Forecasting Preliminary Order Cost to Increase Order Management Performance

Author:

Günsari Tüzin Akçinar1,Kaya Aysegül1,Ekinci Yeliz2ORCID

Affiliation:

1. TYH Textile, Turkey

2. İstanbul Bilgi University, Turkey

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.

Publisher

IGI Global

Subject

Strategy and Management,Business and International Management

Reference35 articles.

1. Acar Ugurlu, Y., & Demir Pali, C. (2019). Preparation and Implementation of a Warehouse Management System: A Case Study. In International Conference on Recent Social Studies and Research (pp. 481-489). International Association of Social Science Research (IASSR).

2. Coordination mechanism, risk sharing, and risk aversion in a five-level textile supply chain under demand and supply uncertainty

3. Impacts of optimization in apparel supply chain focusing on ANN and Genetic Algorithm.;S.Ahmad;Proceedings of the International Conference on Industrial Engineering and Operations Management,2020

4. A neural network application to subcontractor rating in construction firms

5. Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3