LSM733-PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF LECTURE 30 1

Summary of last Session CHAPTER : CAPACITY AND CONSTRAINT MANAGEMENT  Capacity  Design and Effective Capacity  Capacity and Strategy  Capacity Considerations  Managing Demand  Demand and Capacity Management in the Service Sector 2

Summary of last Session(Contd.)  Bottleneck Analysis and Theory of Constraints  Process Times for Stations, Systems, and Cycles  Theory of Constraints  Bottleneck Management  Break-Even Analysis  Single-Product Case 3

Summary of last Session (Contd.)  Applying Expected Monetary Value to Capacity Decisions 4

Agenda for this Session Additional Chapter: Quality Control Quality / Quality Control Phases of Quality Assurance Inspection Statistical Control Control charts SPC Errors 5

Agenda for this Session (Contd.) Control Charts for Variables – Mean control charts – Range control charts Counting Runs Process capability 6

ADDITIONAL CHAPTER: QUALITY CONTROL 7

What is Quality ? In manufacturing, a measure of excellence or a state of being free from defects, deficiencies and significant variations. It is brought about by strict and consistent commitment to certain standards that achieve uniformity of a product in order to satisfy specific customer or user requirements. ISO standard defines quality as "the totality of features and characteristics of a product or service that bears its ability to satisfy stated or implied needs." 8

What is Quality Control ? Quality control (QC) is a procedure or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. 9

What is Quality Assurance ? QC is similar to, but not identical with, quality assurance (QA). QA is defined as a procedure or set of procedures intended to ensure that a product or service under development (before work is complete, as opposed to afterwards) meets specified requirements. QA is sometimes expressed together with QC as a single expression, quality assurance and control (QA/QC).QA 10

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 11

Inspection How Much/How Often Where/When Centralized vs. On-site InputsTransformationOutputs Acceptance sampling Process control Acceptance sampling 12

Cost Optimal Amount of Inspection Inspection Costs Cost of inspection Cost of passing defectives Total Cost 13

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 14

Examples of Inspection Points 15

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 16

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 17

Control Chart UCL LCL Sample number Mean Out of control Normal variation due to chance Abnormal variation due to assignable sources 18

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. 19

Statistical Process Control The Control Process – Define – Measure – Compare – Evaluate – Correct – Monitor results 20

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 21

Sampling Distribution Sampling distribution Process distribution Mean 22

Normal Distribution Mean  95.44% 99.74%  Standard deviation 23

Control Limits Sampling distribution Process distribution Mean Lower control limit Upper control limit 24

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. 25

10-26 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

Type I Error Mean LCLUCL  /2  Probability of Type I error 27

Observations from Sample Distribution Sample number UCL LCL

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. 29

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 30

x-Chart UCL Does not reveal increase Mean and Range Charts UCL LCL R-chart Reveals increase (process variability is increasing) Sampling Distribution 31

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. 32

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 33

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 34

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 35

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 36

Nonrandom Patterns in Control charts Trend Cycles Bias Mean shift Too much dispersion 37

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 38

NonRandom Variation Managers should have response plans to investigate cause May be false alarm (Type I error) May be assignable variation 39

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 40

Process Capability 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 41

Process mean Lower specification Upper specification 1350 ppm 1.7 ppm +/- 3 Sigma +/- 6 Sigma 3 Sigma and 6 Sigma Quality 42

Improving Process Capability Simplify Standardize Mistake-proof Upgrade equipment Automate 43

Taguchi Loss Function Cost Target Lower spec Upper spec Traditional cost function Taguchi cost function 44

Summary of the Session Additional Chapter: Quality Control Quality / Quality Control Phases of Quality Assurance Inspection Statistical Control Control charts SPC Errors 45

Summary of the Session (Contd.) Control Charts for Variables – Mean control charts – Range control charts Counting Runs Process capability 46

THANK YOU 47