcoming the obstacles of renewable energy integration, since smart grids produce large amounts of data. Thus, big data technologies are becoming more and more essential in load forecasting as electric vehicles are gradually being introduced [
28]. In their review article, Habbak et al. (2023) also mentioned that, in comparison to other approaches, AI-based load forecasting techniques utilizing machine learning and neural network models in the energy industry have demonstrated the highest forecast performance, obtaining higher overall RMS and MAPE values [
29].
Tang et al. (2023) outlined the main functions of the AI-predicting inventory model and offered optimization recommendations for e-commerce enterprises. When compared to other models in the case company, the AI forecasting model XGBoost exhibited the highest predictive accuracy and significantly increased the accuracy of inventory forecasting [
30].
Swaminathan and Venkitasubramony (2024) classified the forecasting demand methods in fashion products into two categories: qualitative and quantitative methods. While the qualitative methods include techniques such as expert judgment, Delphi, and analytic hierarchy process, the quantitative methods embrace statistical and heuristics-based methods (time series-based models, Bayesian method-based, heuristic-based, panel data-based models, and others); artificial intelligence (AI); machine learning (ML); and hybrid methods (fuzzy-based, NN-based, clustering and classification, and grey-based hybrid models). AI and ML-based forecasting methods are classified as neural networks (NNs); deep learning; convolutional neural networks (CNNs); evolutionary neural networks (ENNs); extreme learning machine (ELM); and classification (Bayesian networks, genetic algorithm, random forest, and k-nearest neighbor). According to the authors, the utilization of machine learning forecasting models to tackle the demand fluctuations in the fashion industry has been recognized as an area where ongoing research would have a significant impact on the business. As companies expand into new channels or markets, ML-based forecasting has helped them produce more accurate projections and increase consumer involvement [
31].
Gaboitaolelwe et al. (2024) compared the forecasting performance of ML models, including AdaBoost, multilayer perceptron neural network, support vector regression (SVR), random forest (RF), lasso regression, gradient boost, and extreme gradient boost, by using historical power production data. They found that the random forest model performs the best when forecasting model performances are ranked according to skill score results. The optimized multilayer perceptron NN forecasting model is the least effective aside from the baseline models [
32]. According to Bender et al. (2024), the use of ML and AI approaches is becoming more prevalent in demand forecasting systems, which are constantly progressing and offer promising tools to increase forecasting accuracy [
33].
2.3. Original Contribution of the Work
It is possible to come across studies in the literature on estimating the sales of businesses. When the nearly 50 years of studies in the literature are evaluated, demand forecasting studies, which started with time series, continue with artificial neural networks, deep learning, heuristic methods, and studies using various models. Demand in the market for information technology products in a heavily competitive environment is unstable due to increasing competition, changing consumer trends, and rapid innovation in product processing technologies. In this case, the use of time series analysis techniques such as moving average, regression, exponential smoothing, seasonal exponential smoothing, and causal modeling allows for scalable deviations to occur. Traditional models are biased, and these biased estimates can adversely affect production planning and product scheduling [
34]. However, these models, which are insufficient alone, can reproduce more successful results when combined. The method of combining estimates is an estimation method that is used to combat the question of which methods should be used together instead of choosing the best method [
35]. The consolidation of estimates is usually achieved by multiplying and summing individual method estimates with a given weight ratio. In this way, it is possible to obtain a new and more successful forecast from existing forecasts. Demand forecasting studies, which find wide applications in many sectors, have difficulty in finding the same application area in the e-commerce sector. Developing new technologies and the use of smart devices have brought people access to information and ease of transactions, and the e-commerce market has expanded rapidly in the last 10 years. With the effect of the recent pandemic, traditional face-to-face trade became impossible for a long time, and at this point, the e-commerce market has grown incredibly and has become the first market for the whole world. The expansion of the e-commerce market has also increased the variety of online products offered on the market in this field. As a result of this, e-commerce managers difficulty in making and monitoring decisions. On the other hand, it is possible to accelerate product management decisions for these businesses by using the demand forecasting method. As an important contribution to the literature in this study, which finds little application area in the e-commerce sector, it has been tried to provide a model proposal with better forecast performance than other methods to realize the demand forecasting of sales for multiple products and achieve rapid results in a huge product variety while doing this.
The genetic algorithm method, which has the quality of lifesaving in optimization problems with a large solution space, cluster analysis made with the k-means technique, which has an important place in data mining, and historical forecasting models will increase the prediction performance in e-commerce. This created an important belief about our tests. The example of success achieved in this study will ensure that similar studies will increase in the future, so that more studies will take place in e-commerce in the future, and a unique auxiliary tool will be obtained when these studies are put into practice, like the software developed in this study. In an environment of uncertainty, managers will be able to find answers to many questions, such as which products should we sell, how should we determine the inventory policies on the products, and which products should we use for promotions with this assistant.