İŞL 343 Üretim İşlemler Yönetimi Bahar Dönemi. Chapters 9-10 Management and Control of Quality. Melike Meterelliyoz Kuyzu

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1 İŞL 343 Üretim İşlemler Yönetimi Bahar Dönemi Chapters 9-10 Management and Control of Quality Melike Meterelliyoz Kuyzu

2 What is quality? Quality does not mean goodness is the ability of a product or service to consistently meet or exceed customer expectations.

3 Evolution of Quality Management Craft production Skilled craftsmen, responsibility for entire product Industrial Revolution (1770s) Division of labor, less personal accountability for the completed product's performance. Foreman had quality responsibility. Consider quality early in the design stage (1900s) Taylor, Father of Scientific Management : product inspection and gauging Radford: quality consideration in design stage; connection between high quality, increased productivity, and low costs. Statistical process control charts (1924) and tables for acceptance sampling (1930) Bell Labs

4 Evolution of Quality Management Statistical sampling techniques (1940s) Army refined sampling techniques to management large shipments from suppliers American Society for Quality Control Quality assurance/tqc (1950s) Quality in product design and incoming material Zero defects (1960s ) Employee motivation and awareness Quality assurance in services (1970s) Government operations, health care, banking Quality as strategic goal (1980s) Proactive, preventing mistakes Customer satisfaction

5 Key Contributors to Quality Management Table 9.2 Contributor Shewhart Deming Key Contributions Father of statistical quality control Control charts; variance reduction 14 points; special & common causes of variation Juran Crosby Quality is fitness-for-use; quality trilogy Quality is free; zero defects Ishikawa Cause-and-effect diagram; quality circles Taguchi Taguchi loss function

6 Quality Dimensions (Product) Dimension Examples (Automobile) 1. Performance Everything works, fit & finish Ride, handling, acceleration 2. Aesthetics Interior and exterior design 3. Special features Convenience: placement of gauges High tech: Cell phone, dvd player Safety: anti- skid, airbags 4. Conformance Car matches manufacturer s specifications 5. Reliability Infrequency need for repairs 6. Durability Useful life in miles, resistance to rust 7. Perceived quality Top- rated car 8. Serviceability Service after sale

7 Quality Dimensions (Service) Dimension Examples (Car Repairing) 1. Tangibles Were the facilities clean, personnel neat? 2. Convenience Was the service center conveniently located? 3. Reliability Was the problem fixed? 4. Responsiveness Were customer service personnel willing and able to answer questions? 5. Time How long did the customer wait? 6. Assurance Did the customer service personnel seem knowledgeable about the repair? 7. Courtesy Were customer service personnel and the cashier friendly and courteous?

8 Determinants of Quality Design Ease of use Quality of design: Intention of designers to include or exclude features in a product or service Quality of conformance: The degree to which goods or services conform to the intent of the designers Conforms to design Service

9 How much is the cost of quality? ** Porsche recalls 18,627 of its 911 Carreras to replace exhaust pipe: the welding seams on the exhaust pipe could weaken, causing it to fall off. The 911 Carrera S retails for about $81,400 US. ** December, 2005 : Dell recalled about 22,000 laptops in the U.S., as batteries can overheat, which could pose a fire risk. Dell will provide a free replacement battery. August 2006: Dell recalled almost 4.1 million batteries sold in laptops. ** Manufacturer Toshiba announced it was recalling 340,000 batteries across its Dynabook and Satellite lines. ** Fujitsu recalled 287,000 battery packs worldwide.

10 How much is the cost of quality? ** September, In cooperation with the U.S. Consumer Product Safety Commission (CPSC), Kolcraft Enterprises Inc., of Chicago is recalling about 425,000 infant play yards following the death of a child. ** June CPSC, in cooperation with Simplicity Inc.,, announced a recall of about 40,000 Nursery-in-a-Box Cribs. The assembly instructions provided with the cribs incorrectly instruct consumers how to attach the crib's drop side. If improperly installed, the drop side can disengage from the crib. Additionally, the metal locking pins on the drop side can pop off, presenting a choking hazard.

11 How much is the cost of quality? ** The Associated Press November 16, 2007 Volkswagen recalling 2,400 Passat models Volkswagen AG's Shanghai venture announced a recall of 2,440 of its Passat model cars this week because of problems with the locking system.

