DOCSLIB.ORG

Use Process Capability to Ensure Product Quality

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Use Process Capability to Ensure Product Quality Use Process Capability to Ensure Product Quality Lawrence X. Yu, Ph.D. Director (acting) Office of Pharmaceutical Science, CDER, FDA FDA/ PQRI Conference on Evolving Product Quality September 16-17, 2104, Bethesda, MD 1 2 Quality by Testing vs. Quality by Design Quality by Testing – Specification acceptance criteria are based on one or more batch data (process capability) – Testing must be made to release batches Quality by Design – Specification acceptance criteria are based on performance – Testing may not be necessary to release batches L. X. Yu. Pharm. Res. 25:781-791 (2008) 3 ICH Q6A: Test Procedures and Acceptance Criteria… 4 5 Pharmaceutical QbD Objectives Achieve meaningful product quality specifications that are based on assuring clinical performance Increase process capability and reduce product variability and defects by enhancing product and process design, understanding, and control Increase product development and manufacturing efficiencies Enhance root cause analysis and post-approval change management 6 Concept of Process Capability First introduced in Statistical Quality Control Handbook by the Western Electric Company (1956). – “process capability” is defined as “the natural or undisturbed performance after extraneous influences are eliminated. This is determined by plotting data on a control chart.” ISO, AIAG, ASQ, ASTM ….. published their guideline or manual on process capability index calculation 7 Nomenclature Four indices: – Cp: process capability index – Cpk: minimum process capability index – Pp: process performance index – Ppk: minimum process performance index ASTM E2281: Standard Practice for Process and Measurement Capability Indices 8 Calculation Formula (USL LSL) C (USL LSL) p P 6 p 6SD Mean LSL Cpkl Mean LSL Ppkl 3 3SD USL Mean USL Mean Cpku Ppku 3 3SD Cpk= min (Cpkl, Cpku) Ppk= min (Ppkl, Ppku) USL: upper specification limit; LSL: lower specification limit; Mean: grand average of all the data Sigma hat: estimated inherent variability (noise) of a stable process SD: overall variability 9 A Perfectly Centered Process… USL LSL For this case: LSL USL USL= +4σ LSL = -4σ USL-LSL= 8σ Cp= 1.333 Cpku=1.333 Cpkl=1.333 Cpk=1.333 -5 -4 -3 -2 -1 0 1 2 3 4 5 Mean (μ ), Sigma (σ) 10 Process Mean is not Centered… For this case: USL= +4σ LSL = -4σ USL-LSL= 8σ Cp= 1.333 Cpkl = 1.667 Cpku = 1.0 Cpk= 1.0 When the process is not centered, or deliberately run off-center for economic reasons, or only a single specification limit is involved, Cpk should be used. Similarly, Ppk offsets Pp weakness by introducing process mean in the calculation formula. 11 Cpk, Sigma Value, and PPM Area under Non conforming parts per million (ppm) Cpk Sigma normal Capability Value Value distribution Rating** Unilateral specification Bilateral specification* curve (%)* 0.333 1 68.27 158650 317300 Terrible 0.667 2 95.45 22750 45500 Poor Marginally 1.0 3 99.73 1350 2700 capable 1.333 4 99.993636 32 64 Capable 1.667 5 99.999942 0.29 0.58 Good 2.0 6 99.9999998 0.001 0.002 Excellent *Process mean is centered at middle of the specification limits and has normal distribution **Bothe, D. R., Measuring Process Capability, Cedarburg, W.I., Landmark Publishing Inc., 2001 12 Difference between Cpk and Ppk inherent variability overall variability R MR S N (X X )2 or or SD i d2 d2 c4 i1 N 1 SD: standard deviation of all individual (observed) values, which accounts for both common cause variability (noise) and special cause variability. It is often referred as overall variability. : the inherent variability (noise) due to common cause of a stable process. It is often estimated by using within subgroup variability which is linked to the use of control charts. 