完整后设资料纪录
DC 栏位语言
dc.contributor.author李宗翰zh_TW
dc.contributor.author姜齐zh_TW
dc.contributor.authorLI , Tsung-Hanen_US
dc.date.accessioned2018-01-24T07:36:17Z-
dc.date.available2018-01-24T07:36:17Z-
dc.date.issued2016en_US
dc.identifier.urihttp://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070363126en_US
dc.identifier.urihttp://hdl.handle.net/11536/138675-
dc.description.abstract台湾为一出口导向的国家,其中出口占国家GDP约70%,电子制造业又占了出口的30%。需求预测在各行各业都是少不了,从日常生活的蔬菜需求到整体国家经济预测,从企业财务规划、库存管理、生产计划、到客户管理等各层面,需求预测都是一个最关键也是最基本的数字。这个数字是如何产生,大家都只有一个希望,那就是愈准确愈好。不准确的需求预测会带来什么样的结果呢?当预测大于实际需求会造成过多的库存、库存成本资金积压、产生转运成本增高、报废库存增高、降低获益率。当预测小于实际需求,会造成生产排程效率降低、较高的产品成本、错失销售时机、客户满意度降低。因此如何提高需求预测的正确性,成为企业经营中一个重要的课题。
要如何降低需求预测的不确定性呢?对于具备历史资料的产品来说,利用过去的资料进行预测是最简便的方法,然而因为LED处于产业上游位置,所以需要用一些简单的转换关系,就可以产生客观的预测结果。本研究应用不同的预测模式,线性回归、非线性回归、ARIMA以及SARIMA来探讨智慧型手机LED封装体未来需求量。利用2009年1月至2014年12月智慧型手机出货量为样本内资料,藉由不同的预测模型来进行样本内的预测,并将此需求预测模型所产生之2015年预测结果与该年实际智慧型手机进行比较,最后再进行LED需求转换。这种做法,可以减少长鞭效应的产生。结果显示,本研究所使用之非线性回归预测模型MAPE值最佳,得到最佳化的LED需求预测。整体而言也会比使用线性回归、ARIMA之需求预测绩效来得好,增进约7~9倍。
要百分之百准确的预估未来几乎是不可能的事,但我们仍可以藉由增加不同参数变化,来改变需求预测模型。本论文希望透过相关之需求预测模型,改变LED需求预测的方式,做为产业界在从事需求规划时的一个参考,进而能提升LED产业之需求
测品质,增进供应链管理之效率。
zh_TW
dc.description.abstractTaiwan is an export-oriented country, with exports accounting for about 70% of the GDP. The electronics manufacturing industry has accounted for 30 percent of exports. Forecast has only one question, how to be more accurate and better. If inaccurate forecasting what will bring results? When the forecast is greater than actual demand will result in excess inventory, inventory backlog of capital costs, resulting in higher transport costs, increased inventory scrap and reduce the benefit rate. When less than the actual demand forecasting, production scheduling will cause reduced efficiency, higher product costs and missed sales opportunity, customer satisfaction decreases. So how to improve demand forecasting accuracy? This has become an important problem.
How to reduce the uncertainty of demand forecasting? In this study, different forecasting models, linear regression, nonlinear regression, ARIMA and SARIMA to discuss the future demand for smartphones LED package. Use from January 2009 to December 2014 smartphone shipments in the sample data, with different forecasting models to predict within the sample, and this demand forecasting model arising in 2015 and predictions. This approach can reduce the production of bullwhip effect. The results show that the nonlinear regression model has the best MAPE value than other demand forecast model. Overall, the ratio will be using linear regression it can increase of about 7 to 9 times than linear regression or ARIMA.
To be 100% accurate estimate of the future is almost impossible, but we can still increase by different parameters, to change the demand forecasting model. The paper hoped that through the relevant demand forecasting model, change the LED demand in a predictable manner which in turn can enhance the needs of the LED industry measuring quality, enhance the efficiency of supply chain management.
en_US
dc.language.isozh_TWen_US
dc.subject发光二极体zh_TW
dc.subject需求预测zh_TW
dc.subject时间序列zh_TW
dc.subjectLight-emitting diodesen_US
dc.subjectForecastingen_US
dc.subjectTime seriesen_US
dc.titleLED 封装体需求预测-以智慧型手机产业为例-zh_TW
dc.titleForecasting Light-Emitting Diode Package For the Smart Phone Industryen_US
dc.typeThesisen_US
dc.contributor.department管理学院管理科学学程zh_TW
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