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Quality Management & ControlSeung-Kuk Paik, Ph.D.Systems a.docx
- 1. Quality Management & Control Seung-Kuk Paik, Ph.D. Systems and Operations Management CSU, Northridge What is Quality? "Quality" can be defined in many ways. 1.Quality is defined as “FITNESS FOR USE”: How well a service or product performs its intended purpose. 2.Quality is also defined as “CONFORMANCE TO REQUIREMENTS”: How a service or product conforms to performance specifications. What is Quality? 3.In a wider sense, "QUALITY" is often considered the degree of excellence whereby products and services may be ranked against each other on a relative basis for selected features and characteristics. American Society of Quality (ASQ) has accepted the following definition: QUALITY: The totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs. What is Quality?
- 2. DIMENSIONS OF QUALITY 1.Performance - A product´s primary operating characteristics. 2.Features - Supplements to a product´s basic functioning characteristics 3.Reliability – Consistency of performance What is Quality? DIMENSIONS OF QUALITY 4.Durability - A measure of product life. Serviceability - The speed and ease of repair 6.Aesthetics - Appearance of a product 7.Safety - Will the product perform its function without unnecessarily endangering the user? Quality and Productivity Historically, quality was viewed by some as a controlling activity which took place somewhere near the end of a production process, an after-the-fact measurement of production performance. Efforts to obtain quality products increased the costs associated with making that product. Thus, quality and productivity were viewed as conflicting; one was increased at the expense of the other.
- 3. 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. 7 Deming’s Chain Reaction Quality and Costs Costs decrease because of less rework, fewer mistakes, fewer delays, and better use of time and materials Improve quality Productivity improves Stay in Business Provides jobs and more jobs Capture the market
- 4. Quality Improvement DEMING´S 14 POINTS 1.Create constancy of purpose toward improvement of products 2.Adopt a quality philosophy 3.Cease dependence on mass inspection 4.End the practice of selecting suppliers on the basis of price alone 5.Improve constantly 6.Institute training on the job 7.Institute leadership 8.Drive out fear DEMING´S 14 POINTS 9.Break down barriers between departments 10. Eliminate slogans and targets 11. Eliminate work standards that prescribe numerical quotas 12. Remove barriers that rob workers and managers of pride in workmanship 13. Institute a vigorous program of education and self- improvement 14. Put everybody in the organization to work to accomplish the transformation Quality Improvement Deming’s Process Improvement Cycle Establish Expectations Formalize improvements
- 5. Implement Obtain Support Compare Adjust Define Process Pilot Assessment Establish measurements Flow Diagram Cause and Effect Diagram Benchmarking Brainstorm Check Plan Do Act
- 6. 1. It aids in developing methods for measuring and evaluating capabilities of a process. 2. SPC enables us to monitor a process and indicate when the process is in control and when corrective action is needed. 3. It increases efficiency by eliminating redundant or unnecessary activities. Statistical Process Control Statistical process control (SPC) is a system or a set of specific techniques for controlling and improving production and service processes. Some Useful Characteristics: 12 Statistical Process Control One of the most important tools of statistical process control is the process control chart. Process Control Chart: A time ordered plot of sample statistics obtained from an on going process used to distinguish between random and nonrandom variability Purpose: to monitor process output to see if it is random The essence of process control chart is to assure that the output of a process is random so that future output will be random Upper and lower control limits define the range of acceptable variation Causes of Variation Two basic categories of variation in output include common causes and assignable causes.
- 7. Common causes are the purely random, unidentifiable sources of variation that are unavoidable with the current process. Assignable causes of variation are any variation-causing factors that can be identified and eliminated, such as a machine needing repair. 14 Control Chart
- 9. LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources Abnormal variation due to assignable sources Control Charts A control chart is a graph of the performance of a process over time. For example, a chart might show the temperatures of successive meals during one night. Superimposed on each graph are lines called upper and lower control limits (UCL and LCL) for the process. The control limits are so drawn that if the process ever performs outside the control limits, something unusual (assignable cause) has almost certainly happened. A centerline (usually the mean of the process parameter) is also drawn on the graph. Thus, a control chart gives a visual answer to the question: Is this process behaving the way it usually does or something has changed?