13 Difference between Cpk and Ppk Cpk represents the potential process capability (i.e. how well a given process could perform when all special causes have been eliminated). Ppk addresses how the process has performed without the demonstration of the process to be stable. Forecast future batch failure rate – Cpk (Yes) ; Ppk (No) 14 Control Chart CL: the grand average UCL and LCL: • Typically: 3SD from CL • Should not be confused with upper and lower specification limits To evaluate if a process is in a state of statistical control – Western Electric 8 Rules Two Types of Control Chart – Variable control chart: continuous numeric measurements (e.g. assay, dissolution, uniformity, impurity level) – Attribute control chart: discrete data (pass or fail, or counts of defects) 15 Variable Control Chart The average chart (X-bar chart) The variability chart – Moving range chart (MR chart, n=1) – Range chart (R-chart, subgroup size 2-10) – Standard deviation chart (S-chart, subgroup size >10) The average and variability charts are usually prepared and analyzed in pairs. 16 Example Xbar-R Chart Process Capability Analysis of Tablet Assay (first 25 batches, subgroup size =3) Xbar Chart Capability Histogram UCL=102.108 LSL USL n 102 a Specifications e _ M _ LSL 96 p X=100.287 u 100 USL 104 o r g b u LCL=98.466 S 98 1 3 5 7 9 11 13 15 17 19 21 23 25 Batch No. 96 98 100 102 104 R Chart Normal Prob Plot A D: 0.636, P: 0.094 e UCL=4.582 g 4 n a R p _ u 2 o R=1.78 r g b u S 0 LCL=0 1 3 5 7 9 11 13 15 17 19 21 23 25 Batch No. 96 99 102 105 Run Chart Capability Plot Cp: 1.27 104 Within Within Overall ) 102 StDev 1.051 StDev 1.079 % Cp 1.27 Pp 1.24 ( 100 Cpk 1.18 Ppk 1.15 y Overall Cpk: 1.18 a s PPM 229.14 Cpm * s 98 A PPM 323.15 96 Specs 5 10 15 20 25 Ppk: 1.15 Batch No. Data source: Chopra, V., Bairagi, M., Trivedi, P., et al., “A case study: application of statistical process control tool for determining process capability and sigma level,” PDA J Pharm Sci and Tech, 66 (2), 2012, pp. 98-115 17 Attribute Control Chart Control chart for fraction occurrence of an event (p chart) – For example: % of unsuccessful batch at Site A every month – Binominal distribution Control chart for counts of occurrence in a defined time or space increment (c chart) – For example: number of particulate matter in an injection vial – Poisson distribution Other types of control chart: – cumulative sum control chart (CUSUM) – exponentially weighted moving average control charts (EWMA) – etc. ASTM E2587- Standard Practice for Use of Control Charts in Statistical Process Control 18 Example P chart % of “unsuccessful batch”/month at Site A (# of lots attempted: 20-30/month) Binomial Process Capability Analysis of Unsuccess Batch P Chart Unsuccess Rate e UC L=0.1809 t a 0.15 R 20 n o s i s t e r 0.10 c o c p 10 u _ o s r 0.05 P=0.0437 n P U 0.00 LC L=0 0 % 1 3 5 7 9 11 13 15 17 19 21 23 25 20 25 30 Month T otal Batch Manufactured/Month Tests performed w ith unequal sample sizes Histogram Cumulative Unsuccess Rate Process-Z: Tar e Summary Stats t a 10.0 (95.0% confidence) 1.709 R 6 s s % Defectiv e: 4.37 Binomial process e 7.5 y c 5 Low er C I: 2.79 c c u n s capability index: Upper C I: 6.49 e n 4 u 5.0 q U Target: 0.00 e e 0.569 PPM Def: 43726 r v 3 F i t 2.5 a Low er C I: 27917 l Lower 95% u 2 Upper C I: 64891 m u Process Z: 1.7090 0.0 confidence bound C 5 10 15 20 25 Low er C I: 1.5150 0 3 6 9 12 0.505 Month Upper C I: 1.9123 % Unsuccess Rate Similar principles can be used to evaluate process capability of a single product, a product class, different manufacture sites, or a manufacturer global sites. 19 Summary: Process Capability Indices Patient first: clinical relevant specification Consider not only process mean & variability but also in relation to the specification Quantitative and action enabling Applicable for cross sectors (brand, generic, OTC and biotech) No additional testing is required since batch release data is available per current regulation A simple and powerful indicator to ensure product quality and process robustness. 20 Acknowledgements Daniel Peng Alex Viehmann Karthik Iyer 21 .
Load more

Recommended publications
  • Implementing SPC for Non-Normal Processes with the I-MR Chart: a Case Study
    Implementing SPC for non-normal processes with the I-MR chart: A case study Axl Elisson Master of Science Thesis TPRMM 2017 KTH Industrial Engineering and Management Production Engineering and Management SE-100 44 STOCKHOLM Acknowledgements This master thesis was performed at the brake manufacturer Haldex as my master of science degree project in Industrial Engineering and Management at the Royal Institute of Technology (KTH) in Stockholm, Sweden. It was conducted during the spring semester of 2017. I would first like to thank my supervisor at Haldex, Roman Berg, and Annika Carlius for their daily support and guidance which made this project possible. I would also like to thank the quality department, production engineers and operators at Haldex for all insight in different subjects. Finally, I would like to thank my supervisor at KTH, Jerzy Mikler, for his support during my thesis. All of your combined expertise have been very valuable. Stockholm, July 2017 Axl Elisson Abstract The application of statistical process control (SPC) requires normal distributed data that is in statistical control in order to determine valid process capability indices and to set control limits that reflects the process’ true variation. This study examines a case of several non-normal processes and evaluates methods to estimate the process capability and set control limits that is in relation to the processes’ distributions. Box-Cox transformation, Johnson transformation, Clements method and process performance indices were compared to estimate the process capability and the Anderson-Darling goodness-of-fit test was used to identify process distribution. Control limits were compared using Clements method, the sample standard deviation and from machine tool variation.
    [Show full text]
  • Process Capability Analysis
    6 Process capability analysis In general, process capability indices have been quite controversial. (Ryan, 2000, p. 186) Overview Capability indices are widely used in assessing how well processes perform in relation to customer requirements. The most widely used indices will be defined and links with the concept of sigma quality level established. Minitab facilities for capability analysis of both measurement and attribute data will be introduced. 6.1 Process capability 6.1.1 Process capability analysis with measurement data Imagine that four processes produce bottles of the same type for a customer who specifies that weight should lie between 485 and 495 g, with a target of 490 g. Imagine, too, that all four processes are behaving in a stable and predictable manner as indicated by control charting of data from regular samples of bottles from the processes. Let us suppose that the distribution of weight is normal in all four cases, with the parameters in Table 6.1. The four distributions of weight are displayed in Figure 6.1, together with reference lines showing lower specification limit (LSL), upper specification limit (USL) and Target (T). How well are these processes performing in relation to the customer requirements? In the long term the fall-out, in terms of nonconforming bottles, would be as shown in the penultimate column of Table 6.1. The fall-out is given as number of parts bottles) per million (ppm) that would fail to meet the customer specifications. The table in Appendix 1 indicates that these fall-outs correspond to sigma quality levels of 4.64, 3.50, 2.81 and 3.72 respectively for lines 1–4.
    [Show full text]
  • The Effects of Autocorrelation in the Estimation of Process Capability Indices." (1998)
    Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1998 The ffecE ts of Autocorrelation in the Estimation of Process Capability Indices. Lawrence Lee Magee Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Magee, Lawrence Lee, "The Effects of Autocorrelation in the Estimation of Process Capability Indices." (1998). LSU Historical Dissertations and Theses. 6847. https://digitalcommons.lsu.edu/gradschool_disstheses/6847 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. U M I films the text directly from die original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type o f computer printer. The quality o f this reproduction is dependent upon the quality o f the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. hi the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note w ill indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps.
    [Show full text]
  • Improving Process Capability Database Usage for Robust Design Engineering by Generalising Measurement Data
    Downloaded from orbit.dtu.dk on: Sep 25, 2021 Improving process capability database usage for robust design engineering by generalising measurement data Okholm, A.B.; Rask, M.; Ebro, Martin ; Eifler, Tobias; Holmberg, M.; Howard, Thomas J. Published in: 13th International Design Conference - Design 2014 Publication date: 2014 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Okholm, A. B., Rask, M., Ebro, M., Eifler, T., Holmberg, M., & Howard, T. J. (2014). Improving process capability database usage for robust design engineering by generalising measurement data. In 13th International Design Conference - Design 2014 (pp. 1133-1144). Design Society. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. INTERNATIONAL DESIGN CONFERENCE - DESIGN 2014 Dubrovnik - Croatia, May 19 - 22, 2014. IMPROVING PROCESS CAPABILITY DATABASE USAGE FOR ROBUST DESIGN ENGINEERING BY GENERALISING MEASUREMENT DATA A. B. Okholm, M. Rask, M.
    [Show full text]
  • Assignment 9 Control Charts, Process Capability and QFD
    Assignment 9 Control Charts, Process capability and QFD Instructions: 1. Total No. of Questions: 25. Each question carries one point. 2. All questions are objective type. Only one answer is correct per numbered item. 1. How do you find the process capability? a) By Process Capability Ratio = Cp = − b) By USL and LSL only 6 c) By normal distribution curve d) By spread and mean shift of the process 2. In a certain process it is given that USL is 14, LSL is zero. The process has a mean of 10 and standard deviation 2. What will be the Process Capability Ratio? a) 0.83 b) 1.17 c) 1.33 d) 1.5 3. In a process the inverse of process capability ratio is 0.65. Which statement is correct? a) The process is capable. b) The process is incapable. c) The process is capable with tight control. d) None of the above 4. A process having mean 8.80 and process standard deviation 0.12 has spread of specification limit: 9.0±0.4. What will be the process capability index? a) 0.55 b) 1.67 c) 1.33 d) 0.83 5. How is different than ? a) Looks at the centrality of the process. b) Looks at the overall variability of the process. c) Both look at the overall variability of the process. d) Looks at the centrality of the process. 6. A QC scheme is in operation for a process producing ball-bearings. A sample of 6 bearings is taken every hour and diameters is measured.
    [Show full text]
  • Mistakeproofing the Design of Construction Processes Using Inventive Problem Solving (TRIZ)
    www.cpwr.com • www.elcosh.org Mistakeproofing The Design of Construction Processes Using Inventive Problem Solving (TRIZ) Iris D. Tommelein Sevilay Demirkesen University of California, Berkeley February 2018 8484 Georgia Avenue Suite 1000 Silver Spring, MD 20910 phone: 301.578.8500 fax: 301.578.8572 ©2018, CPWR-The Center for Construction Research and Training. All rights reserved. CPWR is the research and training arm of NABTU. Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH. MISTAKEPROOFING THE DESIGN OF CONSTRUCTION PROCESSES USING INVENTIVE PROBLEM SOLVING (TRIZ) Iris D. Tommelein and Sevilay Demirkesen University of California, Berkeley February 2018 CPWR Small Study Final Report 8484 Georgia Avenue, Suite 1000 Silver Spring, MD 20910 www. cpwr.com • www.elcosh.org TEL: 301.578.8500 © 2018, CPWR – The Center for Construction Research and Training. CPWR, the research and training arm of the Building and Construction Trades Department, AFL-CIO, is uniquely situated to serve construction workers, contractors, practitioners, and the scientific community. This report was prepared by the authors noted. Funding for this research study was made possible by a cooperative agreement (U60 OH009762, CFDA #93.262) with the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH or CPWR. i ABOUT THE PROJECT PRODUCTION SYSTEMS LABORATORY (P2SL) AT UC BERKELEY The Project Production Systems Laboratory (P2SL) at UC Berkeley is a research institute dedicated to developing and deploying knowledge and tools for project management.
    [Show full text]
  • Chapter 6: Process Capability Analysis for Six Sigma
    Six Sigma Quality: Concepts & Cases‐ Volume I STATISTICAL TOOLS IN SIX SIGMA DMAIC PROCESS WITH MINITAB® APPLICATIONS Chapter 6 PROCESS CAPABILITY ANALYSIS FOR SIX SIGMA © Amar Sahay, Ph.D. Master Black Belt 1 Chapter 6: Process Capability Analysis for Six Sigma CHAPTER HIGHLIGHTS This chapter deals with the concepts and applications of process capability analysis in Six Sigma. Process Capability Analysis is an important part of an overall quality improvement program. Here we discuss the following topics relating to process capability and Six Sigma: 1. Process capability concepts and fundamentals 2. Connection between the process capability and Six Sigma 3. Specification limits and process capability indices 4. Short‐term and long‐term variability in the process and how they relate to process capability 5. Calculating the short‐term or long‐term process capability 6. Using the process capability analysis to: assess the process variability establish specification limits (or, setting up realistic tolerances) determine how well the process will hold the tolerances (the difference between specifications) determine the process variability relative to the specifications reduce or eliminate the variability to a great extent 7. Use the process capability to answer the following questions: Is the process meeting customer specifications? How will the process perform in the future? Are improvements needed in the process? Have we sustained these improvements, or has the process regressed to its previous unimproved state? 8. Calculating process
    [Show full text]
  • Lean Six Sigma Rapid Cycle Improvement Agenda
    Lean Six Sigma Rapid Cycle Improvement Agenda 1. History of Lean and Six Sigma 2. DMAIC 3. Rapid Continuous Improvement – Quick Wins – PDSA – Kaizen Lean Six Sigma Lean Manufacturing Six Sigma (Toyota Production System) DMAIC • T.I.M.W.O.O.D • PROJECT CHARTER • 5S • FMEA • SMED • PDSA/PDCA • TAKT TIME • SWOT • KAN BAN • ROOT CAUSE ANALYSIS • JUST IN TIME • FMEA • ANDON • SIPOC • KAIZEN • PROCESS MAP • VALUE STREAM MAP • STATISTICAL CONTROLS Process Improvement 3 Lean Manufacturing • Lean has been around a long time: – Pioneered by Ford in the early 1900’s (33 hrs from iron ore to finished Model T, almost zero inventory but also zero flexibility!) – Perfected by Toyota post WWII (multiple models/colors/options, rapid setups, Kanban, mistake-proofing, almost zero inventory with maximum flexibility!) • Known by many names: – Toyota Production System – Just-In-Time – Continuous Flow • Outwardly focused on being flexible to meet customer demand, inwardly focused on reducing/eliminating the waste and cost in all processes Six Sigma • Motorola was the first advocate in the 80’s • Six Sigma Black Belt methodology began in late 80’s/early 90’s • Project implementers names includes “Black Belts”, “Top Guns”, “Change Agents”, “Trailblazers”, etc. • Implementers are expected to deliver annual benefits between $500,000 and $1,000,000 through 3-5 projects per year • Outwardly focused on Voice of the Customer, inwardly focused on using statistical tools on projects that yield high return on investment DMAIC Define Measure Analyze Improve Control • Project Charter • Value Stream Mapping • Replenishment Pull/Kanban • Mistake-Proofing/ • Process Constraint ID and • Voice of the Customer • Value of Speed (Process • Stocking Strategy Zero Defects Takt Time Analysis and Kano Analysis Cycle Efficiency / Little’s • Process Flow Improvement • Standard Operating • Cause & Effect Analysis • SIPOC Map Law) • Process Balancing Procedures (SOP’s) • FMEA • Project Valuation / • Operational Definitions • Analytical Batch Sizing • Process Control Plans • Hypothesis Tests/Conf.
    [Show full text]
  • Chapter 15 Statistics for Quality: Control and Capability
    Aaron_Amat/Deposit Photos Statistics for Quality: 15 Control and Capability Introduction CHAPTER OUTLINE For nearly 100 years, manufacturers have benefited from a variety of statis­ tical tools for the monitoring and control of their critical processes. But in 15.1 Statistical Process more recent years, companies have learned to integrate these tools into their Control corporate management systems dedicated to continual improvement of their 15.2 Variable Control processes. Charts ● Health care organizations are increasingly using quality improvement methods 15.3 Process Capability to improve operations, outcomes, and patient satisfaction. The Mayo Clinic, Johns Indices Hopkins Hospital, and New York-Presbyterian Hospital employ hundreds of quality professionals trained in Six Sigma techniques. As a result of having these focused 15.4 Attribute Control quality professionals, these hospitals have achieved numerous improvements Charts ranging from reduced blood waste due to better control of temperature variation to reduced waiting time for treatment of potential heart attack victims. ● Acushnet Company is the maker of Titleist golf balls, which is among the most popular brands used by professional and recreational golfers. To maintain consistency of the balls, Acushnet relies on statistical process control methods to control manufacturing processes. ● Cree Incorporated is a market-leading innovator of LED (light-emitting diode) lighting. Cree’s light bulbs were used to glow several venues at the Beijing Olympics and are being used in the first U.S. LED-based highway lighting system in Minneapolis. Cree’s mission is to continually improve upon its manufacturing processes so as to produce energy-efficient, defect-free, and environmentally 15-1 19_psbe5e_10900_ch15_15-1_15-58.indd 1 09/10/19 9:23 AM 15-2 Chapter 15 Statistics for Quality: Control and Capability friendly LEDs.
    [Show full text]
  • Statistical Quality Control and Process Capability Analysis for Variability
    ering & ine M g a et al., n n E Rábago-Remy Ind Eng Manage 2014, 3:4 a l g a i e r m t s DOI: 10.4172/2169-0316.1000137 e u n d t n I Industrial Engineering & Management ISSN: 2169-0316 Research Article Open Access Statistical Quality Control and Process Capability Analysis for Variability Reduction of the Tomato Paste Filling Process Dulce María Rábago-Remy, Edith Padilla-Gasca and Jesús Gabriel Rangel-Peraza* Departamento de Estudios de Posgrado e Investigación, Instituto Tecnológico de Culiacán, Juan de Dios Batiz 310 Pte. Col. Guadalupe. Culiacán Sinaloa, México Abstract In this research some techniques for process statistical control were applied, such as frequency histograms, Pareto diagrams, process capability analysis and control charts. The purpose of this investigation was to reduce the variability of the canned tomato paste filling process coming from a tomato processing food industry that has problems with the net weight of their processed product. The results of the process capability analysis showed that 35.52% of the observations were out of the specifications during the months in study, which generates a real capability of the process (Cpk) of 0.124, and it indicates that the process does not have enough ability to fulfill the required specifications by the firm. The process potential capability (Cp) is 0.676. Given that Cpk < Cp, then it is concluded that the process is not centered, indicating that the measurement of the filling process is away from the center of the specifications. The process fits 0.371 sigma between the process mean and the nearest specification limit.
    [Show full text]
  • Poka-Yoke and Quality Control on Traub Machine for Kick Starter Driven Shaft
    Journal of Material Science and Mechanical Engineering (JMSME) Print ISSN: 2393-9095; Online ISSN: 2393-9109; Volume 2, Number 7; April-June, 2015 pp. 10-15 © Krishi Sanskriti Publications http://www.krishisanskriti.org/jmsme.html Poka-Yoke and Quality Control on Traub Machine for Kick Starter Driven Shaft Atif Jamal 1, Ujjwal Kumar 2, Aftab A. Ansari 3, Balwant Singh 4 1,2,3,4 M.Tech Scholar, SET, Sharda University, GN, U.P [email protected], [email protected] [email protected], [email protected] Abstract: This study was conducted in manufacturing creation of process stoppages, and provides tools and methods environment and focused at machining operation. Fair Products for designing them. India, which manufactures auto ancillaries, was selected for this research. Fair Products has been facing tremendous pressure of In study Chen et al. (1996) , has also considers that a Poka- quality level and in house rejection due to under sizing of the parts manufactured on Traub Machine. After sometime the yoke is a mechanism for detecting, eliminating, and correcting process begins to fail as number of defects begins to increase as errors at their source, before they reach the customer. such the management has thrown challenge to the manufacturing team to find ways to improve the outgoing quality at machining C M Hinckley (2003) although the occurrence of mistakes is operation. Thus, Error Proofing method was adopted for inevitable, non-conformances and defects is not. To prevent implementation at Traub Machining operation. Experimental defects caused by mistakes, our approach to quality control research was carried out to see the effectiveness of Error must include several new elements.
    [Show full text]
  • Chapter 5: Quality Tools for Six Sigma 2
    Six Sigma Quality: Concepts & Cases‐ Volume I STATISTICAL TOOLS IN SIX SIGMA DMAIC PROCESS WITH MINITAB® APPLICATIONS Chapter 5 Quality Tools for Six Sigma Basic Quality Tools and Seven New Tools ©Amar Sahay, Ph.D. 1 Chapter 5: Quality Tools for Six Sigma 2 Chapter Highlights This chapter deals with the quality tools widely used in Six Sigma and quality improvement programs. The chapter includes the seven basic tools of quality, the seven new tools of quality, and another set of useful tools in Lean Six Sigma that we refer to –“beyond the basic and new tools of quality.” The objective of this chapter is to enable you to master these tools of quality and use these tools in detecting and solving quality problems in Six Sigma projects. You will find these tools to be extremely useful in different phases of Six Sigma. They are easy to learn and very useful in drawing meaningful conclusions from data. In this chapter, you will learn the concepts, various applications, and computer instructions for these quality tools of Six Sigma. This chapter will enable you to: 1. Learn the seven graphical tools ‐ considered the basic tools of quality. These are: (i) Process Maps (ii) Check sheets (iii) Histograms (iv) Scatter Diagrams (v) Run Charts/Control Charts (vi) Cause‐and‐Effect (Ishikawa)/Fishbone Diagrams (vii) Pareto Charts/Pareto Analysis 2. Construct the above charts using MINITAB 3. Apply these quality tools in Six Sigma projects 4. Learn the seven new tools of quality and their applications: (i) Affinity Diagram (ii) Interrelationship Digraph (iii) Tree Diagram (iv) Prioritizing Matrices (v) Matrix Diagram (vi) Process Decision Program Chart (vii) Activity Network Diagram 5.
    [Show full text]