基于机器学习方法的食品行业需求预测模型

IF 0.7 Q3 COMPUTER SCIENCE, THEORY & METHODS
Nouran Nassibi, Heba A. Fasihuddin, L. Hsairi
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引用次数: 0

摘要

——持续的全球经济不稳定和不确定性给销售预测带来了困难。因此,许多部门和决策者正面临着新的、紧迫的挑战。在供应链管理中,食品行业是一个关键部门,其中食品产品的销售运动和需求预测更难以预测。准确的销售预测有助于最大限度地减少各个商店的库存和过期商品,从而减少这些过期产品的潜在损失。为了帮助食品公司适应快速变化并更有效地管理其供应链,有必要利用机器学习(ML)方法,因为ML能够有效地处理和评估大量数据。本研究比较了沙特阿拉伯最大的分销公司之一的糖果产品的两种预测模型,以提高该公司使用机器学习算法预测其产品需求的能力。为了实现这一目标,使用了支持向量机(SVM)和长短期记忆(LSTM)算法。此外,还对模型在季度时间序列预测中的表现进行了评价。当与需求预测模型进行比较时,两种算法都提供了强有力的结果,但总体而言,LSTM优于SVM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches
—Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML’s ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company’s ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
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来源期刊
CiteScore
2.30
自引率
22.20%
发文量
519
期刊介绍: IJACSA is a scholarly computer science journal representing the best in research. Its mission is to provide an outlet for quality research to be publicised and published to a global audience. The journal aims to publish papers selected through rigorous double-blind peer review to ensure originality, timeliness, relevance, and readability. In sync with the Journal''s vision "to be a respected publication that publishes peer reviewed research articles, as well as review and survey papers contributed by International community of Authors", we have drawn reviewers and editors from Institutions and Universities across the globe. A double blind peer review process is conducted to ensure that we retain high standards. At IJACSA, we stand strong because we know that global challenges make way for new innovations, new ways and new talent. International Journal of Advanced Computer Science and Applications publishes carefully refereed research, review and survey papers which offer a significant contribution to the computer science literature, and which are of interest to a wide audience. Coverage extends to all main-stream branches of computer science and related applications
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