重塑 3PL 运营:减轻和管理损害参数的机器学习方法

Yunus Emre Yeti̇ş, Safiye Turgay, Bi̇lal Erdemi̇r
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摘要

在第三方物流(3PL)环境中,减少损坏参数、提高运营效率和降低成本非常重要。本研究旨在通过机器学习分析损坏控制所涉及的参数,制定重塑第三方物流运营的策略。随着时间的推移,人们对物流行业的潜力有了更深入的了解。物流业中的产品损坏,尤其是在运输和存储过程中的损坏,不仅会造成经济损失,还会影响客户的生产率和运营效率。利用人工智能技术,可以确定消费者的期望,预测损坏损失,并通过应用机器学习算法制定创新战略。同时,随着人工智能技术的出现,无人驾驶车辆、用于仓储和货架的机器人以及系统内大数据的便捷使用等方案,都能最大限度地减少物流行业的失误。人工智能在物流业的应用提高了企业的效率。本研究包括利用机器学习方法对物流服务行业的误差参数进行估算。应用中使用了一家第三方物流公司过去 5 年的真实数据。对于 3PL 公司的成功来说,仓储和无损产品交付非常重要。受损产品越少,公司的价值就越高。本研究中考察的公司保留了其损坏数据,并希望对其进行分析,以便采取相应的预防措施,走上更有利可图的道路。因此,研究的重点是错误和损坏数据。本研究说明了这类公司可能会出现哪些问题,以及第三方物流公司如何评估这些问题,以提高客户服务质量和成本效益。
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Reshaping 3PL Operations: Machine Learning Approaches to Mitigate and Manage Damage Parameters
In the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.
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