SPC Made Easier, Simpler, More Statistically Powerful
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The 1999 ASA Quality and Productivity Research Conference Presentation Abstracts Wednesday Keynote Address
The 1999 ASA Quality and Productivity Research Conference Presentation Abstracts Wednesday Keynote Address Six Sigma: From Strategic Deployment to Bottom-line Results A. Blanton Godfrey, Joseph A. DeFeo and Richard Chua Juran Institute, Inc. There are many challenges in creating a strong, competitive position today. They start with creating the right strategic direction for the organization and developing an aggressive, yet realistic strategic plan. But the plan is only a starting point. Without deployment, the strategic planning process is just another non- valued-added activity. In the last few years a small number of companies have used the emerging ideas, concepts and tools of Six Sigma to develop deployment plans leading to stunning results. We shall review some of the successful approaches - and some of the failed ones - and share lessons learned from a number of case studies. 1999 ASA QPRC Presentation Abstracts 1 Six Sigma Implementation I Applying Six Sigma in an Innovative R&D Organization Jane C. Li, James Rockwell and Anne Raich UOP LLC UOP has a proud history of innovation as a world leader in development and commercialization of new technologies for petroleum refining and petrochemical process industries. Two years after successful introduction of Six Sigma to manufacturing in 1995, over 250 UOP engineers, scientists and managers began participation in an accelerated in-house customized Six Sigma program. Long-term success through new product and process design is emphasized with a broad training approach. A culture change has begun toward aligning complex technical and business issues, providing a fresh view of catalysis, material, and process research and development, and improving testing efficiency. -
X-Bar Charts
NCSS Statistical Software NCSS.com Chapter 244 X-bar Charts Introduction This procedure generates X-bar control charts for variables. The format of the control charts is fully customizable. The data for the subgroups can be in a single column or in multiple columns. This procedure permits the defining of stages. The center line can be entered directly or estimated from the data, or a sub-set of the data. Sigma may be estimated from the data or a standard sigma value may be entered. A list of out-of-control points can be produced in the output, if desired, and means may be stored to the spreadsheet. X-bar Control Charts X-bar charts are used to monitor the mean of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.). The measurements of the samples at a given time constitute a subgroup. Typically, an initial series of subgroups is used to estimate the mean and standard deviation of a process. The mean and standard deviation are then used to produce control limits for the mean of each subgroup. During this initial phase, the process should be in control. If points are out-of-control during the initial (estimation) phase, the assignable cause should be determined and the subgroup should be removed from estimation. Determining the process capability (see R & R Study and Capability Analysis procedures) may also be useful at this phase. Once the control limits have been established of the X-bar charts, these limits may be used to monitor the mean of the process going forward. -
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. -
A Brief History of QUALITY’S Seminal Moments
A Brief History of QUALITY’s Seminal Moments 1 A SAMPLING OF THE SEMINAL MOMENTS IN THE HISTORY OF QUALITY, AND THE ASSOCIATED PEOPLE, THAT HAVE MADE A DIFFERENCE Stephen N. Luko Statistician, engineering Fellow United Technologies Aerospace Systems November 11, 2018 Forward 2 This presentation is about some of the important moments and people contributing key concepts and knowledge to the theory and application of Quality. It is therefore a sample of these and not the whole story. The people, concepts and techniques reviewed are a testimony to a field that has blossomed in our lifetime to become the “Quality Sciences”. Today, Quality is a multidisciplinary field and applied in many disciplines, comprising such diverse fields as management, psychology, mathematics, statistics, planning, and leadership among others. One thought before we begin 3 It cannot be helped that most of the people in this story were scientists, engineers, mathematicians or statisticians by training, so much of the following presentation has a technical bedrock. Beginning’s 4 Quality goes back a long time 13th century to the early 19th century - The “Guilds” of medieval Europe and elsewhere throughout the world was the banner of quality goods for centuries. Guilds fostered craftsmanship, apprenticeship, and individual piece to piece inspection and quality. This system continued until the modern period and the dawn of the industrial revolution. Artifacts and antiquities have left us materials thousands of years old. Beginning’s, Shewhart’s long view 5 From: Shewhart, “Statistical Method from the Viewpoint of Quality Control”, 1939 Beginning’s - Shewhart’s long view 6 From: Shewhart, “Statistical Method from the Viewpoint of Quality Control”, 1939. -
A Guide to Creating and Interpreting Run and Control Charts Turning Data Into Information for Improvement Using This Guide
Institute for Innovation and Improvement A guide to creating and interpreting run and control charts Turning Data into Information for Improvement Using this guide The NHS Institute has developed this guide as a reminder to commissioners how to create and analyse time-series data as part of their improvement work. It should help you ask the right questions and to better assess whether a change has led to an improvement. Contents The importance of time based measurements .........................................4 Run Charts ...............................................6 Control Charts .......................................12 Process Changes ....................................26 Recommended Reading .........................29 The Improving immunisation rates importance Before and after the implementation of a new recall system This example shows yearly figures for immunisation rates of time-based before and after a new system was introduced. The aggregated measurements data seems to indicate the change was a success. 90 Wow! A “significant improvement” from 86% 79% to 86% -up % take 79% 75 Time 1 Time 2 Conclusion - The change was a success! 4 Improving immunisation rates Before and after the implementation of a new recall system However, viewing how the rates have changed within the two periods tells a 100 very different story. Here New system implemented here we see that immunisation rates were actually improving until the new 86% – system was introduced. X They then became worse. 79% – Seeing this more detailed X 75 time based picture prompts a different response. % take-up Now what do you conclude about the impact of the new system? 50 24 Months 5 Run Charts Elements of a run chart A run chart shows a measurement on the y-axis plotted over time (on the x-axis). -
Medians and the Individuals Control Chart the Individuals Control Chart
Medians and the Individuals Control Chart The individuals control chart is used quite often to monitor processes. It can be used in almost all cases, so it makes the art of deciding which control chart to use extremely easy at times. Not sure? Use the individuals control chart. The individuals control chart is composed of two charts: the X chart where the individual values are plotted and the moving range (mR) chart where the range between consecutive values are plotted. The averages and control limits are also part of the charts. The average moving range (R̅) is used to calculate the control limits on the X chart. If the mR chart has a lot of out of control points, the average moving range will be inflated leading to wider control limits on the X chart. This can lead to missed signals (out of control points) on the X chart. Is there anything you can do when the mR chart has many out of control points to help miss fewer signals on the X chart? Yes, there is. One method is to use the median moving range. The median moving range is impacted much less by large moving range values than the average. Another option is to use the median of the X values in place of the overall average on the X chart. This publication explores using median values for the centerlines on the X and mR charts. We will start with a review of the individuals control chart. Then, the individuals control charts using median values is introduced. The charts look the same – the only difference is the location of the centerlines and control limits. -
Lean Process Improvement Assessment
LEAN PROCESS IMPROVEMENT ASSESSMENT INSTRUCTIONS: Read each item, then check a box on the 1-to-5 scale to show how often this occurs in your workplace: If you checked "often" or "very often" then consider: 1 = never 2 = rarely 3 = sometimes 4 = often 5 = very often In my work area, we spend too much time looking for the items we need to do our 5S+ 1 2 3 4 5 1 jobs: files, materials, equipment, and so on. If the physical work area was better 5S+ stands for sort, straighten, shine, standardize, sustain, organized, we’d be more productive and less frustrated. and safety. When we get inputs from customers or from other sources (from paper forms or Poka-Yoke online web forms, for example), we often find that the incoming information is 1 2 3 4 5 From the Japanese term for mistake-proofing, poka-yoke is 2 incomplete or inaccurate. We have to circle back to people to get the information all about putting measures in place to prevent errors from we need. If we received complete information from them the first time around, we’d occurring as a process unfolds. be a lot more efficient. We find ourselves wondering what our customers are thinking. Are we meeting their Voice of the Customer needs and expectations? Are they happy with our work? Are there things we can do 1 2 3 4 5 When it comes to improvement, understanding the 3 to increase their satisfaction? If we had answers to these questions, we could serve customer’s spoken and unspoken requirements is essential. -
Phase I and Phase II - Control Charts for the Variance and Generalized Variance
Phase I and Phase II - Control Charts for the Variance and Generalized Variance R. van Zyl1, A.J. van der Merwe2 1Quintiles International, [email protected] 2University of the Free State 1 Abstract By extending the results of Human, Chakraborti, and Smit(2010), Phase I control charts are derived for the generalized variance when the mean vector and covariance matrix of multivariate normally distributed data are unknown and estimated from m independent samples, each of size n. In Phase II predictive distributions based on a Bayesian approach are used to construct Shewart-type control limits for the variance and generalized variance. The posterior distribution is obtained by combining the likelihood (the observed data in Phase I) and the uncertainty of the unknown parameters via the prior distribution. By using the posterior distribution the unconditional predictive density functions are derived. Keywords: Shewart-type Control Charts, Variance, Generalized Variance, Phase I, Phase II, Predictive Density 1 Introduction Quality control is a process which is used to maintain the standards of products produced or services delivered. It is nowadays commonly accepted by most statisticians that statistical processes should be implemented in two phases: 1. Phase I where the primary interest is to assess process stability; and 2. Phase II where online monitoring of the process is done. Bayarri and Garcia-Donato(2005) gave the following reasons for recommending Bayesian analysis for the determining of control chart limits: • Control charts are based on future observations and Bayesian methods are very natural for prediction. • Uncertainty in the estimation of the unknown parameters are adequately handled. -
Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes
Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes Jim Ellis Certified Master Black Belt J&J Process Excellence Director – US Sales Strategic Accounts [email protected] Intent: Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes The purpose of this presentation is to share with the attendees my experience over the past 5 years using the DMAIC Process how to determine what Lean and/or 6 Sigma tools is the best to use when for assuring successful outcomes of your projects. Learning Objectives: 1.) Clear understanding of DAMIC Process 2.) Clear understanding of which common Lean and 6 Sigma Tools are best used in what stage of DMAIC 3.) Clear understanding of how to assess success for a project and know if you have achieved it. After Attending the attendees will: 1.) Know what the DMAIC process is and how it is applied in project planning 2.) Understand which Lean and 6 Sigma tools are used in what sequence to assure successful project outcomes © 2008 Ortho Clinical Diagnostics The Driving Forces of Change Today’s Healthcare Delivery Challenges… Declining Resources $29.9 Space Vacancy Capital Funds billion Skilled Labor Rates Medicare/Medicaid Reimbursement Laboratory Techs 5.9% Shortfall Imaging Techs 5.9% Pharmacists 8.1% RNs 8.1% Inpatient Admissions2 ED Visits2 Volume New Technology Market Pressures Increasing Pressures © 2008 Ortho Clinical Diagnostics Why are labs changing? “I need to transform my lab!” Why? • 2X Volume • ½ the -
Simplificaton of Six Sigma Implementation Using Shainin Tools for Process Improvement Justin T
International Journal of Scientific & Engineering Research, Volume 8, Issue 4, April-2017 ISSN 2229-5518 390 Simplificaton of Six Sigma Implementation Using Shainin Tools for Process Improvement Justin T. Aichail, Arju D. Jadhav, Aniket P. Baviskar, Chandan S. Gajare, R. S. Patil Abstract— The purpose of this paper is to provide a brief overview of Six Sigma and Shainin methodology and to propose the modification of Six Sigma methodology in order to achieve improved efficiency of DMAIC in the process improvement journey by using some of the Shainin tools. The success of the Six Sigma program in an organization depends to a large extent on the success of the Six Sigma project, which in turn depends on how combination of tools is being applied to address the root cause. Shainin Design of Experiments (DOE) offers powerful and effective experimental design approach for solving the chronic quality problems that plague manufacturers worldwide. The objective of this paper is to examine as to how to simplify the implementation of Six Sigma methodology by the applicability of a simpler but not very frequently used methodology known as Shainin methodology. The comparative study of various approaches of Six Sigma implementation has been done. Index Terms— DOE, DMAIC, Process Improvement, Quality Tools, Shainin, Simplification, Six Sigma Implementation. —————————— —————————— 1 INTRODUCTION HE demands placed on an organization in today‘s global long been used to identify the key input variables responsible T business environment are driven by customer satisfaction for variations in the output. as well as the fulfilment of the expectation of stakeholders A technical feature that distinguishes Six Sigma from other regarding cost reduction, improving business performance quality approaches is its ability to use statistical methods with- and maintaining a competitive advantage. -
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 -
Introduction to Using Control Charts Brought to You by NICHQ
Introduction to Using Control Charts Brought to you by NICHQ Control Chart Purpose A control chart is a statistical tool that can help users identify variation and use that knowledge to inform the development of changes for improvement. Control charts provide a method to distinguish between the two types of causes of variation in a measure: Common Causes - those causes that are inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system. Using your morning commute as an example, it may take between 35-45 minutes to commute to work each morning. It does not take exactly 40 minutes each morning because there is variation in common causes, such as the number of red lights or traffic volume. Special Causes - those causes that are not part of the system all the time or do not affect everyone, but arise because of specific circumstances. For example, it may take you 2 hours to get to work one morning because of a special cause, such as a major accident. Variation in data is expected and the type of variation that affects your system will inform your course of action for making improvements. A stable system, or one that is in a state of statistical control, is a system that has only common causes affecting the outcomes (common cause variation only). A stable system implies that the variation is predictable within statistically established limits, but does not imply that the system is performing well. An unstable system is a system with both common and special causes affecting the outcomes.