12 The Costs of Quality Appraisal costs: Costs of activities designed to ensure quality or uncover defects: inspection of equipment, testing, labs, inspectors, interruption of production. Prevention costs: Costs of preventing defects from occurring. Planning and administration systems Working with vendors Training Quality control procedure Extra attention in both the design and production phases to decrease the probability of defective workmanship

13 The Costs of Quality (Cont.) Failure costs: Cost caused by defective parts or products, faulty services, mis-calibrated equipment, poor methods, sloppy execution. Internal failure: Failure discovered during production: rework, scrap, downtime, material and product losses. External failure: Failures discovered after delivery to the customer: returns, recalls, liability, warranty costs, damage to reputation. Per unit external failure cost is typically greater than internal cost Failure costs represent costs related to poor quality Appraisal and prevention costs represent investments for achieving good quality

14 Evaluate Quality -- Inspection

15 Evaluate Quality The Baldrige Award The Deming Prize Quality Certifications: ISO 9001 ISO Türkiye: TSE

16 Inspection Inspection is an appraisal activity that compares goods or services to a standard. How Much/How Often Where/When Centralized vs. On-site Attributes vs. Variables Inputs Transformation Outputs Acceptance sampling Process control Shall we perform 100% inspection? Acceptance sampling

17 How much Trade-offs in Inspect Low-cost, high-volume items vs. high-cost, lowvolume items Costs of inspection vs. costs of passing defectives High human involvement operations vs. mechanical operations How often Stable process vs. unstable process Many small lots vs. a few large lots

18 Figure 10.3 Inspection Costs Trade-off Cost Total Cost Cost of passing defectives Cost of inspection Optimal Amount of Inspection

19 Where to Inspect in the Process Manufacturing Raw materials and purchased parts Finished products Before a costly operation Before an irreversible process Services Incoming purchased materials and suppliers Personnel Service interfaces Outgoing completed work

20 Centralized vs. On-Site Inspection Some have-to examples On-site: inspecting the hull of a ship for cracks Lab inspections: medical tests, analyzing food samples, testing metals for hardness Lab vs. On-Site Whether the advantages of specialized lab tests are worth the time and interruption On-site: quicker decisions and avoidance of introduction of extraneous factors Lab inspections: specialized equipment and favorable test environment

21 Statistical Process Control Statistical evaluation of the output of a process during production The essence of statistical process control is to assure that the output of a process is random so that future output will be random.

22 Process Variations Random Variation: Natural variations in the output of process, created by countless minor factors. -- also called as Common variability, Chance variation Assignable variation: In process output, a variation whose cause can be identified -- also called as Special variation

23 Control Chart A time-ordered plot of representative sample statistics obtained from an on going process Upper and lower control limits define the range of acceptable variation Purpose: to monitor process output to see if it is random In control all data points fall between the UCL and LCL Out of control stop the process and correct it

24 Figure 10.4 Example of a Control Chart Abnormal variation due to assignable sources Out of control UCL Normal variation due to chance Abnormal variation due to assignable sources Mean LCL Sample number

25 Variables vs. Attributes Variables data are measured, usually on a continuous scale. Examples: length, width or weight of a part, amount of time needed to complete a task Attribute data are counted. Examples: the number of defective parts in a sample, the number of calls per day

26 Statistical Tools Two statistical tools are used for quality control 1. Control charts Control charts for variables Mean charts (x-bar charts) Range charts (R charts) Control charts for attributes p-chart c-chart 2. Run tests Above-Below / Up-Down

27 Variables Data Charts Process Centering X bar Chart X-bar is a sample mean X n i = 1 = n X i Process Dispersion R Chart (consistency) R is a sample range R = max( X i ) min( X i ) Variables: generate data that are measured.

28 Normal Distribution Figure 10.6 σ=standard deviation Mean -3σ -2σ +2σ +3σ 95.44% 99.74%

29 Control Limits Control limits: the dividing lines between random and nonrandom deviations from the mean of the sampling distribution Sampling distribution Standard deviation = σ/ observation # Process distribution Standard deviation =σ Lower control limit Mean Upper control limit Why Sampling? All the samples are within the control limits random deviation process under control A sample is out side of control limits non-random deviation process out of control

30 SPC Errors Type I error: Concluding a process is not in control when it actually is. The probability of this happening is α which is the sum of the probability under two tails. Type II error: Concluding a process is in control when it is not. Non-random variations are mistakenly thought to be random. Statistical process control is not 100% correct!

