臺灣博碩士論文加值系統

1939年T.P. Wright首先發表學習曲線的理論與計算方式，他觀察到一個人重複做一件相同或相類似的工作，在經過一段時間後，會因熟練度的提昇而增進其工作效率，進而減少浪費、降低產品生產成本，以提昇產品的競爭能力。之後有關學習曲線研究的文獻陸續發表出來，且已成功的應用在傳統製造產業上。
通訊產業，有別於傳統製造業，是一種高技術高資本的行業，需要投注大量資金於昂貴的自動化設備，認為如此才能享有競爭優勢。但事實上並非如此單純，因為全世界的通訊網路製造廠商都可購買相同的設備，其生產力的大小乃決定於其訓練及學習效果。
對於此一現象，本文針對通訊產業的學習曲線及其效果做一衡量，以提供給相關廠商參考並發展廠商的自我學習曲線。由於良率具有學習效應的現象已經被證實，所以本文除了持續研究良率的學習現象之外，並基於統計分析工具的發達，提出多變數學習曲線模式，進而推導出良率的多變數學習曲線模式，並利用此學習曲線模式來修正生產日程量、人員績效評估、品質管制以及產品價格制訂等生產模式，並和已往只用單一變數的學習曲線模式相互比較，且探討兩者使用時機及其優缺點。。
實證結果顯示，有考慮學習效應的生產模式能夠適當地反映出生產過程的學習變化。在有考量學習效應的生產環境下，多變數的學習曲線模式會比單一變數學習曲線模式更能反映實際生產變動。利用這樣的研究結果，可以讓生管人員在規劃生產日程量時降低不必要的規劃量，在人員績效制訂上確實地反映出作業人員的學習變化；對於品管人員來說，能夠繪製出合理的品質管制圖；對財務人員而言，也能擬定出合理的產品價格、節省預算的浪費。
根據上述的這些優點，顯示本研究所探討的學習曲線生產模式於實際通訊產業生產管理應用之可行性與有效性。

T.P Wright, the first presented the theory and calculation of learning curve in 1939, found workers learn and improve by repeating the same or similar operation. This learning condition improves worker’s efficiency, reduces the waste, lowers the production cost and raises competition of enterprise. After that, the studies about learning curves presented continually and applied the conventional industry successfully.
Telecommunication industry, more capital and high technology industry differed from conventional industry, needs a great quantity of capital to invest in expensive automatic machinery in order to maintain the advantage of a good competition. The fact is not simple because the manufactures of telecommunication in the world can also buy the same equipment. The advantage of competition doesn’t decide on equipment but on the effect of worker’s training and learning.
For the order change issue, this paper develops a learning curve fitting telecommunication industry and provides manufactures the method how to expand itself learning curve. This paper not only continues to study defect rate, confirmed to have learning effect, but also presents multivariate learning curves to modify master production schedule, human performance, quality control and product price model. This paper will compare these results between multivariate learning curves and univariate ones.
The case study shows that production models considering the effect of learning could actually response the variance of learning process. Under the learning circumstances, the change of production considering multivariate learning curve is more sensitive than the change of univariate one. By means of the learning curve tools, managers can understand these changes and do proper methods to improve the learning rate to reach the plateauing earlier. Besides, it can reduce the unnecessary quantity in planning MPS, certainly response the learning process in human performance, draw a reasonable quality control graph. We also find that the product price model considering learning effect can appropriately reacts actual variance of good cost. It is also great beneficial for supervisors to draft firm’s budget, make strategy of good price and set up cost performance.
Thus, the application of learning curve on production models proposed in this paper is effective and efficient.

第一章 緒論1
1.1研究背景與動機1
1.2研究目的3
1.3研究範圍與限制4
1.4研究方法4
第二章 文獻探討7
2.1學習定義7
2.2學習曲線的意義、類型8
2.2.1學習曲線的意義8
2.2.2學習曲線的類型9
2.3學習曲線的特性10
2.3.1起伏現象10
2.3.2平台現象（Plateauing）11
2.4學習曲線的效應12
2.4.1效應來源12
2.4.2影響效應因素13
2.5學習曲線的模式15
2.5.1單一變數模式：17
2.5.2多變數模式：22
2.6參數估計26
2.6.1 經驗估計法26
2.6.2統計分析法27
2.6.3 MTM動素估計法27
2.6.4合成作業估計法27
2.7學習曲線應用範圍29
第三章 應用模式的構建30
3.1問題分析30
3.2考量學習效用之生產模式31
3.2.1生產日程量31
3.2.2人員績效制訂34
3.2.3品質管制35
3.2.4產品價格制定36
第四章 實證研究38
4.1個案公司簡介38
4.2公司生產型態39
4.3原始資料來源41
4.4學習曲線模式的建立41
4.4.1 SMD站學習曲線模式45
4.4.2 Touch Up站學習曲線模式54
4.4.3 Burn In站學習曲線模式55
4.4.4 Final Test站學習曲線模式56
4.3學習效用之生產模式分析及比較57
4.3.1 生產日程量57
4.3.2 人員績效評估64
4.3.3 品質管制68
4.3.4 產品價格制訂72
第五章 結論與未來研究方向76
5.1結 論76
5.2未來研究方向79
參考文獻80
附錄一 各製程站原始資料84
附錄二 各工作站學習曲線模式求取過程95
SMT站不良率雙變數學習曲線模式分析97
SMT站不良率單變數學習曲線模式分析101
SMT站時間單變數學習曲線模式分析105
Touch Up站不良率雙變數學習曲線模式分析（四月份之前）109
Touch Up站不良率雙變數學習曲線模式分析（四月份之後）113
Touch Up站不良率單變數學習曲線模式分析（四月份之前）117
Touch Up站不良率單變數學習曲線模式分析（四月份之後）121
Touch Up站時間單變數學習曲線模式分析（四月份之前）125
Touch Up站時間單變數學習曲線模式分析（四月份之後）129
Burn In站時間單變數學習曲線模式分析（四月份之前）133
Burn In站時間單變數學習曲線模式分析（四月份之後）137
Final Test站不良率雙變數學習曲線模式分析（四月份之前）141
Final Test站不良率雙變數學習曲線模式分析（四月份之後）145
Final Test站時間單變數學習曲線模式分析（四月份之前）149
Final Test站時間單變數學習曲線模式分析（四月份之後）153

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