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Quality Management

Quality Management

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Quality Management

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  1. Quality Management

  2. Statistical Quality Control Methods Statistical Quality Control Methods Acceptance Sampling Statistical Process Control Attributes Variables Attributes Variables Type of Data

  3. Statistical Quality Control Methods Attribute Data: data which count items, such as the number of defective items on a sample Variable Data: data which measure a particular product characteristic such as length or weight

  4. The population or process is actually Good or in control Bad or out of control The sample says that the population or process is Good or in control In agreement • or Type II error Bad or out of control In agreement  or Type I error Statistical Quality Control Methods Sampling Error Sample results are not representative of the actual population or process Customer’s risk Prducer’s risk

  5. Cost incurred by passing a reject Cost to Inspect Acceptance Sampling Designing a Sampling Plan for Attribute Costs to justify inspection Full or 100% inspection or not?

  6. Acceptance Sampling Purpose of Sampling Plan • Find its quality • Ensure that the quality is what it is supposed to be

  7. Acceptance Sampling Designing a Sampling Plan for Attribute n: Number of units in the sample depended on the lot size c: the acceptance number AQL (acceptable quality level): maximum percentage of defects that a company is willing to accept LTPD (lot tolerance percent defective): minimum percentage of defects that a company is willing to reject : producer’s risk : consumer’s risk

  8. Acceptance Sampling Designing a Sampling Plan for Attribute • c LTPD/AQL nAQL • 0 44.890 0.052 • 10.946 0.355 • 6.509 0.818 • 4.890 1.366 • 4.057 1.970 • 3.549 2.613 • 3.206 3.286 • 2.957 3.981 • 2.768 4.695 • 2.618 5.426 =0.05 =0.10 MIL-STD-105E

  9. Acceptance Sampling -Example AQL=2% =0.05 =0.10 LTPD=8%

  10. Operating Characteristic Curve

  11. Operating Characteristic Curve P(r  c) p np 0.99 0.97 1% 0.95 2% 1.98 …

  12. Acceptance Sampling Determine a Sampling Plan for Variables Control Limit: Points on an acceptance sampling chart that distinguish the accept and reject regions. Also, points on a process control chart that distinguish between a process being in and out of control. Sample size

  13. Acceptance Sampling • Example ABC Electronics Company buys a 50-ohm resistor from an outside Vendor. From historical data, the standarddeviation for the resistor is 3 ohms. Determine the appropriate control limits if we use a sample Size of n=100 and we want to be 95% confident that the sample Results are truly representative of the total population.

  14. Acceptance Sampling Determine a Sampling Plan for Variables

  15. Example Acceptance Sampling Continuing with the resistor problem, we can tolerate some variation in the number of ohms in each resistor. However, if the number of ohms falls below 49, then we would have a serious problem in our electrical circuit. What is the probability of us accepting a lot when the average resistance is 49 ohms or less.

  16. LCL Acceptance Sampling

  17. Acceptance Sampling • Example 8.53%

  18. Statistical Process Control Statistical process control (SPC) Statistical method for determining whether a particular process is in or out of control. Central Limit Theorem

  19. Statistical Process Control

  20. Statistical Process Control

  21. Statistical Process Control SPC Using Attribute Measurement Attribute data are data that are counted, such as good or bad units produced by a machine. Samples defects Sample size=6 defects=2

  22. Statistical Process Control SPC Using Attribute Measurement Center line = = Long-run average percent defective Standard deviation of sample = Note: x~Bernoulli distribution

  23. Statistical Process Control Variable Measurements Using X and R Charts An X chart tracks the changes in the means of samples by plotting the means that were taken from a process. An R chart tracks the changes in the variability by plotting the range within each sample.

  24. Statistical Process Control Notations: m = total number of samples n = total number of items in the sample Rj = difference between the highest and lowest values in sample j

  25. Statistical Process Control Variable Measurements Using X and R Charts Setup Control Chart: • At least 25 samples • Setup control limits Control limits for Upper control limit for Lower control limit for

  26. Statistical Process Control n A2 D3 D4 n A2 D3 D4 • 1.88 0 3.27 • 1.02 0 2.57 • 0.73 0 2.28 • 0.58 0 2.11 • 0.48 0 2.00 • 0.42 0.08 1.92 • 0.37 0.14 1.86 • 0.34 0.18 1.82 • 0.31 0.22 1.78 • 0.29 0.26 1.74 • 0.27 0.28 1.72 • 0.25 0.31 1.69 • 0.24 0.33 1.67 • 0.22 0.35 1.65 • 0.21 0.36 1.64 • 0.20 0.38 1.62 • 0.19 0.39 1.61 • 0.19 0.40 1.60 • 0.18 0.41 1.59

  27. Statistical Process Control

  28. Process Capability

  29. Process Capability Process Capability Ratio The larger the ratio, the greater the potential for producing parts within tolerance from the specified process.

