Week 11 Quality Management 2024 PDF

1 WEEK 11 QUALITY MANAGEMENT Opening case of Hyundai Quality Definitions Dimensions of Quality TQM: A “Total” View of Quality Six sigma quality management  2 Opening case of Hyundai Brand Turnarou...

1 WEEK 11 QUALITY MANAGEMENT Opening case of Hyundai Quality Definitions Dimensions of Quality TQM: A “Total” View of Quality Six sigma quality management  2 Opening case of Hyundai Brand Turnaround– What Hyundai Did Increased the number of workers on the quality control team from 100 to more than 850. Instituted mandatory seminars for all workers on the importance of quality. Invoked the direct involvement of its CEO in twice-monthly meetings comparing Hyundai quality with that of its rivals. Made capital investments in problem areas, including $30 million invested in a computer center to test electronic systems.  3 Opening case of Hyundai Brand Turnaround at Hyundai – Results of Changes Brand loyalty for Hyundai surpassed that of Honda and Toyota to take the No. 1 spot. Five Hyundai cars among its “Best Bets” for safety, reliability, and fuel efficiency. Genesis models made Hyundai a strong competitor in the luxury market, where excellent quality is imperative. In 2014 Hyundai placed first in the J.D. Power Initial Quality Study. Hyundai and its sister company Kia averaged 90 problems per 100 vehicles, 20 percent fewer problems than those found in European, Japanese, and American cars, on average.  4 Opening case of Hyundai As we can see from the experiences of Hyundai, quality offers firms a way of enhancing their competitiveness and strategic position in the marketplace. The reality in today’s competitive world is that no firm can afford to forget quality; no firm can afford to compromise on quality. Quality is expected and must be delivered. To be delivered, it must be understood  5 Quality Definitions Quality can be broadly defined by the following terms: Product quality is a product’s fitness for consumption—how well it meets customers’ needs and desires. Fitness for consumption is determined by both a product’s design quality and its conformance quality. Design quality is a measure of how well a product’s designed features match up to the requirements of a given customer group. Conformance quality is a measure of whether or not a delivered product meets its design specifications. Quality management is a management approach that establishes an organization wide focus on quality, merging the development of a quality-oriented corporate culture with intensive use of managerial and statistical tools.  6 Quality Definitions Dimensions of quality Dimension of Description for Description for Product Quality a Tangible Good an Intangible Service Performance The degree to which the product meets or exceeds certain operating characteristics Features Presence of unique product characteristics that supplement basic functions Length of time a product performs Ability to perform the promised service Reliability before it must be repaired dependably and accurately Durability Length of product life or the amount of use one gets before a product deteriorates Conformance The degree to which a product meets its design specifications Subjective assessment of a product’s Appearance of physical facilities, equipment, Aesthetics look, feel, sound, taste, or smell personnel, and communication materials Competence of product support in Support/ Willingness to help customers and provide terms of installation, information, Responsiveness prompt service maintenance, or repair Perceived Subjective assessment based on Subjective assessment of the knowledge Quality image, advertising, brand names, and courtesy of employees and their (Reputation/ reputation, or other information indirectly ability to convey trust and confidence Assurance/ associated with the product’s Subjective assessment of the caring, individualized Empathy) attributes attention paid to customers  7 Quality Definitions Total Quality Management (TQM) An integrated strategy aimed at embedding awareness of quality. The word total has important connotations: 1. A product’s quality is determined by customer’s acceptance and use: Any discussion of product quality must include all of the attributes that targeted customers will care most about 2. Quality management is a total, organization-wide activity rather than a technical task: Every employee has a stake in product quality, and almost everyone has some direct or indirect influence on it. 3. Quality improvement requires a total commitment from all employees: A quality product results from good design combined with effective production and delivery methods. Everyone in a company has some role either directly or indirectly related with the quality of the products  8 Quality Definitions Cost of quality (COQ) refers to a framework for quantifying the total cost of quality-related efforts and deficiencies Prevention Costs - Costs associated with preventing defects and limiting failure and appraisal costs (e.g., training, improvement projects, data gathering, analysis). Appraisal Costs - Costs associated with inspection to assess quality levels (e.g. staff, tools, training, etc.) Internal Failure Costs - Costs from defects found before delivery to the customer (e.g., rework, scrap, etc.) External Failure Costs - Costs associated with defects found after delivery to customer (e.g., warranty, recall, etc.) Get real in p. 179 in the pdf version of the handout  9 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. Six Sigma A statistical