# Chapter 4 Shewhart Control Charts in Phase I

## 4.1 Introduction

Statistical Quality Control (SQC) consists of methods to improve the quality of process outputs. These methods are the central focus of the book by (Christensen, Betz, and Stein 2013). Statistical Process Control (SPC) is a subset of Statistical Quality Control (SQC). The objective for SPC is to specifically understand, monitor, and improve processes to better meet customer needs. The output of all processes, whether they are manufacturing processes or processes that provide a service of some kind, are subject to variability. Variability makes it more difficult for processes to generate outputs that fall within desirable specification limits.

(Shewhart 1931) designated the two causes for variability in process outputs to be common causes and special or assignable causes. Common causes of variability are due to the inherent nature of the process. They can’t be eliminated or reduced without changing the actual process. Assignable causes for variability, on the other hand, are unusual disruptions to normal operation. They should be identified and removed in order to reduce variability and make the process more capable of meeting the specifications.

Control charts are statistical tools. Their use is the most effective way to distinguish between common and assignable cause for variability when monitoring process output in real time. Although control charts alone cannot reduce process variability, they can help to prevent over-reaction to common causes for variability (which may make things worse), and help to prevent ignoring assignable cause signals. When the presence of an assignable cause for variability is recognized, knowledge of the process can lead to adjustments to remove this cause and reduce the variability in process output.

To illustrate how control charts distinguish between common and assignable causes, consider the simple example described by (Joiner 1994). Eleven year old Patrick Nolan needed a science project. After talking with his father, a statistician, he decided to collect some data on something he cared about; his school bus. He recorded the time the school bus arrived to pick him up each morning, and made notes about anything he considered to be unusual that morning. After about 5 weeks, he made the chart shown in Figure 4.1 that summarized the information he collected. Figure 4.1 Patrick’s Chart

A control chart of the number of minutes past 8:00AM that the bus arrived is shown in Figure 4.2. In this figure, the upper control limit (UCL) is three standard deviations above the mean, and the lower control limit (LCL) is three standard deviations below the mean. The two points in red are above the upper control limit, and they correspond to the day when Patrick noted that the school bus door opener was broken, and the day when there was a new driver on the bus. The control chart shows that these two delayed pickup times were due to special or assignable causes (which were noted as unusual by Patrick). The other points on the chart are within the control limits and appear to be due to common causes. Common causes like normal variation in amount of traffic, normal variation in the time the bus driver started the route, normal variation in passenger boarding time at previous stops, and slight variations in weather conditions are always present and prevent the pickup times from being exactly the same each day. Figure 4.2 Control Chart of Patrick’s Data

There are different types of control charts, and two different situations where they are used (Phase I, and Phase II). The book by (Christensen, Betz, and Stein 2013) describes the use of Shewhart control charts in what would be described as Phase II process monitoring. The book also describes how the control limits are calculated by hand and used to monitor a process.

Sometimes Shewhart control charts are maintained manually in Phase II real time monitoring by process operators who have the knowledge to make adjustments when needed. However, we will describe other types of control charts that are more effective than Shewhart control charts for Phase II monitoring in Chapter 6. In the next two sections of this chapter, we will describe the use of Shewhart control charts in Phase I.

## 4.2 Variables Control Charts in Phase I

In Phase I, control charts are used retrospectively on data to calculate preliminary control limits and determine if the process had been in control. When assignable causes are detected on the control chart using the historical data, an investigation is conducted to find the cause. If the cause is found, and it can be prevented in the future, then the data corresponding to the out of control points on the chart are eliminated and the control limits are recalculated.

This is an iterative process and it is repeated until control chart limits are refined, a chart is produced that does not appear to contain any assignable causes, and the process operates at an acceptable level. The information gained from the Phase I control chart is then used as the basis of Phase II monitoring. Since control chart limits are calculated repeatedly in Phase I, the calculations are usually performed using a computer. This chapter will illustrate the use of R to calculate control chart limits and display the charts.

Another important purpose for using control charts in Phase I is to document the process knowledge gained through an Out of Control Action Plan (OCAP). This OCAP is a list of the causes for out of control points. This list is used when monitoring the process in Phase II using the revised control limits determined in Phase I. When out of control signals appear on the chart in Phase II, the OCAP should give an indication of what can be adjusted to bring the process back into control.

The Automotive Industry Action Group (TaskSubcommittee 1992) recommends preparatory steps be taken before variable control charts can be used effectively. These steps are summarized in the list below.

1. Establish an environment suitable for action Management must provide resources and support actions to improve processes based on knowledge gained from use of control charts.

