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Editorial

机译:社论

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摘要

Welcome to volume 9. Time passes so quickly and here we are in our ninth year. This is an interesting mixed feature issue for the start of the ninth volume because it encompasses novel and traditional methodologies, the perennial financial issues of cash flow, the resurgence of optimisation modelling, traditional contextual domain of banking and the surprising element of dealing with military supply chain (SC) measures. According to Sucky's literature research and conversations with apparel manufacturers' specialists, there is not any common analytic method for demand forecasting in apparel industry and to our knowledge, there is not adequate number of study in literature to forecast the demand with adaptive-network-based fuzzy inference system (ANFIS) for apparel manufacturers. The purpose of this paper is constructing an effective demand forecasting system for apparel manufacturers. The ANFIS is used forecasting the demand for apparel manufacturers. The results of the Sucky's study showed that an ANFIS-based demand forecasting system can help apparel manufacturers to forecast demand accurately, effectively and simply.
机译:欢迎来到第9卷。时间过得真快,我们已经进入了第九个年头。对于第九卷的开头,这是一个有趣的混合特征问题,因为它涵盖了新颖和传统的方法,现金流的常年财务问题,优化建模的复兴,传统的银行业背景域以及处理军事供应的令人惊讶的要素链(SC)措施。根据Sucky的文献研究以及与服装制造商专家的对话,服装行业的需求预测没有任何通用的分析方法,据我们所知,文献中没有足够的研究来基于自适应网络来预测需求服装制造商的模糊推理系统(ANFIS)。本文的目的是为服装制造商构建有效的需求预测系统。 ANFIS用于预测服装制造商的需求。 Sucky的研究结果表明,基于ANFIS的需求预测系统可以帮助服装制造商准确,有效和简单地预测需求。

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