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Chapter 6 Quality Management Types of Quality to Consider • User based quality – Eyes of the customer, sometimes not measurable • Product based quality – Measurable Quality characteristics • Manufacturing based Quality – Conforms to standards / designs Dimensions of Quality (Garvin) 1. Performance Basic operating characteristics 2. Features “Extra” items added to basic features 3. Reliability Probability product will operate over time Dimensions of Quality (Garvin) 4. Conformance Meeting pre-established standards 5. Durability Life span before replacement 6. Serviceability Ease of getting repairs, speed & competence of repairs Dimensions of Quality (Garvin) 7. Aesthetics Look, feel, sound, smell or taste 8. Safety Freedom from injury or harm 9. Other perceptions Subjective perceptions based on brand name, advertising, etc Total Quality Management 1. 2. 3. 4. 5. 6. 7. 8. Customer defined quality Top management leadership Quality as a strategic issue All employees responsible for quality Continuous improvement / Kaizen Shared problem solving Statistical quality control Training & education for all employees Cost of Quality Cost of achieving good quality Prevention Planning, Product design, Process, Training, Information Appraisal Inspection and testing, Test equipment, Operator Cost of Quality Cost of poor quality Internal failure costs Scrap, Rework, Process failure, Process downtime, Pricedowngrading External failure costs Customer complaints, Product return, Warranty, Product liability, Lost sales Quality–Cost Relationship Increased prevention costs lead to decreased failure costs Improved quality leads to increased sales and market share Quality improvement at the design stage Higher quality products can command higher prices Statistical Process Control The objective of a process control system is to provide a statistical signal when assignable causes of variation are present © 2014 Pearson Education, Inc. S6 - 10 Statistical Process Control (SPC) ► Variability is inherent in every process ► Natural or common causes ► Special or assignable causes ► Provides a statistical signal when assignable causes are present ► Detect and eliminate assignable causes of variation © 2014 Pearson Education, Inc. S6 - 11 Natural Variations ► Also called common causes ► Affect virtually all production processes ► Expected amount of variation ► Output measures follow a probability distribution ► For any distribution there is a measure of central tendency and dispersion ► If the distribution of outputs falls within acceptable limits, the process is said to be “in control” © 2014 Pearson Education, Inc. S6 - 12 Assignable Variations ► Also called special causes of variation ► Generally this is some change in the process ► Variations that can be traced to a specific reason ► The objective is to discover when assignable causes are present ► Eliminate the bad causes ► Incorporate the good causes © 2014 Pearson Education, Inc. S6 - 13 Samples To measure the process, we take samples and analyze the sample statistics following these steps Each of these represents one sample of five boxes of cereal (a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency # # # # # # # # # # # # # # # # # Figure S6.1 © 2014 Pearson Education, Inc. # # # # # # # # # Weight S6 - 14 Samples To measure the process, we take samples and analyze the sample statistics following these steps Frequency (b) After enough samples are taken from a stable process, they form a pattern called a distribution The solid line represents the distribution Weight Figure S6.1 © 2014 Pearson Education, Inc. S6 - 15 Samples To measure the process, we take samples and analyze the sample statistics following these steps (c) There are many types of distributions, including the normal (bellshaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Frequency Figure S6.1 Central tendency Weight © 2014 Pearson Education, Inc. Variation Weight Shape Weight S6 - 16 Samples To measure the process, we take samples and analyze the sample statistics following these steps Prediction Frequency (d) If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight Figure S6.1 © 2014 Pearson Education, Inc. S6 - 17 Samples To measure the process, we take samples and analyze the sample statistics following these steps Prediction Frequency (e) If assignable causes are present, the process output is not stable over time and is not predicable ? ?? ?? ? ? ? ? ? ? ? ? ? ?? ?? ? Weight Figure S6.1 © 2014 Pearson Education, Inc. S6 - 18 Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes © 2014 Pearson Education, Inc. S6 - 19 Process Control Frequency Lower control limit (a) In statistical control and capable of producing within control limits Upper control limit (b) In statistical control but not capable of producing within control limits (c) Out of control Size (weight, length, speed, etc.) © 2014 Pearson Education, Inc. Figure S6.2 S6 - 20 Control Charts for Variables ► Characteristics that can take any real value ► May be in whole or in fractional numbers ► Continuous random variables x-chart tracks changes in the central tendency R-chart indicates a gain or loss of dispersion © 2014 Pearson Education, Inc. S6 - 21 Setting Chart Limits For x-Charts when we know s Lower control limit (UCL) = x= - zs x Upper control limit (UCL) = x= + zs x Where x= = mean of the sample means or a target value set for the process z = number of normal standard deviations sx = standard deviation of the sample means = s / n s = population (process) standard deviation n = sample size © 2014 Pearson Education, Inc. S6 - 22 Setting Control Limits ▶ Randomly select and weigh nine (n = 9) boxes each hour Average weight in 17 +13 +16 +18 +17 +16 +15 +17 +16 = = 16.1 ounces the first sample 9 WEIGHT OF SAMPLE HOUR (AVG. OF 9 BOXES) 1 WEIGHT OF SAMPLE WEIGHT OF SAMPLE HOUR (AVG. OF 9 BOXES) HOUR (AVG. OF 9 BOXES) 16.1 5 16.5 9 16.3 2 16.8 6 16.4 10 14.8 3 15.5 7 15.2 11 14.2 4 16.5 8 16.4 12 17.3 © 2014 Pearson Education, Inc. S6 - 23 Setting Control Limits 12 é Avg of 9 boxes ê å Average mean ê = i=1 = x= of 12 samples ê 12 ê ë ( © 2014 Pearson Education, Inc. ) ù ú ú ú ú û x= = 16 ounces n=9 z=3 s = 1 ounce S6 - 24 Setting Control Limits 12 é Avg of 9 boxes ê å Average mean ê = i=1 = x= of 12 samples ê 12 ê ë ( ) ù ú ú ú ú û x= = 16 ounces n=9 z=3 s = 1 ounce æ 1 ö æ 1ö UCL x = x + zs x = 16 + 3 ç ÷ = 16 + 3 ç ÷ = 17 ounces è3ø è 9ø = æ 1 ö æ 1ö LCL x = x - zs x = 16 - 3 ç ÷ = 16 - 3 ç ÷ = 15 ounces è3ø è 9ø = © 2014 Pearson Education, Inc. S6 - 25 Setting Control Limits Control Chart for samples of 9 boxes Variation due to assignable causes Out of control 17 = UCL Variation due to natural causes 16 = Mean 15 = LCL | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 Sample number © 2014 Pearson Education, Inc. Out of control Variation due to assignable causes S6 - 26 Restaurant Control Limits Sample Mean For salmon filets at Darden Restaurants x Bar Chart 11.5 – UCL = 11.524 11.0 – = x = – 10.959 10.5 – | | | | | | | | | 1 3 5 7 9 11 13 15 17 LCL = – 10.394 Sample Range Range Chart 0.8 – UCL = 0.6943 0.4 – – R = 0.2125 0.0 – | 1 © 2014 Pearson Education, Inc. | | | | | | | | 3 5 7 9 11 13 15 17 LCL = 0 S6 - 27 Mean and Range Charts (a) (Sampling mean is shifting upward, but range is consistent) These sampling distributions result in the charts below UCL (x-chart detects shift in central tendency) x-chart LCL UCL (R-chart does not detect change in mean) R-chart Figure S6.5 LCL © 2014 Pearson Education, Inc. S6 - 28 Mean and Range Charts (b) These sampling distributions result in the charts below (Sampling mean is constant, but dispersion is increasing) UCL (x-chart indicates no change in central tendency) x-chart LCL UCL (R-chart detects increase in dispersion) R-chart Figure S6.5 LCL © 2014 Pearson Education, Inc. S6 - 29 Control Charts for Attributes ► For variables that are categorical ► Defective/nondefective, good/bad, yes/no, acceptable/unacceptable ► Measurement is typically counting defectives ► Charts may measure 1. Percent defective (p-chart) 2. Number of defects (c-chart) © 2014 Pearson Education, Inc. S6 - 30 Six Sigma • Developed by Motorola, a disciplined approach calling for at most 3 defects for every million units of production / customers served • Seven Tools will not be covered in depth Benchmarking • Comparing product / service against best-in-class • In computing – Standard test cases • Auto safety – Crash tests • In a large company, internal benchmarking: – Compare different divisions or departments • In General: Who does it the best and how do we compare? What is JIT ? Producing only what is needed, when it is needed A philosophy An integrated management system JIT’s mandate: Eliminate all waste “Poka-yokes” used to eliminate errors and wasteful repetition Kaizen Continuous improvement Requires total employee involvement Essence of JIT is willingness of workers to: Spot quality problems Halt production when necessary Generate ideas for improvement Analyze problems Perform different functions Visual Control Visual Control Visual Control