Stevenson 9 Management of Quality

Learning Objectives Define the term quality. Explain why quality is important and the consequences of poor quality. Identify the determinants of quality. Describe the costs associated with quality. Describe TQM. Describe Lean Production. Give an overview of problem solving. Describe and use various quality tools.

Key Contributors to Quality Management

Quality Management What does the term quality mean? Quality is the ability of a product or service to consistently meet or exceed customer expectations.

Quality Assurance vs. Strategic Approach Emphasis on finding and correcting defects before reaching market Strategic Approach Proactive, focusing on preventing mistakes from occurring Greater emphasis on customer satisfaction

Dimensions of Quality Performance - main characteristics of the product/service Aesthetics - appearance, feel, smell, taste Special Features - extra characteristics Conformance - how well product/service conforms to customer’s expectations Reliability - consistency of performance

Dimensions of Quality (Cont’d) Durability - useful life of the product/service Perceived Quality - indirect evaluation of quality (e.g. reputation) Serviceability - service after sale

Examples of Quality Dimensions

Examples of Quality Dimensions (Cont’d)

Service Quality Convenience Reliability Responsiveness Time Assurance Courtesy Tangibles

Examples of Service Quality Dimension Examples 1. Convenience Was the service center conveniently located? 2. Reliability Was the problem fixed? 3. Responsiveness Were customer service personnel willing and able to answer questions? 4. Time How long did the customer wait? 5. Assurance Did the customer service personnel seem knowledgeable about the repair? 6. Courtesy Were customer service personnel and the cashier friendly and courteous? 7. Tangibles Were the facilities clean, personnel neat?

Challenges with Service Quality Customer expectations often change Different customers have different expectations Each customer contact is a “moment of truth” Customer participation can affect perception of quality “Fail-safe” must be designed into the system (for customer self-service)

Determinants of Quality Quality of design Intention of designers to include or exclude features in a product or service Quality of conformance The degree to which goods or services conform to the intent of the designers

The Consequences of Poor Quality Loss of business Liability Productivity Costs

Responsibility for Quality Top management Design Procurement Production/operations Quality assurance Packaging and shipping Marketing and sales Customer service IDEALLY, EVERYONE IS RESPONSIBLE FOR QUALITY

Costs of Quality Failure Costs - costs incurred by defective parts/products or faulty services. Internal Failure Costs Costs incurred to fix problems that are detected before the product/service is delivered to the customer. External Failure Costs All costs incurred to fix problems that are detected after the product/service is delivered to the customer.

Costs of Quality (continued) Appraisal Costs Costs of activities designed to ensure quality or uncover defects Prevention Costs All TQ training, TQ planning, customer assessment, process control, and quality improvement costs to prevent defects from occurring

Ethics and Quality Substandard work Defective products Substandard service Poor designs Shoddy workmanship Substandard parts and materials Having knowledge of this and failing to correct and report it in a timely manner is unethical.

Quality Certification ISO 9000 Set of international standards on quality management and quality assurance, critical to international business ISO 14000 A set of international standards for assessing a company’s environmental performance

ISO 9000 Quality Management Principles Customer focus Leadership People involvement Process approach A systems approach to management Continual improvement Factual approach to decision making Mutually beneficial supplier relationships

ISO 14000 Management systems Operations Environmental systems Systems development and integration of environmental responsibilities into business planning Operations Consumption of natural resources and energy Environmental systems Measuring, assessing and managing emissions, effluents, and other waste

Total Quality Management A philosophy that involves everyone in an organization in a continual effort to improve quality and achieve customer satisfaction. T Q M

The TQM Approach Find out what the customer wants Design a product or service that meets or exceeds customer wants Design processes that facilitates doing the job right the first time Keep track of results Extend these concepts to suppliers

Elements of TQM Continual improvement Competitive benchmarking Employee empowerment Team approach Decisions based on facts Knowledge of tools Supplier quality Champion Quality at the source Suppliers

Continuous Improvement Philosophy that seeks to make never-ending improvements to the process of converting inputs into outputs. Kaizen: Japanese word for continuous improvement.

