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1 Quality Management and Control Presented by: Mohammad Saleh Owlia, Visiting Professor, University of Malaya Adopted from: Operations Management for Competitive Advantage, Eleventh Edition (2006) Richard B. Chase, F. Robert Jacobs and Nicholas J. Aquilano

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2 What is Quality?

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3 Garvins Product Quality Dimension Garvins Product Quality Dimension Performance Features Reliability Conformance Durability Serviceability Aesthetics Perceived Quality

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4 Service Quality Dimensions Service Quality Dimensions Parasuraman, Zeithamel, and Berrys Service Quality Dimensions Tangibles Service Reliability Responsiveness Assurance Empathy

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5 Total Quality Management TQM may be defined as managing the entire organization so that it excels on all dimensions of products and services that are important to the customer.

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6 Quality Specifications Design quality (consumers view) – inherent value of the product in the marketplace and therefore, has strategic implications. C onformance quality (producers view) – degree to which the product or service design specifications are met

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7 Costs of Quality Appraisal costs Appraisal costs Prevention costs Internal failure costs External Failure costs inspection and testing scrap, rework, yield loss, downtime complaint adjustment, allowances, warranty work quality planning and training

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Cost per good unit of product 0100%Quality level (q) Optimum quality level Total quality costs Internal and external failure costs Minimum total cost Prevention and appraisal costs Quality Cost: Traditional View

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Cost per good unit of product 0 100% Quality level (q) Optimum quality level Total quality costs Internal and external failure costs Minimum total cost Prevention and appraisal costs Quality Cost Minimized at Zero Defects

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Cost per good unit of product 0 100% Quality level (q) Optimum quality level Total quality costs Internal and external failure costs Minimum total cost Prevention and appraisal costs Improvement Through Simplification

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11 Ways in Which Quality Can Improve Profitability Market Gains – Improved response – Economies of Scale – Improved reputation Reduced Costs Increased productivity Lower rework and scrap costs Lower warranty costs Increased Profits Improved Quality

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12 Phases of Quality Assurance Acceptance sampling Process control Continuous improvement Inspection before/after production Corrective action during production Quality built into the process The least progressive The most progressive

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13 PDCA Cycle (Deming Wheel) 1. Plan a change aimed at improvement. 1. Plan 2. Execute the change. 2. Do 3. Study the results; did it work? 3. Check 4. Institutionalize the change or abandon or do it again. 4. Act

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14 Ishikawas Basic Tools of Quality HistogramHistogram Pareto Charts Cause & Effect Diagrams Check Sheets Scatter Diagrams FlowchartsFlowcharts Control Charts

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15 Histograms Number of Lots Data Ranges Defects in lot Can be used to identify the frequency of quality defect occurrence and display quality performance. Graphical representation of data in a bar chart format

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16 Can be used to find when 80% of the problems may be attributed to 20% of the causes. Can be used to find when 80% of the problems may be attributed to 20% of the causes. Assy. Instruct. Frequency DesignPurch. Training Other 80% Pareto Charts

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17 Pareto Charts The Steps Used in Pareto Analysis Include: –Gathering categorical data relating to quality problems. –Drawing a histogram of the data. –Focusing on the tallest bars in the histogram first when solving the problem

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18 Cause and Effect Diagrams Cause and Effect (or Fishbone or Ishikawa) Diagram –A diagram designed to help workers focus on the causes of a problem rather than the symptoms. –The diagram looks like the skeleton of a fish, with the problem being the head of the fish, major causes being the ribs of the fish and subcauses forming smaller bones off the ribs.

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19 Cause & Effect Diagram Effect ManMachine MaterialMethod Environment Possible causes: The results or effect Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)

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20 Cause and Effect Diagrams

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21 Billing Errors Wrong Account Wrong Amount A/R Errors Wrong Account Wrong Amount Monday Can be used to keep track of defects or used to make sure people collect data in a correct manner. Check Sheets

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22 Check Sheets Setting Up a Check Sheet –Identify common defects occurring in the process. –Draw a table with common defects in the left column and time period across the tops of the columns to track the defects. –The user of the check sheet then places check marks on the sheet whenever the defect is encountered.

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23 Check Sheets

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Hours of Training Defects Can be used to illustrate the relationships between variables (Example: quality performance and training). Scatter Diagrams

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25 Scatter Diagrams Used to examine the relationships between variables: Steps in Setting Up a Scatter Plot –Determine your X (independent) and Y (dependent) variables. –Gather process data relating to the variables identified in step 1. –Plot the data on a two-dimensional Cartesian plane. –Observe the plotted data to see whether there is a relationship between the variables.

