Operations Management Framework Insert New Resource/Profit Model

7 C H A P T E R Quality Tools L E A R N I N G O B J E C T I V E S Explain the function of the general-purpose quality analysis tools. Explain how each quality tool aids in the QI story and DMAIC processes. Describe and make computations for process capability using Cp and Cpk capability indices. Describe how statistical process control can be used to prevent defects from occurring. Describe how acceptance sampling works and the role of the operating characteristics curve. Understand the Kano model. Explain how the Six Sigma quality relates to process capability. Describe service quality applications, including service blueprinting and moment-of-truth analysis. Describe how “recovery” applies to quality failures.

Quality Analysis Six Sigma’s DMAIC and TQM’s QI Story provide structure, but neither defines how activities are to be accomplished. That can be determined through the use of a broad set of analysis tools. Insert exhibit 7.1 DMAIC and QI 3

General-Purpose Quality Analysis Tools Flow Charts Run Charts Cause & Effect Diagram Pareto Charts Histograms Check Sheets Scatter Diagrams Control Charts

General-Purpose Quality Analysis Tools: Flow Chart Flow Chart: A diagram of the steps in a process

General-Purpose Quality Analysis Tools: Run Charts Run Charts: Plotting a variable against time.

General-Purpose Quality Analysis Tools: Cause & Effect Diagram Possible causes: The results or effect Man Machine Material Method Environment Effect Can be used to systematically track backwards to find a possible cause of a quality problem (or effect) 17

General-Purpose Quality Analysis Tools: Cause & Effect Diagram Also known as: Ishikawa Diagrams Fishbone Diagrams Root Cause Analysis

General-Purpose Quality Analysis Tools: Checksheet Monday Billing Errors Wrong Account Wrong Amount A/R Errors Can be used to keep track of defects or used to make sure people collect data in a correct manner 16

Data Analysis Example Exhibit 7.6: SleepCheap Hotel Survey Data

General-Purpose Quality Analysis Tools: Histogram Can be used to identify the frequency of quality defect occurrence and display quality performance 14

General-Purpose Quality Analysis Tools: Pareto Analysis 63.5% of complaints are about the bathroom 50.5% of complaints are that something is dirty Variant of histogram that helps rank order quality problems so that most important can be identified

General-Purpose Quality Analysis Tools: Scatter Plots

General-Purpose Quality Analysis Tools: Control Charts 970 980 990 1000 1010 1020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 LCL UCL Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality

Statistical Process Control (SPC) Takes advantage of our knowledge about the standardized distribution of these measures Process Capability Uses sampling to determine if the process can produce consistently within acceptable customer limits Cp and Cpk Process Control Identifies potential problems before defects are created by watching the process unfold X-bar & R Charts 3

SPC Steps Measure a sample of the process output Four to five units of output for most applications Many (>25) samples Calculate sample means ( X ), grand mean (X), & ranges (R) Calculate “process capability” Can you deliver within tolerances defined by the customer Traditional standard is “correct 99.74% of the time” Monitor “process control” Is anything changing about the process? In terms of mean or variation 3

Process Capability Capability Index: quantifying the relationship between control limits and customer specifications Cp -- Used to determine “capability” when the process is “mean-centered” Exhibit 7.14: Process Control Chart for Soft Drink Can

Process Capability Capability Index: quantifying the relationship between control limits and customer specifications Cpk -- Used to determine “capability” when the process is “mean-shifted” Exhibit 7.15 Process Shifted Downward From Center Difference between Cp and Cpk is minimal Cpk approach works fine to calculate capability of mean-centered process (but not vice versa!!!)

Cpk Calculation LCS - Lower control specification UCS - Upper control specification X - “Grand” mean of process performance  - Standard deviation of process performance If Cpk is > 1.000 then the process is “Capable” Translation, we will produce good parts at least 99.74% of the time

Example 7.3: Cpk Calculation Customer specification Mean of .375 inches + or - .002 inches Therefore, customer specification limits at .373 and .377 Process performance Actual mean is .376 Standard deviation is 0.0003 Cpk = min[ 0.376 – 0.373 , 0.377 – 0.376 ] 0.0009 0.0009 = min [3.333, 1.111] = 1.111 The process is capable.

Cp Calculation is Simpler Version of Cpk LCS - Lower control specification UCS - Upper control specification  - Standard deviation of process performance Mean is assumed to sit exactly between UCS and LCS!! If Cp is > 1.000 then the process is “Capable” Translation, we will produce good parts at least 99.74% of the time

Example 7.2 Cp Calculation Customer specification Mean of .375 inches + or - .002 inches Therefore, customer specification limits at .373 and .377 Process performance Actual mean is .375 Standard deviation is 0.0024 Solution: Cp = 0.377 – 0.373 = 0.27778 6(0.0024)

Another Cp Calculation: Metal Fabrication A metal fabricator produces connecting rods with an outer diameter that has a 1 ± .01 inch specification. A machine operator takes several sample measurements over time and determines the sample mean outer diameter to be 1.002 inches with a standard deviation of .003 inch. Calculate the process capability for this example. What does this solution tell you about the process?