Quality at the Source The philosophy of making each worker responsible for the quality of his or her work.

Six Sigma Statistically Conceptually Having no more than 3.4 defects per million Conceptually Program designed to reduce defects Requires the use of certain tools and techniques Six sigma: A business process for improving quality, reducing costs, and increasing customer satisfaction.

Six Sigma Programs Six Sigma programs Employed in Improve quality Save time Cut costs Employed in Design Production Service Inventory management Delivery

Six Sigma Management Providing strong leadership Defining performance metrics Selecting projects likely to succeed Selecting and training appropriate people

Six Sigma Technical Improving process performance Reducing variation Utilizing statistical models Designing a structured improvement strategy

Six Sigma Team Top management Program champions Master “black belts” “Green belts”

Six Sigma Process Define Measure Analyze Improve Control DMAIC

Obstacles to Implementing TQM Lack of: Company-wide definition of quality Strategic plan for change Customer focus Real employee empowerment Strong motivation Time to devote to quality initiatives Leadership

Obstacles to Implementing TQM Poor inter-organizational communication View of quality as a “quick fix” Emphasis on short-term financial results Internal political and “turf” wars

Basic Steps in Problem Solving Define the problem and establish an improvement goal Define measures and collect data Analyze the problem Generate potential solutions Choose a solution Implement the solution Monitor the solution to see if it accomplishes the goal

The PDSA Cycle Plan Do Study Act

Process Improvement Process Improvement: A systematic approach to improving a process Process mapping Analyze the process Redesign the process

The Process Improvement Cycle Implement the Improved process Select a process Study/document Seek ways to Improve it Design an Evaluate Document

Basic Quality Tools Flowcharts Check sheets Histograms Pareto Charts Scatter diagrams Control charts Cause-and-effect diagrams Run charts

Check Sheet Monday Billing Errors A/R Errors Wrong Account Wrong Amount A/R Errors Monday

80% of the problems may be attributed to 20% of the Pareto Analysis 80% of the problems may be attributed to 20% of the causes. Smeared print Number of defects Off center Missing label Loose Other

Control Chart 970 980 990 1000 1010 1020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 UCL LCL

Cause-and-Effect Diagram Materials Methods Equipment People Environment Cause

Run Chart Time (Hours) Diameter

Tracking Improvements UCL LCL Process not centered and not stable Process centered and stable Additional improvements made to the process

Methods for Generating Ideas Brainstorming Quality circles Interviewing Benchmarking

Benchmarking Process Identify a critical process that needs improving Identify an organization that excels in this process Contact that organization Analyze the data Improve the critical process

Lean Production “A systematic approach to identifying and eliminating waste(non-value-added activities) through continuous improvement by flowing the product at the pull of the customer in pursuit of perfection.”

Basic Elements of Lean

Waste in Operations

Waste in Operations

Pull System Basics Material is pulled through the system when needed Reversal of traditional push system where material is pushed according to a schedule Forces cooperation Prevent over and underproduction While push systems rely on a predetermined schedule, pull systems rely on customer requests

Benefits of Lean Systems Reduced inventory Improved quality Lower costs Reduced space requirements Shorter lead time Increased productivity Greater flexibility Better relations with suppliers Simplified scheduling and control activities Increased capacity Better use of human resources More product variety

Stevenson 10 Quality Control

Learning Objectives List and briefly explain the elements of the control process. Explain how control charts are used to monitor a process, and the concepts that underlie their use. Use and interpret control charts. Use run tests to check for nonrandomness in process output. Assess process capability.

