| DC 欄位 |
值 |
語言 |
| DC.contributor | 工業管理研究所在職專班 | zh_TW |
| DC.creator | 鄭聿均 | zh_TW |
| DC.creator | Yu-Jiun Cheng | en_US |
| dc.date.accessioned | 2019-7-25T07:39:07Z | |
| dc.date.available | 2019-7-25T07:39:07Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=106456032 | |
| 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.description | National Central University | en_US |
| dc.description.abstract | 消費者選購筆記型電腦也會將其售後服務的維修品質與維修時效做為考量指標之一，國外筆記型電腦評測網站Laptop每年公布的品牌愛好排行榜，其中服務與保固在評測指標中就占了20%，而國內的品牌廠也陸續推出快速維修的服務，來強調自家品牌售後服務的高水準，而要達成快速維修其影響因素很多，其中維修中心的維修零組件備品準確度更是影響維修時效的關鍵因素之一。筆記型電腦是由上百種零件所組成，而維修零組件需求通常是隨機且是間歇性發生，且在眾多的零組件品項中，絕大多數品項需求是零，而不為零的品項其需求數量也有很大的變異，在預測隨機需求時很難找到其規律性，增加預測的困難度。
本研究將以某筆記型電腦製造商的維修資料庫做為個案資料，並將維修紀錄分為保固期外與保固期內，再依照零組件需求數量、特性分類，並從中挑選適合做為本研究預測標的的零組件；另收集工廠出貨數量、業務銷售數量與不良品送修量，應用迴歸分析找出與送修不良品之關聯性，且透過迴歸來預測筆記型電腦維修零組件備料需求，並與移動平均法、指數平滑法做比較，透過平均絕對百分比誤差作為主要衡量指標，平均絕對誤差與平均平方差為參考指標，藉此來評估預測模型的績效。
研究結果顯示三種預測方法中保固期內以迴歸分析的預測準度結果最佳，其次是移動平均法，指數平滑法的預測準度則不甚理想；保固期外則是應用移動平均法可以獲得較佳的預測結果，迴歸預測法與指數平滑法結果皆不甚理想。
| zh_TW |
| dc.description.abstract | After-sales service is the key factor that influencing consumer’s notebook purchase decisions. According to the annual list of notebook brand ranking by Laptop Mag, after-sales service and warranty accounted for 20% in the evaluation. Most of the notebook brands and manufacturers launched fast repair service to improve after-sales service quality and customer satisfaction. And for the fast repair service requirement, the accuracy of the preparation of spare parts will be the most important key factor.
The results of this study is to use Regression Analysis to construct a main reference index. This study analyzed the notebook repair database of a notebook manufacturer as the case study, and select the spare parts suitable for the prediction of this research by demand and feature of the spare parts. And also find the shipment quantity, and repair quantity data to finish the analysis.
The results of this study shows that for the notebooks within warranty, the prediction accuracy of Regression Analysis is better than Moving Average Period or Exponential Smoothing. But for the notebooks which already out of warranty, the prediction accuracy of Moving Average Period is better than Regression Analysis and Exponential Smoothing. And the management can rivise the demand for spare parts according to the results of this study.
| en_US |
| DC.subject | 筆記型電腦 | zh_TW |
| DC.subject | 維修備料 | zh_TW |
| DC.subject | 需求預測 | zh_TW |
| DC.subject | 迴歸分析 | zh_TW |
| DC.subject | Notebook | en_US |
| DC.subject | Spare parts | en_US |
| DC.subject | Demand forecasting | en_US |
| DC.subject | Regression Analysis | en_US |
| DC.title | 運用迴歸分析筆記型電腦維修零件需求 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |