探索在线课程在 COVID 中的作用--19 供应链领域的危机管理--使用模糊认知图(FCM)模型进行预测

Dimitrios K. Nasiopoulos, Dimitrios A. Arvanitidis, Dimitrios M. Mastrakoulis, N. Kanellos, Thomas Fotiadis, Dimitrios E. Koulouriotis
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引用次数: 0

摘要

近年来,全球化愈演愈烈,需要准确的预测。传统的供应链已演变成跨国网络,并随着时间的推移不断扩大,变得更加脆弱。这些危险有可能扰乱商品流通或若干计划行动。因此,提高抵御威胁组织生存的各类风险的能力至关重要。确定一个组织所面临的风险大小的方法之一,就是衡量它在购买公众中的受欢迎程度。虽然风险不可能消除,但有效的预测和供应链风险管理可以帮助企业识别、评估和降低风险。因此,良好的供应链风险管理,包括预测,对每家公司都至关重要。为了衡量企业的受欢迎程度,有一些离散值(跳出率、全球排名、有机流量、非品牌流量、品牌流量)被称为 KPI。下面是影响这些数值的一些假设,以及这些数值之间相互作用的模型。研究结果表明,对于一个组织来说,提高其知名度、加强在股东社区的宣传以及能够预测其未来需求是多么重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Role of Online Courses in COVID-19 Crisis Management in the Supply Chain Sector—Forecasting Using Fuzzy Cognitive Map (FCM) Models
Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements.
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CiteScore
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