2. Define the process The process including people, equipment, material, methods, and environment must be understood in relation to the upstream suppliers and downstream customers. Methods such as flowcharts, SIPOC diagrams (to be described in the next chapter), and cause-and-effect diagrams described by (Christensen, Betz, and Stein 2013) are useful here.

3. Determine characteristics to be charted This step should consider customer needs, current and potential problems, and correlations between characteristics. For example, if a characteristic of the customer’s need is difficult to measure, track a correlated characteristic that is easier to measure.

4. Define the measurement system Make sure the characteristic measured is defined so that it will have the same meaning in the future and the precision and accuracy of the measurement is predictable. Tools such as Guage R&R Studies described in chapter 3 are useful here.

Data for Shewhart control charts are gathered in subgroups. Subgroups, properly called rational subgroups, should be chosen so that the chance for variation among the process outputs within a subgroup is small and represents the inherent process variation. This is often accomplished by grouping process outputs generated consecutively together in a subgroup, then spacing subgroups far enough apart in time to allow for possible disruptions to occur between subgroups. In that way, any unusual or assignable variation that occurs between groups should be recognized with the help of a control chart. If a control chart is able to detect out of control signals, and the cause for these signals can be identified, it is an indication that the rational subgroupings were effective.

The data gathering plan for Phase I studies are defined as follows:

1. Define the subgroup size Initially this is a constant number of 4 or 5 items per each subgroup taken over a short enough interval of time so that variation among them is due only to common causes.

2. Define the Subgroup Frequency The subgroups collected should be spaced out in time, but collected often enough so that they can represent opportunities for the process to change.

3. Define the number of subgroups Generally 25 or more subgroups are necessary to establish the characteristics of a stable process. If some subgroups are eliminated before calculating the revised control limits due to discovery of assignable causes, additional subgroups may need to be collected so that there are at least 25 subgroups used in calculating the revised limits.

### 4.2.1 Use of $$\overline{X}$$-R charts in Phase I

The control limits for the $$\overline{X}$$-chart are $$\overline{\overline{X}}\pm A_2\overline{R}$$, and the control limits for the $$R$$-chart are $$D_3\overline{R}$$ and $$D_4\overline{R}.$$ The constants $$A_2$$, $$D_3$$, and $$D_4$$ are indexed by the rational subgroup size and are given in Appendix D of (Christensen, Betz, and Stein 2013). These constants are used when calculating control limits manually. We will illustrate the use of a computer for creating $$\overline{X}-R$$ Charts in Phase I using the R package $$\verb!qcc!$$ and data data from (Mitra 1998). He presents the following example in which subgroups of 5 were taken from a process that manufactured coils. The resistance values in ohms was measured on each coil, and 25 subgroups of data were available. The data is shown in Table 4.1.

Table 4.1 Coil Resistance

Subgroup Ohms
1 20 22 21 23 22
2 19 18 22 20 20
3 25 18 20 17 22
4 20 21 22 21 21
5 19 24 23 22 20
6 22 20 18 18 19
7 18 20 19 18 20
8 20 18 23 20 21
9 21 20 24 23 22
10 21 19 20 20 20
11 20 20 23 22 20
12 22 21 20 22 23
13 19 22 19 18 19
14 20 21 22 21 22
15 20 24 24 23 23
16 21 20 24 20 21
17 20 18 18 20 20
18 20 24 22 23 23
19 20 19 23 20 19
20 22 21 21 24 22
21 23 22 22 20 22
22 21 18 18 17 19
23 21 24 24 23 23
24 20 22 21 21 20
25 19 20 21 21 22

### 4.2.2$$\overline{X}$$ and R Charts

To begin, this historical data is entered into a computer file so that it can be analyzed. For example, Figure 4.3 shows the first three lines of a .csv (comma separated) file containing this data. The first line contains names for the variables in each subgroup. Figure 4.3 Coil.csv file

The data is read into an R data frame using the commands shown below, and the $$\verb!qcc!$$ function in the R package $$\verb!qcc!$$ is used to create the R-chart shown in Figure 4.4.

#Example from Mitra
library(qcc)
qcc(CoilG, type="R") Figure 4.4 R-chart for Coil Resistance

In Figure 4.4, the range for subgroup 3 is out of the limits for the R-chart, indicating an assignable cause was present. Including this out-of-control subgroup in the calculations increases the average range to $$\overline{R}=3.48$$, and will broaden the control limits for both the $$\overline{X}$$ chart and R-chart and reduce the chances of detecting other assignable causes.

When the cause of the wide range in subgroup 3 was investigated, past information showed that was the day that a new vender of raw materials and components was used. The quality was low, resulting in the wide variability in coil resistance values produced on that day. Based on that fact, management instituted a policy to require new vendors to provide documentation that their process could meet the required standards. With this in mind, subgroup 3 is not representative of the process after implemention of this policy, so it should be removed before calculating and displaying the control charts.

