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Slide #1.
Process Performance and Quality Chapter 6 © 2007 Pearson Education
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Slide #2.
Costs of Poor Process Performance Defects: Any instance when a process fails to satisfy its customer. Prevention costs are associated with preventing defects before they happen. Appraisal costs are incurred when the firm assesses the performance level of its processes. Internal failure costs result from defects that are discovered during production of services or products. External failure costs arise when a defect is discovered after the customer receives the service or product. © 2007 Pearson Education
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Slide #3.
Total Quality Management Quality: A term used by customers to describe their general satisfaction with a service or product. Total quality management (TQM) is a philosophy that stresses three principles for achieving high levels of process performance and quality: 1. Customer satisfaction 2. Employee involvement 3. Continuous improvement in performance © 2007 Pearson Education
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Slide #4.
TQM Wheel Customer satisfaction © 2007 Pearson Education
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Slide #5.
Customer Satisfaction Customers, internal or external, are satisfied when their expectations regarding a service or product have been met or exceeded. Conformance: How a service or product conforms to performance specifications. Value: How well the service or product serves its intended purpose at a price customers are willing to pay. Fitness for use: How well a service or product performs its intended purpose. Support: Support provided by the company after a service or product has been purchased. Psychological impressions: atmosphere, image, or aesthetics © 2007 Pearson Education
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Slide #6.
Employee Involvement One of the important elements of TQM is employee involvement. Quality at the source is a philosophy whereby defects are caught and corrected where they were created. Teams: Small groups of people who have a common purpose, set their own performance goals and approaches, and hold themselves accountable for success. Employee empowerment is an approach to teamwork that moves responsibility for decisions further down the organizational chart to the level of the employee actually doing the job. © 2007 Pearson Education
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Slide #7.
Team Approaches Quality circles: Another name for problem-solving teams; small groups of supervisors and employees who meet to identify, analyze, and solve process and quality problems. Special-purpose teams: Groups that address issues of paramount concern to management, labor, or both. Self-managed team: A small group of employees who work together to produce a major portion, or sometimes all, of a service or product. © 2007 Pearson Education
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Slide #8.
Continuous Improvement Continuous improvement is the philosophy of continually seeking ways to improve processes based on a Japanese concept called kaizen. 1. Train employees in the methods of statistical process control (SPC) and other tools. 2. Make SPC methods a normal aspect of operations. 3. Build work teams and encourage employee involvement. 4. Utilize problem-solving tools within the work teams. 5. Develop a sense of operator ownership in the process. © 2007 Pearson Education
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Slide #9.
Statistical Process Control Statistical process control is the application of statistical techniques to determine whether a process is delivering what the customer wants. Acceptance sampling is the application of statistical techniques to determine whether a quantity of material should be accepted or rejected based on the inspection or test of a sample. Variables: Service or product characteristics that can be measured, such as weight, length, volume, or time. Attributes: Service or product characteristics that can be quickly counted for acceptable performance. © 2007 Pearson Education
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Slide #10.
Sampling Sampling plan: A plan that specifies a sample size, the time between successive samples, and decision rules that determine when action should be taken. Sample size: A quantity of randomly selected observations of process outputs. © 2007 Pearson Education
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Slide #11.
Sample Means and the Process Distribution Sample statistics have their own distribution, which we call a sampling distribution. © 2007 Pearson Education
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Slide #12.
Sampling Distributions A sample mean is the sum of the observations divided by the total number of observations. Sample Mean n x i x i 1 n where xi = observations of a quality characteristic such as time. n = total number of observations x = mean The distribution of sample means can be approximated by the normal distribution. © 2007 Pearson Education
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Slide #13.
Sample Range The range is the difference between the largest observation in a sample and the smallest. The standard deviation is the square root of the variance of a distribution. where x x i n 1 2 = standard deviation of a sample n = total number of observations xi = observations of a quality characteristic x = mean © 2007 Pearson Education
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Slide #14.
Process Distributions A process distribution can be characterized by its location, spread, and shape. Location is measured by the mean of the distribution and spread is measured by the range or standard deviation. The shape of process distributions can be characterized as either symmetric or skewed. A symmetric distribution has the same number of observations above and below the mean. A skewed distribution has a greater number of observations either above or below the mean. © 2007 Pearson Education
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Slide #15.