Phases of Quality Assurance Inspection and corrective action during production Inspection of lots before/after production Quality built into the process Acceptance sampling Process control Continuous improvement The least progressive The most progressive

Inspection How Much/How Often Where/When Centralized vs. On-site Inputs Transformation Outputs Acceptance sampling Process control Acceptance sampling

Inspection Costs Cost Optimal Amount of Inspection Total Cost Cost of inspection Cost of passing defectives

Where to Inspect in the Process Raw materials and purchased parts Finished products Before a costly operation Before an irreversible process Before a covering process (e.g., painting/final assembly)

Examples of Inspection Points

Statistical Control Statistical Process Control: Statistical evaluation of the output of a process during production Quality of Conformance: A product or service conforms to specifications

Control Chart Control Chart Purpose: to monitor process output to see if it is random A time ordered plot representative sample statistics obtained from an on going process (e.g. sample means) Upper and lower control limits define the range of acceptable variation

Control Chart 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 UCL LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources

Statistical Process Control The essence of statistical process control is to assure that the output of a process is random so that future output will be random.

Statistical Process Control The Control Process Define Measure Compare Evaluate Correct Monitor results

Statistical Process Control Variations and Control Random variation: Natural variations in the output of a process, created by countless minor factors Assignable variation: A variation whose source can be identified

Normal Distribution Mean     95.44% 99.74% Standard deviation

Control Limits Sampling distribution Process distribution Mean Lower control limit Upper control limit

SPC Errors Type I error Type II error Concluding a process is not in control when it actually is. Type II error Concluding a process is in control when it is not.

Type I and Type II Errors

Type I Error Mean LCL UCL /2 Probability of Type I error

Observations from Sample Distribution Sample number UCL LCL 1 2 3 4

Control Charts for Variables Variables generate data that are measured. Mean control charts Used to monitor the central tendency of a process. X bar charts Range control charts Used to monitor the process dispersion R charts

Mean Control Chart for Variables  

Mean Control Chart for Variables  

Range Control Chart for Variables Monitors Process Dispersion D3 and D4 are found in the Table for 3-σ Control Limits

Table for 3-σ Control Limits

Mean and Range Charts Detects shift x-Chart Does not detect shift (process mean is shifting upward) Sampling Distribution UCL x-Chart Detects shift LCL UCL Does not detect shift R-chart LCL

Mean and Range Charts Does not reveal increase x-Chart R-chart Sampling Distribution (process variability is increasing) UCL Does not reveal increase x-Chart LCL UCL R-chart Reveals increase LCL

Control Chart for Variables/Example A quality inspector took five samples, each with four observations (n = 4), of the length of time for glue to dry. The analyst computed the mean of each sample and then computed the grand mean. All values are in minutes. Use this information to obtain three-sigma (i.e., z = 3) control limits for means of future times. It is known from previous experience that the standard deviation of the process is .02 minute.

Control Chart for Variables/Example

Control Chart for Variables/Example A quality inspector took five samples, each with four observations (n = 4), of the length of time for glue to dry. The analyst computed the mean of each sample and then computed the grand mean. All values are in minutes. Use this information to obtain three-sigma (i.e., z = 3) control limits for means of future times.

Control Chart for Variables/Example Standard Deviation is not given  

Control Chart for Variables/Example Plot the sample means in to control chart.

Control Chart for Variables/Example For previous example, construct a Range chart From the Table for 3-σ Control Limits, for n= 4 (observations per sample), D4 = 2.28 and D3=0 UCL = 2.28(0.046) = 0.105 LCL = 0(0.046) = 0 Note that each sample’s range falls within these Control Limits

Control Chart for Attributes p-Chart - Control chart used to monitor the proportion of defectives in a process c-Chart - Control chart used to monitor the number of defects per unit Note for both p-Charts and c-Charts, if LCL is negative, then set LCL to zero. Attributes generate data that are counted.