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26 Scatter Diagrams Prevention in Costs and Conformance

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27 FlowchartsFlowcharts Flowcharts: Picture of a process Allows a company to see process weaknesses Sometimes the first step in many process improvement projects to see how the process exists You have to be able to know the process before you can improve it

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28 Example: Process Flow Chart No, Continue… Material Received from Supplier Inspect Material for Defects Defects found? Return to Supplier for Credit Yes Can be used to find quality problems.

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29 FlowchartsFlowcharts Basic Flowcharting Symbols

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30 FlowchartsFlowcharts Steps in Flowcharting Include –Settle on a standard set of flowcharting symbols to be used. –Clearly communicate the purpose of the flowcharting to all the individuals involved in the flowcharting exercise. –Observe the work being performed by shadowing the workers performing the work. –Develop a flowchart of the process.

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31 Control Charts –Control charts are used to determine whether a process will produce a product or service with consistent measurable properties. –Control charts are discussed in detail in Technical Note 7.

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Example: Run Chart Time (Hours) Diameter Can be used to identify when equipment or processes are not behaving according to specifications.

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Example: Control Chart LCLUCL Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality.

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34 Six Sigma Quality A philosophy and set of methods companies use to eliminate defects in their products and processes Seeks to reduce variation in the processes that lead to product defects The name, six sigma refers to the variation that exists within plus or minus six standard deviations of the process outputs

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35 Six Sigma Quality (Continued) Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO) 1,000,000 x units of No. x unit pererrorfor iesopportunit ofNumber defects ofNumber DPMO

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36 Six Sigma Quality (Continued) Example of Defects Per Million Opportunities (DPMO) calculation. Suppose we observe 200 letters delivered incorrectly to the wrong addresses in a small city during a single day when a total of 200,000 letters were delivered. What is the DPMO in this situation? So, for every one million letters delivered this citys postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?

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37 Six Sigma Quality: DMAIC Cycle Define, Measure, Analyze, Improve, and Control (DMAIC) Developed by General Electric as a means of focusing effort on quality using a methodological approach Overall focus of the methodology is to understand and achieve what the customer wants DMAIC consists of five steps….

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38 Six Sigma Quality: DMAIC Cycle (Continued) 1. Define (D) 2. Measure (M) 3. Analyze (A) 4. Improve (I) 5. Control (C) Customers and their priorities Process and its performance Causes of defects Remove causes of defects Maintain quality

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39 Six Sigma Roles and Responsibilities 1. Executive leaders must champion the process of improvement 2. Corporation-wide training in Six Sigma concepts and tools 3. Setting stretch objectives for improvement 4. Continuous reinforcement and rewards

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40 { { { { Customer requirements Design requirements Part/ item characteristics Process operations Operations requirements Product planning Product design Process planning Process control QFD Overview

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X X X X AB A B B A Correlation: Strong positive Positive Negative Strong negative X * Completely clean Perfect press No delays at counter Quick turn-around Friendly service Good training Clean DC solvent Clean DC filters No rust in SP lines Firm press pads Good equip maint. Importance to Customer Operating requirements Customer requirements Competitive evaluation X: Us A: Comp.A B: Comp.B (5 is best) X AB A B 4-hr formal 2-wk OJT Visual daily Visual daily, clean monthly Visual daily Change monthly Monthly, plus as needed Target values X XX X X X BB BB BBA A A A A A Technical evaluation Relationships Strong = 9 Medium = 3 Small = 1 Importance weighting QFD - House of Quality

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42 Second Part Statistical Process Control

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43 Statistical Thinking All work occurs in a system of interconnected processes Variation exists in all processes Understanding and reducing variation are the keys to success

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44 Sources of Variation in Production Processes Materials Tools OperatorsMethods Measurement Instruments Human Inspection Performance EnvironmentMachines INPUTSPROCESSOUTPUTS

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45 Variation Many sources of uncontrollable variation exist (common causes) Special (assignable) causes of variation can be recognized and controlled Failure to understand these differences can increase variation in a system

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46 Problems Created by Variation Variation increases unpredictability. Variation reduces capacity utilization. Variation makes it difficult to find root causes. Variation makes it difficult to detect potential problems early.

