10 March 2016Materi ke-3 Lecture 3 Statistical Process Control Using Control Charts

10 March 2016Materi ke-3 Review What is control chart / Shewhart control chart ? Describe the importantce of pareto diagrams in process improvement ?

10 March 2016Materi ke-3 Outline Introduction Cause of Variation Statistical Basis for Control Charts Selection of rational Subgroups Analysis of Patterns in Control Chart Maintenance of Control Chart

10 March 2016Materi ke-3 Introduction Statistical process control is a collection of tools that when used together can result in process stability and variability reduction Control Chart : Graphical tool for monitoring the activity of on going process

10 March 2016Materi ke-3 Introduction Benefits using control chart When to take corrective action Type remedial action necessary When to leave a process alone Process capability Possible means of quality improvement How to set product specifications

10 March 2016Materi ke-3 Cause of Variation Variability is a part of any process The causes of variations Chance Cause Something inherent to a process ( as the natural variation in a process ) Assignable Cause Something for which an identifiable reason can determined Example : wrong tool, operator error

10 March 2016Materi ke-3 Cause of Variation Chance and Assignable Causes of Quality Variation A process that is operating with only chance causes of variation present is said to be in statistical control. Natural variability or background noise. Fluctuations A process that is operating in the presence of assignable causes is said to be out of control. E.g. Operator errors, defective raw material, improper settings. The eventual goal of SPC is reduction or elimination of variability in the process by identification of assignable causes.

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Statistical Basis for Control Charts Basic Principles Assumed to have approximately normal distribution Control limits : % ( 3  limits ) A control chart : on line process control Making inference

10 March 2016Materi ke-3 Statistical Basis for Control Charts Selection of Control Limits Let  represent a quality characteristic of interest and represent an estimate of  If k = 3  of a sample statistic falling outside

10 March 2016Materi ke-3 Statistical Basis for Control Charts Errors in making inference from control chart Type I : process is out of control when it is actually in control Type II : process is in control when it is really out of control Effect of control limits on errors in making inference Warning limit Usually 2 standard deviation Effect of sample size on control limits Influence in standard deviation

10 March 2016Materi ke-3 Statistical Basis for Control Charts Basic Principles Basic Principles A typical control chart has control limits set at values such that if the process is in control, nearly all points will lie between the upper control limit (UCL) and the lower control limit (LCL).

10 March 2016Materi ke-3 Statistical Basis for Control Charts A control chart contains A center line An upper control limit A lower control limit A point that plots within the control limits indicates the process is in control No action is necessary A point that plots outside the control limits is evidence that the process is out of control Investigation and corrective action are required to find and eliminate assignable cause(s) There is a close connection between control charts and hypothesis testing

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Photolithography Example Important quality characteristic in hard bake is resist flow width Process is monitored by average flow width Sample of 5 wafers Process mean is 1.5 microns Process standard deviation is 0.15 microns Note that all plotted points fall inside the control limits Process is considered to be in statistical control

10 March 2016Materi ke-3

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Selection of rational Subgroups The premise : chosen is such manner that the variation within it is considered to due only to chance causes. Basis : Time order Two approaches ( Besterfield, 1990 ) Instance of time method Period of time method Subgroup Size ( the number of items in each group ) Frequency of sampling

10 March 2016Materi ke-3 Selection of rational Subgroups Subgroups or samples should be selected so that if assignable causes are present, the chance for differences between subgroups will be maximized, while the chance for differences due to these assignable causes within a subgroup will be minimized.

10 March 2016Materi ke-3 Selection of rational Subgroups Selection of Rational Subgroups Two general approaches to constructing rational subgroups. Select consecutive units of production. Each sample consists of units that were produced at the same time (or as closely together as possible) Provides a “snapshot” of the process. Effective at detecting process shifts.

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Select a random sample over the entire sampling interval. Often used to make decisions about the acceptance of all units of product that have been produced since the last sample. Can be effective at detecting if the mean has wandered out-of-control and then back in-control.

10 March 2016Materi ke-3

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Analysis of Patterns in Control Chart Five Rules for identifying an out-of-control process 1.A single point outside the control limits 2.Two out of three consecutive points fall outside the 2  warning limits on the same side 3.Four out of five consecutive points fall beyond the 1  warning limits on the same side 4.Eight or more consecutive points fall to one side 5.A run of eight or more consecutive points –up, down, above or below the CL, or above or bellow the median

10 March 2016Materi ke-3 Analysis of Patterns in Control Chart Nonrandom patterns can indicate out-of- control conditions Patterns such as cycles, trends, are often of considerable diagnostic value (more about this in Chapter 5) Look for “runs” - this is a sequence of observations of the same type (all above the center line, or all below the center line) Runs of say 8 observations or more could indicate an out-of-control situation. Run up: a series of observations are increasing Run down: a series of observations are decreasing

10 March 2016Materi ke-3 An x chart with a nonrandom, up-run, down-run patterns

10 March 2016Materi ke-3 An x chart with a cyclic pattern

10 March 2016Materi ke-3

10 March 2016Materi ke-3

10 March 2016Materi ke-3

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Analysis of Patterns in Control Chart Interpretation of Plots ( Non random pattern ) Determination of causes associated with out-of-control points Require a thorough knowledge of the process and the sensitivity of the output quality characteristic to the process parameters The pattern and associated causes Change in the level of the plotted pattern ( a jump ) ( change quality raw material, change operator, failure component ) Trend in the plotted pattern ( tool wear, change in pressure ) Cyclic behavior in the plotted pattern ( seasonal effects of quality, operator fatigue ) Concentration of points near the control limits ( two or more operator plotted on the same chart, different production method )

10 March 2016Materi ke-3

10 March 2016Materi ke-3 Maintenance of Control Chart Proper placement of the control charts on the shop floor is important  easy to access The control chart should draw the attention and curiosity of everyone involved

10 March 2016Materi ke-3 Quiz 1.What are benefits of using control chart ? 2.Explain the different between chance causes and assignable causes ? give example of each ? 3.How are rational subgroups selected ? explain the importance of this in the total quality systems approach?