Machine Learning in Predicting Demand for Fast-Moving Consumer Goods: An Exploratory Research

Tarallo, Elcio; Akabane, Getulio Kazue; SHIMABUKURO, CAMILO ILZO; Mello, José André Villas Bôas; Amancio, Douglas

doi:10.1016/j.ifacol.2019.11.203

Cited by 51 publications

(22 citation statements)

References 20 publications

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“…Xu and Chan [31] used big data and machine learning methods to implement forecasting of medical device demand to establish a univariate device demand forecasting method, concluding that model prediction accuracy can be further improved by introducing big data into the forecasting model. Tarallo et al [32] presented an exploratory study of machine learning methods for forecasting demand for products with a short shelf life and concluded that demand forecasting for FMCG products can improve the supply chain inventory balance and increase corporate profits.…”

Section: Literature Reviewmentioning

confidence: 99%

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Chen

2022

Complexity

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Establishing a rapid-response mechanism to manage customer orders is very important in managing demand surges. In this study, combined with predicting order requests, we established a multiobjective optimization model to solve the warehouse space allocation problem. First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. The following three modes were tested to allocate warehouse storage space: random, ABC, and prediction-oriented. Finally, using actual order data, we conducted a comparative analysis of the three modes regarding their efficiency in goods selection. The method proposed by this study improved goods selection efficiency by a sizable margin (23.8%).

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Section: Literature Reviewmentioning

confidence: 99%

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Chen

2022

Complexity

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“…Many scholars have investigated the problem of managing the inventory of perishable products [69], especially considering the growing number of perishables in retailing that are disposed of due to spoilage, which is reported to be even around 15% [70]. According to [19], more accurate forecasts in the fresh food sector result in a reduction in both losses from products that reach their expiration dates and the costs of transportation and storage of refrigerated products. Food sales prediction is also extremely important in the case of products facing seasonal changes in demand, which may depend on many hidden contexts, not always easily recognised [13].…”

Section: A Premises For Food Demand Predictionmentioning

confidence: 99%

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Lutosławski

Hernes²,

Radomska

et al. 2021

IEEE Access

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Food demand prediction is a significant issue for both businesses processes improvement and sustainable development issues. The data science methods, including artificial intelligence methods, are often used for this purpose. The aim of this research is to develop the models for food demand prediction based on the Nonlinear Autoregressive Exogenous Neural Network. The research focuses on the processed food, such as bread or butter. Developed models' architectures differing in the number of hidden layers and the number of neurons in the hidden layers, as well as with different sizes of the delay-line, were tested for a given product. Results of the research show that depending on the type of product, prediction performance slightly differed. The results of the R 2 measure ranged from 96,2399 to 99,6477 depending on particular products. The proposed models can be used in a company's intelligent management system for rational control of inventories and food production. It can also lead to reducing food waste.INDEX TERMS food industry, sustainable development, neural networks, machine learning, demand forecasting.

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“…In the paper [15] a radial basis function (RBF) neural network and a designed genetic algorithm were successfully used for forecasting the sales of fresh milk. In the aspect of FMCG, the authors of [16] showed benefits of applying Machine Learning methods in creating demand forecasting models. The use of the Autoregressive Distributed Lag model was presented in the paper [17].…”

Section: Related Workmentioning

confidence: 99%

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Henzel¹,

Sikora²

2020

Annals of Computer Science and Information Systems

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In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.

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Machine Learning in Predicting Demand for Fast-Moving Consumer Goods: An Exploratory Research

Cited by 51 publications

References 20 publications

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

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