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Assist. Prof. Dr. Benhr SATIR 02/12/2014

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Outline Variation Dimensions of Quality Definition of Quality Descriptive Statistics Statistical Methods for Quality Improvement: Acceptance Sampling Designed Experiments Statistical Process Control & Magnificient Seven Total Quality Management Quality Related Costs Benefits 14.12.20122

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Variation Which has higher variation? mam ada McDonalds What do you understand from this question? # of different meals the same taste for a specific meal Something else? 14.12.20123

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Variation Which one you like more? mam ada McDonalds 14.12.20124

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Variation Which one you like more as an IE? mam ada McDonalds 14.12.20125

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Variation Variation is an enemy for an IE We hate variation! Ford T Model: Henry Ford said "You can have any colour as long as it's black." If it is unavoidable, try to cope with it Only black color for a Mercedes in 2015? 14.12.20126

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Dimensions of Quality Garvin (1987) 1. Performance: Will the product/service do the intended job? 2. Reliability: How often does the product/service fail? 3. Durability: How long does the product/service last? 4. Serviceability: How easy to repair the product / to solve the problems in service? 14.12.20127

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Dimensions of Quality 5. Aesthetics: What does the product/service look/smell/sound/feel like? 6. Features: What does the product do/ service give? 7. Perceived Quality: What is the reputation of the company or its products/services? 8. Conformance to Standards: Is the product/service made exactly as the designer/standard intended? 9. What else? What do YOU think? 14.12.20128

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Quality in Different Areas of Society 14.12.20129 AreaExamples AirlinesOn-time, comfortable, low-cost service Food ServicesGood product, fast delivery, good environment Postal Servicesfast delivery, correct delivery, cost containment Consumer ProductsProperly made, defect-free, cost effective InsurancePayoff on time, reasonable cost AutomotiveDefect-free CommunicationsClearer, faster, cheaper service

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Definition of Quality No two products are identical; i.e. There is always a certain amount of variability. Modern Defn : Quality is inversely proportional to variability. Quality Improvement : reduction of variability in processes and products. 14.12.201210

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Quality Engineering Terminology Quality characteristics : parameters that jointly describe the quality from customers view point. Physical : length, weight, viscocity. Sensory : taste, color, appearance. Time orientation : reliability, durability, serviceability. Nominal (Target) Value : Desired value for a quality characteristic. Upper Specification Limit (USL) : largest allowable value for a quality characteristic that will not influence the funciton or performance of theproduct. Lower Specification Limit (LSL) : Similar to USL, it is the smallest allowable value. 14.12.201211

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Quality Engineering Terminology Nonconformity : Specific type of failure. Failure : Fail to meet the specification. Defect : Nonconformities that are serious enough to significantly affect the safe or effective use of the product. Defective: A product is defective if it has one or more defects. 14.12.201212

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Quality Engineering Terminology Quality Engineering : Set of operational, managerial and engineering applications to ensure that the quality characteristics are at their corresponding nominal values or required levels. Remember : There is always variability and quality is inversely proportional to it. Only way of describing variability : Statistics. Use of statistical methods are crucial in quality improvements. 14.12.201213

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Some Important Statistical Definitions Population Sample Use parameters to summarize features Use statistics to summarize features Inference on the population from the sample 14.12.201214

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Some Important Statistical Definitions A Population (Universe) is the whole collection of things under consideration. A Sample is a portion of the population selected for analysis. A Parameter is a summary measure computed to describe a characteristic of the population. A Statistic is a summary measure computed to describe a characteristic of the sample. 14.12.201215

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Summary Measures Central Tendency MeanMedianModeQuartileVariationRangeVariance Standard Devation 14.12.201216

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Mean Population Mean: (parameter) For a finite population with N measurements, the population mean is A reasonable estimate of the population mean is the sample mean. Sample Mean: (statistic) If the n observations in a sample are denoted by x 1, x 2, , x n, the sample mean is 14.12.201217

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Mean Example: Suppose that an engineer is developing a rubber compound for use in O-rings. The O-rings are to be employed as seals in plasma etching tools used in the semiconductor industry, so their resistance to acids and other corrosive substances is an important characteristic. The data from the modified rubber compound are: 1037 1047 1066 1048 1059 1073 1070 1040. The sample mean strength (psi) for the eight observations on strength is 14.12.201218

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Mean Sample mean is affected by the extreme values and/or outliers. 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 12 14 14.12.201219

