Download presentation

Presentation is loading. Please wait.

Published byLeonard Bafford Modified about 1 year ago

1
1 6 6 Managing Quality PowerPoint presentation to accompany Heizer and Render Operations Management, 10e Principles of Operations Management, 8e PowerPoint slides by Jeff Heyl

2
2 Outline Defining Quality Implications of Quality Ethics and Quality Management Total Quality Management Continuous Improvement Six Sigma Employee Empowerment TQM in Services Statistical Process Control (SPC) Control Charts for Variables Control Charts for Attributes Process Capability Process Capability Ratio (C p ) Process Capability Index (C pk )

3
3 Learning Objectives

4
4 Two Ways Quality Improves Profitability Improved Quality Increased Profits Increased productivity Lower rework and scrap costs Lower warranty costs Reduced Costs via Improved response Flexible pricing Improved reputation Sales Gains via Figure 6.1

5
5 Defining Quality The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs American Society for Quality Different Views User-based Manufacturing-based Product-based

6
6 Key Dimensions of Quality Performance Features Reliability Conformance Durability Serviceability Aesthetics Perceived quality Value

7
7 Ethics and Quality Management Operations managers must deliver healthy, safe, quality products and services Poor quality risks injuries, lawsuits, recalls, and regulation Organizations are judged by how they respond to problems All stakeholders much be considered

8
8 Deming’s Fourteen Points Table Create consistency of purpose 2.Lead to promote change 3.Build quality into the product; stop depending on inspections 4.Build long-term relationships based on performance instead of awarding business on price 5.Continuously improve product, quality, and service 6.Start training 7.Emphasize leadership

9
9 Deming’s Fourteen Points Table Drive out fear 7.Break down barriers between departments 8.Stop haranguing workers 9.Support, help, and improve 12.Remove barriers to pride in work 13.Institute education and self-improvement 14.Put everyone to work on the transformation

10
10 Continuous Improvement Represents continual improvement of all processes Involves all operations and work centers including suppliers and customers People, Equipment, Materials, Procedures

11
11 Six Sigma Program A highly structured program developed by Motorola A discipline – DMAIC Also, Statistical definition of a process that is % capable, 3.4 defects per million opportunities (DPMO) 66 66 Mean Lower limitsUpper limits 3.4 defects/million ±6 2,700 defects/million ±3 Figure 6.4

12
12 Six Sigma 1.Define critical outputs and identify gaps for improvement 2.Measure the work and collect process data 3.Analyze the data 4.Improve the process 5.Control the new process to make sure new performance is maintained DMAIC Approach

13
13 Employee Empowerment Getting employees involved in product and process improvements 85% of quality problems are due to process and material Techniques Build communication networks that include employees Develop open, supportive supervisors Move responsibility to employees Build a high-morale organization Create formal team structures

14
14 TQM In Services Service quality is more difficult to measure than the quality of goods Service quality perceptions depend on Intangible differences between products Intangible expectations customers have of those products

15
15 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 Statistical Process Control (SPC)

16
16 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”

17
17 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

18
18 Types of Data Characteristics that can take any real value May be in whole or in fractional numbers Continuous random variables VariablesAttributes Defect-related characteristics Classify products as either good or bad or count defects Categorical or discrete random variables

19
19 Control Charts for Variables For variables that have continuous dimensions Weight, speed, length, strength, etc. x-charts are to control the central tendency of the process R-charts are to control the dispersion of the process These two charts must be used together

20
20 Setting Chart Limits For x-Charts when we know Upper control limit (UCL) = x + z x Lower control limit (LCL) = x - z x wherex=mean of the sample means or a target value set for the process z=number of normal standard deviations x =standard deviation of the sample means = / n =population standard deviation n=sample size

21
21 Setting Control Limits Hour 1 SampleWeight of NumberOat Flakes Mean16.1 =1 HourMeanHourMean n = 9 LCL x = x - z x = (1/3) = 15 ozs For 99.73% control limits, z = 3 UCL x = x + z x = (1/3) = 17 ozs