Another Cp Calculation: Metal Fabrication Cp or Cpk? Cpk – it is not a mean centered process Customer specification Mean 1 inch LCS .99 inch = (1 inch – .01 inch) UCS 1.01 inches = (1 inch + .01 inch) Fabrication process performance Actual mean 1.002 inches Standard deviation .003 inch Solution: Cpk = min[ 1.002 –.99 , 1.01 – 1.002 ] 3(.003) 3(.003) = min [1.333, 0.889] = 0.889 Process, as configured, is not capable. How can it be made capable?

Process Control Cp and Cpk tell us whether the process will produce defective output as part of its normal operation. i.e., is it “capable”? Control charts are maintained on an ongoing basis so that operators can ensure that a process is not changing i.e., drifting to a different level of performance i.e., is it “in control” 3

SPC Steps Measure a sample of the process output Four to five units of output for most applications Many (>25) samples Calculate sample means ( X ), grand mean (X), & ranges (R) Calculate “process capability” Can you deliver within tolerances defined by the customer Traditional standard is “correct 99.74% of the time” Monitor “process control” Is anything changing about the process? In terms of mean or variation 3

X-Bar and R-Chart Construction Insert Exhibit 7.17 3

Control Charts: X-bar Steps Distinguishing between random fluctuation and fluctuation due to an assignable cause. X-bar chart tracks the trend in sample means to see if any disturbing patterns emerge. Exhibit 7.18 X-bar Chart for Example 7.4 Steps Calculate Upper & Lower Control Limits (UCL & LCL). Use special charts based on sample size Plot X-bar value for each sample Investigate “Nonrandom” patterns

Control Charts: R Steps Provide monitoring of variation within each sample. i.e., within each subgroup that you measure when calculating process capability Always paired with X-bar charts. Exhibit 7.19 R-Chart for Example 7.4 Steps Calculate Upper & Lower Control Limits (UCL & LCL). Use special charts based on sample size Different from those used in X-bar chart Plot R value for each sample Investigate “Nonrandom” patterns

Nonrandom Patterns on Control Charts Investigate the process if X-bar or R chart illustrates: One data point above +3 or below -3 2 out of 3 data points between +2  and +3  or between -2  and -3  4 out of 5 data points between +1  and +3  or between -1  and -3  8 successive points above the grand mean or 8 successive points below the grand mean.

Acceptance Sampling Purposes Advantages Sampling to accept or reject the immediate lot of product at hand 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) 4

Acceptance Sampling (Continued) Disadvantages Risks of accepting “bad” lots and rejecting “good” lots Added planning and documentation Sample provides less information than 100-percent inspection 5

Acceptance Sampling Acceptable Quality Level (AQL) Max. acceptable percentage of defectives that defines a “good” lot Producer’s risk is the probability of rejecting a good lot Lot tolerance percent defective (LTPD) Percentage of defectives that defines consumer’s rejection point Consumer’s risk is the probability of accepting a bad lot Plan developed based on risk tolerance to determine size of sample and number in sample that can be defective Exhibit 7.21 Operating Characteristics Curve

Six Sigma Quality – Role of interdependencies At 3, the probability that an assembly of interdependent parts works, given “n” parts: 1 part = .99741 = 99.74% 10 parts = .997410 = 97.43% 50 parts = .997450 = 87.79% 100 parts = .9974100 = 77.08% 267 parts = .9974267 = 49.90% 1000 parts = .99741000 = 7.40% Simulation

Six Sigma Quality “Six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs Exhibit 7.23 Process Capability for Six Sigma Quality

Six Sigma and Failure Rates Odds of random fluctuation creating a result that is 6 from the mean are 2 in 1 billion 99.9999998% confident of a good outcome In practice, process mean is allowed to shift ±1.5 

Six Sigma and Failure Rates Failure Rates in the presence of component interdependencies 6 (mean-centered) line 6 (1.5 mean-shift) line 3 line

Moment-of-Truth Analysis Moment-of-Truth Analysis: The identification of the critical instances when a customer judges service quality and determines the experience enhancers, standard expectations, and experience detractors. Experience enhancers: Experiences that make the customer feel good about the interaction and make the interaction better. Standard expectations: Experiences that are expected and taken for granted. Experience detractors: Experiences viewed by the customer as reducing the quality of service. 5

Customer Relationship Management (CRM) Customer loyalty increases profitability: Advances in technologies and techniques have enhanced companies’ ability to manage relationships with customers. CRM: Systems designed to improve relationships with customers and improve the business’ ability to identify valuable customers. Includes call center management software, sales tracking, and customer service. 5

Recovery There will always be times when customers do not get what they want. Failure to meet customers’ expectations does not have to mean lost customers. Recovery plans: Policies for how employees are to deal with quality failures so that customers will return. Example: A recovery for a customer who has had a bad meal at a restaurant might include eliminating the charges for the meal, apologizing, and offering gift certificates for future meals. 5