- 10. Appropriate Use of SPC Use SPC if process is Measurable Repetitive Prone to occasional drift or dramatic shift Has quantitative output Steps In Creating Control Charts Take samples from the population and compute the appropriate sample statistic Use the sample statistic to calculate control limits and draw the control chart Plot sample results on the control chart and determine the state of the process (in or out of control) Investigate possible assignable causes and take any indicated actions Continue sampling from the process and reset the control limits when necessary Control Chart for Attributes Example: Consider a data entry operation that makes numerous entries daily. On each of 24 consecutive days, subgroups of 200 entries are inspected. The resulting raw data follows: Day Number of Entries Insp Number Defective FD 12006.030 22006.030 32006.030 42005.025 52000.000 62006.030 720014.070
- 11. 82004.020 92000.000 102001.005 112008.040 122002.010 132004.020 142007.035 152001.005 162003.015 172001.005 182004.020 192000.000 202004.020 212000.000 2220015.075 232004.020 242001.005 Control Chart for Attributes When the data consists of a series of fraction defectives, the appropriate control chart is a p-chart. A p-chart is suitable when a process outcome does not have a numerical value. Centerline of a p-chart, p = Total Number of Defectives / (N*n) where, N=Number of samples, and n=sample size, and Total Number of Defectives is the aggregate of defectives from all samples, and Control limits, called 3-sigma limits are given by: In the earlier example, = UCL(p) = LCL(p) =
- 12. P-Chart Day Control Charts for Variables Two common types of control charts The X-bar( )control chart measures the mean of each sample reveals any tendency for the process mean to drift or jump around over time The R (range) chart measures the range of each sample reveals any tendency of the process to behave more randomly or less randomly over time. 22 Constructing Control Charts The followings steps are needed: First, choose the sampling plan and sample size (n). Collect several samples (usually more than 20). Calculate the range (R) and mean ( ) of each sample Find the average of the means Calculate average range ( ) of all the sample Calculate upper and lower control limits for X-bar chart as below:
- 13. Calculate upper and lower control limits for R-bar Factors for Computing Control Charts Sample Size, nMean Factor, A2Upper Range, D4Lower Range, D3 21.8803.2680 31.0232.5740 4 .7292.2820 5 .5772.1140 6 .4832.0040 7 .4191.9240.076 8 .3731.8640.136 9 .3371.8160.184 10 .3081.7770.223 Source: Special Technical Publication 15-C, American Society for Testing Materials, “Quality Control of Materials,” pp. 63 and 72, 1951 Example for X-bar & R Charts
- 14. A restaurant manager is concerned about the temperature of a food item being served. The manager never wanted the dish to be cooler than 140 degrees F or warmer than 160 degrees F when it left the kitchen to be served. The manager decided to do a capability study over 10 days. Each day, she randomly picked three servings and tested the temperature just as the dish was being served. The following are the data that she collected. Day Reading, 0FRange, 0F , 0F 1150160155 10155.0 214015015515148.3 =8.50F 3145150150 5148.3 4150150155 5151.6 =151.50F 513015515025145.0 6140 140145 5141.6 7150150150 0150.0 8155155160 5156.7 9160160160 0160.0 1015016016515158.3 Example of Initial Capability R chart Limits:
- 15. Center Control Line = = 8.5 0F chart Limits: Center Control Line = Initial Capability Study UCL (160.2) LCL (142.8) Process not stable, not in control 141.6 CL (151.5) Initial Capability
- 16. UCL(21.8) LCL(0) 25 Process not stable, not in control CL (8.5) Capability Study of Improved Process DayReading, 0FRange,0F , 0F 111511471504149.3 121511501474149.3 131501521512151.0 141501561516152.3 151501481524150.0 161481511557151.3 171521571498152.6 181471511504149.3 191501501566152.0 201501551515152.0 = 5.0 = 150.9 R chart limits: LCL = 0, UCL = 12.9 chart limits: LCL = 145.8, UCL = 156.0
- 17. Improved Process Process stable, and in control UCL(156) LCL(145.8) CL(150.9) Improved Process Process stable, and in control UCL(12.9) LCL(0) CL(5.0) 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.
- 18. A process in statistical control does not necessarily meet the design specification. Just because a process is in control does not necessarily mean that it is within tolerances. 32 Process Capability Lower Specification Upper Specification Process variability matches specifications Lower Specification Upper Specification Process variability well within specifications Lower Specification
- 19. Upper Specification Process variability exceeds specifications Range of Possible Solution s Redesign the process so that it can achieve the desired output Use an alternative process that can achieve the desired output Retain the current process but attempt to eliminate unacceptable output using 100% inspection Examine the specifications to see whether they really necessary or could be relaxed without adversely affecting customer satisfaction Capability analysis is the determination of whether the inherent variability of the process output falls within the acceptable range of variability allowed by the specifications for the process output Process Capability Analysis
- 20. Process Capability Ratio Process capability ratio, Cp = specification width process width Upper specification – lower specification Cp = 1. For a process to be capable, it must have a ratio of at least 1.0 2. A ratio of 1.0 means that 99.73% of the output of a process can be expected to be within the specifications. 3. The greater the ratio, the greater the probability that the output of a machine or process will fall within design specifications. Cp = Upper Specification - Lower Specification 6s Insurance claims process Process mean x = 210.0 minutes
- 21. Process standard deviation s = .516 minutes Design specification = 210 ± 3 minutes = = 1.938 213 - 207 6(.516) Process Capability Ratio Example UCL p = p + 3 p ( 1 - p ) / n LCL p
- 24. 155 160 12345678910 Days X-bar Chart2155148.3148.3151.6145141.6150156.7160158.3 Days X-bar X-bar Chart Sheet1X- barR15510148.315148.35151.6514525141.651500156.75160015 8.315 Sheet10000000000 Days X-bar X-bar Chart Sheet2 Sheet3 R Chart 0 5 10 15 20 25
- 25. 30 12345678910 Days Range Chart310155525505015 Days Range R Chart Sheet1X- barR15510148.315148.35151.6514525141.651500156.75160015 8.315 Sheet1 Days X-bar X-bar Chart Sheet2 Days Range R Chart Sheet3 X-bar Chart 140 145 150