22
22 17 = UCL 15 = LCL 16 = Mean Setting Control Limits Control Chart for sample of 9 boxes Sample number |||||||||||| Variation due to assignable causes Variation due to natural causes Out of control

23
23 Setting Chart Limits For x-Charts when we don’t know Lower control limit (LCL) = x - A 2 R Upper control limit (UCL) = x + A 2 R whereR=average range of the samples A 2 =control chart factor found in Table S6.1 x=mean of the sample means

24
24 Control Chart Factors Table S6.1 Sample Size Mean Factor Upper Range Lower Range n A 2 D 4 D

25
25 Setting Control Limits Process average x = 12 ounces Average range R =.25 Sample size n = 5

26
26 Setting Control Limits UCL x = x + A 2 R = 12 + (.577)(.25) = = ounces Process average x = 12 ounces Average range R =.25 Sample size n = 5 From Table S6.1

27
27 Setting Control Limits UCL x = x + A 2 R = 12 + (.577)(.25) = = ounces LCL x = x - A 2 R = = ounces Process average x = 12 ounces Average range R =.25 Sample size n = 5 UCL = Mean = 12 LCL =

28
28 Restaurant Control Limits For salmon filets at Darden Restaurants Sample Mean x Bar Chart UCL = x – LCL – ||||||||| – 11.0 – 10.5 – Sample Range Range Chart UCL = R = LCL = 0 ||||||||| – 0.4 – 0.0 –

29
29 R – Chart Type of variables control chart Shows sample ranges over time Difference between smallest and largest values in sample Monitors process variability Independent from process mean

30
30 Setting Chart Limits For R-Charts Lower control limit (LCL R ) = D 3 R Upper control limit (UCL R ) = D 4 R where R=average range of the samples D 3 and D 4 =control chart factors from Table S6.1

31
31 Setting Control Limits UCL R = D 4 R = (2.115)(5.3) = 11.2 pounds LCL R = D 3 R = (0)(5.3) = 0 pounds Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D 4 = 2.115, D 3 = 0 UCL = 11.2 Mean = 5.3 LCL = 0

32
32 Mean and Range Charts (a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) R-chart (R-chart does not detect change in mean) UCL LCL Figure S6.5 x-chart (x-chart detects shift in central tendency) UCL LCL

33
33 Mean and Range Charts R-chart (R-chart detects increase in dispersion) UCL LCL Figure S6.5 (b) These sampling distributions result in the charts below (Sampling mean is constant but dispersion is increasing) x-chart (x-chart does not detect the increase in dispersion) UCL LCL

34
34 Control Charts for Attributes For variables that are categorical Good/bad, yes/no, acceptable/unacceptable Measurement is typically counting defectives Charts may measure Percent defective (p-chart) Number of defects (c-chart)

35
35 Control Limits for p-Charts Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics UCL p = p + z p ^ LCL p = p - z p ^ wherep=mean fraction defective in the sample z=number of standard deviations p =standard deviation of the sampling distribution n=sample size ^ p(1 - p) n p = ^

36
36 p-Chart for Data Entry SampleNumberFractionSampleNumberFraction Numberof ErrorsDefectiveNumberof ErrorsDefective Total = 80 (.04)(1 -.04) 100 p = =.02 ^ p = = (100)(20)

37
37.11 –.10 –.09 –.08 –.07 –.06 –.05 –.04 –.03 –.02 –.01 –.00 – Sample number Fraction defective |||||||||| p-Chart for Data Entry UCL p = p + z p = (.02) =.10 ^ LCL p = p - z p = (.02) = 0 ^ UCL p = 0.10 LCL p = 0.00 p = 0.04

38
38.11 –.10 –.09 –.08 –.07 –.06 –.05 –.04 –.03 –.02 –.01 –.00 – Sample number Fraction defective |||||||||| p-Chart for Data Entry UCL p = p + z p = (.02) =.10 ^ LCL p = p - z p = (.02) = 0 ^ UCL p = 0.10 LCL p = 0.00 p = 0.04 Possible assignable causes present

39
39 Which Control Chart to Use Table S6.3 Variables Data Using an x-Chart and R-Chart 1.Observations are variables 2.Collect samples of n = 4, or n = 5, or more, each from a stable process and compute the mean for the x-chart and range for the R- chart 3.Track samples of n observations each.