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Median Robust measure of central tendency Not affected by extreme values In an ordered array, the median is the middle number If n or N is odd, the median is the middle number If n or N is even, the median is the average of the 2 middle numbers 0 1 2 3 4 5 6 7 8 9 100 1 2 3 4 5 6 7 8 9 10 12 14 14.12.201220

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Median Example : Consider the O-rings example. 1037 1047 1066 1048 1059 1073 1070 1040. To find the median, 14.12.201221

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Mode A Measure of central tendency Value that occurs most often Not affected by extreme values There may not be a mode There may be several modes Used for either numerical or categorical data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 14.12.201222

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Summary Measures Mean = 15.5 11 12 13 14 15 16 17 18 19 20 21 Data B Data A Mean = 15.5 11 12 13 14 15 16 17 18 19 20 21 Mean = 15.5 Data C 14.12.201223

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Range Measure of variation Difference between the largest and the smallest observations: Ignores how data are distributed 7 8 9 10 11 12 14.12.201224

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Sample Variance & Standard Deviation Most important measure of variation Shows variation about the mean 14.12.201225

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Sample Variance & Standard Deviation If the n observations in a sample are denoted by x 1, x 2,, x n, then the sample variance is The sample standard deviation, s, is the positive square root of the sample variance. 14.12.201226

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Sample Variance & Standard Deviation Example : Consider the O-rings example. 1037 1047 1066 1048 1059 1073 1070 1040. ixixi 1234567812345678 1037 1047 1066 1048 1059 1073 1070 1040 8440 14.12.201227

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Sample Variance & Standard Deviation 11 12 13 14 15 16 17 18 19 20 21 Data B Data A 11 12 13 14 15 16 17 18 19 20 21 Data C Mean = 15.5 s = 3.338 Mean = 15.5 s =.9258 Mean = 15.5 s = 4.57 14.12.201228

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Histogram The most commonly used graph to show frequency distributions, i.e. how often each different value in a set of data occurs. Used to visualize the distribution. Birthdate example 14.12.2012 29

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Pareto Chart Organizes and displays information to show the relative importance of various problems or causes of problems. A special form of a vertical bar chart that puts items in order (from the highest to the lowest) relative to some measurable effect of interest: frequency, cost, time. Are arranged with longest bars on the left and the shortest to the right. Helps teams to focus efforts where they can have the greatest potential impact. 14.12.201230

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Example Pareto Chart 14.12.201231

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Cause & Effect Diagram Also called Ishikawa diagram, fishbone diagram. Understand the root causes of a problem BEFORE you put a solution into place. Identify and display many different possible causes for a problem. See the relationships between the many causes. Helps determine which data to collect. 14.12.201232

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Cause & Effect Diagram 14.12.201233 Clearly define the focused problem. Use brainstorming to identify possible causes. Sort causes into reasonable clusters (no less than 3, not more than 6). Label the clusters (consider people, policies, procedures, materials if you have not already identified labels). Develop and arrange bones in each cluster. Check the logical validity of each causal chain. Focused problem Root cause

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Cause & Effect Diagram 14.12.201234 Bones should not include solutions. Bones should not include lists of process steps. Bones include the possible causes. Turnover in staff Policies People Procedures Materials Inadequate training Burnout Lack of supervision Minimal benefits No policy on staff screening Paperwork overwhelming Lack of office space Back-biting environment Restrictive budget Location Escorting clients to appointments and having to wait

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Statistical Methods for Quality Improvement 3 major areas: Acceptance Sampling Statistical Process Control (SPC) Design of Experiments 14.12.201235

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Acceptance Sampling Inspection and testing of Raw materials Semifinished products Finished products Based on inspection Accept or Reject the product Type of inspection procedure is called acceptance sampling. Can do either 100% inspection, or inspect a sample of a few items taken from the lot. 14.12.201236

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Statistical Process Control SPC is a statistics-based methodology for achieving process stability and improving capability by reducing variability. All processes have variation in output: Some of the variation is caused by factors that can be identified and managed (assignable causes). Ex: improperly adjusted machines, operator errors, defective raw materials etc. Some of the variation is inherent in the process (background noise) : cumulative effect of many small, unavoidable causes. Also named as chance causes of variation. A process is said to be in statistical control, if only chance causes of variation is present and it is out of control, if there are assignable causes of variation. SPC is aimed at discovering variation resulting from assignable causes so that adjustments can be made and bad output is not produced. 14.12.201237