  30. Process Capability Capability Index To determine whether the process mean is closer to the upper specification limit, or the lower specification limit.

  31. Quality Certification • ISO 9000 • Set of international standards on quality management and quality assurance, critical to international business • ISO 14000 • A set of international standards for assessing a company’s environmental performance

  32. Total Quality Management • A philosophy that involves everyone in an organization in a continual effort to improve quality and achieve customer satisfaction.

  33. The TQM Approach • Find out what the customer wants • Design a product or service that meets or exceeds customer wants • Design processes that facilitates doing the job right the first time • Keep track of results • Extend these concepts to suppliers

  34. Elements of TQM • Continual improvement • Competitive benchmarking • Employee empowerment • Team approach • Decisions based on facts • Knowledge of tools • Supplier quality • Champion • Quality at the source • Suppliers (partners in the process )

  35. Continuous Improvement • Philosophy that seeks to make never-ending improvements to the process of converting inputs into outputs. • Kaizen: Japanese word for continuous improvement.

  36. Quality at the Source The philosophy of making each worker responsible for the quality of his or her work.

  37. Six Sigma • 什麼是高品質? 在一般大眾的想像中,品質管理是個抽象的名詞,如何做才能達到所要求的高品質要求,其主要的問題在於缺乏一個指標來告訴大家真實的狀況,我們產品的品質的真實情形.

  38. Six Sigma • Motorola公司在1970年代中期到年代中期的十年間,由於品質競爭失利,節節敗退。彩色電視機廠在1974年關閉,音響廠在1980年停業,電腦記憶晶片也在1985年向日本廠商降服,眼看就要倒閉了。當時該公司董事長一面向美國政府要求保護,一方面提出高品質策略全面向6σ品質邁進,使生產線不良率降低至PPM水準。終於其無線呼叫器在日本市場大獲全勝,成為美國公司起死回生的典範。其重返競技場的力量即為高品質的產品與服務,1988年該公司獲得第一屆美國品質獎(The First Annual Malcolm Bealdrige National Quality Award)。

  39. Six Sigma • 品質大敵-品質變異 萬物皆有變化,工業產品也隨時伴有差異,同種產品間功能或尺寸的差異被稱之為變異(Variation)。變異小不影響顧客的滿意程度或後緒工程的作業,是可以容許的。一旦變異影響客戶的滿意程度,那麼變異就成了品質的大敵了。在Motorola有句口號:Variation is the Enemy of Customer Satisfaction。 具有連續性的品質特性,在製程正常時會呈常態分配,由常態分配可算出超出規格的不良率。在農業時代或輕工業時代,產品特性只要有99%良好,就很好了。可是現今的工業產品複雜無比,如用99%良品率的來裝配噴射客機,那麼恐怕沒有一架飛機飛得起來。道理很簡單,如果那架飛機用了10,000個零件,每個都是99%良品率,那麼總成的良品率幾近於零。所以要製造飛機,除了設計能力外,零件工業的力量是很重要的。

  40. Six Sigma Quality improvement program developed by Motorola to reduce process variation to 50% of design tolerance Cp=1; defect rate = 2700 per million parts Cp=2; defect rate = 3.4 per million parts

  41. Six Sigma Our Customers Feel the Variance, Not the Mean Often, our inside-out view of the business is based on average or mean-based measures of our recent past. Customers don’t judge us on averages, they feel the variance in each transaction, each product we ship. Six Sigma focuses first on reducing process variation and then on improving the process capability. Customers value consistent, predictable business processes that deliver world-class levels of quality. This is what Six Sigma strives to produce. Source: GE

  42. Six Sigma • 在Motorola,6σ品質水準的意義如下: • 3.4PPM(不良率或缺點數為百萬分之三點四) • 99.99966%產品為無缺點。 • 提供一個與競爭者比較的基準,為TQM提供一個衡量的基準。 • 可以瞭解距離無缺點有多遠。