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47 Importance of Understanding Variation time PREDICTABLE ? UNPREDECTIBLE

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48 Two Fundamental Management Mistakes 1. Treating as a special cause any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to common causes 2. Attributing to common causes any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to a special cause

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49 Process Control Charts Process Control Charts

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50 Number or percent of defective items in a lot. Number of defects per item. Types of defects. Value assigned to defects (minor=1, major=5, critical=10) Length Weight Time Diameter Tensile Strength Strength of Solution Height Volume Temperature Types of Data Things we count Variables Data Attribute Data Things we measure

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51 Process Control Charts Process Control Charts Variables and Attributes –To select the proper process chart, we must differentiate between variables and attributes. A variable is a continuous measurement such as weight, height, or volume. An attribute is the result of a binomial process that results in an either-or-situation. –The most common types of variable and attribute charts are shown in the following slide.

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52 Process Control Charts Process Control Charts Variables and Attributes Variables Attributes X (process population average)P (proportion defective) X-bar (mean for average)np (number defective) R (range)C (number conforming) MR (moving range)U (number nonconforming) S (standard deviation)

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53 Process Control Charts Process Control Charts A Generalized Procedure for Developing Process Charts –Identify critical operations in the process where inspection might be needed. These are operations in which, if the operation is performed improperly, the product will be negatively affected. –Identify critical product characteristics. These are the attributes of the product that will result in either good or poor function of the product.

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54 Process Control Charts Process Control Charts A Generalized Procedure for Developing Process Charts (continued) –Determine whether the critical product characteristic is a variable or an attribute. –Select the appropriate process control chart from among the many types of control charts. This decision process and types of charts available are discussed later. –Establish the control limits and use the chart to continually improve.

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55 Process Control Charts Process Control Charts A Generalized Procedure for Developing Process Charts (continued) –Update the limits when changes have been made to the process.

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56 Process Control Charts Process Control Charts Understanding Control Charts –A process chart is nothing more than an application of hypothesis testing where the null hypothesis is that the product meets requirements. An X-bar chart is a variables chart that monitors average measurement. An example of how to best understand control charts is provided under the heading Understanding Control Charts in the textbook.

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57 Process Control Charts Process Control Charts X-bar and R Charts –The X-bar chart is a process chart used to monitor the average of the characteristics being measured. To set up an X-bar chart select samples from the process for the characteristic being measured. Then form the samples into rational subgroups. Next, find the average value of each sample by dividing the sums of the measurements by the sample size and plot the value on the process control X-bar chart.

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58 Process Control Charts Process Control Charts X-bar and R Charts (continued) –The R chart is used to monitor the variability or dispersion of the process. It is used in conjunction with the X-bar chart when the process characteristic is variable. To develop an R chart, collect samples from the process and organize them into subgroups, usually of three to six items. Next, compute the range, R, by taking the difference of the high value in the subgroup minus the low value. Then plot the R values on the R chart.

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59 Process Control Charts Process Control Charts X-bar and R Charts

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60 Example of x-Bar and R Charts: Required Data

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61 Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.

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62 Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values

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63 Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot Values UCL LCL

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64 Example of x-bar and R charts: Steps 5&6. Calculate R-chart and Plot Values UCL LCL

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65 UCL LCL Samples over time UCL LCL Samples over time UCL LCL Samples over time Normal Behavior Possible problem, investigate Interpreting Control Charts

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66 Process Control Charts Process Control Charts Implications of a Process Out of Control –If a process loses control and becomes nonrandom, the process should be stopped immediately. –In many modern process industries where just-in-time is used, this will result in the stoppage of several work stations. –The team of workers who are to address the problem should use a structured problem solving process.

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67 Process Control Charts Control Charts for Attributes –We now shift to charts for attributes. These charts deal with binomial and Poisson processes that are not measurements. –We will now be thinking in terms of defects and defectives rather than diameters or widths. A defect is an irregularity or problem with a larger unit. A defective is a unit that, as a whole, is not acceptable or does not meet specifications.

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68 Process Control Charts Process Control Charts p Charts for Proportion Defective –The p chart is a process chart that is used to graph the proportion of items in a sample that are defective (nonconforming to specifications) –p charts are effectively used to determine when there has been a shift in the proportion defective for a particular product or service. –Typical applications of the p chart include things like late deliveries, incomplete orders, and clerical errors on written forms.