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Control Chart A control chart is a presentation of data in which the control values are plotted against time. Used to study how a process changes over time and to determine if variation is chance or assignable cause. Immediate visualisation of problems. Control charts have a central line, upper and lower warning limits, and upper and lower action limits. 14.12.201238

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Control chart - Illustration of construction 0102030405060708090100 0.8 0.9 1.0 1.1 1.2 1.3 X-chart Copper Warning limit Action limit Central line Control value 14.12.201239

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Design of Experiments Helpful in discovering the key variables influencing the quality characteristics of interest. Systematically change the controllable factors in the process and determine the effect of them on the output product parameters. Statistically designed experiments are useful to reduce the variability in the quality characteristics and to determine the levels of controllable factors that optimize process performance. 14.12.201240

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TQM Consists of organization-wide efforts to install and make permanent a climate in which an organization continuously improves its ability to deliver high-quality products and services to customers. W. Edwards Deming 14.12.201241

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Demings 14 Points for the Transformation of Management 1. Create constancy of purpose toward improvement of product and service, with the aim to become competitive and to stay in business, and to provide jobs. 2. Adopt the new philosophy. We are in a new economic age. Western management must awaken to the challenge, must learn their responsibilities, and take on leadership for change. 3. Cease dependence on inspection to achieve quality. Eliminate the need for inspection on a mass basis by building quality into the product in the first place. 4. End the practice of awarding business on the basis of price tag. Instead, minimize total cost. Move toward a single supplier for any one item, on a long-term relationship of loyalty and trust. 5. Improve constantly and forever the system of production and service, to improve quality and productivity, and thus constantly decrease costs. 6. Institute training on the job. 7. Institute leadership (see Point 12 and Ch. 8). The aim of supervision should be to help people and machines and gadgets to do a better job. Supervision of management is in need of overhaul, as well as supervision of production workers. 8. Drive out fear, so that everyone may work effectively for the company (see Ch. 3). 9. Break down barriers between departments. People in research, design, sales, and production must work as a team, to foresee problems of production and in use that may be encountered with the product or service. 10. Eliminate slogans, exhortations, and targets for the work force asking for zero defects and new levels of productivity. Such exhortations only create adversarial relationships, as the bulk of the causes of low quality and low productivity belong to the system and thus lie beyond the power of the work force. Eliminate work standards (quotas) on the factory floor. Substitute leadership. Eliminate management by objective. Eliminate management by numbers, numerical goals. Substitute leadership. 11. Remove barriers that rob the hourly worker of his right to pride of workmanship. The responsibility of supervisors must be changed from sheer numbers to quality. 12. Remove barriers that rob people in management and in engineering of their right to pride of workmanship. This means, inter alia, abolishment of the annual or merit rating and of management by objective (see Ch. 3). 13. Institute a vigorous program of education and self-improvement. 14. Put everybody in the company to work to accomplish the transformation. The transformation is everybody's job. 14.12.201242

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Demings 7 Deadly Diseases 1. Lack of constancy of purpose to plan product and service that will have a market and keep the company in business, and provide jobs. 2. Emphasis on short-term profits: short-term thinking (just the opposite from constancy of purpose to stay in business), fed by fear of unfriendly takeover, and by push from bankers and owners for dividends. 3. Evaluation of performance, merit rating, or annual review. 4. Mobility of management; job hopping. 5. Management by use only of visible figures, with little or no consideration of figures that are unknown or unknowable. 6. Excessive medical costs. 7. Excessive costs of liability, swelled by lawyers that work on contingency fees. 14.12.201243

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Demings Circle PDSA: plandostudyact OPDCA: observation-PDSA PDCA: plandocheckact or plandocheckadjust Iterative four-step management method used in business for the control and continuous improvement of processes and products. 14.12.201244

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Quality-Related Costs Prevention costs Appraisal costs Correction costs: Internal Failure Costs External Failure Costs Costs of Conformance i.e. : The cost of doing things right the first time Costs of Non-Conformance i.e. : The cost incurred as a result of things not being done right the first time 14.12.201245

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Benefits 14.12.201246 Reduces costs Increases dependability Increases speed Boosts morale Increases customer retention Increases profit Internal BenefitsExternal Benefits Customer gets correct product or service Correct specifications Appropriate intangibles Customer satisfaction Customer retention