How short-run SPC methods are introduced
Many companies have difficulty applying Statistical Process Control (SPC) because they think it is only for high-volume, repetitive operations. That’s simply not true. This chapter is designed to introduce short-run SPC methods that take advantage of the limited data that is available from processes like those that produce:
- nuts and bolts in 6-minute runs,
- one submarine every 18 months,
- chemicals in batches,
- one-of-a-kind prototypes.
Processes and Controlling Variability
SPC is not only about parts. It’s also about processes and controlling variability. We see a process with many variables:
new shipments of raw material, tool wear, shift changes, and machine updates and replacements. Each of these changes introduces variability. This variability affects the process and must be controlled whether the quantity of parts you produce is large or small.
Traditional SPC can be difficult to apply to a short-run process where statistically meaningful data cannot be collected. Types of processes considered to be short-run include those that make:
- a large number of parts in a short period of time,
- a large complex part over a long period of time,
- only one part per run,
- parts that are difficult to place into subgroups.
Short-run SPC means we have to look at our work in a different way. Conventional x & R charts are limited to processes that generate enough subgroups to calculate centerlines and control limits. To accommodate short production runs, some modifications are needed.
The short-run methods are a combination of statistical and graphical approaches that attempt to get the most from limited information. In this chapter, we will deal with six basic charts that can be used for short-run applications:
- Nominal charts
- Individual charts with a moving range
- Moving average moving range charts
- Standardized x2 & R charts
- Short-run variables charts
- Group charts
The information in this chapter assumes that the reader understands x2 & R charting techniques. For more information on x2 & R charts, see Chapter 3.