term to describe the quality goal of no more than 3.4 defects out of every million units. It aims near quality perfection (the statistical likelihood of non- defects 99.99966% of the time) Refer to the Excel file (six sigma)  10 Six Sigma Quality Defects per Million Opportunities (DPMO) A metric used to describe the variability of the process. Requires three pieces of data: Unit – the item produced or being serviced. Defect – any item or event that does not meet the customer’s requirements. Opportunity – a chance for a defect to occur Number of defects DPMO = ×1,000,000 Number of opportunities for error per unit × Number of units  11 Six Sigma Quality Six Sigma Methodology (DMAIC) Define - identify customers and their priorities, a project suitable for Six Sigma efforts, and CTQ s (critical-to-quality characteristics) Measure – determine how to measure the process and how it is performing and identify the key internal processes that influence CTQ s. Analyze – determine the most likely causes of defects and identify the key variables. Improve – identify means to remove the causes of defects, confirm the key variables, identify the maximum acceptance ranges of the key variables. Control – determine how to maintain the improvements and put tools in place to ensure that the key variables remain within maximum acceptance ranges.  12 Six Sigma Quality - Process capability analysis Process capability analysis A tool for assessing the ability of a process to consistently meet or exceed a product’s design specifications. Considers the tolerances allowed by product or service design specifications the natural variability in the process A measure of process capability that compares the specification width with the process width. Specification width S Cp = =. Process width P  13 Six Sigma Quality - Process capability analysis Example K Computers offers a medical equipment cart. This cart requires piping (which is cut internally) for the frame. A scatter diagram that compares the speed of a conveyor line and the lengths of cut metal tubing A positive relationship between the conveyor speed and the cut length; an increase in conveyor speed seems associated with longer pieces It is not possible to cut each tube to exactly the same length  14 Six Sigma Quality - Process capability analysis Example The design tolerances for tubing parts designate how much the lengths can vary yet still fit together properly in the cart assembly. Specification width (S) The design specification for a tube length is 1030mm ± 10mm (1020 to 1040) Process width (P) The actual range of outcomes generated by the production process itself Processes have natural variation (due to machine vibration, cutting tool wear, worker experience, and metal characteristics) E.g., if the process can maintain length from 1025 to 1035, it is capable since P ≤ S  15 Six Sigma Quality - Process capability analysis The Cp is essentially the ratio of the specification width to the process width USL LSL P is a function of σ because most process output distributions have probability Managers in the past have chosen to set P = 6σ to define a range that covers about 99.7 percent of the output for processes that vary Thus, a Cp value less than 1 would indicate that more than 0.3 percent of produced units will not meet design specifications.  16 ❖ Some samples Three bell-shaped distributions are shown, but with varying widths and heights. All three graphs mark LSL at 1020 and USL at 1040. Graph A, the graphed line is shorter and wider than the others and extends past the LSL and USL. Cp value of 0.67 - an incapable process Graph B, the graphed line is taller and narrower than in A and stops at LSL and USL. Cp value of 1 – a capable process Graph C has the tallest and narrowest line with endpoints at 1024, 1036, well inside the LSL and USL. Cp value of 1.67 - an incapable process that deal with many unplanned but short-term variations in P.  17 ❖ Another sample Deceptive Cp Value: The Problem of Lack of Centering Cp value effectively measures process capability, when the center of its output distribution is the same as the center of the product specification range In this case, the centre of the graph to the right of the LSL and the endpoints are at 1016 and 1028. X-bar = 1022. The process width and specification width are the same as in distribution C from the previous slide but many of the units of output from this distribution will have values that are outside the specification range.  18 Six Sigma Quality - Process capability analysis To deal with non-centered process distributions, we must use an adjusted version of the Cp metric known as the Cpk The Cpk and Cp are almost the same, except for the correction term, (1 − K). The calculation of K has a new parameter, D, which is the design center of the specification width S. D is the target value for performance data, while X bar is the process average. D equals (closes to) X bar then Cpk is identical to Cp  19 Six Sigma Quality - Process capability analysis Returning to the process of p.17 Given the process mean = 1022, we calculate the Cpk value as follows abs: absolute value Cpk is less than 1 It indicates an unreliable process that cannot reliably meet design specifications.  20 Six Sigma Quality - Process capability analysis Cpk, PPM, and Process Management PPM Process Cpk (Defective parts per million) Implications Process is incapable; 100% 0.50 133,610 inspection may be needed. 1.00 2,700 Blank Process capable, normal sampling 1.33 64 would be typical. 