2. Quality Control Pertemuan 12 Mata kuliah: J0444 - Manajemen Operasional Tahun: 2010

3. 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.

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

5. Inspection How Much/How Often Where/When Centralized vs. On-site InputsTransformationOutputs Acceptance sampling Process control Acceptance sampling

6. 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

7. Examples of Inspection Points

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

9. 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

10. Control Chart 0123456789101112131415 UCL LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources

11. 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.

12. Produce Good Provide Service Stop Process Yes No Assign. Causes? Take Sample Inspect Sample Find Out Why Create Control Chart Start Statistical Process Control Steps

13. 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

14. Sampling Distribution Sampling distribution Process distribution Mean

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

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

17. SPC Errors Type I 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.

18. 10-17 Type I and Type II Errors In controlOut of control In controlNo ErrorType I error (producers risk) Out of control Type II Error (consumers risk) No error

19. Type I Error Mean LCLUCL  /2  Probability of Type I error

20. Observations from Sample Distribution Sample number UCL LCL 1234

21. Control Charts for Variables 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 Variables generate data that are measured.

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

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

24. 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 Attributes generate data that are counted.

25. Use of p-Charts When observations can be placed into two categories. – Good or bad – Pass or fail – Operate or don’t operate When the data consists of multiple samples of several observations each

26. 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

27. 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

28. 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

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

30. 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 Counting Runs

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

32. Tolerances or specifications –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 Process Capability

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

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

35. Limitations of Capability Indexes 1.Process may not be stable 2.Process output may not be normally distributed 3.Process not centered but C p is used

36. 10-35 Example Machine Standard Deviation Machine CapabilityCpCp A0.130.780.80/0.78 = 1.03 B0.080.480.80/0.48 = 1.67 C0.160.960.80/0.96 = 0.83 Cp > 1.33 is desirable Cp = 1.00 process is barely capable Cp < 1.00 process is not capable

37. The End