Predictable inventory management within dairy supply chain operations

Rosario Huerta-Soto, Edwin Ramirez-Asis, John Tarazona-Jiménez, Laura Nivin-Vargas, Roger Norabuena-Figueroa, Magna Guzman-Avalos, Carla Reyes-Reyes
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引用次数: 1

Abstract

PurposeWith the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC managementDesign/methodology/approachPreferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.FindingsAs a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.Practical implicationsMany sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.Originality/valueThis paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.
乳品供应链操作中可预测的库存管理
随着当前乳制品行业的现代化浪潮,全球乳制品市场发生了重大变化。机器学习(ML)充分利用库存规划,最大限度地提高了商品从一个地点到另一个地点的流动。通过促进众多组件之间的废物减少和质量改进,它减少了运营费用。本研究的重点是分析现有的乳制品供应链优化策略,并寻找进一步改善乳制品供应链的途径。本研究旨在提高DSC管理的运营卓越性和优化策略的持续改进设计/方法/方法系统评价的首选报告项目和系统评价的元分析(PRISMA)标准是本研究方法的灵感来源。在系统评价和荟萃分析中报告证据的公认方案是PRISMA。健康科学协会和出版物支持这些标准。在这项研究中,作者依靠描述性统计。这一现代化举措的结果是,乳制品行业已经能够通过使用尖端的优化策略来提高运营效率。历史上,DSC研究人员一直依赖于数学建模工具,但最近作者开始使用人工智能(AI)和基于ml的方法。虽然基于数学建模的方法仍然是最常用的,但基于AI/ ml的方法正在迅速成为首选方法。在运输阶段,云计算、共享数据库和软件实际上将数据传输给分销商、物流公司和零售商。该公司已经开发了全面的部署,分销和存储空间选择方法以及供应链路线图。许多种类的环境退化,包括导致气候变化的温室气体的大量排放,都是由乳制品行业造成的。这个行业不仅危害环境,而且给动物造成了很大的痛苦。规模较小的奶场很难以大型奶场设定的低价生产牛奶,而大型奶场往往得到补贴和其他财政激励措施的支持。原创性/价值本文通过提供优化方法的入门,概述了农民和分销商如何提高乳制品加工设施的效率,从而解决了乳制品行业的需求。大多数研究只是简单地提到供应链优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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