In the R code shown below, subgroup 3 is removed from the updated data frame $$\verb!Coilm3!$$ and the $$\overline{X}$$ chart shown in Figure 4.5 was produced.

# Eliminate subgroup 3
library(qcc)
Coilm3<-CoilG[-c(3), ]
qcc(Coilm3, type="xbar") Figure 4.5 $$\overline{X}$$-chart for Coil Resistance

When investigating the causes for the the high averages for subgroups 15 and 23, and the low average for subgroup 22, it was found that the oven temperature was too high for subgroup 22, and the wrong die was used for subgroup 23, but no apparent reason for the high average for subgroup 15 could be found.

Assuming high oven temperatures and use of the wrong die can be avoided in the future, the control chart limits were recomputed eliminating subgroups 22 and 23. This resulted in the upper and lower control limits for the R chart (LCL=0, UCL=6.919) and the $$\overline{X}$$ chart (LCL=18.975, UCL=22.753). However, one subgroup (15) remains out of the control limits on the $$\overline{X}$$ chart, and there are less than 25 total subgroups of data used in calculating the limits.

The next step would be to eliminate the data from subgroup 15, collect additional data, and recompute the limits and plot the chart again. It may take many iterations before charts can be produced that do not exhibit any assignable causes. As this is done, the assignable causes discovered in the process should be documented into a OCAP. For example, in the two iterations described above, three assignable causes were discovered. An OCAP based on just these three causes mentioned may look something like the list shown below.

OCAP

Out of Control on R Chart–High within subgroup variability

1. Verify that the vendor of raw material and component has documented their ability supply within specs. If not, switch vendors.

Out of control on $$\overline{X}$$ chart

1. Check to make sure the proper die was used. If not, switch to the proper die.

2. Check to make sure oven temperature was set correctly. If not, set correctly for the next production run.

In practice, considerable historical data from process operation may already be routinely collected and stored in data bases. The R package $$\verb!readr!$$ has several functions that can be used to load plain text rectangular files into R data frames (see (Wickham and Grolemund 2017)). Most large companies maintain a Quality Information System (like those the FDA requires companies in the pharmaceutical industry to maintain (see I.B.3 (Burke and Silvestrini 2017)). These systems usually employ $$\verb!SQL!$$ relational databases. There are also functions in other R packages such as $$\verb!RODBC!$$, $$\verb!RJDBC!$$, and $$\verb!rsqlserver!$$ that can connect to an SQL data base and fetch or read portions of the data. An article giving examples of this is available at https://www.red-gate.com/simple-talk/sql/reporting-services/making-data-analytics-simpler-sql-server-and-r/. As long as the data that is retrieved can be formatted as rational subgroups (with only common cause variability within subgroups), this is a good source of historical data for Phase I control chart applications.

The discovery of assignable causes for out of control signals in real applications can sometimes be a difficult task. If there is not an obvious cause, then the Cause and Effect (or Ishakawa) Diagram presented in Chapter 9-Basic Quality Tools (Christensen, Betz, and Stein 2013) is useful for organizing and recording ideas for possible assignable causes. These diagrams are best made in a group setting where everyone that is knowledgeable about the process participates. Each person participating should in turn present one possible factor or cause to be added to the chart without judgement or criticism from others in the group. Once the group runs out of ideas the diagram is complete. The $$\verb!cause.and.effect!$$ function in the $$\verb!qcc!$$ package can be used to print the final diagram.

When the diagram is complete, the group can test the ideas to see if the out of control signal can be replicated. By consensus, the group should pick what they feel is the most likely factor on the diagram causing the problem. Next, they should follow the Plan-Do-Check-Act cycle described in Chapter 10-Continuous Improvement Tools (Christensen, Betz, and Stein 2013). In this context, the Plan Step is already complete when the group has picked the factor they want to study. The Do step consists of purposely changing the factor to the level the group felt caused the problem. The Check step consists of determining if implementing the change results in the same type of out of control signal for which the group is trying to find the cause. If it does, the OCAP should be updated noting that changes to this factor can cause an out of control signal. Finally, the Act step of the PDCA consists of implementing procedures to prevent changes to this factor from occurring in the future.

Frequently an out of control signal is caused by a combination of changes to several factors simultaneously. If that is the case, testing changes to one-factor-at a time using the PDCA approach is inefficient and will never discover the cause. Several factors can be tested simultaneously using Experimental Design plans. Some of these methods will be presented in the next chapter and more details can be found in (Lawson 2015).

