Blockchain technology and mitigating bullwhip effect in supply chains with uncertain markets: a horizontal layer product distribution strategy

Aidin Delgoshaei, Mohd Khairol Anuar Ariffin
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Abstract

Product distribution in supply chain management has been hotly debated during the last decade. However, during COVID-19, many supply chains suffered from sudden changes in local market demands. Such changes cause a bullwhip effect throughout a supply chain, making them unable to respond rapidly. This research develops a new model for distributing products in the food chain using real urban and geographical data of blockchain technology. The aim is to re-adjust the product distribution plans by using a horizontal layer product distribution readjustment strategy while local markets confront sudden market changes. To address the problem, a heuristic was proposed and coded by Python based on the largest density-distance rule. Then, to evaluate the performance of the proposed method, the schedules are assessed with some metrics gathered in the literature. For this purpose, a Full Factorial design of experiments is generated by Python. Moreover, the outcomes are compared with those gained from short-traveling time and greedy loading-based heuristics. The results showed that using the horizontal layer product distribution readjustment strategy for modifying the initial schedules could prevent lost sales in all studied cases. Besides, by responding to sudden market demand changes rapidly, which subsequently causes preventing lost sales, more profits were gained in 58.3% of the studied cases. In addition, in 61.11% of studied cases, the proposed method was faster than other studied heuristics in terms of computational time.
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区块链技术与减轻不确定市场供应链中的牛鞭效应:水平层产品分销战略
过去十年间,供应链管理中的产品分配问题一直备受争议。然而,在 COVID-19 期间,许多供应链都受到了当地市场需求突然变化的影响。这种变化会对整个供应链产生牛鞭效应,使其无法做出快速反应。本研究利用区块链技术的真实城市和地理数据,开发了一种新的食品链产品分配模式。其目的是在当地市场面临突如其来的市场变化时,通过使用水平层产品分销再调整战略来重新调整产品分销计划。针对这一问题,我们提出了一种基于最大密度-距离规则的启发式,并用 Python 进行了编码。然后,为了评估所提出方法的性能,利用文献中收集的一些指标对计划进行了评估。为此,Python 生成了一个全因子实验设计。此外,实验结果还与短程时间和基于贪婪负载的启发式方法进行了比较。结果表明,在所有研究案例中,使用水平层产品分布重新调整策略来修改初始计划可以避免销售损失。此外,在 58.3% 的研究案例中,通过快速应对市场需求的突然变化,从而避免了销售损失,获得了更多利润。此外,在 61.11% 的研究案例中,就计算时间而言,建议的方法比其他研究的启发式方法更快。
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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