Causes of Variation Two basic categories of variation in output include common causes and assignable causes. Common causes are the purely random, unidentifiable sources of variation that are unavoidable with the current process. If process variability results solely from common causes of variation, a typical assumption is that the distribution is symmetric, with most observations near the center. Assignable causes of variation are any variationcausing factors that can be identified and eliminated, such as a machine needing repair. © 2007 Pearson Education
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Slide #16.
Assignable Causes The red distribution line below indicates that the process produced a preponderance of the tests in less than average time. Such a distribution is skewed, or no longer symmetric to the average value. A process is said to be in statistical control when the location, spread, or shape of its distribution does not change over time. After the process is in statistical control, managers use SPC procedures to detect the onset of assignable causes so that they can be eliminated. Location Spread © 2007 Pearson Education © 2007 Pearson Education Shape
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Slide #17.
Control Charts Control chart: A time-ordered diagram that is used to determine whether observed variations are abnormal. A sample statistic that falls between the UCL and the LCL indicates that the process is exhibiting common causes of variation; a statistic that falls outside the control limits indicates that the process is exhibiting assignable causes of variation. © 2007 Pearson Education
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Slide #18.
Control Chart Examples © 2007 Pearson Education
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Slide #19.
Type I and II Errors Control charts are not perfect tools for detecting shifts in the process distribution because they are based on sampling distributions. Two types of error are possible with the use of control charts. Type I error occurs when the employee concludes that the process is out of control based on a sample result that falls outside the control limits, when in fact it was due to pure randomness. Type II error occurs when the employee concludes that the process is in control and only randomness is present, when actually the process is out of statistical control. © 2007 Pearson Education
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Slide #20.
Statistical Process Control Methods Control Charts for variables are used to monitor the mean and variability of the process distribution. R-chart (Range Chart) is used to monitor process variability. x-chart is used to see whether the process is generating output, on average, consistent with a target value set by management for the process or whether its current performance, with respect to the average of the performance measure, is consistent with past performance. If the standard deviation of the process is known, we can place UCL and LCL at “z” standard deviations from the mean at the desired confidence level. © 2007 Pearson Education
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Slide #21.
Control Limits The control limits for the x-chart are: UCL–x = =x + A2R and LCLx–= x=- A2R Where = X = central line of the chart, which can be either the average of past sample means or a target value set for the process. A2 = constant to provide three-sigma limits for the sample mean. The control limits for the R-chart are UCLR = D4R and LCLR = D3R where R = average of several past R values and the central line of the chart. D3,D4 = constants that provide 3 standard deviations (three-sigma) limits for a given sample size. © 2007 Pearson Education
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Slide #22.
Calculating Three-Sigma Limits Table 6.1 © 2007 Pearson Education
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Slide #23.
West Allis Industries Example 6.1 West Allis is concerned about their production of a special metal screw used by their largest customers. The diameter of the screw is critical. Data from five samples is shown in the table below. Sample size is 4. Is the process in statistical control? © 2007 Pearson Education
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Slide #24.
West Allis Industries Control Chart Development Example 6.1 0.5027 – 0.5009 = 0.0018 Special Metal Screw Sample Number 1 2 3 4 5 1 0.5014 0.5021 0.5018 0.5008 0.5041 © 2007 Pearson Education Sample 2 3 0.5022 0.5009 0.5041 0.5024 0.5026 0.5035 0.5034 0.5024 0.5056 0.5034 4 0.5027 0.5020 0.5023 0.5015 0.5039 R 0.0018 _ x 0.5018 (0.5014 + 0.5022 + 0.5009 + 0.5027)/4 = 0.5018
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Slide #25.
