# Application of Neural Networks to Explore Manufacturing Sales Prediction
## 2 Literature Review
### Artificial Neural Network Prediction  
Artificial neural networks were developed in 1950s, and the original neuron-like perceptron model was proposed in 1969 [25]. However, the original theory was somewhat simple and was not taken seriously until [PERSON] defined the modern ANN form in 1982. Many diverse ANNs have been subsequently proposed incorporating new architectures and theories, and the enormous increase of computing power has allowed modern and powerful ANNs to be widely used, with many practical ANN applications across various industries.  
Various market sales prediction models have been proposed based on back-propagation ANNs, incorporating improved methods to compensate for known back-propagation inadequacies, particularly for problems with agnostic rules. Although the PCB industry has significant impact on the Taiwanese economy, serious inventory dumping and material shortages remained. Therefore, [PERSON] et al. [26] established an ANN-based prediction model to solve these inventory and material shortage problems. [PERSON] et al. [27] developed an optimized ANN prediction system for clothing sales prediction using two years of clothing sales data. They compared model performance with basic fully connected ANN and traditional predictive models, and showed that the proposed algorithm was superior to traditional prediction methods for products with low demand uncertainty and weak seasonal trends. Thus, the method was suitable for fashion retailers to produce short-term retail predictions.  
[PERSON] and [PERSON] [28] used back-propagation networks (BNPs) to predict future food sales for large wholesalers in Victoria. They showed that the proposed BPN method provided superior prediction results compared with traditional methods for trending and market analysis, i.e., simple linear regression models. Hence, the BPN model could provide a useful tool for sales prediction. [PERSON] et al. [29] used an ANN trained by BPN to predict future time series values, including weekly demand for supermarket items, considering prices, advertising activity, and holiday impacts.  
Convenience stores are an integral part of the retail industry, distributing goods to consumers from suppliers. Some convenience stores were out of stock of customer desired products, where others had excess of the same products. However, customer satisfaction was always significantly impacted where desired goods were out of stock, regardless of how good the service was. Thus, controlling ordering and inventory has become a very important issue for store convenience store management. Therefore, [PERSON] et al. [30] developed an ANN based system to control orders and manage the inventory in convenience stores based on business circle and sales prediction operational characteristics. The proposed system improved order and discard rates to better ensure ordering the right items in the correct quantities.  
[PERSON] and [PERSON] [31] considered several sales prediction algorithms and developed an inverse transfer-like ANN model to predict oral care product sales and error rates for several different products. [PERSON] et al. [32] proposed an optimized BPN method to improve supermarket daily average rice sales prediction accuracy. [PERSON] [5] proposed an electricity price forecasting. This review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or may be encountered. [PERSON] et al. [33] used an ANN to analyze electricity spot-prices of the Italian Power Exchange.