Since the control charts produced with the data in Table 4.1 detected out of control signals, it is an indication that the rational subgroupings were effective. There was less variability within the subgroups than from group to group, and therefore subgroups with high or low averages were identified on the $$\overline{X}$$ chart as potential assignable causes. If data is retrieved from a historical database, and no assignable causes are identified on either the $$\overline{X}$$ or R charts and subgroup means are close to the center line of the $$\overline{X}$$ chart, it would indicate that the subgrouping has included more variability within the subgroups than from subgroup to subgroup. These subgroupings will be ineffective in identifying assignable causes.

Notice that the control limits for the $$\overline{X}$$ and R charts described by (Christensen, Betz, and Stein 2013) in Chapter 14 Section 3, and computed by the $$\verb!qcc!$$ function as shown in Figure 4.3, have no relationship with the specification limits described in Chapter 3. Specification limits are determined by a design engineer or another familiar with the customer needs (that may be the next process). The control limits for the control chart are calculated from observed data, and they show the limits of what the process is currently capable of. The Center (or $$\overline{\overline{X}}$$) and the $$\verb!StDev!$$ (or standard deviation) shown in the group of statistics at the bottom of the chart in Figure 4.5 are estimates of the current process average and standard deviation. Specification limits should never be added to the control charts because they will be misleading.

The goal in Phase I is to discover and find ways to eliminate as many assignable causes as possible. The revised control chart limits and OCAP will then be used in Phase II to keep the process operating at the optimal level with minimum variation in output.

### 4.2.3 Interpreting Charts for Assignable Cause Signals

In addition to just checking individual points against the control limits to detect assignable causes, (Christensen, Betz, and Stein 2013) shows a list of additional indicators that should be checked (in Figure 14.5, p 194). This list is reproduced below.

1. A point above or below the upper control limit or below the lower control limit

2. Seven consecutive points above (or below) the center line

3. Seven consecutive points trending up (or down)

4. Middle one-third of the chart includes more than 90% or fewer than 40% of the points after at least 25 points are plotted on the chart

5. Obviously non-random patterns

These items are based on the Western Electric Rules proposed in the 1930s. If potential assignable cause signals are investigated whenever any one of the indicators are violated (with the possible exception of number 3.), it will make the control charts more sensitive for detecting assignable causes. It has been shown that the trends of consecutive points, as described in 3, can sometimes occur by random chance, and strict application of indicator 3 may lead to false positive signals and wasted time searching for assignable causes that are not present.

### 4.2.4$$\overline{X}$$ and s Charts

The $$\overline{X}$$ and s Charts described by (Christensen, Betz, and Stein 2013) can also be computed by the $$\verb!qcc!$$ function by changing the option $$\verb!type="R"!$$ to $$\verb!type="S"!$$ in the function call for the R -chart. When the subgroup size is 4 or 5, as normally recommended in Phase I studies, there is little difference between $$\overline{X}$$ and s charts and $$\overline{X}$$ and R Charts, and it doesn’t matter which is used.

### 4.2.5 Variable Control Charts for Individual Values

In some situations there may not be a way of grouping observations into rational subgroups. Examples are continuous chemical manufacturing processes, or administrative processes where the time to complete repetitive tasks (i.e., cycle time) are recorded and monitored. In this case a control chart of individual values can be created. Using $$\verb!qcc!$$ function and the option $$\verb!type="xbar.one"!$$ a control chart of individual values is made. The block of R code below shows how the chart shown in Figure 4.1 was made.

minutes<-c(15,17,18,20,21,16,17,18,15.5,16,22,28,21.5,16,17,16,18,17,19,21,27.5,17.5,21,16,18.75
,21.5)
library(qcc)
qcc(minutes, type="xbar.one", std.dev="MR",title="Control Chart of Minutes after 8:00AM")