West Allis Industries Completed Control Chart Data Example 6.1 Special Metal Screw Sample Number 1 2 3 4 5 1 0.5014 0.5021 0.5018 0.5008 0.5041 © 2007 Pearson Education Sample 2 3 0.5022 0.5009 0.5041 0.5024 0.5026 0.5035 0.5034 0.5024 0.5056 0.5034 4 0.5027 0.5020 0.5023 0.5015 0.5047 R= R 0.0018 0.0021 0.0017 0.0026 0.0022 0.0021 x= = _ x 0.5018 0.5027 0.5026 0.5020 0.5045 0.5027
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Slide #26.
West Allis Industries R-chart Control Chart Factors Example 6.1 Factor Size of for Sample Charts (n) Factor for UCL Factor for and LCL for LCL for x-Charts R-Charts (A2) (D3) 2 1.880 0 3.267 3 1.023 0 R = 0.0021 2.575 4 0.729 0 D4 = 2.282 2.282 5 0.577 0 UCLR = D4R = 2.282 (0.0021) = 0.00479 in. D3 = 0 2.115 LCLR = D3R 0.483 0 (0.0021) = 0 in. 0 © 2007 Pearson Education 6 2.004 UCL R(D4)
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Slide #27.
West Allis Industries Range Chart Example 6.1 © 2007 Pearson Education
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Slide #28.
West Allis Industries x-chart Control Chart Factor Example 6.1 Factor Size of for Sample Charts (n) Factor for UCL Factor for and LCL for LCL for x-Charts R-Charts (A2) (D3) 2 1.880 0 3.267 3 1.023 0 R = 0.0021 A2 = 0.729 =x = 0.5027 2.575 0.729+ 0.729 (0.0021) 0= 0.5042 in. UCLx4= x= + A2R = 0.5027 2.282 = LCLx 5= x - A2R = 0.5027 0.577– 0.729 (0.0021) =0 0.5012 in. © 2007 Pearson Education 2.115 UCL R(D4)
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Slide #29.
West Allis Industries x-Chart Example 6.1 © 2007 Pearson Education Sample the process Find the assignable cause Eliminate the problem Repeat the cycle
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Slide #30.
Application 6.1 © 2007 Pearson Education
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Slide #31.
Application 6.1 UCLR D4 R 1.864(0.38) 0.708 LCLR D3 R 0.136(0.38) 0.052 © 2007 Pearson Education
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Slide #32.
Application 6.1 © 2007 Pearson Education
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Slide #33.
Sunny Dale Bank Example 6.2 Sunny Dale Bank management determined the mean time to process a customer is 5 minutes, with a standard deviation of 1.5 minutes. Management wants to monitor mean time to process a customer by periodically using a sample size of six customers. Design an x-chart that has a type I error of 5 percent. That is, set the control limits so that there is a 2.5 percent chance a sample result will fall below the LCL and a 2.5 percent chance that a sample result will fall above the UCL. Sunny Dale Bank x= = 5.0 minutes = 1.5 minutes n = 6 customers z = 1.96 Control Limits = + z UCLx = x x UCLx = 5.0 + 1.96(1.5)/ 6 = 6.20 min = – z LCL = x x x x = /n LCLx = 5.0 – 1.96(1.5)/ 6 = 3.80 min After several weeks of sampling, two successive samples came in at 3.70 and 3.68 minutes, respectively. Is the customer service process in statistical control? © 2007 Pearson Education
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Slide #34.
Control Charts for Attributes p-chart: A chart used for controlling the proportion of defective services or products generated by the process. p = p(1 – p)/n Where n = sample size p = central line on the chart, which can be either the historical average population proportion defective or a target value. – and LCL = p−z – Control limits are: UCLp = p+z p p p z = normal deviate (number of standard deviations from the average) © 2007 Pearson Education
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Slide #35.
Hometown Bank Example 6.3 The operations manager of the booking services department of Hometown Bank is concerned about the number of wrong customer account numbers recorded by Hometown personnel. Each week a random sample of 2,500 deposits is taken, and the number of incorrect account numbers is recorded. The results for the past 12 weeks are shown in the following table. Is the booking process out of statistical control? Use three-sigma control limits. © 2007 Pearson Education
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Slide #36.
