Sales Prediction through Neural Networks for a Small Dataset

Croda, Rosa María Cantón; Gibaja-Romero, Damián-Emilio; Morales, Santiago

doi:10.9781/ijimai.2018.04.003

Cited by 16 publications

(5 citation statements)

References 21 publications

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“…Ahangar et al (2010) establish the superior performance of ANNs compared to OLS in predicting the stock price of listed companies. Croda et al (2019) establish that ANNs have a very high predictive accuracy compared to traditional statistical techniques in sales forecasting, even when presented with a small dataset. Accordingly, alternative methods aiming to improve OLS, such as the LASSO technique as per Equation ( 3), have been established (Casella et al, 2017;Tibshirani, 2011).…”

Section: Existing Comparative F Indings On the Three Toolsmentioning

confidence: 88%

“…Droomer and Bekker (2020), utilising a large database of US online grocery stores, find that ANNs outperform other modern and complex algorithms like XGBoost in predicting customers' purchasing behaviour. Croda et al (2019), using a small Mexican chemicals wholesaler dataset, establish that ANNs produce highly accurate sales predictions. Wang et al (2019) demonstrate the high accuracy of ANNs in predicting the annual sales of Taiwanese manufacturing enterprises.…”

Section: Existing Comparative F Indings On the Three Toolsmentioning

confidence: 95%

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Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

Zhou

Gumbo

2021

Afr. j. inf. commun. (Online)

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The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.

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Section: Existing Comparative F Indings On the Three Toolsmentioning

confidence: 88%

Section: Existing Comparative F Indings On the Three Toolsmentioning

confidence: 95%

Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

Zhou

Gumbo

2021

Afr. j. inf. commun. (Online)

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“…For example, we implemented standard forecast methods which, based on the weekly/monthly periods, provided different levels of accuracy. In this case, more complex forecast methods such as those based on Artificial Neural Networks (ANNs) must be considered [22]. Future work is focused on extending the quantitative tools portfolio to address these limitations.…”

Section: Discussionmentioning

confidence: 99%

Forecast Methods and Periodic Review Inventory Model for Supply Planning to Reduce Food Waste

Ceballos-Palomares

Nava-Jiménez

Caballero‐Morales

et al. 2021

AJEBA

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Food waste is an important economic and resource problem in all countries around the world. Particularly, the restaurant sector highly contributes to food waste and limited efforts or studies have been performed to overcome this problem. In this context, the present study addresses an alternative to improve the supply planning for perishable products in the restaurant sector through the application of specific forecasting methods and a stochastic inventory control model. For this purpose, a real enterprise within this economic sector was considered. Our findings support that monthly forecasts can be more appropriate for accurate demand estimation and supply planning of perishable products, which is important to reduce unnecessary products. Also, the periodic review inventory control model can lead to a more appropriate supply scheme to reduce the waste of surplus food. These findings and the proposed techniques can be used for other economic entities to reduce product waste due to poor supply planning.

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“…It has to be from a selection of several models. In addition, it is a challenge to create the most perfect forecasting model with only limited data available when using data mining (Cantón Croda, Gibaja, & Caballero, 2018).…”

Section: (Neisyafitri and Ongkunaruk)mentioning

confidence: 99%

The Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia

Neisyafitri

Ongkunaruk

2022

J AGRIB RURAL DEV RES

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The Indonesian beef consumption increases sharply during Ramadan and made a difference between supply and demand. The research aimed to study the demand pattern of burger patties and determine a suitable forecasting method compared between quantitative and intervention forecasting methods. The actual demand was intervened by experts based on reasons such as supply shortage, holidays, promotion, and government projects. The daily sales of burger patties were collected for a year. Then, the data were divided into training and testing data. Later, time-series forecasting was performed by software. Then, the best forecasting method for daily data was selected between Individual forecasting and Top-Down forecasting. Similarly, for weekly data, the best forecasting method was compared between aggregate forecasting and Bottom-Up forecasting. Then, repeat the process for the intervened sales data. The result revealed that the mean absolute percentage error was improved after intervention by about 3.64%-58.83%. The combination of quantitative and qualitative approaches improved forecast accuracy. In addition, the aggregate level or weekly sales forecast had higher forecast accuracy than the disaggregated level. The Bottom-Up forecast performs better than the aggregate forecast. Hence, we recommended the company plans based on weekly data and implement Every Low Price to reduce the demand fluctuation.

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Sales Prediction through Neural Networks for a Small Dataset

Cited by 16 publications

References 21 publications

Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms

Forecast Methods and Periodic Review Inventory Model for Supply Planning to Reduce Food Waste

The Use of Intervention Approach in Individual and Aggregate Forecasting Methods for Burger Patties: A Case in Indonesia

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