The option $$\verb!std.dev="MR"!$$ instructs the function to estimate the standard deviation by taking the average moving range of two consecutive points (i.e., $$(|17-15|+|18-17|, \ldots |21.5-18.75|)/25$$). The average moving range is then scaled by dividing by $$d_2$$ (from the factors for control charts with subgroup size $$n=2$$). This is how the shown at the bottom of Figure 4.1 was obtained. This standard deviation was used in constructing the control limits $$UCL=18.89423+3\times2.819149=27.35168$$ and $$LCL=18.89423-3\times2.819149=10.43678$$. $$\verb!std.dev="MR"!$$ is the default, and leaving it out will not change the result. The default can be changed by specifying $$\verb!std.dev="SD"!$$. This specification will use the sample standard deviation of all the data as an estimate of the standard deviation. However, this is usually not recommended, because any assignable causes in the data will inflate the sample standard deviation, widen the control limits, and possibly conceal the assignable causes within the limits. ## 4.3 Attribute Control Charts in Phase I While variable control charts track measured quantities related to the quality of process outputs, attribute charts track counts of nonconforming items. Attribute charts are not as informative as variables charts for Phase I studies. A shift above the upper or below the lower control limit or a run of points above or below the center line on a variables chart may give a hint about the cause. However, a change in the number of nonconforming items may give no such hint. Still attribute charts have value in Phase I. In service industries and other non-manufacturing areas, counted data may be abundant but numerical measurements rare. Additionally, many characteristics of process outputs can be considered simultaneously using attribute charts. (Christensen, Betz, and Stein 2013) describes $$p$$, $$np$$, $$c$$ and $$u$$ charts for attribute data. All of these attribute charts can be made with the $$\verb!qcc!$$ function in the R package $$\verb!qcc!$$. The formulas for the control limits for the $$p$$-chart are: \begin{align} UCL&=\overline{p}+3\sqrt{\frac{\overline{p}(1-\overline{p})}{n}} \\ LCL&=\overline{p}-3\sqrt{\frac{\overline{p}(1-\overline{p})}{n}}, \tag{4.1} \end{align} where $$n$$ is the number of items in each subgroup. The control limits will be different for each subgroup if the subgroup sizes $$n$$ are not constant. For example, the R code below produces a $$p$$ chart with varying control limits using the data in Table 14.1 on page 189 of (Christensen, Betz, and Stein 2013). When the lower control limit is negative, it is always set to zero. library(qcc) #Christensen's Table 14.1 d<-c(3,6,2,3,5,4,1,0,1,0,2,5,3,6,2,4,1,1,6,5,6,4,3,4,1,2,5,0,0) n<-c(48,45,47,51,48,47,48,50,46,45,47,48,50,50,49,46,50,52,48, 47,49,49,51,50,48,47,47,49,49) qcc(d,sizes=n,type="p") In the code, the vector $$\verb!d!$$ is the number of nonconforming, and the vector $$\verb!n!$$ is the sample size for each subgroup. The first argument to $$\verb!qcc!$$ is the number of nonconforming, and the second argument $$\verb!sizes=!$$ designates the sample sizes. It is a required argument for $$\verb!type="p"!$$, $$\verb!type="np"!$$, or $$\verb!type="u"!$$ attribute charts. The argument $$\verb!sizes!$$ can be a vector, as shown in this example, or as a constant. ### 4.3.1 Use of a $$p$$ Chart in Phase I The $$\verb!qcc!$$ package includes data from a Phase I study using a $$p$$ chart taken from (Montgomery 2013). The study was on a process to produce cardboard cans for frozen orange juice. The plant management had asked that the use of control charts be implemented in effort to improve the process. In the Phase I study, 30 subgroups of 50 cans each were initially inspected at half-hour intervals and classified as either conforming or nonconforming. The R code below taken from the $$\verb!qcc!$$ package documentation makes the data available. The first three lines of the data frame $$\verb!orangejuice!$$ are shown below the code. library(qcc) data(orangejuice) attach(orangejuice) orangejuice sample D size trial 1 1 12 50 TRUE 2 2 15 50 TRUE 3 3 8 50 TRUE . . . The column $$\verb!sample!$$ in the data frame is the subgroup number, $$\verb!D!$$ is the number of nonconforming cans in the subgroup, $$\verb!size!$$ is the number of cans inspected in the subgroup, and $$\verb!trial!$$ is an indicator variable. Its value is $$\verb!TRUE!$$ for each of the 30 subgroups in the initial sample (there are additional subgroups in the data frame). The code below produces the initial $$p$$ chart shown in Figure 4.6. library(qcc) qcc(D[trial], sizes=size[trial], type="p") In this code the $$\verb![trial]!$$ (after D and size), restricts the data to the initial 30 samples where $$\verb!trial=TRUE!$$. Figure 4.6 $$p$$ chart of the number nonfoncorming In this chart, the proportion nonconforming for subgroups 15 and 23 fell above the upper control limit. With only, conforming-nonconforming information available on each sample of 50 cans, it could be difficult to determine the cause of a high proportion nonconforming. A good way to gain insight about the possible cause of an out of control point is to classify the nonconforming items. For example, if the 50 nonconforming items in sample 23 were classified into 6 types of nonconformites. A Pareto diagram could be contsructed using the $$\verb!pareto.chart()!