Hometown Bank Using a p-Chart to monitor a process n = 2500 p= p = p = 147 = 0.0049 12(2500) p(1 – p)/n 0.0049(1 – 0.0049)/2500 p = 0.0014 UCLp = 0.0049 + 3(0.0014) = 0.0049 0.0091 – 3(0.0014) LCLp = = 0.0007 © 2007 Pearson Education Sample Number Wrong Account # Proportion Defective 1 2 3 4 5 6 7 8 9 10 11 12 15 12 19 2 19 4 24 7 10 17 15 3 0.006 0.0048 0.0076 0.0008 0.0076 0.0016 0.0096 0.0028 0.004 0.0068 0.006 0.0012 Total 147
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Slide #37.
Hometown Bank Using a p-Chart to monitor a process Example 6.3 © 2007 Pearson Education
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Slide #38.
Application 6.2 © 2007 Pearson Education
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Slide #39.
Application 6.2 Total number of leaky tubes 72 p 0.025 Total number of tubes 20144 p 1 p 0.0251 0.025 p 0.01301 n 144 UCL p p z p 0.025 3 0.01301 0.06403 LCL p p z p 0.025 3 0.01301 0.01403 LCL p 0 © 2007 Pearson Education
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Slide #40.
c-Charts c-chart: A chart used for controlling the number of defects when more than one defect can be present in a service or product. The underlying sampling distribution for a c-chart is the Poisson distribution. The mean of the distribution is c The standard deviation is c A useful tactic is to use the normal approximation to the Poisson so that the central line of the chart is c and the control limits are UCLc = c+z c © 2007 Pearson Education and LCLc = c−z c
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Slide #41.
Woodland Paper Company Example 6.4 In the Woodland Paper Company’s final step in their paper production process, the paper passes through a machine that measures various product quality characteristics. When the paper production process is in control, it averages 20 defects per roll. a) Set up a control chart for the number of defects per roll. Use twosigma control limits. b) Five rolls had the following number of defects: 16, 21, 17, 22, and 24, respectively. The sixth roll, using pulp from a different supplier, had 5 defects. Is the paper production process in control? c = 20 z=3 © 2007 Pearson Education UCLc = c+3 c = 20 + 3 20 = 33.42 LCLc = c−3 c = 20 - 3 20 = 6.58
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Slide #42.
Woodland Paper Company Using a c-Chart to monitor a process Example 6.4 Number of Defects Solver - c-Charts Sample Number © 2007 Pearson Education
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Slide #43.
Application 6.3 c 6 5 0 4 6 4 1 6 5 0 9 2 4 12 UCLc c z c 4 2 2 8 © 2007 Pearson Education c 4 2 LCLc c z c 4 2 2 0
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Slide #44.
Process Capability Process capability is the ability of the process to meet the design specifications for a service or product. Nominal value is a target for design specifications. Tolerance is an allowance above or below the nominal value. © 2007 Pearson Education
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Slide #45.
Process Capability Nominal value Process distribution Upper specification Lower specification 20 25 Process is capable © 2007 Pearson Education 30 Minutes
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Slide #46.
Process Capability Nominal value Process distribution Upper specification Lower specification 20 25 30 Process is not capable © 2007 Pearson Education Minutes
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Slide #47.
Effects of Reducing Variability on Process Capability Nominal value Six sigma Four sigma Two sigma Lower specification © 2007 Pearson Education Upper specification Mean
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Slide #48.
Process Capability Index, Cpk Process Capability Index, Cpk, is an index that measures the potential for a process to generate defective outputs relative to either upper or lower specifications. Cpk = Minimum of x= – Lower specification 3 , Upper specification – x= 3 We take the minimum of the two ratios because it gives the worst-case situation. © 2007 Pearson Education
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Slide #49.
Process Capability Ratio, Cp Process capability ratio, Cp, is the tolerance width divided by 6 standard deviations (process variability). Cp = © 2007 Pearson Education Upper specification - Lower specification 6
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Slide #50.
Intensive Care Lab Example 6.5 The intensive care unit lab process has an average turnaround time of 26.2 minutes and a standard deviation of 1.35 minutes. The nominal value for this service is 25 minutes with an upper specification limit of 30 minutes and a lower specification limit of 20 minutes. The administrator of the lab wants to have four-sigma performance for her lab. Is the lab process capable of this level of performance? Upper specification = 30 minutes Lower specification = 20 minutes Average service = 26.2 minutes = 1.35 minutes © 2007 Pearson Education
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Slide #51.
