DOCSLIB.ORG

DMAIC Problem Solving Process

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
DMAIC Problem Solving Process QETools Tutorial - Part I QE Tools Software Tutorial An Excel-based Statistical Software Add-In for Lean-Six Sigma qetools.com Topics Note: Not all tools are shown in this tutorial. See help files for additional examples. 2 1 www.qetools.com QETools Tutorial - Part I Topics (Continued) 3 QE Tools Menu of Tools 4 2 www.qetools.com QETools Tutorial - Part I QE Tools Tutorial Part 1 – Getting Started and Data Manipulation 1.1 Getting Started 1.2 Data Manipulation Tools Subset Data Split Data by Group 1.3 Six Sigma Methods – Tool Roadmap qetools.com 5 1.1 Getting Started Excel Menu QE Tools appears as a menu option in the main Excel toolbar 6 3 www.qetools.com QETools Tutorial - Part I 1.1 Getting Started New Data Sheet QE Tools uses a dedicated data sheet (own worksheet) for performing analyses. Begin by creating an initial blank datasheet using the New Data Sheet menu pick. 7 1.1 Getting Started Data Sheet . A new, pre-formatted worksheet is inserted with name “Datasheet” (Warning: Do not change sheet name). After you create a data sheet, you can add and manipulate data in ways familiar to you in Excel (e.g., copy, paste, add formulas, etc.) . Note: You must define a variable name for each data series (Datasheet Row: “Var Name”) . Optional, may include Upper and Lower specification Limits (USL and LSL) as well as a Target (nominal) value for each variable. These will automatically be referenced for those tools that use specification limits for analysis. TIP: Use Datasheet to only store raw data 8 4 www.qetools.com QETools Tutorial - Part I 1.1 Getting Started Macro-Enabled File Format Be sure to save the file as a “.xlsm” macro- enabled format file to ensure the QETools functionality works properly 9 1.1 Getting Started Entering Data – “Datasheet” . If all data in a column are numerical values, QETools will recognize as “Data” in the “Var Type” row. Otherwise, data will be listed as “Text.” . Important: Certain tools are restricted to the use of Data Variables Only. Note: You may also copy data from other locations (e.g., Excel File). TIP: Do not copy entire worksheet. First rows on data sheet are reserved for data characteristics 10 5 www.qetools.com QETools Tutorial - Part I 1.1 Getting Started Data Format in “DataSheet” . Variables may include Text or Data Variables . Data variables may either be values or calculations (Excel Formulas). Examples: • ‘TotalVisit’ has data values • ‘TotalWait’ and ‘WaittoVisit’ are formula. Data for any variable may be constructed using standard Excel commands. 11 1.1 Getting Started Variable Names When entering variable names, QE Tools may update after you enter them or paste from another worksheet QE Tools uses an algorithm to standardize variable names: Certain characters are not allowed in variable names and are removed: ":", "\", "/", "?", "*", "[", "]", "'", " ", "(", ")", "`", "~", "@", "#", "$", "%", "^", "&", "-", "+", "=", "{", "}", "|", "<", ">") Duplicate names are not allowed to insure QE Tools knows which variable you wish to analyze when running a tool 12 6 www.qetools.com QETools Tutorial - Part I 1.1 Getting Started Number of Worksheet Warnings QETools warns of too many active worksheets because performance may be diminished with increasing file size After 30 worksheets, QETools issues a warning message Recommend creating a second analysis file or removing unused worksheets 13 1.2 Data Manipulation Sorting and other data manipulation may be done using normal Excel Functionality, OR QE Tools has a data manipulation tool for: Subset Data by Variable Split Data Set by Variable 14 7 www.qetools.com QETools Tutorial - Part I 1.2 Data Manipulation Subset Data – Dialogue Box May create a subset of data rows (Select Variables) based on condition(s) satisfied Sample Excel Data File: qetools-sampledata.xlsx 15 1.2 Data Manipulation Subset Data – Sample Result Data are stored on datasheet in next open column to the right with new variable names (suffix added) . Subset to only include data for each row where Team = C 16 8 www.qetools.com QETools Tutorial - Part I 1.2 Data Manipulation Split Data by Group – Dialogue Box Split command creates data subsets of all selected rows of data Select Variables for each unique value of the Grouping Variable identified 17 1.2 Data Manipulation Split Data by Group – Sample Result Data are stored on datasheet in next open column to the right with new variable names (suffix added) The output is separate columns for each Output Variable . ‘TimeWaitingRoom’ . ‘WaitClinician1’ . WaitClinician2’) for each of the selected Grouping Variable . Team A . Team B . Team C 18 9 www.qetools.com QETools Tutorial - Part I 1.3 Six Sigma Methods – Tool Road Map QE Tools also provides a Six Sigma problem solving roadmap with common analysis steps and hyperlinks to analysis tools and templates 19 1.3 Six Sigma Methods – Tool Road Map Tool Roadmap: Define/Measure Phase Task Task Description and Tool Road Map (with Hyperlinks) DEFINE Define Problem Identify Problem, key customers, and assess impact in terms of $$ if possible – Tools: Project Charter SIPOC QFD - House of Quality Scorecard Analysis Pareto Chart Other Tools: Value Stream Map, Flow Chart, Swim Lane Diagram, Affinity Diagram, Cost-Benefit Analysis, Project Plan Identify Critical Y and assess its current state (e.g., Data Pattern, Process Stability, Process Capability/DPM/DPMO). MEASURE: Identify Key Output Note: select method based on Y data type (continuous/discrete, binary, or binary w/ multiple opportunities) Assess Current State Compute DPM, DPMO, Sigma Level, Yield (Unit-based, Rolled Throughput), or Process Capability Index (e.g., Pp, Ppk) Performance Process Capability Process Capability Process Capability Sigma Level and Graphical Summary Graphical Summary Graphical Summary Binary Tabulation DPM Converter (Normal) (Non-Normal) (Binary) FMEA Analysis Scorecard Analysis Yield Analyzer Assess stability of Y Plot Y versus time ( X-axis in time order) and assess if problem is chronic and/or sporadic. (time order) Assess if any trends in data over time (may include comparison to target performance levels or specification limits). Run Chart Blue Text representsAssess hyperlinksif process is in-statistical to controlvarious (process stability analysis). Select appropriate SPC chart based on the if Y is continuous variable data (or may be modeled as continuous)? analysis and templates in QEToolsIf subgroup size = 1 (or unknown), use: I / MR If subgroup size = 2 - 10, use: X-Bar / Range If subgroup size > 10, use X-Bar / S If Y relates to yield or % defective (each unit is defective or not)? If subgroup size is not constant, use: P-Chart If subgroup size is constant, use: P-Chart NP-Chart If Y relates to DPMO (1 or more defect opportunities per unit)? If subgroup size is NOT constant, use: U-Chart If subgroup size is constant, use: U-Chart C-Chart Describe Distribution of Perform distribution analysis. Select method based on Y-data type Y (non-time order) 20 If Y is categorical data (with multiple categories) --> use: Pareto Chart If Y is a set of proportions --> show distribution of sample proportions using: Histogram If Y is discrete/continuous --> show distribution using: Process Capability Process Capability Histogram Single Box Plot Graphical Summary Graphical Summary Dot Plot (Normal) (Non-Normal) Summarize Data and Assess if Mean and/or Compute descriptive statistics of Y and assess if mean and/or variation concern. 10 www.qetools.com Variation Concern Process Capability Process Capability Descriptive Graphical Summary Graphical Summary Statistics (Normal) (Non-Normal) Measurement Systems Assess quality of the measurement data for Y – verify if measurement system is effective (preferably quantitatively Analysis using measurement system analysis techniques) Paired (Repeated) Attribute Matching Gage R&R Study Measurements Study Study ANALYZE: Qualitative Analysis Identify possible key X’s (usually prior to Quantitative Analysis): Possible tools include: Cause-and Effect FMEA Other Tools: P-Diagram, Affinity Diagram Collect Data (check sheets, observational study of X's and Y's, controlled experiment). Quantitative Analysis: Analyze data to identify cause of the problem (key X's). Possible analysis tools include: Stratify Y by a grouping variable X (Stratification Analysis) Desc Statistics Mulitple Box Plot Scatter Plot Pareto Chart Multiple Tabulation (Grouping) Cross Tabulation Binary Tabulation Test for a Statistically Significant Difference Using Hypothesis Test. Select appropriate test on the following: Test variance effect on Y for X at 2 levels (variance Group A vs. Variance B): F-test Test mean effect on Y for X at 2 levels using paired data: Paired t-test Independent Test mean effect on Y for X at 2 levels: Samples t-test Test two proportions (Proportion Group A vs. Proportion Group B): Two proportion test Test mean effect on Y for X at 2 or more levels using independent samples: One-Factor ANOVA Test mean effect on Y for two X variables at 2 or more levels each: Two-Factor ANOVA Examine relationship between a single Y and X variable (prediction equation, Correlation, and R-squared): Scatter Plot Simple Regression Examine relationship between a single Y and mulitple X variables: Multiple Run Chart Multiple Regression Correlation Matrix Queuing Analysis Perform a Controlled Design of Experiment: Control Chart Create an Define Existing Analyze QETools DOE Analysis Experiment Experiment Experiment Other Tools: Multi-vari analysis, stepwise regression IMPROVE: Identify Improvement Identify improvement countermeasure (new procedure, change process setting, poke yoke, process monitoring Compare alternative solutions using tools from analyze phase or evaluate alternatives using: Pugh Matrix Assess the before/after effect of improvement recommendation relative to current state Process Capability Process Capability Process Capability Sigma Level Graphical Summary Graphical Summary Graphical Summary Binary Tabulation Calculator (Normal) (Non-Normal) (Binary) FMEA Analysis Scorecard Analysis Simulation Analysis Other Tools: Future State Map, Revised Process Flow Chart CONTROL: Create Control Plan Prepare a process control plan or document method of control.
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]
  • Improving Performance of Epidemic Healthcare Management During COVID-19 Outbreak Using LSS DMAIC Approach: a Case Study for Bangladesh Aquib Irteza Reshad, Md
    Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, Detroit, Michigan, USA, August 10 - 14, 2020 Improving Performance of Epidemic Healthcare Management during COVID-19 Outbreak using LSS DMAIC Approach: A Case Study for Bangladesh Aquib Irteza Reshad, Md. Mozibur Rahman and Naquib Mahmud Chowdhury Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology(BUET) Dhaka-1000 [email protected], [email protected], [email protected] Abstract The recent outbreak of coronavirus (COVID-19) pandemic has exposed the weakness of the existing healthcare facilities in developing countries like Bangladesh. The increasing amount of patients has made this condition more vulnerable. There is a high possibility that, these increasing amount of symptomatic patients might create a shortage in RT-PCR test kits in upcoming days. The objective of this study is to use Define, Measure, Analyze, Improve, and Control (DMAIC) in improving the epidemic healthcare management system during the COVID-19 outbreak in Bangladesh. The goal of this study is to use LSS methodology, especially the DMAIC improvement format in the existing dedicated healthcare management system for coronavirus treatment. The root cause analysis behind the higher response time and improper service for the Institute of Epidemiology, Disease Control and Research (IEDCR), and other dedicated healthcare providers regarding, coronavirus treatment in the current situation has been performed. FMEA (Failure Mode and Effect Analysis) was conducted in order to assess the potential failure modes in the existing healthcare management system. A simulation study regarding the implementation of pooled testing in Bangladesh for improving efficiency and optimizing the usage of RT-PCR test kits has also been carried out.
    [Show full text]
  • A DMAIC Framework for Improving Software Quality in Organizations: Case Study at RK Company Team Composition
    MCGILL UNIVERSITY Montreal, Quebec September 20 – 21, 2016 A DMAIC Framework for Improving Software Quality in Organizations: Case Study at RK Company Team Composition • Racha Karout • Anjali Awasthi Outline 1. Introduction 1.1 Background 1.2 Problem Definition 1.3 Research Objectives 2. Literature Review 3. Solution Approach 4. Conclusions and Future Works 1.1 Background • To compete in today‘s world, every business needs to improve. • Software has increasingly become a critical component in many industries (telecoms, banking, insurance, … etc.). • Software quality is crucial and poor quality is not acceptable. • Software development has not been consistently successful. What is Software Quality? • Software quality is the degree to which a system, component or process meets specified requirements, in other words, the degree to which a system, component or process meets customer or user needs or expectation (IEEE, 1991). • The software should not have bugs that reduce the quality attributes (functionality, reliability, usability and maintainability) (Chang et al., 2006). • There should not be issues that affect its ability to maintain or re-establish its level of performance. • Easy to use and maintain. 1.2 Problem Definition • In today’s market competition and the need for rapid delivery, software quality is often sacrificed, leading to the failure of the software project • The use of traditional methodology (waterfall) with the current market pace, continuous change of customer requirements and rapid development of technology plays a major role in poor software quality. • People jump to solutions without fully understanding the problem or finding the root cause of poor quality. 1.3 Research Objectives 1.
    [Show full text]
  • Statistical Process Control for Monitoring Nonlinear Profiles: a Six Sigma Project on Curing Process
    This is the author’s final, peer-reviewed manuscript as accepted for publication. The publisher-formatted version may be available through the publisher’s web site or your institution’s library. Statistical process control for monitoring nonlinear profiles: a six sigma project on curing process Shing I. Chang, Tzong-Ru Tsai, Dennis K. J. Lin, Shih-Hsiung Chou, & Yu-Siang Lin How to cite this manuscript If you make reference to this version of the manuscript, use the following information: Chang, S. I., Tsai, T., Lin, D. K. J., Chou, S., & Lin, Y. (2012). Statistical process control for monitoring nonlinear profiles: A six sigma project on curing process. Retrieved from http://krex.ksu.edu Published Version Information Citation: Chang, S. I., Tsai, T., Lin, D. K. J., Chou, S., & Lin, Y. (2012). Statistical process control for monitoring nonlinear profiles: A six sigma project on curing process. Quality Engineering, 24(2), 251-263. Copyright: Copyright © Taylor & Francis Group, LLC. Digital Object Identifier (DOI): doi:10.1080/08982112.2012.641149 Publisher’s Link: http://www.tandfonline.com/doi/abs/10.1080/08982112.2012.641149 This item was retrieved from the K-State Research Exchange (K-REx), the institutional repository of Kansas State University. K-REx is available at http://krex.ksu.edu Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process Shing I Chang1, Tzong‐Ru Tsai2, Dennis K.J. Lin3, Shih‐Hsiung Chou1 & Yu‐Siang Lin4 1Quality Engineering Laboratory, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, USA 2Department of Statistics, Tamkang University, Danshui District, New Taipei City 25137 Taiwan 3Department of Statistics, Pennsylvania State University, USA 4Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan ABSTRACT Curing duration and target temperature are the most critical process parameters for high- pressure hose products.
    [Show full text]
  • Statistical Process Control Tools: a Practical Guide for Jordanian Industrial Organizations
    Volume 4, Number 6, December 2010 ISSN 1995-6665 JJMIE Pages 693 - 700 Jordan Journal of Mechanical and Industrial Engineering www.jjmie.hu.edu.jo Statistical Process Control Tools: A Practical guide for Jordanian Industrial Organizations Rami Hikmat Fouad*, Adnan Mukattash Department of Industrial Engineering, Hashemite University, Jordan. Abstract The general aim of this paper is to identify the key ingredients for successful quality management in any industrial organization. Moreover, to illustrate how is it important to realize the intergradations between Statistical Process Control (SPC) is seven tools (Pareto Diagram, Cause and Effect Diagram, Check Sheets, Process Flow Diagram, Scatter Diagram, Histogram and Control Charts), and how to effectively implement and to earn the full strength of these tools. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialized in producing steel. Flow process chart was constructed, Check Sheets were designed, Pareto Diagram, scatter diagrams, Histograms was used. The vital few problems were identified; it was found that the steel tensile strength is the vital few problem and account for 72% of the total results of the problems. The principal aim of the project is to train quality team on how to held an effective Brainstorming session and exploit these data in cause and effect diagram construction. The major causes of nonconformities and root causes of the quality problems were specified, and possible remedies were proposed. © 2010 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved Keywords: Statistical Process Control, Check sheets, Process Flow Diagram, Pareto Diagram, Histogram, Scatter Diagram, Control Charts, Brainstorming, and Cause and Effect Diagram.
    [Show full text]
  • Cause and Effect Diagrams
    Online Student Guide Cause and Effect Diagrams OpusWorks 2019, All Rights Reserved 1 Table of Contents LEARNING OBJECTIVES ....................................................................................................................................4 INTRODUCTION ..................................................................................................................................................4 WHAT IS A “ROOT CAUSE”? ......................................................................................................................................................... 4 WHAT IS A “ROOT CAUSE ANALYSIS”?...................................................................................................................................... 4 ADDRESSING THE ROOT CAUSE .................................................................................................................................................. 5 ROOT CAUSE ANALYSIS: THREE BASIC STEPS ........................................................................................................................ 5 CAUSE AND EFFECT TOOLS .......................................................................................................................................................... 6 FIVE WHYS ...........................................................................................................................................................6 THE FIVE WHYS ............................................................................................................................................................................
    [Show full text]
  • Root Cause Analysis of Defects in Automobile Fuel Pumps: a Case Study
    International Journal of Management, IT & Engineering Vol. 7 Issue 4, April 2017, ISSN: 2249-0558 Impact Factor: 7.119 Journal Homepage: http://www.ijmra.us, Email: [email protected] Double-Blind Peer Reviewed Refereed Open Access International Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage as well as in Cabell‟s Directories of Publishing Opportunities, U.S.A ROOT CAUSE ANALYSIS OF DEFECTS IN AUTOMOBILE FUEL PUMPS: A CASE STUDY Saurav Adhikari* Nilesh Sachdeva* Dr. D.R. Prajapati** ABSTRACT Quality can be directly measured from the degree to which customer requirements are satisfied. Some problems were reported by the customers of the automobile company under study in the fuel pumps; which is used in an automobile to transfer the fuel from fuel tank to fuel injection system after filtration.This paper presents the implementation of Quality Control tools– Check Sheet, Fishbone Diagram(or Ishikawa Diagram), ParetoChartand 5-Why analysis tools for identification and elimination of the root cause/s responsible for malfunctioning of the fuel pump in customers‟ cars. From the Check sheet and Pareto analysis, two major defects were identified which accounted for more than 80% of the problems being reported. The root causes of these two defects affecting the product quality of the company were then further analyzed using the 5- Why analysis. Keywords: Quality Control Tools, Ishikawa Diagram, Pareto Chart, 5-Why Analysis * Undergraduate Student, Department of Mechanical Engineering, PEC University of Technology, (formerly Punjab Engineering College), Chandigarh ** Associate Professor& Corresponding Author, Department of Mechanical Engineering, PEC University of Technology (formerly Punjab Engineering College), Chandigarh 90 International journal of Management, IT and Engineering http://www.ijmra.us, Email: [email protected] ISSN: 2249-0558Impact Factor: 7.119 1.
    [Show 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
    [Show full text]
  • Operational Excellence
    Chapter 6 Operational Excellence ©2016 Montgomery County Community College Objectives This chapter provides an introduction to the role that Operational Excellence (OPEX or OE) plays in the continuous improvement of biomanufacturing operations. The chapter is not intended to serve as a comprehensive guide to every quality improvement, but rather as an overview of tools and techniques which illustrate many of the basic principles of Statistical Process Control (SPC). SPC is a methodology that uses statistical tools and analysis to monitor variations in a process in order to manage and control it. After completing this chapter the student will be able to: describe a process identify potential sources of waste in a process define when a process is “in control” versus “out of control” explain the simple tools used in Lean and Six Sigma improvement methodology list the steps in a Six Sigma process improvement select and apply general Lean Six Sigma tools to simulated problems recognize deployment challenges to OEX strategies 28 Chapter 6 - Operational Excellence Terms 5S (Sort, Straighten, Shine, Standardize, and Sustain): a workplace discipline used to ensure reliable work practices and a clean working environment; used in the West but originally from Japan. The term 5S is derived from the original Japanese usage of S-prefix words: Seiri, Seition, Seiso, Seiketsu, and Shitsuke. Andon: a visual management tool and component of the lean philosophy; these are lights placed on or adjacent to machines or production lines to indicate operation status. Correlation: a statistical relation between two or more variables such that systematic changes in the value of one variable are accompanied by systematic changes in the other.
    [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]