$$ function in the $$\verb!qcc!$$ package as shown in the code below. library(qcc) nonconformities<-c(2,30,5,1,9,3) names(nonconformities)<-c("Contamination","Sealing Failure","Adhesive Failure", "Material Defects","Printing Off Color", "Ink Migration") paretoChart(nonconformities, ylab = "Nonconformance frequency",main="Pareto Chart") The resulting chart in Figure 4.7, shows the major classes of nonconforming cans. Figure 4.7 Pareto Chart of Noconforming Cans Sample 23 The causes sealing failure and ahesive failure usually reflect operator error. This sparked an investigation of what operator was on duty at that time. It was found that the half-hour period when subgroup 23 was obtained occurred when a temporary and inexperienced operator was assigned to the machine; and that could account for the high fraction nonconforming. A similar classification of the nonconformities in subgroup 15 led to the discovery that a new batch of cardboard stock was put into production during that half hour period. It was known that introduction of new batches of raw material sometimes caused irregular production performance. Prevetitive measures were put in place to prevent use of inexperienced operators and un-tested raw materials in th future. Subsequently, subgroups 15 and 23 were removed from the data, and the limits were recalculated as $$\overline{p}=.2150$$, $$UCL=0.3893$$, and $$LCL=0.0407$$. Subgroup 21 ($$p=.40$$) now falls above the revised $$UCL$$. However, no assignable cause could be discovered for the number nonconforming. There were no other obvious patterns, and at least with respect to operator controllable problems, it was felt that the process was now stable. Nevertheless, the average proportion nonconforming cans is 0.215, which is not an acceptable quality level (AQL). The plant management agreed, and asked the engineering staff to analyze the entire process to see if any improvements could be made. Using methods like those to be described in the next chapter (Chapter5), the engineering staff found that several adjustments could be made to the machine that should improve its performance. After making these adjustments, 24 more subgroups (numbers 31-54) of $$n$$=50 were collected. The R code below plots the fraction nonconforming for these additional subgroups on the chart with the revised limits. The result is shown in Figure 4.8. library(qcc) # remove out-of-control points (see help(orangejuice) for the reasons) inc <- setdiff(which(trial), c(15,23)) qcc(D[inc], sizes=size[inc], type="p", newdata=D[!trial], newsizes=size[!trial]) detach(orangejuice) The statement $$\verb!inc <- setdiff(which(trial), c(15,23))!$$ creates a vector ($$\verb!inc!$$) of the subgroup numbers, where $$\verb!trial=TRUE!$$, excluding subgroups 15 and 23. In the call to the $$\verb!qcc!$$ function, the argument $$\verb!D[inc]!$$ is the vector containing the number of nonconforming cans for all the the subgroups in the original 30 samples minus groups 15 and 23. This is the set that will be used to calculate the control limits. The argument $$\verb!sizes=size[inc]!$$ is the vector of subgroup sizes associated with these 28 subgroups. The argument $$\verb!newdata=D[!!\verb!trial]!$$ specifies that the data for nonconformities in the additional 24 subgroups in data frame $$\verb!orangejuice!$$, where $$\verb!trial=FALSE!$$ should be plotted on the control chart with the previously calculated limits. The argument $$\verb!newsizes=size[!!\verb!trial])!$$ is the vector of subgroup sizes associated with the 24 new subgroups. For more information and examples of selecting subsets of data frames see (Wickham and Grolemund 2017). The immediate impression from the new chart is that the process, after adjustments, is operating at a new lower proportion defective. The run of consecutive points below the center line confirms that impression. The obvious assignable cause for this shift is successful adjustments. Figure 4.8 Revised limits with additional subgroups Based on this improved performance, the control limits should be recalculated based on the new data. This was done, and additional data was collected over the next 5 shifts of operation (subgroups 55 to 94). The R code below first loads the data from subgroups 31 to 54, which is in the data frame $$\verb!orangejuice2!$$ in the R package $$\verb!qcc!$$. In this data set $$\verb!trial=TRUE!$$ for subgroups 31 to 54, and $$\verb!trial=FALSE!$$ for subgroups 55 to 94. The $$\verb!qcc!