Quality Managementand 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
Garvin’s Product Quality Dimension Performance Features Durability Reliability Serviceability Conformance Aesthetics Perceived Quality
Service Quality Dimensions Parasuraman, Zeithamel, and Berry’s Service Quality Dimensions Tangibles Responsiveness Service Reliability Assurance Empathy
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.
Quality Specifications • Design quality (consumer’s view) • inherent value of the product in the marketplace and therefore, has strategic implications. • Conformance quality (producer’s view) • degree to which the product or service design specifications are met
Costs of Quality • Appraisal costs inspection and testing • Prevention costs quality planning and training • Internal failure costs scrap, rework, yield loss, downtime • External Failure costs complaint adjustment, allowances, warranty work
Quality Cost: Traditional View Totalqualitycosts Internaland externalfailurecosts Cost per good unit of product Minimumtotal cost Preventionand appraisalcosts 0 Quality level (q) 100% Optimum quality level
Inspection before/after production Corrective action during production Quality built into the process Acceptance sampling Process control Continuous improvement The least progressive The most progressive Phases of Quality Assurance
1. Plan a change aimed at improvement. 4. Institutionalize the change or abandon or do it again. 4. Act 1. Plan 3. Check 2. Do 3. Study the results; did it work? 2. Execute the change. PDCA Cycle (Deming Wheel)
Ishikawa’s Basic Tools of Quality Check Sheets Histogram Pareto Charts Control Charts Cause & Effect Diagrams Flowcharts Scatter Diagrams
Histograms Graphical representation of data in a bar chart format Can be used to identify the frequency of quality defect occurrence and display quality performance. Number of Lots 0 1 2 3 4 Defectsin lot Data Ranges
Pareto Charts Can be used to find when 80% of the problems may be attributed to 20% of the causes. 80% Frequency Design Assy. Instruct. Purch. Training Other
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
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.
Machine Man Effect Environment Method Material Cause & Effect Diagram The results or effect Possible causes: Can be used to systematically track backwards to find a possible cause of a quality problem (or effect)
Check Sheets Can be used to keep track of defects or used to make sure people collect data in a correct manner. Monday • Billing Errors • Wrong Account • Wrong Amount • A/R Errors • Wrong Account • Wrong Amount
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.
Scatter Diagrams Can be used to illustrate the relationships between variables (Example: quality performance and training). 12 10 8 Defects 6 4 2 0 0 10 20 30 Hours of Training
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.
Scatter Diagrams Prevention in Costs and Conformance
Flowcharts 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”
Example: Process Flow Chart Material Received from Supplier No, Continue… Inspect Material for Defects Defects found? Yes Can be used to find quality problems. Return to Supplier for Credit
Flowcharts Basic Flowcharting Symbols
Flowcharts • 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.
Control Charts • 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.
Example: Run Chart Can be used to identify when equipment or processes are not behaving according to specifications. 0.58 0.56 Diameter 0.54 0.52 0.5 0.48 0.46 0.44 1 2 3 4 5 6 7 8 9 10 11 12 Time (Hours)
UCL LCL Example: Control Chart Can be used to monitor ongoing production process quality and quality conformance to stated standards of quality. 1020 1010 1000 990 980 970 0 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2
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
Six Sigma Quality (Continued) • Six Sigma allows managers to readily describe process performance using a common metric: Defects Per Million Opportunities (DPMO) Number of defects = x 1,000,000 DPMO é ù Number of ê ú opportunit ies x No. of units ê ú for error per ê ú unit ë û
Six Sigma Quality (Continued) So, for every one million letters delivered this city’s postal managers can expect to have 1,000 letters incorrectly sent to the wrong address. 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? Cost of Quality: What might that DPMO mean in terms of over-time employment to correct the errors?
Six Sigma Quality: DMAIC Cycle (Continued) 1. Define (D) Customers and their priorities 2. Measure (M) Process and its performance 3. Analyze (A) Causes of defects 4. Improve (I) Remove causes of defects 5. Control (C) Maintain quality
Second Part Statistical Process Control
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
Sources of Variation in Production Processes Measurement Instruments Operators Methods Materials INPUTS PROCESS OUTPUTS Tools Human Inspection Performance Machines Environment
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
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.
Importance of Understanding Variation time PREDICTABLE ? UNPREDECTIBLE
Two Fundamental Management Mistakes • Treating as a special cause any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to common causes • Attributing to common causes any fault, complaint, mistake, breakdown, accident or shortage when it actually is due to a special cause
Types of Data Variables Data “Things we measure” • Length• Weight• Time • Height• Volume• Temperature • Diameter• Tensile Strength• Strength of Solution Attribute Data “Things we count” • 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)
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)
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.
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.
Process Control Charts X-bar and R Charts
Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.
Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values
UCL LCL Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot Values