An Exploratory Study for Neural Network Forecasting of Retail Sales Trends Using Industry and National Economic Indicators
نویسنده
چکیده
This paper proposes the use of artificial neural networks (feed forward multi-layer perceptron and Elman recurrent networks) in forecasting sales trends at retail by analyzing industry and manufacturer specific metrics along with national economic indicators. Relevant data drivers were gathered based on consultations with the manufacturer as well as experts in the fields of economics and finance. Simulations were run using the proposed system to determine the amount of product sold at retail for the end of the present week, as well as how much product will sell at retail three months from today: three months is the required lead time for the manufacturer to fabricate the products being examined. The initial results of this study indicate that both feed-forward neural networks and Elman recurrent neural networks show potential in being able to forecast sales trends with reasonable accuracy.
منابع مشابه
Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique
Artificial neural network is considered one of the most efficient methods in processing huge data sets that can be analyzed computationally to reveal patterns, trends, prediction, forecasting etc. It has a great prospective in engineering as well as in medical applications. The present work employs artificial neural network-based curve fitting techniques in prediction and forecasting of the Cov...
متن کاملConstructing a Sales Forecasting Model by Integrating GRA and ELM: A Case Study for Retail Industry
Due to the strong competition and economic hardship, sales forecasting is a challenging problem as the demand fluctuation is influenced by many factors. A good forecasting model leads to improve the customers’ satisfaction, reduce destruction of fresh food, increase sales revenue and make production plan efficiently. In this study, the GELM forecasting model integrates Grey Relation Analysis (G...
متن کاملPredicting Consumer Retail Sales Using Neural Networks
Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effectiveness of data preprocessing such as detrendi...
متن کاملUsing Social and Economic Indicators for Modeling, Sensitivity Analysis and Forecasting the Gasoline Demand in the Transportation Sector: An ANN Approach in case study for Tehran metropolis
Compared to the conventional methods, Artificial Neural Networks (ANN) are considered to be one of the reliable tools for modeling of complex phenomena such as demand. Aim of this study is to provide a model for gasoline demand in transportation section of Tehran metropolis through multilayered perceptron neural network and using the presented model in analyzing the sensitivity of the model to ...
متن کاملComparison of Neural Network-Based Forecasting Methods Using Multi-Criteria Decision-Making Tools
Because of today's strong competition, most manufacturing organizations continually try to increase their profits and reduce their costs. Accurate sales forecasting is certainly an inexpensive way to meet these goals because it leads to improved customer service, reduced lost sales and product returns, and more efficient production planning (Doganis et al., 2006). Forecasting future demand is c...
متن کامل