$$ function call in the code then plots all the data on the control chart with limits calculated from just subgroups 31 to 54. The result is shown in Figure 4.9. library(qcc) data(orangejuice2) attach(orangejuice2) names(D) <- sample qcc(D[trial], sizes=size[trial], type="p", newdata=D[!trial], newsizes=size[!trial]) detach(orangejuice2) Figure 4.9 Revised limits with additional subgroups In this chart there is no indication of assignable causes in the new data. The proportion nonconforming varies between 0.02 to 0.22 with an average proportion nonconforming of 0.1108. This is not an acceptable quality level. Due to the fact that no assignable causes are identified in Figure 4.7, the $$p$$ chart alone gives no insight on how to improve the process and reduce the number of nonconforming items. This is one of the shortcomings of attribute charts in Phase I studies. Another shortcoming, pointed out by (Borror and Champ 2001), is that the false alarm rate for $$p$$ and $$np$$ charts increases with the number of subgroups and could lead to wasted time trying to identify phantom assignable causes. To try to improve the process to an acceptable level of nonconformities, the next step might be to classify the 351 nonconforming items found in subgroups 31 to 94 and display them in a Pareto Chart like Figure 4.7. A Seeing the most prevalent types of nonconformities may motivate some ideas among those familiar with the process as to their cause. If there is other recorded information about the processing conditions during the time subgroups were collected, then scatter diagrams and correlation coefficients with the proportion nonconforming may also stimulate ideas about the cause for differences in the proportions. These ideas can be documented in cause-and-effect diagrams, and may lead to some Plan-Do-Check-Act investigations or more efficient investigations using the Experimental Designs to be described in the next chapter. Continual investigations of this type may lead to additional discoveries of ways to lower the proportion nonconforming. The control chart (like Figure 4.5 and 4.6) serves as a logbook to document the timing of interventions and their subsequent effects. Assignable causes discovered along the way are also documented in the OCAP for future use in Phase II. The use of the control charts in this example, along with the other types of analysis suggested in the last two paragraphs, would all be classified as part of the Phase I study. Compared to the simple example from (Mitra 1998) presented in the last section, this example gives a clearer indication of what might be involved in a Phase I study. In Phase I, the control charts are not used to monitor the process but rather as tools to help identify ways to improve and to record and display progress. Phase I would be complete when process conditions are found where the performance is stable (in control) at a level of nonconformities for attribute charts, or a level of process variability for variable charts that is acceptable to the customer or next process step. An additional outcome of the Phase I study is an estimate of the current process average and standard deviation. ### 4.3.2 Constructing other types of Attribute Charts with qcc When the number of items inspected in each subgroup is constant, $$np$$ charts for nonconformites are preferable. The actual number of nonconformites for each subgroup are plotted on the $$np$$ chart rather than the fraction nonconforming. The control limits, which are based on the Binomial distribution, are calculated with the following formulas: \begin{align} UCL&=n\overline{p}+3\sqrt{n\overline{p}(1-\overline{p})}\\ \\ \texttt{Center line}&=n\overline{p}\\ \\ LCL=&n\overline{p}-3\sqrt{n\overline{p}(1-\overline{p})}\\ \tag{4.2} \end{align} where, $$n$$ is constant so that the control limits remain constant for all subgroups. The control limits for a $$p$$ chart, on the other hand, can vary depending upon the subgroup size. The R code below illustrates the commands to produce an $$np$$ chart with using the data from Figure 14.2 in (Christensen, Betz, and Stein 2013). In this code, $$\verb!sizes=1000!$$ is specified as a single constant rather than a vector containing the number of items for each subgroup. library(qcc) d<-c(9,12,13,12,11,9,7,0,12,8,9,7,11,10) qcc(d,sizes=1000,type="np") When charting nonconformities, rather than the number of nonconforming items in a subgroup, a $$c$$-chart is more appropriate. When counting nonconforming (or defective) items, each item in a subgroup is either conforming or nonconforming. Therefore, the number of nonconforming in a subgroup must be between zero and the size of the subgroup. On the other hand, there can be more than one nonconformity in a single inspection unit. In that case, the Poisson distribution is better for modeling the situation. The control limits for $$c$$-chart are based on the Poisson distribution and are given by the following formulas: \begin{align} UCL&=\overline{c}+3\sqrt{\overline{c}}\\ \\ \texttt{Center line}&=\overline{c}\\ \\ LCL=&\overline{c}-3\sqrt{\overline{c}}\\ \tag{4.3} \end{align} where, $$\overline{c}$$ is the average number of defects per inspection unit. (Montgomery 2013) describes an example where a $$c$$-chart was used to study the number of defects found in groups of 100 printed circuit boards. More than one defect could be found in a circuit board so that the upper limit for defects is greater than 100. This data is also included in the $$\verb!qcc!$$ package. When the inspection unit size is constant (i.e. 100 circuit boards) as it is in this example, the $$\verb!sizes=!$$ argument in the call to $$\verb!qcc!$$ is unnecessary. The R code below, from the $$\verb!qcc!$$ documentation, illustrates how to create a $$c$$-chart. library(qcc) data(circuit) attach(circuit) qcc(circuitx[trial], sizes=circuit\$size[trial], type="c")

