ENGINEERING MANAGEMENT
Prof. Dr. Mete Gündoğan
SECTION 1 QUALITY MANAGEMENT Lesson Content: Statistical Quality Control Specific SPC Tools And Procedures; Flow Diagram Cause-And-Effect Or Fishbone Diagram Pareto Charts Histograms, Scatter Plots, Checklist, Control Charts,
Statistical Quality Control Statistical Quality Control (SQC)
Descriptive Statistics
Statistical Process Control (SPC)
Acceptance Sampling
Statistical Quality Control Descriptive Statistics ; ď‚— used to describe the quality characteristics and relationships. ď‚— statistics such as the mean, median, standard deviation, the range, and a measure of the distribution of data are included.
Statistical Quality Control Descriptive Statistics ; Describing certain characteristics of a product & a process Measures of Central Tendency (mean) Measures of Variability (standard deviation & range) Measures of the Distribution of Data
Statistical Quality Control Acceptance sampling ; ď‚— is the process of randomly inspecting a sample of goods and deciding whether to accept the entire lot based on the results. ď‚— Acceptance sampling determines whether a batch of goods should be accepted or rejected.
Statistical Quality Control Acceptance sampling ;
Statistical Quality Control Acceptance sampling Risk ; The Lot is actually Good
The Lot is actually Bad
The Lot is Accepted
Correct Decision Confidence = 1 – α
Incorrect Decision β Risk (Consumer’s Risk)
The Lot is Rejected
Incorrect Decision α Risk (Producer’s Risk)
Correct Decision Power = 1 - β
Statistical Quality Control Statistical process control (SPC) ; ď‚— Statistical process control is a collection of tools that when used together can result in process stability and variance reduction ď‚— involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. ď‚— SPC answers the question of whether the process is functioning properly or not.
Statistical Quality Control Statistical Quality Control (SQC)
Descriptive Statistics
Statistical Process Control (SPC)
Acceptance Sampling
ď‚— All three of these statistical quality control categories are helpful in
measuring and evaluating the quality of products or services. However, statistical process control (SPC) tools are used most frequently because they identify quality problems during the production process
Important of the SPC ď‚— Measure the value of a quality characteristic ď‚— Help to identify a change or variation in some quality
characteristic of the product or process
SPC SPC can be applied to any process. There is inherent variation in any process which can be
measured and “controlled”. SPC doesn’t eliminate variation, but it does allow the user to track special cause variation. “SPC is a statistical method of separating variation resulting from special causes from natural variation and to establish and maintain consistency in the process, enabling process improvement.” (Goetsch & Davis, 2003. p. 631)
SPC Two kinds of variation occur in all manufacturing processes 1- Natural or Common Cause Variation consists of the variation inherent in the process as it is designed. may include variations in temperature, properties of raw materials, strength of an electrical current etc. 2- Special Cause Variation or Assignable-cause Variation With sufficient investigation, a specific cause, such as abnormal raw material or incorrect set-up parameters, can be found for special cause variations.
SPC Process is said to be ‘in control’ and stable If common cause is the only type of variation that exists in the
process It is also predictable within set limits i.e. the probability of any future outcome falling within the limits can be stated approximately. Process is said to be ‘out of control’ and unstable
- Special cause variation exists within the process
SPC Statistical process control -broadly broken down into 3 sets of activities Understanding the process Understanding the causes of variation Elimination of the sources of special cause variation.
Advantages Of Spc Reduces waste Lead to a reduction in the time required to produce the
product or service due to a diminished likelihood that the final product will have to be reworked, identify bottlenecks, wait times, and other sources of delays within the process. A distinct advantage over other quality methods, such as inspection - its emphasis on early detection and prevention of problems Cost reduction Customer satisfaction
Specific SPC Tools And Procedures; QC tools are the means for Colleting data , analyzing data , identifying root causes and measuring the results. Control Chart
Check Sheet
Pareto Chart
Flow Chart
Cause-&-Effect Diagram
Histogram
Scatter Diagram
Cause-And-Effect Or Fishbone Diagram Helps in identifying root causes of the quality failure Wide scope for application Encourages participation and contribution by everyone Diagrams posted in key locations to stimulate continued
reference
Cause-And-Effect Or Fishbone Diagram Method
Usage
Cause and Effect Diagram Man
Effect
Machine
Material Cause (4M’s)
Method Result (Controlled State)
Result
Can obtain a clear overall Used when clarifying a picture of causal relation. cause and effect (A change in the cause relationship.[Used during triggers a variation in the a phase to analyze causes.] result.)○Can clarify the cause and effect Used to develop relationship.
countermeasures.