Use of p-Charts When observations can be placed into two categories. Good or bad Pass or fail Operate or don’t operate

p-Charts  

p-Chart Example An inspector counted the number of defective monthly billing statements of a company telephone in each of 20 samples. Using the following information, construct a control chart that will describe 99.74 percent of the chance variation in the process when the process is in control. Each sample contained 100 statements.

p-Chart Example

p-Chart Example  

p-Chart Example Plot the sample proportions in the Control Chart

Use of c-Charts Use only when the number of occurrences per unit of measure can be counted; non-occurrences cannot be counted. Scratches, chips, dents, or errors per item Cracks or faults per unit of distance Breaks or Tears per unit of area Bacteria or pollutants per unit of volume Calls, complaints, failures per unit of time

c-Charts  

c-Chart Example Rolls of coiled wire are monitored using a c-chart. Eighteen rolls have been examined, and the number of defects per roll has been recorded in the following table. Is the process in control? Plot the values on a control chart using three standard deviation control limits

c-Chart Example

Use of Control Charts At what point in the process to use control charts What size samples to take What type of control chart to use Variables Attributes

Run Tests Run test – a test for randomness Any sort of pattern in the data would suggest a non-random process All points are within the control limits - the process may not be random

Nonrandom Patterns in Control charts Trend Cycles Bias Mean shift Too much dispersion

Counting Runs Counting Above/Below Median Runs (7 runs) Counting Up/Down Runs (8 runs) U U D U D U D U U D B A A B A B B B A A B

NonRandom Variation Managers should have response plans to investigate cause May be false alarm (Type I error) May be assignable variation

Determine whether a process is in control? Transform the data into As and Bs, and Us and Ds. Count the number of Runs in each case N is number of observations

Determine whether a process is in control Expected number of Runs Standard Deviation of Runs Z of Runs

Determine whether a process is in control

Example Twenty sample means have been taken from a process. The means are shown in the following table. Use median and up/down run tests with z = 2 to determine if assignable causes of variation are present. Assume the median is 11.0

Example Although the median test does not reveal any pattern, because its ztest value is within the range ± 2, the up/down test does; its value exceeds +2. Consequently, nonrandom variations are probably present in the data and, hence, the process is not in control

Process Capability Tolerances or specifications Process variability Range of acceptable values established by engineering design or customer requirements Process variability Natural variability in a process Process capability Process variability relative to specification For a process to be capable, its capability index (ideally) should be 1.33 or higher

Process Capability A. Process variability matches specifications Lower Specification Upper Specification A. Process variability matches specifications B. Process variability well within specifications C. Process variability exceeds specifications

Process Capability Ratio If the process is centered use Cp Process capability ratio, Cp = specification width process width Upper specification – lower specification 6 Cp = If the process is not centered use Cpk

Process Capability Ratio Example 1 Determine the capability of each process (i.e., six standard deviations) and compare that value to the specification difference of .80 mm Process capability for Process A is 0.13x6=0.78. Similarly, Process B capability is 0.48, and Process C capability is 0.96

Process Capability Ratio Example 1  

Process Capability Ratio Example 2 A process has a mean of 9.20 grams and a standard deviation of .30 gram. The lower specification limit is 7.50 grams and the upper specification limit is 10.50 grams. Compute Cpk The smaller of the two indexes is 1.44, so this is the Cpk. Because the Cpk is more than 1.33, the process is capable

Limitations of Capability Indexes Process may not be stable Process output may not be normally distributed Process not centered but Cp is used

3 Sigma and 6 Sigma Quality Process mean Lower specification Upper specification 1350 ppm 1.7 ppm +/- 3 Sigma +/- 6 Sigma

Improving Process Capability Simplify Standardize Mistake-proof Upgrade equipment Automate

Traditional cost function Taguchi Loss Function Cost Target Lower spec Upper spec Traditional cost function Taguchi cost function

QUIZ NEXT SESSION QUALITY MANAGEMENT AND CONTROL (STEVENSON CHAPTERS 9 & 10)