Intensive Care Lab Assessing Process Capability Example 6.5 Cpk = Minimum of Cpk = Cpk = Upper specification = 30 minutes Lower specification = 20 minutes Average service = 26.2 minutes = 1.35 minutes x= – Lower specification Minimum of 3 26.2 – 20.0 3(1.35) Minimum of © 2007 Pearson Education , 1.53, 0.94 Upper specification – x= 3 , 30.0 – 26.2 3(1.35) = 0.94 Process Capability Index
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Slide #52.
Intensive Care Lab Assessing Process Capability Example 6.5 Cp = Cp = Upper specification - Lower specification 30 - 20 6(1.35) 6 = 1.23 Process Capability Ratio Does not meet 4 (1.33 Cp) target Before Process Modification Upper specification = 30.0 minutes Lower specification = 20.0 minutes Average service = 26.2 minutes = 1.35 minutes Cpk = 0.94 Cp = 1.23 After Process Modification Upper specification = 30.0 minutes Lower specification = 20.0 minutes Average service = 26.1 minutes = 1.2 minutes Cpk = 1.08 Cp = 1.39 © 2007 Pearson Education
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Slide #53.
Application 6.4 © 2007 Pearson Education
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Slide #54.
Application 6.4 © 2007 Pearson Education
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Slide #55.
Loss (dollars) Taguchi's Quality Loss Function Lower specification © 2007 Pearson Education Nominal value Upper specification
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Slide #56.
Six Sigma Six Sigma is a comprehensive and flexible system for achieving, sustaining, and maximizing business success by minimizing defects and variability in processes. It relies heavily on the principles and tools of TQM. It is driven by a close understanding of customer needs; the disciplined use of facts, data, and statistical analysis; and diligent attention to managing, improving, and reinventing business processes. © 2007 Pearson Education
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Slide #57.
Six Sigma Improvement Model 1. Define Determine the current process characteristics critical to customer satisfaction and identify any gaps. 2. Measure Quantify the work the process does that affects the gap. 3. Analyze Use data on measures to perform process analysis. 4. Improve Modify or redesign existing methods to meet the new performance objectives. 5. Control Monitor the process to make sure high performance levels are maintained. © 2007 Pearson Education
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Slide #58.
Six Sigma Implementation Top Down Commitment from corporate leaders. Measurement Systems to Track Progress Tough Goal Setting through benchmarking best-in-class companies. Education: Employees must be trained in the “whys” and “how-tos” of quality. Communication: Successes are as important to understanding as failures. Customer Priorities: Never lose sight of the customer’s priorities. © 2007 Pearson Education
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Slide #59.
Six Sigma Education Green Belt: An employee who achieved the first level of training in a Six Sigma program and spends part of his or her time teaching and helping teams with their projects. Black Belt: An employee who reached the highest level of training in a Six Sigma program and spends all of his or her time teaching and leading teams involved in Six Sigma projects. Master Black Belt: Full-time teachers and mentors to several black belts. © 2007 Pearson Education
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Slide #60.
International Quality Documentation Standards ISO 9000 ISO 14000 © 2007 Pearson Education A set of standards governing documentation of a quality program. Documentation standards that require participating companies to keep track of their raw materials use and their generation, treatment, and disposal of hazardous wastes.
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Slide #61.
Malcolm Baldrige National Quality Award Named after the late secretary of commerce, a strong proponent of enhancing quality as a means of reducing the trade deficit. The award promotes, recognizes, and publicizes quality strategies and achievements. 1. 2. 3. 4. Category 1 ─ Leadership Category 2 ─ Strategic Planning Category 3 ─ Customer and Market Focus Category 4 ─ Measurement, Analysis, and Knowledge Management 5. Category 5 ─ Human Resource Focus 6. Category 6 ─ Process Management © 2007 Pearson Education 7. Category 7 ─ Business Results 120 points 85 points 85 points 90 points 85 points 85 points 450 points
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