博碩士論文 104456009 完整後設資料紀錄

DC 欄位 語言
DC.contributor工業管理研究所在職專班zh_TW
DC.creator郭永勝zh_TW
DC.creatorYung-Sheng Kuoen_US
dc.date.accessioned2018-6-22T07:39:07Z
dc.date.available2018-6-22T07:39:07Z
dc.date.issued2018
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=104456009
dc.contributor.department工業管理研究所在職專班zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract本研究利用灰色預測模型對個案公司2016年1月至2017年3月槍嘴產品銷售資料進行建模與模擬預測,並將預測結果進行殘差的評析,以驗證模型之預測效能。研究結果顯示,GM(1,1)滾動模型對產品需求預測的效果較佳,尤以資料數目4筆的GM(1,1)滾動模型預測與驗證效果最佳,預測平均準確度92.83%,驗證準確度也都高於90.0%,並由表4-4的分析與比較結果得知,GM(1,1)滾動模型預測2016年1月至2017年6月之銷售資料,其平均預測精度為93.10%優於個案公司以經驗值預估的精確度77.82%。因此,GM(1,1)滾動模型可作為本研究個案公司產品需求預測之參考。zh_TW
dc.description.abstractThis study uses the grey prediction model to model and simulate the sales data of the products from January 2016 to March 2017. The prediction results are evaluated by residuals to verify the prediction performance of the model. The research results show that the GM(1,1) rolling model has a better effect on product demand forecasting, especially the GM(1,1) rolling model with 4 data points has the best prediction and verification effectiveness, and the average prediction accuracy is 92.83%. The verification accuracy is also higher than 90.0%, and from the analysis and comparison results in Table 4-4, the GM(1,1) rolling model predicts sales data from January 2016 to June 2017, and its average forecast accuracy, 93.10% ,was better than that of the case company with an accuracy of 77.82% by using the experience method. Therefore, the GM(1,1) rolling model can be used as a reference for the product demand forecast of the P company in this study.en_US
DC.subject灰色預測zh_TW
DC.subjectGM(1,1)滾動模型zh_TW
DC.subject脫蠟鑄造zh_TW
DC.subjectGrey Predictionen_US
DC.subjectGM(1,1) Rolling Modelen_US
DC.subjectInvestment Castingen_US
DC.title灰色預測應用於脫蠟鑄造產品需求預測之研究-以P公司為例zh_TW
dc.language.isozh-TWzh-TW
DC.titleGrey Prediction Applied to the Demand Forecast of Investment Casting Products - A Case Study of P companyen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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