31 Type I error If we choose a 3σ control limits: LCL = mean - 3 σ, UCL = mean + 3 σ 9974 out of 10,000 of the sample is within the limits 26 out of 10,000 of the sample is outside of the limits Is the process under control? SPC finds 1 sample out of the limits and conclude non-randomness variation

32 Control Charts for Variables Mean control charts Used to monitor the central tendency of a process. x (x-bar) charts Two approaches to construct 1. Using the standard deviation σ 2. Using the sample range R (=X max -X min ) Range control charts Used to monitor the process dispersion R charts

33 Mean Control Charts -- Using Sigma Based on standard deviation σ Upper control limits: UCL = Lower control limits: LCL = where + z σ - z σ is the standard deviation of the distribution of sample means if the process is under control: where σ σ = σ = Process standard deviation. n = Sample size x x σ z = Standard normal deviate (from tables) x = Average of sample means n x x x x

34 Example 1: Mean Control Charts Using Sigma Samples Observations x Find the three-sigma control limits, given that σ = 0.02 Step 1: Compute the mean of each sample x = sum of observation / number of observation

35 Example 1: Mean Control Charts Using Sigma Step 2: Find the average of sample means x = 5 Step 3: Compute sample standard deviation σ 0.02 σ = = = 0.01 x n 4 = Step 4: Find the control limits -- Three-sigma means z = 3 UCL = x + zσ = (0.01) = x LCL x - zσ = (0.01) = = x Step 5: Conclude that the process is in control (all of within UCL and LCL) x s fall

36 Mean Control Charts Using Range Based on the range of sample data R Upper control limits: Lower control limits: UCL = x + A2 R LCL = x - A2 R where R = Average of sample ranges (R =X max -X min ) A 2 : Tabulated values Note: this approach assumes that the range is in control

37 Example 2a: Mean Control Charts Using Range Observations x R Samples Q: Find the three-sigma control limits Step 1: Compute the range for each sample R = max observation min observation

38 Solution to Example 2a (Cont.) Step 2: Find the average range R = = 0.05 Step 3: Look up value A2 Observation size is 4, then A 2 =0.73 Step 4: Find the control limits: UCL = x + A2 R = (0.05) = LCL = x - A2 R = (0.05) = Step 5: Conclude that the process is in control

39 Range Control Chart (R-Chart)( Control chart used to monitor process dispersion Upper control limits: Lower control limits: UCL = D4 LCL = D3 R R where D 3 and D 4 are obtained from Table

40 Example 2b: R-Chart Observations x R Step 1: R = 5 = 0.05 Step 2: Since n=4, we have D 3 =0, and D 4 = Step 3: The control limits are UCL = D4 R = 2.28(0.05) = LCL = D3 R = 0(0.05) = Step 4: Conclusion: the process is in control (all of R s are within UCL and LCL of range chart)

41 Using Mean and Range Charts Mean charts are sensitive to shifts in the process mean Range charts are sensitive to changes in process dispersion Both charts might be used to monitor the same process

42 Figure 10.10A Mean and Range Charts Sampling Distribution (process mean is shifting upward) UCL x-chart Reveals shift LCL UCL R-chart Does not reveal shift LCL

43 Figure 10.10B Mean and Range Charts Sampling Distribution (process variability is increasing) UCL x-chart Does not reveal increase LCL UCL R-chart Reveals increase LCL

Control Chart for Attributes Used when the

$average fraction defective in population Number$ of Defectives Per Unit c Chart c is average

44 Control Chart for Attributes Used when the process characteristic is counted rather than measured Proportion of Defectives p Chart p is average fraction defective in population Number of Defectives Per Unit c Chart c is average number of defects per unit Attributes generate data that are counted.

