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Control charting captures snapshots of the process average and variation over time. When specific signals are observed on the control charts, we conclude that the process is unstable because the probability of observing those signals if the process had not changed is very small.
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Many advocate the replacement of SPC with more simplistic approaches such as "Pre-Control." Unfortunately, in a manufacturing world of increasing complexity, and with a global market demanding the highest quality and reliability, applying "simple" tools at the expense tools with considerably more value (and really not very complex or difficult) doesn't cut it.
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In last month’s article, we introduced the concept of utilizing Deviation from Nominal (DNOM) control charts for short production runs. These charts allow us to monitor process characteristics over time even when the units being controlled have varying nominal values. DNOM charts assume that the process variability (i.e. standard deviation) does not vary significantly by part type. However, often this assumption does not hold. Characteristics with larger nominal values tend to have more variation than characteristics with smaller nominal values. This month we discuss how to test whether or not significant differences in variability exist and if so, how to modify the DNOM methods and charts to handle this situation.
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Traditional SPC methods were developed to support high volume production and long production runs. However, with the trend toward product specialization, product diversity, and flexible manufacturing, short production runs have become more common. Applying SPC in the traditional manner presents challenges in short production runs.
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Enormous efforts and dollars are spent in production operations reacting to, containing, and solving problems. However, considerably more effort is needed in product design and manufacturing to prevent product failures, scrap, and other inefficiencies.
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Many manufacturing leaders believe that their production personnel use SPC properly, but the evidence suggests otherwise.
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Last month’s article focused on the conceptual application of appropriate sample sizes for X-bar charts. This month we describe the sample size formula and its application in detail.
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The key is to specify a subgroup size so that significant shifts are detected with high probability and that insignificant shifts are unlikely to produce a signal.
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Many factors should be considered when choosing a control chart for a given application.
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Clearly, controlling everything is not feasible or a smart use of limited resources.
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Most manufacturers would rate product quality as a key driver of their overall ability to satisfy customers and compete in a global market.
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Nothing and everything. Though they are not directly linked, statistician and SPC expert Steven Wachs cautions that without evidence of process stability, capability data is useless.
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Many statistical tests and procedures assume that data follows a normal distribution
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Ask people involved with the design and manufacture of a product the following question: "What is Quality?" Many, if not most, of the responses will be some form of the following: "Quality is ensuring that our products meet the customer (or engineering) specifications. Unfortunately, this leads to a "conformance to specifications" or a "Product Control" approach to quality.
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This is a crucial distinction that is frequently confused. Basically, specification limits have to do with the voice of the customer while control limits have to do with the voice of the process.
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What is SPC's role, if any, in the world of Lean Manufacturing? Is SPC 'lean'?
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Last month I began to answer this question by examining some of the 14 Principles of Lean Manufacturing according to Jeffery Liker. Let's continue that examination.
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In my last column, I was asked about a capability index called Cpk. Cpk is a measure of a process' ability to conform to specification. A Cpk = 1 means that if nothing changes, 99.73% of the process output will be within specification, as in Figure 1.
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To answer that, we need to start off with what these capability indices represent. Capability indices characterize a process as compared to a specification.
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You can chart deviation-from-target. You do this by creating a Target chart, a chart that plots the deviation of subgroup values from a target value.
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Typical control charts, such as Xbar & R and X & Mr, indicate whether or not a process is statistically stable.
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Control charts are one of the most popular SPC tools used by manufacturers. They are used to determine whether a process is in or out of control.
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Just like control charts tell us about the stability of the process, capability analyses tell us about process capability.
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