# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 2 Literature Review  
In their research, [2] addressed the challenging task of anticipating demand in assembly industries. The accurate prediction of component demand is crucial, particularly when faced with uncertain end-customer demand. To tackle this issue, they suggested a comprehensive approach that integrates the autoregressive moving average model with exogenous inputs (ARIMAX) and machine learning (ML) models. They also conducted a comparison between these advanced methods and traditional univariate benchmarks. The multivariate approach offers several advantages, including a comprehensive understanding of demand shifts using multiple leading indicators, resulting in improved forecasting accuracy of 17.45% NMAE and decreased inventory-related costs of 216.32 \(\upxi\) throughout the component's life cycle. The use of statistical and ML-based models also allows for flexibility in adapting to different stages of the component's life cycle. Ref. [5] addressed the issue of demand forecasting in supply chain management in their study, where they presented an advanced model that combines ML techniques, time series analysis, and deep learning methodologies. Using data from the SOK market in Turkey, a rapidly expanding retail chain, they showcased the efficacy of this model in optimizing stocks, cutting costs, and enhancing sales and customer loyalty. The model attains an average of 24.7% of MAPE. The distinctive feature of their study lies in their combination of deep learning methodology and innovative decision integration techniques. Ref. [13] introduced a technique for demand forecasting in highly competitive business environments. Their method, which utilizes a multi-layer long short-term memory (LSTM) network, excels in its ability to dynamically select models. This is achieved through a grid search approach that considers different combinations of LSTM hyperparameters. The method's main strength lies in its capacity to effectively capture nonlinear patterns in time series data, outperforming established forecasting techniques such as ARIMA, ETS, ANN, KNN, RNN, SVM, and single-layer LSTM. Statistical tests confirm the significant superiority of the proposed approach, particularly when assessing performance measures like RMSE and SMAPE, attaining values of 2.596 and 0.1023, respectively. When comparing computational intelligence techniques like SVM, ANN, and LSTM with traditional statistical methods (ETS and ARIMA), it becomes evident that the former outperforms the latter. Ref. [16] introduced the application of distributed random forest (DRF) and gradient boosting machine (GBM) learning methods for forecasting backorder situations in the supply chain. They utilized a versatile approach that considers different prediction attributes and the diverse attributes of real-time data influenced by errors from both machines and humans. The approach attains classification accuracy of 79% and 85% for training, which demonstrates its efficiency. This approach gives decision managers the ability to adjust to different scenarios. The authors justified their choice of DRF and GBM models by their ability to provide superior explanations. Ref. [11] introduced the UNISON data-driven framework for forecasting intermittent demand for electronic components in the supply chain. They acknowledged the complexities of this task, which are exacerbated by demand fluctuations, short product life cycles, and long production and technology migration timelines. The framework aims to minimize demand series fluctuations while considering the limitations of available information and the need for cross-product modeling variability. The results demonstrated its effectiveness in improving forecast accuracy to 78.51% for various product categories, thereby enabling flexible decision-making and enhancing supply chain resilience for smart production. Ref. [24] examined customer demand prediction for remanufactured products and market development.