Can list up all causes to identify important causes.
Can determine the [Used during a phase to plan direction of action (countermeasure). countermeasures.]
Cause-And-Effect Or Fishbone Diagram Constructing a Fishbone Diagram;
Step 1 - Identify the Problem Step 2 - Draw “spine” and “bones” Step 3 - Identify different areas where problems may arise from Step 4 - Identify what these specific causes could be Step 5 - Use the finished diagram to brainstorm solutions to the main problems.
Cause-And-Effect Or Fishbone Diagram
Pareto Charts ď‚— The Pareto chart can be used to display categories of problems
graphically so they can be properly prioritized. ď‚— Principle is that quality problems are the result of only a few problems e.g. 80% of the problems caused by 20% of causes.
Pareto Charts Method
Usage
Used to identify a problem.
Result
Used to identify the cause of a Pareto Diagram problem. (No. of Occurrences) Used to review the effects of an action to be taken. Used to prioritize actions. (Used during phases to monitor the situation, analyze causes, and review effectiveness of an action.)
Allows clarification of important tasks. Allows identification of a starting point (which task to start with). Allows projection of the effects of a measure to be taken
Pareto Charts CAUSE PERCENTAGE
NUMBER OF DEFECTS
Poor design Wrong part dimensions Defective parts Incorrect machine calibration Operator errors Defective material Surface abrasions
80 16 12 7 4 3 3
64 % 13 10 6 3 2 2
125
100 %
Pareto Charts Percent from each cause
70
(64)
60 50 40 30 20 10
(13)
(10)
(6)
0
Causes of poor quality
(3)
(2)
(2)
Checklist Simple data check-off sheet designed to identify type of quality
problems at each work station per shift per machine per operator. By just entering check marks on a check sheet, data can be collected to
extract necessary information, or a thorough inspection can be performed in an efficient manner, eliminating a possibility of skipping any of the required inspection items. A check sheet is also effective in performing stratification categorization
Checklist Method
Usage
Used to collect data. Check Sheet Day Process Process 1 Process 2 Process 3
Used when performing a thorough inspection.
Result
Ensures collection of required data.
Allows a thorough inspection of all check items. Used during phases to monitor
the situation, analyze causes, Can understand tendencies review effectiveness of an and variations. action, perform Can record required data. standardization, and implement a selected control measure
Checklist • Want to find out cause-wise defects
Checklist More defects on Tuesday. Supply part rusted occurs most frequently.
Histograms ď‚— A chart that shows the frequency distribution of observed
values of a variable. i.e service time at a bank drive-up window.
ď‚— Help in understanding the variation in the process. It also
helps in estimating the process capability.
Histograms Method
Usage
Histogram Standard Range
Range of Variations
X Axis (Values Actually Measured)
Result
•Used to assess the actual conditions. •Used to analyze a process to identify a problem point that needs to be improved by finding the location of the mean value or degree of variations in the graph. •Used to examine that the target quality is maintained throughout the process. •Others [Used during phases to monitor the situation, analyze causes, and review effectiveness of an action.]
• Can identify the location of the mean (central) value or degree of variations. • Can find out the scope of a defect by inserting standard values. • Can identify the condition of distribution (e.g., whether there is an isolated, extreme value).
Histograms Constructing a Histogram From a set of data compute Sum mean (x) Max Min Range (max-min)
Use range to estimate beginning and end Calculate the width of each column by dividing the range by the
number of columns
Histograms
Histograms
Scatter Plots A scatter diagram is used to “examine the relationship between
the two, paired, interrelated data types, ” such as “height and weight of a person.” A scatter diagram provides a means to find whether or not these two data types are interrelated. It is also utilized to determine how closely they are related to identify a problem point that should be controlled or improved. Statistical correlation analysis used to interpret scatter diagrams.
Scatter Plots Method
Usage
Scatter Diagram Abrasion
ｙ Axis
x Axis
Number of Rotations
Result
•Used to identify a relationship between two matters. •Used to identify a relationship between two matters and establish countermeasures based on their cause and effect relation. Example Usage 1.Relationship between thermal treatment temperature of a steel material and its tensile strengths 2.Relationship between visit made by a salesman and volume of sales 3.Relationship between the number of persons visiting a department store and volume of sales 4 Others [Used during phases to monitor the situation, analyze causes, and review effectiveness of an action.]