45 Use of p-charts When observations can be placed into two categories: Good or bad Pass or fail Operate or don t operate When the data consists of multiple samples of several observations each e.g., 15 samples of n = 20 Binomial distribution

46 p-charts Used to monitor the proportion of defectives in a process. Upper control limits: Lower control limits: where and p is the known fraction of defective items in the population. If p is unknown, it can be estimated from samples. The p estimate,, replaces p. Sometimes LCL is negative because of the approximation formula. Use LCL =0. σ p = p(1 n p), UCL LCL p p = = p p + zσ zσ p p

47 Example 3 Sample # Defectives % Defectives Sample # Defectives Total defectives = 220 % Defectives Find a 99.74% control limits. Each sample contained 100 observations.

48 Step 1: Find Solution to Example 3 Total defectives = 220, total observations = 20 (100)=2000 p Step 2: Find σ p p number defectives = number observations σ = p p(1 n p) = = 220 = (1 0.11) 100 = Step 3: 99.74% control limits means z=3: UCL LCL p p = p + zσ p p zσ = p = = (0.03) = (0.03) =

49 Solution to Example 3 (Con t) The process is not in control: sample 8 (22/100=0.22) and sample 15 (21/100=0.21) are above the UCL.

50 Use of c-charts Use only when the number of occurrences per unit of measure can be counted; nonoccurrences cannot be counted. Scratches, chips, dents, or errors per item Cracks or faults per unit of distance Breaks or Tears per unit of area Bacteria or pollutants per unit of volume Calls, complaints, failures per unit of time Poisson distribution

51 c-charts Used to monitor the number of defects per unit. Upper control limit: UCL c = c + z c Lower control limit: LCL c = c z c where c is the mean number of defects per unit c is the standard deviation. Replace c with c in the formulas if c is unknown

52 Example 4 Sample # of Defectives Sample # of Defectives

53 Solution to Example 4 18 samples, total number of defectives found is 45 a) Find: c c = 45 = b) Find control limits: UCL c = c + z c = ( 2.5) = 7.24 LCL c = c z c = 2.5 3( 2.5) = c) Conclusion: the process is in control (all numbers of defects are within UCL and LCL)

54 Use of Control Charts At what point in the process to use control charts Have a tendency to go out of control Critical to the successful operation of the product/services What size samples to take Cost and time are functions of sample size Smaller samples are more likely to reveal a change in the process What type of control chart to use: Variables vs. Attributes Measuring is more costly and time-consuming per unit Measuring provides more information Variable control chart needs a smaller sample size

55 Statistical Process Control -- Run Test

56 Nonrandom Patterns in Control charts Even if all points are within the control limits, the data may still not reflect a random process Trend Cycles Bias Mean shift Too much dispersion

57 Run Tests tests for randomness pattern detection Nonrandom Patterns in Control Charts Trend sustained upward or downward movement Cycle wave pattern Bias more on one side of centerline than other Mean shift average changes Too much dispersion values spread out Run: sequence of observations with certain characteristic followed by one or more observations with different characteristic

58 Counting Runs Counting above (A) & below (B) median B A A B A B B B A A B 7 runs Counting up (U) & down (D) runs U U D U D U D U U D 8 runs

59 Run Test Calculations Number of runs in completely random series z: Number of standard deviations by which the observed # of runs differs from expected number r : # of runs Compare z with ±2 (for 95.5%), ±1.96 (for 95%), ±2.33 (for 98%)

60 Run Test Example Compute # of standard deviations Given: 20 samples, A/B: 10 runs, U/D: 17 runs = 1+ (20/2) = 11 = (2*20 1)/3 = 13 = [(20 1)/4] = 2.18 = [(16*20 29)/90] = 1.80 = (10 11)/(2.18) = 0.46 = (17 13)/(1.80) = Not in control

61 Other Quality Tools

62 Flowcharts Check sheets Histograms Pareto Charts Scatter diagrams Control charts Basic Quality Tools Cause-and-effect diagrams Run charts

63 1. Flowchart A diagram of the steps in process. A flowchart can help identifying possible points in a process where problems occur.

64 2. Check Sheets A tool for recording and organizing data to identify a problem

65 3. Histogram A chart of an empirical frequency distribution

66 4. Pareto Analysis Technique for classifying problem areas according to degree of importance, and focusing on the most important Focus on resolving the most important, leaving the less important "80-20" rule - 80% of the problems may be attributed to 20% of the causes

67 5. Scatter Diagrams A graph that shows the degree and direction of relationship between two variables.

