# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 2 Literature Review
They highlighted the limitations of traditional statistical models in capturing the nonlinear dynamics of customer demand, particularly in e-marketplaces. To overcome this, they utilized advanced machine learning techniques, such as the random forest ensemble regression tree model, to create a precise demand prediction model for remanufactured products. The study used real-world Amazon data and revealed the complexity of predicting remanufactured product demand while demonstrating the effectiveness of machine learning in this field. In [10] study, the integration of machine learning techniques, notably ARIMAX and neural networks, was explored with the aim of enhancing demand forecasting within multi-stage supply chains. The research showed significant improvements in operational and financial performance, proving the effectiveness of machine learning-based methods in reducing inventory costs and enhancing asset efficiency, especially in capital-intensive industries like steel manufacturing. Additionally, the research emphasized the hybrid forecasting model's ability to capture complex relationships among variables at the aggregate level, resulting in more accurate demand predictions. [PERSON]. [31] developed a machine learning technique to enhance the precision of order demand prediction in e-commerce distribution centers. Their approach integrates the unique traits of time series data and employs an adaptive neuro-fuzzy inference system to analyze the patterns of e-commerce order arrivals as time progresses. The implementation framework consists of four stages to support practical application, and a structured model evaluation framework is in place to ensure the reliability of the predictive model. The model shows RMSE of 8.7, 11.33, and 9.55 for retailers 1, 2, and 3, respectively. Additionally, this method allows for the exploration of integration with optimization or heuristic approaches to enable more robust decision-making in the dynamic e-commerce environment. [PERSON]. [37] developed a manufacturer-and-retailer supply chain model considering the retailer's selling price, product green level, and advertisement effort-sensitive demand. It analyzes a sharing contract policy on manufacturer green investment. This model demonstrates how green production methods and investments can boost manufacturer profits while reducing pollution in the environment. However, the analytical method is more complex and time-consuming. In a two-layer supply chain management setting with stochastic conditions, [PERSON] [38] focus on the best replenishment strategies that a retailer may implement to maximize average profit when (s)he is presented with trade offers of credit and price discount from suppliers. In order to maximize average profit, the extended study suggests that the merchant borrow half of the cost of purchases from financial support and make payments at the beginning of the advance payment period. [PERSON]. [39] established a production model that screens out faulty products and reduces greenhouse gas emissions. In this case, it has been assumed that the manufacturing process transfers from a controlled state to an uncontrolled state following an arbitrary, random period of time. The research determines the ideal business and production duration, as well as the ideal production rate, and optimizes the model's predicted average profit. Ref. [38] created a flawed production inventory model that includes warranty and insurance coverage for any unintentionally damaged product parts, rework, preventive machine maintenance, and other features. This study determines the best manufacturing schedule and selling price for the product's various warranty periods in order to maximize profit per unit over the course of the business period. Ref. [40] presented a gated recurrent unit model based on the firefly algorithm (FA-GRU) for intelligent demand forecasting. Here the use of FA helps to select the appropriate parameter for GRU. The inaccuracies in prediction are very low compared to other methods.