Content By Scott A. Hindle

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By: Scott A. Hindle

In everyday language, “in control” and “under control” are synonymous with “in specification.” Requirements have been met. Things are OK. No trouble.

“Out of control,” on the other hand, is synonymous with “out of specification.” Requirements have not been met. Things are not OK. Trouble.

Using this language, an obvious axiom would be: Take action when the process is out of control.

The everyday use of in and out of control is, however, unfortunate for control charts, the major tool of statistical process control (SPC). Why? Because in SPC these terms speak of processes as being stable or unstable. To characterize a process as stable or unstable, process limits, from process data, are needed. Specification limits are not needed.

Given the easy-to-understand basis for the action of meeting or not meeting requirements, coupled with the risk of confusion over the terms in control and out of control, why use control charts? If you are curious to see some of the benefits in doing so, read on. Two case studies are used.

Case one: Part thickness

During a regular review meeting in Plant 17, in- and out-of-specification data on the thickness of part 64 were reviewed.

Scott A. Hindle’s picture

By: Scott A. Hindle

‘Process Capability: What It Is and How It Helps,” parts one, two, three, and four, discussed Alan’s development in the field of process capability1 He’d learned about the mistakes that can be made and how to avoid them in practice to become better at his job. Alan had since passed on his learning to colleagues, one of whom, Owen, had led some successful assessments of process capability.

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By: Scott A. Hindle

Walter Shewhart, father of statistical process control and creator of the control chart, put a premium on the time order sequence of data. Since many statistics and graphs are unaffected by this, you might wonder what the fuss is about. Read on to see why.

Figure 1 shows a series of measurements over 11 months. Each measurement value is from one production batch, with the date of each production given. The date is formatted as day first, and month second, meaning that “06.01”—the first measurement of 69.4—is from January 6.


Figure 1: Measurement data in time order of production.

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By: Scott A. Hindle

I recently got hold of the set of data shown in figure 1. What can be done to analyze and make sense of these 65 data values is the theme of this article. Read on to see what is exceptional about these data, not only statistically speaking.


Figure 1: Example data set.

A good start?

While I was attending a training class several years ago, a recommended starting point in an analysis was to use the “Graphical Summary” in the statistical software, which is in the options for “Basic Statistics.” The default output for figure 1’s data set is shown in figure 2.


Figure 2: Output of the Graphical Summary

Scott A. Hindle
By: Scott A. Hindle, Donald J. Wheeler

In theory, a production process is always predictable. In practice, however, predictable operation is an achievement that has to be sustained, which is easier said than done. Predictable operation means that the process is doing the best that it can currently do—that it is operating with maximum consistency. Maintaining this level of process performance over the long haul can be a challenge. Effective ways of meeting this challenge are discussed below.

Some elements of economic operation

As argued in “What Is the Zone of Economic Production?”, to speak of the economic operation of a manufacturing process, all of the following elements are required:
Element 1: Predictable operation
Element 2: On-target operation
Element 3: Process capability achieved (Cp and Cpk ≥ 1.5)

The notions of on-target operation and process capability are inextricably linked to predictable operation—i.e., demonstrable process stability and consistency over time. Without stability and consistency over time it is impossible to meaningfully talk about either capability or on-target operation.

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By: Scott A. Hindle

In all walks of life, being wrong can come with a penalty. It’s also true that, if you’re lucky, you sometimes get away with it without anybody being the wiser. To understand what this means in relation to the capability indexes Cp and Cpk, read on.

Introduction

In part 3 of “Process Capability: What It Is and How It Helps,” I wrote regarding the interpretation of the two most commonly used capability indexes:
• Predictable processes: Cp and Cpk can be considered reliable indicators of future performance.
• Unpredictable processes: Cp and Cpk may be false, or very misleading, indicators of what the process will give in the future.

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By: Scott A. Hindle


A t the end of part three of this four-part series on process capability, Alan was ready to identify a contact at the factory who could assist in providing some context around the collected data and the overall production process.

Discussion with Joe

Joe, working on the production team, was the person Alan found. Joe was told he needed to provide more context about Product 874 data and give insight into the production process. To facilitate the discussion with Joe, Alan added some questions to Sarah’s X chart (as seen in figure 1).


Figure 1: X chart of Product 874 data with focus on the process changes detected by the software. Click here for larger image.

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By: Scott A. Hindle

Part two of this four-part series on process capability concluded with Alan just about to meet Sarah for a second time. He thought he was making good progress with his analysis of Product 874 data until he was asked to assess process capability, even though it can’t be assessed for an unstable process.

Making sense of the XmR chart

Alan thanked Sarah for the two articles she’d given him. He said that, guided by the second article by Donald Wheeler, he’d created his first XmR chart (figure 1 below), which he interpreted to mean that the process data represented an unstable or not-in-control process. Wheeler’s article noted that it was more important to find the cause of process changes rather than computing statistics.

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By: Scott A. Hindle

In part one of this four-part series, we considered the basics of process capability, as witnessed through the learning curve of Alan in his quest to determine the product characteristics of the powder, Product 874. We pick up with Alan here as he prepares for his second meeting with his colleague Sarah, to discuss his preliminary results.

The second article Sarah had given Alan was titled “Individual Charts Done Right and Wrong,” by Donald J. Wheeler. It helped him to move in a different direction with the data he received to assess process capability. He recalled having been briefly exposed to Shewhart-type control charts, the subject of the paper, during a training class some time back, but he didn’t remember much about them.

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By: Scott A. Hindle

In my August 2015 article, “Process Capability: How Many Data?” I discussed whether 30 data were the “right” number in an analysis of process capability. In this four-part series, the focus is on understanding what process capability is and the pitfalls associated with it, along with how it can help manufacturers develop process knowledge, reach better decisions, and take better actions.

Product 874: What is process capability?

The story starts with Alan, a relative novice in the field of process capability, who was assigned the task of writing a report on the process capability for a key product characteristic of Product 874, a powder product. The 56 data values he received are found in figure 1. Alan’s brief was to use these data to write a report covering:
• The process capability results for the characteristic under study
• An interpretation of the results
• An appendix of all calculations in Excel for traceability purposes