1.50 7 Blank For values of 2 or more, no 2.00 0.00198 inspection may be needed; process is very stable.  21 Six Sigma Quality - Process control analysis Process Control Charts (SPC) It should be monitored over time to ensure that it remains stable, that is, that the range and mean (center) of process output do not change too much. Process control charts or statistical process control (SPC) are tools used to monitor process output to detect such changes.  22 Six Sigma Quality - Process control analysis Intelligent systems plot and compare outputs to a set of limits for the upper and lower boundaries of the process width. The process width is defined by a confidence interval (usually 99 percent or 3σ). For instance, A sample value lies between the upper and lower limits is within the expected normal random variation of the process. Points that fall outside these limits are not likely to have occurred by chance, suggesting that the process may have changed. Thus, process control charts identify when a process has deviated from its normal operation (when it is “out of control”). Such a change prompts the process operator to stop, investigate, and correct the process.  23 Six Sigma Quality - Process control analysis This monitors The output of a process to ensure that sample statistics (e.g., mean and range) are within the expected variation limits of the process. Bell shaped curve with 99% of the sample mean values falling within 3 sigma of the center line. This is the expected zone. The 1% of sample mean values that falls outside of 3 sigma of the center line is called the unexpected zone.  24 Six Sigma Quality - Process control analysis (Xbar&R) An example of Xbar & R You are a hard disk manufacturer. You wants to track hard disk seek times to make sure that the process of building the disks is under control. 1. You collected data samples (20 samples, 5 observations, n=5)  25 2. For each sample, calculate the sample mean x 3. For each sample, find the range, R It is the difference between the largest and smallest values 4. Calculate the grand mean It is the value of = (the summation every x )/n 5. Calculate the mean range It is the value of = (the summation of the every R)/n. In our case it is 0.69 (13.7/20) 6. Compute control limits and construct the charts for each chart using the values of A2, D3, and D4 Note: A2, D3, D4 are given parameters x Chart R Chart Central line = Central line = R. Lower control line = x − A2 R. Lower control line = D3 R. Upper control line = x + A R. Upper control line = D4 R. 2  26 Values for Setting Control Limit Lines  27 Six Sigma Quality - Process control analysis (Xbar&R) Data Points X bar Chart R Chart Central Line 12.14 ms 0.69 Lower Control Limit 12.14 − 0.577*0.69 = 11.74 0 (LCL) Upper Control Limit 12.14 + 0.577*0.69 = 12.54 2.115*0.69 = 1.459 (UCL)  28 Six Sigma Quality - Process control analysis (Xbar&R) Upper Control Limit x Chart for the Example Data a dashed green line at 12.54 Lower Control Limit a dashed green line at 11.74 Central line a red line at 12.14. The x-bar line trends upward from just above the Lower Control limit to above the Central line, with one strong drop below the Central line before sloping upward steeply, passing the upper limit briefly, and sloping back down.  29 Six Sigma Quality - Process control analysis (Xbar&R) Upper Control Limit R Chart for the Example Data a dashed green line at 1.459 Lower Control Limit 0 Central line A red line at 0.69. The x-bar line is a jagged line that moves up and down across the Central line but does not have an upward or downward trend.  30 Sample Means and Ranges  31 Six Sigma Quality - Process control analysis (p Chart) p attribute control char Xbar-R charts used for continuous variables. In some cases, the observed data are attribute variables so you deal with pass/fail, live/die, or good/ bad outcomes. So, managers want to determine if the proportion of nonconforming product or service is stable, i.e., the process is under control. p attribute control chart is used to this case For instance, K Computers developed a new product and want to know if the production process consistently produces fewer than 5 percent defects (a given minimum standard) To do this analysis, 1. Collect and organize the data under normal operating conditions- A table in the next slide  32 Reject Rate  33 Six Sigma Quality - Process control analysis (p Chart) 2. Compute control limits and construct the chart with following equations Standard deviation of sample proportion The “3” in these equations establishes the width of the control limit. ± 3σ. In this case the limits are set at The value of 3 can be changed to increase or decrease this interval For the previous case, (we cannot have a negative LCL)  34 Six Sigma Quality - Process control analysis (p Chart) The graph has the upper limit at 0.09 and the lower limit at 0.00 and p-bar at 0.0335. It shows defects spiking upward to sample 7 and then back down to a low at sample 14 and then climbing again to sample 20.  35 Six Sigma Quality - Process control analysis (p Chart) Interpreting Control Charts Trends – when successive points seem to fall along a line moving upward or downward. Runs – run of points that indicate systemic changes in process. Hugging – points appear so closely grouped that they seem to show no variation. Periodicity – plotted points show the same pattern of change over equal intervals.  36 Process Control Chart Evidence for Investigation https://qi.elft.nhs.uk/wp- content/uploads/2019/04/ Guide-to-intepreting- SPC-charts.pdf