68 6. Control Charts A statistical chart of time-ordered values of a sample statistic. Abnormal variation due to assignable sources Out of control UCL Normal variation due to chance Abnormal variation due to assignable sources Mean LCL Sample number

69 7. Cause-and-effect Diagrams Used to search for the causes of a problem; also called fishbone diagram. They help to identify categories of factors that might be causing problems.

70 8. Run charts Tool for tracking results over a period of time. To identify trends or other patterns.

71 Tracking Improvements Using a control chart to track improvements UCL UCL UCL LCL Process not centered and not stable LCL Process centered and stable LCL Additional improvements made to the process

72 Philosophies of Quality Management -- Total Quality Management

73 Total Quality Management (TQM) A philosophy that involves everyone in an organization in a continual effort to improve quality and achieve customer satisfaction. T Q M

74 Three Key Philosophies Continuous improvement: Never-ending push to improve. (Kaizen) Involvement of everyone in the organization. Customer satisfaction: Meeting or exceeding customer expectations.

75 The TQM Approach 1. Find out what the customer wants 2. Design a product or service that meets or exceeds customer wants 3. Design processes that facilitates doing the job right the first time 4. Keep track of results 5. Extend these concepts to suppliers and to distribution

76 Philosophies of Quality Management -- Six Sigma

77 Statistically Six Sigma Having no more than 3.4 defects per million Conceptually Program designed to reduce defects to achieve lower costs and improved customer satisfaction Requires the use of certain tools and techniques to achieve strategic business results Employed in Design Production Service Inventory management Delivery Objectives: Reducing defects Reducing costs Reducing product and/or process variability Reducing delivery time Increasing productivity Improving customer satisfaction

78 3 Sigma and 6 Sigma Quality Lower specification Upper specification 1350 ppm 1350 ppm 1.7 ppm 1.7 ppm Process mean +/- 3 σ +/- 6 σ

79 Process Capability Tolerances or specifications Range of acceptable values established by engineering design or customer requirements Process variability Natural variability in a process, measured by. σ Process capability Process variability relative to specification

80 Capability Analysis Capability Analysis: Determines whether the inherent variability of the process output falls within the acceptable range of the variability allowed by the design specifications for the process output.

81 Capability Analysis In case C of previous figure: Even the process is in control, we cannot assure it will provide desired output A process should be both in control and within specifications before production begins Possible solutions: Redesign the process Use an alternative process Retain current process and use 100% inspection Reexamine the specifications to see if can be relaxed

82 Process Capability Ratio Process capability ratio, Cp = specification width process width Cp = Upper specification lower specification 6σ For a process to be deemed to be capable: C p 1.33 Some companies use term six-sigma: Motorola Corporation and 3M This requires Cp = 2.

83 Example 7 Three machines can perform a specific job. The tolerance range for the job is 0.8. If the machines have the following standard deviations, which machines are capable of performing the job? σ Machine σ Capability 6σ Cp A 0.13 B 0.08 C /0.78= /0.48= /0.96=0.83

84 Lower specification Cp can be misleading If the process mean is not centered between the upper and lower specifications. Upper specification Process mean +/- 3 σ

85 Cpk Ratio If the process is not centered, we can calculate C pk ratio Cpk Upper specification Process mean = min {, 3σ Process mean - Lower specification } 3σ Example: process mean = 9.2, σ = 0.3, USL = 10.5 and LSL = 7.5. Cpk = min {, } 3(0.3) 3(0.3) = min { 1.44, 1.89} = 1.44>1.33, it s capable

86 Improving Process Capability Requires changing the process target value and/or reducing the process variability Simplify: eliminate steps, reduce the number of parts, use modular design Standardize: use standard parts, procedures Mistake-proof: design parts that can only be assembled the correct way Upgrade equipment: Upgrade equipment: replace worn out equipment, take advantage of technological improvements

87 Limitations of Capability Indexes 1. Process may not be stable 2. Process output may not be normally distributed 3. Process not centered but C p is used

Operations Management

9-1 Management of Quality Operations Management William J. Stevenson 8 th edition 9-2 Management of Quality CHAPTER 9 Management of Quality McGraw-Hill/Irwin Operations Management, Eighth Edition, by William