40
40 Which Control Chart to Use Table S6.3 Attribute Data Using the p-Chart 1.Observations are attributes that can be categorized as good or bad (or pass–fail, or functional–broken), that is, in two states. 2.We deal with fraction, proportion, or percent defectives. 3.There are several samples, with many observations in each. For example, 20 samples of n = 100 observations in each.

41
41 UCL Target LCL Erratic behavior. UCL Target LCL Run of 5 above (or below) central line. UCL Target LCL Two plots very near lower (or upper) control. Normal behavior. Process is “in control.” UCL Target LCL Patterns in Control Charts UCL Target LCL One plot out above (or below). Process is “out of control.” UCL Target LCL Trends in either direction, 5 plots. Progressive change.

42
42 Process Capability The natural variation of a process should be small enough to produce products that meet the standards required A process in statistical control does not necessarily meet the design specifications Process capability is a measure of the relationship between the natural variation of the process and the design specifications

43
43 Process Capability Ratio C p = Upper Specification - Lower Specification 6 A capable process must have a C p of at least 1.0 Does not look at how well the process is centered in the specification range Often a target value of C p = 1.33 is used to allow for off-center processes Six Sigma quality requires a C p = 2.0

44
44 Process Capability Ratio C p = Upper Specification - Lower Specification 6 Insurance claims process Process mean x = minutes Process standard deviation =.516 minutes Design specification = 210 ± 3 minutes

45
45 Process Capability Ratio C p = Upper Specification - Lower Specification 6 Insurance claims process Process mean x = minutes Process standard deviation =.516 minutes Design specification = 210 ± 3 minutes = = (.516)

46
46 Process Capability Ratio C p = Upper Specification - Lower Specification 6 Insurance claims process Process mean x = minutes Process standard deviation =.516 minutes Design specification = 210 ± 3 minutes = = (.516) Process is capable

47
47 Process Capability Index A capable process must have a C pk of at least 1.0 A capable process is not necessarily in the center of the specification, but it falls within the specification limit at both extremes C pk = minimum of, Upper Specification - x Limit Lower x -Specification Limit

48
48 Process Capability Index New Cutting Machine New process mean x =.250 inches Process standard deviation =.0005 inches Upper Specification Limit =.251 inches Lower Specification Limit =.249 inches

49
49 Process Capability Index New Cutting Machine New process mean x =.250 inches Process standard deviation =.0005 inches Upper Specification Limit =.251 inches Lower Specification Limit =.249 inches C pk = minimum of, (.251) (3).0005

50
50 Process Capability Index New Cutting Machine New process mean x =.250 inches Process standard deviation =.0005 inches Upper Specification Limit =.251 inches Lower Specification Limit =.249 inches C pk = = New machine is NOT capable C pk = minimum of, (.251) (3) (.249) (3).0005 Both calculations result in

51
51 Interpreting C pk C pk = negative number C pk = zero C pk = between 0 and 1 C pk = 1 C pk > 1 Figure S6.8

52
52 SPC and Process Variability (a)Acceptance sampling (Some bad units accepted) (b)Statistical process control (Keep the process in control) (c)C pk >1 (Design a process that is in control) Lower specification limit Upper specification limit Process mean, Figure S6.10

53
53 In-Class Problems from the Lecture Guide Practice Problems

54
54 In-Class Problems from the Lecture Guide Practice Problems Problem 2: Several samples of size have been taken from today’s production of fence posts. The average post was 3 yards in length and the average sample range was yard. Find the 99.73% upper and lower control limits.

55
55 In-Class Problems from the Lecture Guide Practice Problems Problem 3: The average range of a process is 10 lbs. The sample size is 10. Use Table S6.1 to develop upper and lower control limits on the range.

56
56 In-Class Problems from the Lecture Guide Practice Problems Problem 4: Based on samples of 20 IRS auditors, each handling 100 files, we find that the total number of mistakes in handling files is 220. Find the 95.45% upper and lower control limits.

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google