•Can identify cause and effect relation. •Can understand the relationship between two results.
Scatter Plots
Control Charts Show the variation in a measurement during the time period
that the process is observed. Monitor processes to show how the process is performing and how the process and capabilities are affected by changes to the process. This information is then used to make quality improvements. A time ordered sequence of data, with a centre line calculated by the mean. Used to determine the capability of the process. Help to identify special or assignable causes for factors that peak performance.
Control Charts Control charts have four key features: i. Data Points: Either averages of subgroup measurements or individual measurements plotted on the x/y axis and joined by a line. Time is always on the x-axis. ii.
The Average or Center Line The average or mean of the data points and is drawn across the middle section of the graph, usually as a heavy or solid line.
iii. The Upper Control Limit (UCL)
Drawn above the centerline and annotated as "UCL". This is often called the “+ 3 sigma” line. iv.
The Lower Control Limit (LCL) Drawn below the centerline and annotated as "LCL". This is called the “3 sigma” line.
Control Charts
Control Charts Control limits define the zone where the observed data for a
stable and consistent process occurs virtually all of the time (99.7%). Any fluctuations within these limits come from common causes inherent to the system, such as choice of equipment, scheduled maintenance or the precision of the operation that results from the design. An outcome beyond the control limits results from a special cause. The automatic control limits have been set at 3-sigma limits.
Control Charts
Type Of Control Charts Variable control charts
Variable data are measured on a continuous scale. For example:
time, weight, distance or temperature can be measured in fractions or decimals. Applied to data with continuous distribution Attribute control charts
Attribute data are counted and cannot have fractions or
decimals. Attribute data arise when you are determining only the presence or absence of something: success or failure, accept or reject, correct or not correct. For example, a report can have four errors or five errors, but it cannot have four and a half errors. Applied to data following discrete distribution
Variable Control Charts X-bar Charts
Variable Control Charts
Variable Control Charts R Charts
X AND R CHART EXAMPLE IN-CLASS EXERCISE
X AND R CHART EXAMPLE IN-CLASS EXERCISE
X AND R CHART EXAMPLE IN-CLASS EXERCISE
X AND R CHART EXAMPLE IN-CLASS EXERCISE UCL = x + A2 R 10.728 .58( 0.2204 )=10.856 LCL = x - A2 R 10.728-.58( 0.2204 )=10.601 10.900 10.850
Means
10.800 10.750 10.700
Sample mean UCL
10.650
LCL
10.600
grand mean of x
10.550 1
2
3
4
5
6
7
8
Sample
9
10
11
12
13
14
15
X AND R CHART EXAMPLE IN-CLASS EXERCISE
Attributes Control Charts p Charts ď‚— To evaluate process stability when counting the fraction
defective. ď‚— It is used when the sample size varies: the total number of circuit boards, meals, or bills delivered varies from one sampling period to the next.
Constructing a p-Chart: Example: A Production manager for a tire company has inspected the number of defective tires in five random samples with 20 tires in each sample. The table below shows the number of defective tires in each sample of 20 tires.
Step 1: Calculate the Percent defective of Each Sample and the Overall Percent Defective (P-Bar)
Step 2: Calculate the Standard Deviation of P.
p(1-p) (.09)(.91) Ďƒp= = =0.064 n 20
Step 3: Calculate CL, UCL, LCL
Step 4: Draw the Chart
Attributes Control Charts Example: The number of nonconforming switches in samples of size 150 are shown in Table. Construct a fraction nonconforming control chart for these data. Does the process appear to be in control?
Attributes Control Charts c Charts The c charts are used to monitor the number of occurrences of an event. It requires that the opportunity for events remains the same from observation to observation. Its use is best illustrated by examples of typical applications which include: Number of failures/breakdowns/alarms. Number of non-conformances during an audit. Number of defects in an inspection lot.
Constructing a C-Chart: Example: The number of weekly customer complaints are monitored in a large hotel. Develop a three sigma control limits For a C-Chart using the data table On the right.
Calculate CL, UCL, LCL
Specific SPC Tools To sum up 7 QC tools (Numerical data ) are used as follow: Pareto Diagram Stratification
data Scatter Diagram Cause and Effect Dyg. Histogram Check Sheet Control Chart current status
To identify the current status and issues Basic processing performed when collecting To identify the relationship between two things To identify the cause and effect relationship To see the distribution of data To record data collection To find out abnormalities and identify the