Forecasting Preliminary Order Cost to Increase Order Management Performance

Tüzin Akçinar Günsari, A. Kaya, Y. Ekinci
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Abstract

In this study, the cost estimation to be used in the optimization of proposed order price offer is made by artificial neural network (ANN) method. A case study is performed by the real data of a company, and the forecast results of the traditional arithmetic model used by the company and the proposed ANN based method are compared and it is seen that the proposed method results outperform the other. The biggest contribution of this study to companies is to increase the company’s order management performance by helping the company to make more accurate pricing due to more accurate cost estimation. Moreover, to the best of our knowledge, this is the first study on forecasting preliminary order cost in the apparel industry and fills an important gap in the literature.
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预测初步订单成本以提高订单管理绩效
在本研究中,采用人工神经网络(ANN)方法对拟用订单报价的优化进行了成本估计。以某公司的实际数据为例,对该公司使用的传统算法模型与所提出的基于人工神经网络的方法的预测结果进行了比较,结果表明,所提出的方法优于其他方法。这项研究对公司最大的贡献是通过帮助公司因更准确的成本估计而做出更准确的定价,从而提高公司的订单管理绩效。此外,据我们所知,这是服装行业首次对初步订单成本进行预测的研究,填补了文献中的一个重要空白。
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来源期刊
CiteScore
2.30
自引率
27.30%
发文量
35
期刊介绍: The main objective of the International Journal of Business Analytics (IJBAN) is to advance the next frontier of decision sciences and provide an international forum for practitioners and researchers in business and governmental organizations—as well as information technology professionals, software developers, and vendors—to exchange, share, and present useful and innovative ideas and work. The journal encourages exploration of different models, methods, processes, and principles in profitable and actionable manners.
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