【正文】 確認所有數(shù)據(jù)采集點標示各工序標準控制文件各步驟標明為增值性（VA）或非增值性（NVA）確認各工藝步驟的 X 和 Y標明可能消除的NVA 步驟加入并標明“隱形工廠”工段標明為VA或NVA，標明可能消除的步驟標明須指定控制文件的步驟加入DUP，RTY，COPQ，循環(huán)周期等估計值標明須進行量具和工藝能力研究的步驟通過直接或秘密觀察確認準確性4誰將受到影響？《6 Sigma項目運作實例》《定義階段》如何進行項目問題陳述如何進行問題陳述？分六個方面進行問題陳述：我們簡單介紹以下項目是如何定義的。b目的c耗費3項目的選擇b1減少缺陷的70%b3項目完成周期為4個月b5黑帶的第一個項目必須滿足培訓目標《6 Sigma項目運作實例》《定義階段》我們在定義階段做什么我們在定義階段需要做什么？1，完成項目陳述。？6，完成目標陳述?！? Sigma項目運作實例》《測量階段》如何進行項目描述如何進行項目描述: 1，目標陳述2，Metric 圖3，月節(jié)省額如何繪制工藝流程圖:召集小組:流程圖繪制是集體努力的結果小組包括：流程負責人：項目結果的負責人工程部門工藝，產(chǎn)品，設計及設備生產(chǎn)部門操作員，各班次主管，培訓員，操作班長，維修技師流程圖所需信息腦力風暴觀察/經(jīng)歷操作手冊工程標準，工作指示六大方面（人，機，方法，測量，材料，環(huán)境）工藝流程細圖程序：1，從流程圖中列出工藝步驟2，加入下列內容輸出指標輸出指標標準，若存在輸入指標輸入指標標準，若存在工藝能力或量具能力指標所用設備3，標明隱形工廠步驟4，標明各步驟屬于增值性（VA）或非增值性（NVA）5，標明各步驟屬于可控性的（C）或噪音性的（N）6，確認各設備的輸入指標設置7，確認流程圖準確性8，必要時更改及更新流程*6mman, machine ,method, measurement, mother nature (environment)(6M:人員，機器，測量方法，原材料，環(huán)境)使用定性型量具 Ramp。標準化分數(shù)如果員工時常與標準不一致，則需要改變測量系統(tǒng)（或局部標準）工藝能力分析:為何測量工藝能力?使我們根據(jù)數(shù)據(jù)分配資源！ （這可不常見！）缺陷率得以量化確認可以改進機會分析工藝能力可使組織預測其所有產(chǎn)品和服務的真實質量水平確認工藝發(fā)生問題的本質居中程度或分散度R 模型測量系統(tǒng) μ總和=μ工藝+Δμ測量系統(tǒng)偏離度： 觀察值=實際真實值+測量偏移通過“校準計劃” Δ 測量偏移來評估 真實值 測量值（準確度）測量系統(tǒng) σ2 總合=σ2工藝+σ2測量系統(tǒng)偏離度： 觀察的偏差=工藝的偏差+測量的偏差通過“校準計劃”來評估 真實值 測量值（準確度）R 使用方法說明:1，校準量具或確認最近校準仍然有效2，收集10個代表工藝偏差全部范圍的樣本3，從每日使用這種測量方法的員工中選出檢驗員4，運用 ClacMake Patterned Data 準備量具研究數(shù)據(jù)表5，讓員工測量所有無標識，隨機次序的樣本6，分別讓另外其他員工測量所有無標識，隨機次序的樣本7，重復第五步及第六步循環(huán)三次。Minitab 默認計算P/SV量具Ramp。 DetectionFMEA Examples
Plating ExampleAn aerospace plating pany was shipping product to itscustomers with nickel plating that was too thin. Parts were failingcorrosion testing at the customer.
Shipping ExampleThe shipping department of an electronics pany is unable toship an assembly without its clam shell protective packaging. Thiscauses occasional late shipments to the customer.
In the following examples, a single line from the FMEA is used as anillustration for each of the above examples. 圖形技術分析:Graphical MethodsProcess Variation
Noise variation from discrete inputs
Different operators, machines, setups
Different days, shifts
Different batches, mixtures, raw materials
Noise variation from continuous inputs
Ambient temperature, humidity, pressure
Wear, drift, erosion, chemical depletion) ,..., , ( 2 1 k Process x x x f y =) ,..., , ( 2 1 k Noise n n n f +Intentional Unwanted The equation just means that any output isdetermined by the intentional process settingsand the unwanted noise variation.Common Classification of Noise Variables
Positional (within part variation) Variation within a single production unit
Thickness variation across a plated part Variation across a unit containing many parts
Variation across a semiconductor wafer with many die Variation by position in a batch process
Cavitytocavity variations in an injection molding operation
Cyclical (parttopart variation) Variation between consecutive production units Batchtobatch average differences – consecutive batches
Temporal (timetotime variation) Shifttoshift, DaytoDay, Setuptosetup Variation not accounted for by Positional or Cyclical2 2 2 2Temporal Cyclical Positional Noise σ σ σ ++=Graphical Analysis – Example
Injection molding is used to make a type of socket, four pieces at a time, onepiece per slot. Measurements of the sockets consist of thickness values inexcess of millimeters. The gauges measure in hundredths of amillimeter. The specification is 11 177。 x) – A Few ThoughtsPg 8 ?March 01, Breakthrough Management Group. Unpublished proprietary work available only under license. All rights reserved. March 16, 2001
Make sure the process settings cover the likely productionrange (but not too far).
Too great a range
points outside the normal range mayhave too great an effect on the model.
Too small a range
Error term may dominate the fit.
Take several replicates at each input setting (x).
Replicate runs help increase the model accuracy.
Randomize runs whenever practical.
Run order is often significant factor.
The output (y) at different inputs (x抯) is not alwaysindependent of previous settings.A good spread in the data is required for agood model. Consider two examples:All of the data is collected at the normalprocess settings. In this case, regression willtry to fit a linear model to a bination ofrandom process variation and randommeasurement variation. The results will be ofno value.The second case is when most of the datais clustered around the standard settingsexcept for a couple of points at the extremeranges. In this case, the extreme pointscontrol the fit of the model. If one of theextreme points is a flyer, then the model willbe in error due to the flyer.The ideal case is for the Black Belt tocollect a range of data throughout the processspace. 置信區(qū)間:Confidence Intervals
A population is the set of all measurements of interest to the experimenter
A sample is a subset of measurements selected from the population
An inference is a statement about a population parameter based oninformation contained in a sample Two types of inference
Estimation A poll has been devised to determine the public’s reaction to anew political scandal. The purpose is to estimate the reactionof all Americans by polling a representative sample
Hypothesis testing A vaccine for Lyme disease has been developed but the rateof negative side effects is %. A new vaccine has beendeveloped and it is desired to know if the rate o