CHAPTER 27 • Statistical Process Control

27-4 CHAPTER 27 • Statistical Process Control
This chapter focuses on just one aspect of statistics for improving quality:
statistical process control. The techniques are simple and are based on sampling
distributions (Chapter 11), but the underlying ideas are important and a
bit subtle.
PROCESSES
In thinking about statistical inference, we distinguish between the sample data we
have in hand and the wider population that the data represent. We hope to use
the sample to draw conclusions about the population. In thinking about quality
improvement, it is often more natural to speak of processes rather than populations.
This is because work is organized in processes. Some examples are
■ processing an application for admission to a university and deciding whether
or not to admit the student;
■ reviewing an employee’s expense report for a business trip and issuing a
reimbursement check;
■ hot forging to shape a billet of titanium into a blank that, after machining,
will become part of a medical implant for hip, knee, or shoulder replacement.
Each of these processes is made up of several successive operations that eventually
produce the output—an admission decision, reimbursement check, or metal
component.
PROCESS
A process is a chain of activities that turns inputs into outputs.
We can accommodate processes in our sample-versus-population framework:
think of the population as containing all the outputs that would be produced by
the process if it ran forever in its present state. The outputs produced today or this
week are a sample from this population. Because the population doesn’t actually
exist now, it is simpler to speak of a process and of recent output as a sample from
the process in its present state.
flowchart
cause-and-effect diagram
DESCRIBING PROCESSES
The first step in improving a process is to understand it. Process understanding
is often presented graphically using two simple tools: flowcharts and cause-andeffect
diagrams. A flowchart is a picture of the stages of a process. A cause-andeffect
diagram organizes the logical relationships between the inputs and stages of
a process and an output. Sometimes the output is successful completion of the
process task; sometimes it is a quality problem that we hope to solve. A good
starting outline for a cause-and-effect diagram appears in Figure 27.1. The main