  43. Six Sigma Motorola公司認為數據是滿足顧客的關鍵: 如果不能用Data表示我們所知的,那麼我們對它所知不多 (If we cannot express what we know in numbers, we don't know much about it) 如果對它所知不多,又怎樣控制它 (If we don't know much about it, we cannot control it) 如果我們不能控制它,那只有靠運氣了 (If we cannot control it, we are at the mercy of chance)

  44. Six Sigma • 6 Sigma 的推動手法類似於一般品管圈的手法應用,但「6 sigma」在推行上以D.M.A.I.C(Define,Measure,Analyse,Improve,Control五大方式)來做其運用的手法,不同於品管圈的是它導入了統計學的觀念,使得品質改善上更加的量化. 導入統計學方式的好處是,在推動品質改善時你可以更明確的了解改善的成果,例如在改善縮短病患候診時間的改善案中,一般在品管圈的方式或許測量到平均時間的縮短,即認為品質有改善,但是不經過統計的確認,無法得知改善前和改善後是否有其真正的差異;6sigma就是以統計科學為基礎加上品管改善手法應用來進行高品質改善的專案計劃。

  45. Six Sigma 成員 • 盟主(Champion) • 高階主管、領導並管理階層、肩負成敗責任、 選定專案、提供所需資源。 • 黑帶 (Black Belt) • 全職身分、領導團隊之執行(步驟DMAIC)、 著重關鍵流程改善、指導綠帶。 • 綠帶 (Green Belt) • 技能訓練與黑帶類似但非全職身分,由中階經理兼任、以本身的業務範圍作為專案的改善目標。 • 大黑帶 (Master Black Belt) • 有專案改善技術、教學和領導能力、協助各階層的訓練(訓練黑帶、綠帶) 發揮策略層面的技巧。

  46. 6標準差小組 • 由黑帶或綠帶領導 • 3~10人 • 5~6人較理想

  47. DMAIC • 一、界定(Define):針對流程,界定問題,衡量問題的嚴重程度,然後再分析數據,面對數據指標,擬定方案,採取行動改善問題。你如果能夠越正確地界定問題(了解顧客的心聲),你的目標就能越明確,那你就越能夠對症下藥。 • 二、衡量(Measure):蒐集資料,衡量整個流程或操作過程中,有多少產生誤差的機會,並衡量出影響結果最大的品質關鍵性 ( 流程中的瑕疪 ) 的詳實數據資料予以量化。 投入 流程 產出 準時交貨 訂單量 訂貨類型 內部重做 單件成本 受訓時數 準時交貨 定單完成 整體成本

  48. DMAIC • 三、分析(Analyze):分析數據,評估流程的優缺點何在,並和同業比較,找出專案結果最後可以改善的最大限度到哪裡。之後訂出目標數據,亦即你想達成的目標在哪裏。確認目前運作水準與目標水準的差距,分析與判斷造成缺點的根本原因,並分析其對問題的影響程度,以決定改善的優先順序。假如你能回答,誤差出現於何時,何地、和如何出現的話,那就是你已經找到癥結所在了。但是千萬不要只局限在表面的探討,還要找到誤差的真正發源點才對。六標準差教學員們統計學的理由,主要就是為了要幫助黑帶用更有效的方式,整理和使用數據資料。找出影響結果的潛在變數,或是瑕疪發生的最重要根源,以及如何加以量測,研究出數據資料的因應對策,朝理想流程改進。這是六標準差當中非常困難的部分。

  49. DMAIC • 四、改善(Improve):決定解決方案,並加以執行。列出那些未達我們期望的品質關鍵性要項,針對每一要項做改進,這樣一來,整個流程都能被改進了。黑帶,必須清楚地將每一項能夠改進的細節列出,外加持續改善的決心。 • 五、控制(Control):控制新流程,保持成果不輟。黑帶需要根據所設定的操作標準範圍,定期測量幾項關鍵變數,區分統計流程監督和統計流程控制,以確保成果都在目標範圍之內,並監督是否有足以影響資料的新變數出現。確保所做的改善能夠持續下去,衡量不能中斷,才能避免錯誤再度發生。控制的目的就是要將改善的成果繼續保持下去。將工程標準化,專案都必須具備「防呆」機制,即使人員有異動,流程也不會改變,制度還是可以照常進行。

  50. Six Sigma • 從1980年代Motorola公司利用六個標準品質策略以提昇全面品質水準以來,該公司將,SPC、問題解決(Problem solving)、連續改善實驗計畫直交設計(Taguchi Method)等合在一起,擬出六個達成六標準差品質的步驟: