...Chapter 6 Statistical Process Control 6.0 Introduction One of the axioms or truisms in law of nature is “No two items of any category at any instant in the universe are the same”. Manufacturing process is no exception to it. It means that variability is part of life and is an inherent property of any process. Measuring, monitoring and managing are rather engineers’ primary job in the global competition. A typical manufacturing scenario can be viewed as shown in the Figure 6.1. That is if one measures the quality characteristic of the output, he will come to know that no two measured characteristics assume same value. This way the variablility conforms one of the axioms or truisms of law of nature; no two items in the universe under any category at any instant will be exactly the same. In maunufacturing scenario, this variability is due to the factors (Random variables) acting upon the input during the process of adding value. Thus the process which is nothing but value adding activity is bound ot experience variability as it is inherent and integral part of the process. Quality had been defined in many ways. Quality is fitness for use is the most common way of looking at it. This fitness for use is governed by the variability. In a maufacturing scenario, despite the fact that a machine operator uses the same precision methods and machines and endeavours to produce identical parts, but the finished products will show a definite variablity. The variability of a product...
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...Summary Seattle Concrete Company (SCC) produces bags of concrete mix for its customers. These bags have a stated weight of 40 pounds per bag. Given the steep penalties for under filling bags, SCC sets a target of 41 pounds per bag to ensure under filling due to natural variations still remain above the 40 pound threshold. Historically, they have operated with a single production shift, but have recently added a second shift to meet their growing demand. Management is interested in a full analysis of their process to ensure that the control limits and capabilities are within reason, and to ensure that the addition of a second shift isn’t causing issues to their process. To accomplish this, a data set was constructed. Ten bags were tested every hour during each of the two separate eight hour shifts. This continued for five days to give us 800 observations, 400 from each shift. The samples provided were analyzed for management using statistical process control methods, control charts for attributes, and capability analysis. This included X & R control charts, P-charts, C-charts, and capability analysis. The interesting finding was that all the control charts displayed variations and fill levels completely within the limits. Also, defective bags, defined as those filled to less than 40 pounds, occurred within acceptable limits. Despite all this, the capability analysis with our chosen bounds of 40 pounds to 42 pounds failed to meet the 3σ threshold. Upon this determination...
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...Statistical Engineering: Principles and Examples September 20, 2011 Lynne B. Hare, Ph.D. Statistical Strategies, LLC S i i lS i C lynne.hare@comcast.net Outline 1. Motivation o a o a. b. c. d. Not enough statisticians Management demand Balance with other staff Other technologies in competition p Background Definition Principles Comments 3 3. Examples a p es NIST Standard Reference Materials b. Industrial consumer b I d i l complaints [Group Exercise] a. Closed loop dosing systems c. Process Variation c Process Variation Reduction d. Lean Six Sigma Slide 2 a. 2. Statistical Engineering a. b. c. d. d Lynne s Background (Why me?) Lynne’s Background (Why me?) • 40+ years in industry – Nabisco, then Kraft , – Unilever (Lipton) – Hunt‐Wesson Foods • Academic – AB Math The Colorado AB Math, The Colorado College – MS, Applied and Mathematical Statistics, Rutgers – PhD, Interdisciplinary, Rutgers • Government: NIST Government: NIST • Academia – Cal. St. Fullerton (MBAs) – Rutgers (Experiment Station) ( ) • ASA – Chair P&Q Division – Fellow ‘94 • Consulting – Consumer goods – Pharmaceuticals – R&D, Manufacturing, Quality • ASQ – Chair Statistics Division – Fellow ’86 – Column Quality Progress Column: Quality Progress Slide 3 Statisticians?? Not sure N t Yes No Don’t care. This is the only session that looked remotely interesting. that looked remotely interesting 4 How can you tell? How can you tell? • If you...
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...Summary Beaver Brothers, Inc. is conducting a study to assess the accuracy, and consistency of their 150-gram bar soap production line. They are interested in seeking; the reliability, average weight and range of each soap bar. By conducting this study Beaver Brothers, Inc. wants to see the consistency of their machines, dependability of their marketing tools and the level of assurance that they are promoting and supplying trustworthy products to their consumers. Beaver Brother, Inc. achieves their wanted information by collecting 25 samples of 5 observations and recorded the data in a table. The task is to use these collected samples to create; means, graphs, and tables to present the data and draw conclusions about the production line. This is to be done using quantitative and qualitative data, consisting of reliable and relevant information. Answers and Conclusions a) The first assignment was to create the control charts showing that the process of the machinery is in statistical control. This was accomplished using the x-bar charts program on the POM software. By viewing Graph 1, you are able to see the range of the weight within each sample group. You can see that the range diversifies from up to eleven grams in study 22, as low as less than one gram in study 24. There is a consistent trend however, that the average range of each five observations ranges at around 6.59 grams. Graph 1 is inconclusive by itself. It does not demonstrate the gram weight of each soap...
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...Introduction 3 What does TQM cover? 3 Deming 14 Principles of TQM 3 Total Quality and Six Sigma 5 Six sigma DMAIC process elements 6 Application where Six sigma is better 6 Difference between Six Sigma & Total Quality Management 6 Reasons for adopting Total quality Management 7 Accurate time to adopt Total Quality Management (TQM) 8 Total Quality Management Tools 9 Principles of Total Quality Management 11 1. Customer centric approach 12 2. Employee involvement 12 3. Process approach 13 4. Integrated system 13 5. Strategic & systematic approach 13 6. Fact-based decision making 14 7. Continual improvement 14 8. Communication 14 The Concept of Continuous Improvement by Total Quality Management 14 Benefits of Total Quality Management in an Organization 16 Conclusion 17 References: 18 Introduction Total Quality Management (TQM) is a philosophy with the enthusiasm of accomplishing the overall performance of the organization. This globally recognized strategic technique has several benefits that make TQM trustworthy and applicable toward the business people. These benefits may include improving consumer satisfaction, focusing on employee motivation, reducing waste and improving overall performance of the organization. TQM is a problem solving technique that particularly focuses on continuous improvement of the quality of product, process and service. In this approach the management and employee can work together for the overall improvement by using...
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...Sigma * is a set of techniques and tools for process improvement developed by Motorola in 1986. Jack Welch made it central to his business strategy at General Electric in 1995. Today, it is used in many industrial sectors. * seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes. It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization ("Champions", "Black Belts", "Green Belts", "Yellow Belts", etc.) who are experts in these methods. Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified value targets, for example: reduce process cycle time, reduce pollution, reduce costs, increase customer satisfaction, and increase profits. These are also core to principles of Total Quality Management (TQM) as described by Peter Drucker and Tom Peters (particularly in his book "In Search of Excellence" in which he refers to the Motorola six sigma principles). * The term is originated from terminology associated with manufacturing, specifically terms associated with statistical modeling of manufacturing processes. The maturity of a manufacturing process can be described by a sigma rating indicating its yield or the percentage of defect-free products it creates. A six sigma process is one in which 99.99966% of the products manufactured...
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...Quality and Productivity in Manufacturing Process by using Quality Control Chart and Statistical Process Control Including Sampling and Six Sigma By Ghazi Abu Taher & Md. Jahangir Alam Khulna University of Engineering & Technology, Bangladesh Abstract- The aim of study is to find out the effective way of improving the quality and productivity of a production line in manufacturing industry. The objective is to identify the defect of the company and create a better solution to improve the production line performance. Various industrial engineering technique and tools is implementing in this study in order to investigate and solve the problem that occurs in the production. However, 7 Quality Control tools are the main tools that will be applied to this study. Data for the selected assembly line factory are collected, studied and analyzed. The defect with the highest frequency will be the main target to be improved. Various causes of the defect will be analyzed and various solving method will be present. The best solving method will be chosen and propose to the company and compare to the previous result or production. However, the implementation of the solving methods is depending on the company whether they wanted to apply or not. Keywords: statistical process control, control chart, tqm, 6-sigma, sampling, histogram, pareto diagram, cause and effect diagram, AQL, LTPD, process performance index, process potential index, process centering index. GJRE-G Classification : FOR...
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...Prentice Hall Upper Saddle River, New Jersey 07458 About the Authors vi Foreword xx Preface xxi PART i l l 1, Introduction to Operations Management 1 Operations and Productivity 1 Global Company Profile: Hard Rock Cafe 2 What Is Operations Management? 4 Organizing to Produce Goods and Services 4 Why Study OM? 4 What Operations Managers Do 7 How This Book Is Organized 7 The Heritage of Operations Management 8 Operations in the Service Sector 9 Differences between Goods and Services 10 Growth of Services 11 Service Pay 12 Exciting New Trends in Operations Management 12 The Productivity Challenge 14 Productivity Measurement 15 Productivity Variables 17 Productivity and the Service Sector 19 Ethics and Social Responsibility 20 Summary 20 • Key Terms 21 • Solved Problems 21 < Self-Test 22 • Internet and Student CD-ROM/DVD Exercises 22 • Discussion Questions 22 • Ethical Dilemma 23 • Problems 23 • Case Studies: National Air Express 24; Zychol Chemicals Corporation 25 • Video Case Study: Hard Rock Cafe: Operations Management in Services 25 • Additional Case Study 26 • Bibliography 26 • Internet Resources 26 Developing Missions and Strategies 34 Mission 34 Strategy 35 Achieving Competitive Advantage Through Operations 36 Competing on Differentiation 36 Competing on Cost 37 Competing on Response 37 Ten Strategic OM Decisions 39 Issues in Operations Strategy 42 Research 42 Preconditions 43 Dynamics 43 Strategy Development and Implementation 43 Critical Success Factors and...
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...Table of Contents 2 Executive Summary 4 3 Situation analysis 5 3.1 Company overview 5 3.2 Evaluation of Process Management approach at D2D 6 3.3 Evaluation of Just in Time (JIT) techique 10 3.4 Benchmarking 11 3.5 SWOT Analysis 12 4. RECOMMENDATIONS 13 Creating a value system: 14 Development of a Strategy Map 15 Reviewing the Organisational Strategy 15 Organisational Performance 16 5. Conclusion 18 6. Bibliography 19 EXECUTIVE SUMMARY The author conducted a situation analysis for D2D through analysing the approach to process management followed by D2D as well as the quality improvement techniques used by D2D. The analysis revealed the following: * Process Management at D2D- The approach followed by the company is very effective as every process is controlled and measured. This is applicable to all areas within D2D. However, the author is of the opinion that D2D should avoid implementing to many control systems to avoid their employees losing focus. It was also not clear what D2D’s market strategy; if any entails. * Just in Time (JIT) technique – The author found that certain elements such as Lead time reduction; flexible workforce and the required supplier quality assurance and implement a zero defects quality programme were being implemented at D2D. The author is however of the opinion that this is sufficient and that there is no need for D2D to implement additional elements of JIT. * Benchmarking – The assessment showed that D2D is very...
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...EXECUTIVE SUMMARY This research paper acknowledges different quality management tools. It is about the evolution of different quality tools and defining the best practice Strategic model in IT organization in relation to achieving quality within their business processes and integrating business processes to achieve strategic goals. Previous research and study indicates the various quality system tools in order to achieve quality management objectives in the competitive business environment. This research paper investigates and finds out the evolution of different quality system tools and significance of various quality tools and then designing best practice strategic model for future in Information Technology organization. The importance of this proposed research is that currently IT companies are experiencing increased global competition and parameters like complexities of the product, different markets, customer focus, high quality of product, decision making and integration among various business units in the global environment and the environment in which the firm operates are the issues of concern. Traditional quality tool in IT systems are not capable of coping with these demands and still stay competitive. Changes are needed in order to achieve sustainable results and maintaining fitness and overcome the problems of traditional tools. Today industrial companies need stable position socially and economically...
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...Six Sigma is a set of tools and techniques/strategies for process improvement originally developed by Motorola in 1981.[1][2] Six Sigma became well known after Jack Welch made it a central focus of his business strategy at General Electric in 1995,[3] and today it is used in different sectors of industry.[4] Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes.[5] It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization ("Champions", "Black Belts", "Green Belts", "Yellow Belts", etc.) who are experts in the methods.[5] Each Six Sigma project carried out within an organization follows a defined sequence of steps and has quantified value targets, for example; process cycle time reduction, customer satisfaction, reduction in pollution, cost reduction and/or profit increase.[5] The term Six Sigma originated from terminology associated with manufacturing, specifically terms associated with statistical modeling of manufacturing processes. The maturity of a manufacturing process can be described by a sigma rating indicating its yield or the percentage of defect-free products it creates. A six sigma process is one in which 99.99966% of the products manufactured are statistically expected to be free of defects (3.4 defects per million), although, as discussed below...
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...Control Charts and Introduction to Six Sigma Session 12: Control Charts and Introduction to Six Sigma concepts Control Charts and Introduction to Six Sigma Control Charts - Topics of Discussion – – – – – Control Chart History Control Limits Individuals and Moving Range Charts X-bar and R Charts Subgrouping Control Charts and Introduction to Six Sigma Typical Process Metrics • • • • • • • • • Cycle times Lead times Productivity Schedule variance Budget variance Employee satisfaction Customer satisfaction Safety incidents System users (# hits) • • • • • • • • • Days sales outstanding Customer service calls Request for quotes Proposal development Attrition/retention Bid win rate Transactional defects Sales orders Revenue dollars What are some metrics associated with your projects? What are some metrics associated with your projects? Control Charts and Introduction to Six Sigma Control Charts – “While every process displays Variation, some processes display controlled variation, while other processes display uncontrolled variation” (Walter Shewhart). – Controlled Variation is characterised by a stable and consistent pattern of variation over time. Associated with Common Causes. – Process A shows controlled variation. X-Bar Chart for Process A X-Bar Chart for Process A UCL=77.20 UCL=77.20 75 X a C a fo P ce B -B r h rt r ro ss 8 0 U L 7 .2 C= 7 7 Sample Mean 7 0 X= 0 8 7 .9 L L 6 .7 C= 4 0 6 0 5 0 Special Causes 0 5 1...
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...Management, Six Sigma And Related Enterprise Process Improvement Methods Kirkor Bozdogan Center for Technology, Policy and Industrial Development Massachusetts Institute of Technology Cambridge, MA USA 02139 Tel. 617 253-8540 bozdogan mit.edu ESD-WP-2010-05 August 2010 esd.mit.edu/wps ESD Working Paper Series Towards An Integration Of The Lean Enterprise System, Total Quality Management, Six Sigma And Related Enterprise Process Improvement Methods Kirkor Bozdogan Center for Technology, Policy and Industrial Development Massachusetts Institute of Technology Cambridge, MA USA 02139 Tel. 617 253-8540 bozdogan mit.edu ESD-WP-2010-05 August 2010 esd.mit.edu/wps TOWARDS AN INTEGRATION OF THE LEAN ENTERPRISE SYSTEM, TOTAL QUALITY MANAGEMENT, SIX SIGMA AND RELATED + ENTERPRISE PROCESS IMPROVEMENT METHODS Kirkor Bozdogan # Center for Technology, Policy and Industrial Development, Massachusetts Institute of Technology, Cambridge, MA USA 02139 August 5, 2010 Key Words: Lean manufacturing; just-in-time-production (JIT); Toyota Production System (TPS); lean enterprise system; total quality management (TQM); six sigma; lean six sigma; theory of constraints (TOC); agile manufacturing; business process reengineering (BPR); enterprise change and transformation; evidence-based management practices Abstract: The lean enterprise system, total quality management, six sigma, theory of constraints, agile manufacturing, and business process reengineering have been introduced...
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...Operations Exam Framework Exam writing * Use headings and titles * Be short and clear * Executive summary is useful * Use exhibits + quantitative analysis * Don’t repeat case facts Strong Exams * Support claims with evidence * Are specific * Address root causes * Prioritize time and actions * Impact of actions * Organization of report * Use exhibits for assumptions * Actions consistent with analysis Read the Case Executive Summary – must do Think of Decision and make analysis lead to it Context * Role * Limitations of the role * Other stakeholders? * Issue: Write a sentence outlining the core problem * Prioritize the issues * Key issues symptoms outcomes (financial concerns = revenue/profit) * (Design (product/process matrix), Capacity, Inventory (SCM), Quality) * Goal: Long term plans and goals – motivation * Decision * Constraints and other considerations * Time, money, scope – tradeoffs External Economy: Implications Industry Size-up * Trends in the industry (growth?) Stage of growth (prospect if start-up but low revenues, if mature there is competition and revenues grow slower, if stable cost control is important and maybe look to differentiate) * What are customers looking for? * Political, Social, Technology * Where do we fit in the industry? * Nature of industry volume or niche? Operational approach...
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...Statistical Databases Jaideep Srivastava and Hung Q. Ngo, Department of Computer Science, University of Minnesota, 200 Union street, EE/CS Building, room 4-192, Minneapolis, MN 55455 e-mail: srivasta, hngo @cs.umn.edu, ¡ 1 Introduction A statistical database management system (SDBMS) is a database management system that can model, store and manipulate data in a manner well suited to the needs of users who want to perform statistical analyses on the data. Statistical databases have some special characteristics and requirements that are not supported by existing commercial database management systems. For example, while basic aggregation operations like SUM and AVG are part of SQL, there is no support for other commonly used operations like variance and co-variance. Such computations, as well as more advanced ones like regression and principal component analysis, are usually performed using statistical packages and libraries, such as SAS [1] and SPSS [2]. From the end user’s perspective, whether the statistical calculations are being performed in the database or in a statistical package can be quite transparent, especially from a functionality viewpoint. However, once the datasets to be analyzed grow beyond a certain size, the statistical package approach becomes infeasible, either due to its inability to handle large volumes of data, or the unacceptable computation times which make interactive analysis impossible. With the increasing sophistication of data collection instrumentation...
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