Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model

Vhatkar, Sangeeta; Dias, J. M.

doi:10.1016/j.procs.2016.03.031

Cited by 28 publications

(11 citation statements)

References 3 publications

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“…Qualitative sales forecasting methods mainly include the subjective probability method, expert judgment opinion method, Delphi method, mutual influence method, scenario prediction method, etc [ 2 – 4 ]. Quantitative sales forecasting methods mainly include the time-series method (e.g., autoregressive series analysis [ 5 ], ARIMA model [ 6 ]), machine learning method [ 7 ] (e.g., artificial neural network [ 8 , 9 ], extreme learning machine [ 10 ], support vector machine (SVR) [ 11 ], and ensemble algorithms), and deep learning method.…”

Section: Related Work Of Demand Forecastingmentioning

confidence: 99%

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Li¹,

Zhang²

2022

Computational Intelligence and Neuroscience

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Based on real sales data, this article constructed LGBM and LSTM sales prediction models to compare and verify the performance of the proposed models. In this article, we forecast the product sales of stores in the future T + 3 days and use MAPE as the evaluation index. The experiment shows that the product sales prediction model based on the convolutional LSTM (ConvLSTM) network has better prediction accuracy. From a store point of view, ConvLSTM prediction model MAPE was 0.42 lower than the long short-term memory (LSTM) network and 0.68 lower than LGBM. From the perspective of commodity categories, different commodity categories are suitable for different forecasting methods. Some categories are suitable for regression forecasting, while others are suitable for time-series forecasting. Among the categories suitable for time-series forecasting, the ConvLSTM model performs the best.

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Section: Related Work Of Demand Forecastingmentioning

confidence: 99%

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Li¹,

Zhang²

2022

Computational Intelligence and Neuroscience

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“…As the literature reveals, artificial neural networks (ANN) are widely applied for demand forecasting [82][83][84][85]. To improve the accuracy of ANN-based demand predictions, Liu et al [86] proposed a combination of a grey model and a stacked auto encoder applied to a case study of predicting demand in a Brazilian logistics company subject to transportation disruption [87].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

2020

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Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

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“…Vhatkar and Dias [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. Mo et al [32] proposed an optimized BPN method to improve supermarket daily average rice sales prediction accuracy.…”

Section: Artificial Neural Network Predictionmentioning

confidence: 99%

Application of Neural Networks to Explore Manufacturing Sales Prediction

2019

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Manufacturing sales prediction is an important measure of national economic development trends. The plastic injection molding machine industry has its own independent R and D energy and mass production technology, with all products sold globally through international brands. However, most previous injection molding machine studies have focused on R and D, production processes, and maintenance, with little consideration of sales activity. With the development and transformation of Industry 4.0 and the impact of the global economy, Taiwan’s injection molding machine industry growth rate has gradually flattened or even declined, with company sales and profits falling below expectations. Therefore, this study collected key indicators for Taiwan’s export economy from 2008 to 2017 to help understand the impact of economic indicators on injection molding sales. We collected 35 indicators, including net entry rate of employees into manufacturing industries, trend indices, manufacturing industry sales volume indices, and customs export values. We used correlation analysis to select variables affecting plastic injection machine sales and artificial neural networks (ANN) were applied to predict injection molding machine sales at each level. Prediction results were verified against the correlation indicators, and seven key external economic factors were identified to predict accurate changes in company annual sales prediction, which will be helpful for effective resource and risk management.

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Oral-Care Goods Sales Forecasting Using Artificial Neural Network Model

Cited by 28 publications

References 3 publications

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Short-Term Demand Forecast of E-Commerce Platform Based on ConvLSTM Network

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

Application of Neural Networks to Explore Manufacturing Sales Prediction

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