PROCESS CAPABILITY AND SPC Chapter 9 A 9

PROCESS CAPABILITY AND SPC Chapter 9 A

9 A-2 OBJECTIVES 1. 2. 3. Explain what statistical quality control is. Calculate the capability of a process. Understand how processes are monitored with control charts.

Types of Situations where SPC can be Applied How many paint defects are there in the finish of a car? How long does it take to execute market orders? How well are we able to maintain the dimensional tolerance on our ball bearing assembly? How long do customers wait to be served from our drive-through window? LO 1

9 A-4 Basic Forms of Variation Assignable variation is caused by factors that can be clearly identified and possibly managed Common variation is inherent in the production process Example: A poorly trained employee that creates variation in finished product output. Example: A molding process that always leaves “burrs” or flaws on a molded item.

Testing examples

Taguchi’s View of Variation LO 1 Traditional view is that quality within the range is good and that the cost of quality outside this range is constant Taguchi views costs as increasing as variability increases, so seek to achieve zero defects and that will truly minimize quality costs

Process Capability Taguchi argues that tolerance is not a yes/no decision, but a continuous function Other experts argue that the process should be so good the probability of generating a defect should be very low LO 2

9 A-8 Types of Statistical Sampling Attribute (Go or no-go information) Defectives refers to the acceptability of product across a range of characteristics. Defects refers to the number of defects per unit which may be higher than the number of defectives. p-chart application Variable (Continuous) Usually measured by the mean and the standard deviation. X-bar and R chart applications

Statistical Process Normal Behavior Control (SPC) Charts 9 A-9 UCL LCL 1 2 3 4 5 6 Samples over time UCL Possible problem, investigate LCL 1 2 3 4 5 6 UCL Samples over time Possible problem, investigate LCL 1 2 3 4 5 6 Samples over time

9 A-10 Control Limits are based on the Normal Curve m -3 -2 -1 Standard deviation units or “z” units. 0 1 2 3 x z

9 A-11 Control Limits We establish the Upper Control Limits (UCL) and the Lower Control Limits (LCL) with plus or minus 3 standard deviations from some x-bar or mean value. Based on this we can expect 99. 7% of our sample observations to fall within these limits. 99. 7% LCL UCL x

Process Control with Attribute Measurement: Using ρ Charts LO 3 Created for good/bad attributes Use simple statistics to create the control limits

9 A-13 Example of Constructing a p-Chart: Required Data Sample No. of No. Samples Number of defects found in each sample

9 A-14 Example of Constructing a p-chart: Step 1 1. Calculate the sample proportions, p (these are what can be plotted on the p-chart) for each sample

9 A-15 Example of Constructing a p-chart: Steps 2&3 2. Calculate the average of the sample proportions 3. Calculate the standard deviation of the sample proportion

9 A-16 Example of Constructing a p-chart: Step 4 4. Calculate the control limits UCL = 0. 0924 LCL = -0. 0204 (or 0)

9 A-17 Example of Constructing a p-Chart: Step 5 5. Plot the individual sample proportions, the average of the proportions, and the control limits

How to Construct x and R Charts LO 3

9 A-19 Example of x-bar and R Charts: Required Data

9 A-20 Example of x-bar and R charts: Step 1. Calculate sample means, sample ranges, mean of means, and mean of ranges.

9 A-21 Example of x-bar and R charts: Step 2. Determine Control Limit Formulas and Necessary Tabled Values

9 A-22 Example of x-bar and R charts: Steps 3&4. Calculate x-bar Chart and Plot Values Then plot both graphs: Means to the Mean chart and Ranges to the Range chart.

9 A-23 ANY QUESTIONS?