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69 Process Control Charts Process Control Charts np Charts –The np chart is a graph of the number of defectives (or nonconforming units) in a subgroup. The np chart requires that the sample size of each subgroup be the same each time a sample is drawn. –When subgroup sizes are equal, either the p or np chart can be used. They are essentially the same chart.

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70 Example of Constructing a p-Chart: Required Data

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71 Statistical Process Control Formulas: Attribute Measurements (p-Chart) Given: Compute control limits:

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72 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample. Example of Constructing a p-chart: Step 1

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73 2. Calculate the average of the sample proportions. 3. Calculate the standard deviation of the sample proportion Example of Constructing a p-chart: Steps 2&3

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74 4. Calculate the control limits. UCL = LCL = (or 0) Example of Constructing a p-chart: Step 4

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75 Example of Constructing a p-Chart: Step 5 5. Plot the individual sample proportions, the average of the proportions, and the control limits Observation p UCL LCL

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76 Process Capability Process Stability and Capability –Once a process is stable, the next emphasis is to ensure that the process is capable. –Process capability refers to the ability of a process to produce a product that meets specifications.

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77 Process Capability Process limits Tolerance limits How do the limits relate to one another?

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78 If the process capability of a normally distributed process is.084, the process is in control, and is centered at.550. What are the upper and lower control limits for this process? Process Capability = 6 6 =.084 =.014 UCL = (.014)=.592 LCL = (.014)=.508

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79 X cm Tolerance band Process capability (6 ) LTLUTL Output out of spec Process output distribution s Process Capability Chart

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80 This process is CAPABLE of producing all good output. ä Control the process. This process is NOT CAPABLE. ä INSPECT - Sort out the defectives× Lower Tolerance Limit Upper Tolerance Limit Process Capability

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81 Process Capability Index, C pk Shifts in Process Mean Capability Index shows how well parts being produced fit into design limit specifications. As a production process produces items small shifts in equipment or systems can cause differences in production performance from differing samples.

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82 Given: process mean = =.001 =.001 LTL =.994 UTL = Process Capability Index- Example 3 Upper Tol Limit - X 3 X - Lower Tol Limit – OR { C pk = Smaller of: C pk = min (.001)3 (.001) or C pk = min [2.5 or 1.5] = 1.5 –

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83 LTLUTL C pk = 1.0 LTLUTL C pk = 0.60 LTLUTL C pk = 1.33 LTLUTL C pk = 0.80 (f) LTLUTL C pk = 1.0 (d) LTLUTL C pk = 3.0 Process Capability: C pk Varieties

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84 Process Control Charts Some Control Chart Concepts (continued) –Corrective Action. When a process is out of control, corrective action is needed. Correction action steps are similar to continuous improvement processes. They are Carefully identify the problem. Form the correct team to evaluate and solve the problem. Use structured brainstorming along with fishbone diagrams or affinity diagrams to identify causes of the problem.

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85 Process Control Charts Process Control Charts Some Control Chart Concepts (continued) –Corrective Action (continued) Brainstorm to identify potential solutions to problems. Eliminate the cause. Restart the process. Document the problem, root causes, and solutions. Communicate the results of the process to all personnel so that this process becomes reinforced and ingrained in the operations.

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86 Acceptance Sampling –A statistical quality control technique used in deciding to accept or reject a shipment of input or output. –Acceptance sampling inspection can range from 100% of the Lot to a relatively few items from the Lot (N=2) from which the receiving firm draws inferences about the whole shipment.

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87 Acceptance Sampling Purposes – Determine quality level – Ensure quality is within predetermined level Advantages – Economy – Less handling damage – Fewer inspectors – Upgrading of the inspection job – Applicability to destructive testing – Entire lot rejection (motivation for improvement)

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88 Acceptance Sampling Disadvantages – Risks of accepting bad lots and rejecting good lots – Added planning and documentation – Sample provides less information than 100- percent inspection

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89 Acceptance Sampling Examples of when acceptance sampling might be needed When dealing with new or unproven suppliers. During start-ups and when building new products. When products can be damaged in shipment. When dealing with extremely sensitive products that can be damaged easily. When products can spoil during shipment.

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90 Statistical Sampling Techniques n and c –The bottom line in acceptance sampling is that acceptance sampling plans are designed to give us two things: n and c, where n = the sample size of a particular sampling plan c = the maximum number of defective pieces for a c = the maximum number of defective pieces for a sample to be rejected

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