$$u$$-charts are also used for charting defects when the inspection unit does not have a constant size. The control limits for the $$u$$-chart are given by the following formulas:

\begin{align} UCL&=\overline{u}+3\sqrt{\overline{u}/k}\\ \\ \texttt{Center line}&=\overline{u}\\ \\ LCL=&\overline{u}+3\sqrt{\overline{u}/k}\\ \tag{4.4} \end{align}

where, $$\overline{u}$$ is the average number of defects per a standardized inspection unit and $$k$$ is the size of each individual inspection unit. The R code to create an $$u$$-chart using the data in Figure 14.3 in (Christensen, Betz, and Stein 2013) is shown below. The $$\verb!qcc!$$ function used in that code uses the formulas for the control limits shown in Equation (4.4) (3.1)and are not constant. (Christensen, Betz, and Stein 2013) replaces $$k$$, in (4.4), with $$\overline{k}$$, the average inspection unit size. Therefore, the control limits shown in that book are constant. If you make the chart using the code below, you will see that the control limits are not constant.

library(qcc)
d<-c(6,7,8,8,6,7,7,6,3,1,2,3,3,4)
k<-c(12,10,8,9,8,9,8,10,10,10,9,12,10,12)
qcc(d,sizes=k,type="u")

## 4.4 Process Capability Analysis

At the completion of a Phase I study, where control charts have shown that the process is in control at what appears to be an acceptable level, then calculating a process capability ratio (PCR) is an appropriate way to document the current state of the process.

When variables control charts were used in Phase I, then the PCR’s $$C_p$$ and $$C_{pk}$$ are appropriate. These indices can be calculated with the formulas:

$\begin{equation} C_p=\frac{USL-LSL}{6\sigma} \tag{4.5} \end{equation}$ $\begin{equation} C_{pk}=\frac{Min(C_{Pu},C_{Pl})}{3\sigma}, \tag{4.6} \end{equation}$

where

\begin{align} C_{Pu}&=\frac{USL-\overline{X}}{\sigma}\\ \\ C_{Pl}&=\frac{\overline{X}-LSL}{\sigma}\\ \tag{4.7} \end{align}

The measure of the standard deviation $$\sigma$$ used in these formulas is estimated from the subgroup data in Phase I control charts. If $$\overline{X} - R$$ charts were used, then $$\sigma=\overline{R}/d_2$$, where $$d_2$$ is taken from the table in Appendix D on page 302 of (Christensen, Betz, and Stein 2013) for the appropriate subgroup size $$n$$. If $$\overline{X} - s$$ charts were used, then $$\sigma=\overline{s}/c_4$$, where $$c_4$$ is taken from the table in Appendix D on page 301 for the appropriate subgroup size $$n$$.

The $$\verb!process.capability!$$ function in the R package $$\verb!qcc!$$ can compute the capability indices and display a histogram of the data with the specification limits superimposed. For example, the R code below creates $$\overline{X} - R$$ charts using the data from Table 14.2 in (Christensen, Betz, and Stein 2013). Assuming this data shows the process is in control, then the $$\verb!process.capability!$$ function is called to create the PCR’s, in the report below the code, and it produces the graphical representation shown in Figure 4.10.

If the process is exactly centered between the lower and upper specification limits, then $$C_p = C_{pk}$$. In the example below, the center line $$\overline{\overline{X}}=7.1249$$ is very close to the center of the specification range and therefore $$C_p$$ and $$C_{pk}$$ are nearly equal. When the process is not centered, $$C_{pk}$$ is a more appropriate measure than $$C_p$$. The indices $$C_{p_l}=1.573$$ and $$C_{p_u}=1.605$$ are called $$Z_L$$ and $$Z_U$$ by (Christensen, Betz, and Stein 2013), and they would be appropriate if there was only a lower or upper specification limit. Otherwise $$C_{pk}=min(C_{p_l}, C_{p_u})$$ Since the capability indices are estimated from data, the report also gives 95% confidence intervals for them.

Lathe<-read.table("Lathe.csv",header=TRUE,sep=",",na.strings="NA",dec=".",strip.white=TRUE)
library(qcc)
qcc(Lathe,type="R")
pc<-qcc(Lathe,type="xbar")
processCapability(pc,spec.limits=c(7.115,7.135))
-- Process Capability Analysis -------------------

Number of obs = 100          Target = 7.125
Center        = 7.1249       LSL    = 7.115
StdDev        = 0.002098106  USL    = 7.135

Capability indices  Value  2.5%  97.5%
Cp     1.59  1.37   1.81
Cp_l   1.57  1.38   1.76
Cp_u   1.60  1.41   1.80
Cp_k   1.57  1.34   1.80
Cpm    1.59  1.37   1.81

Exp<LSL 0%   Obs<LSL 0%
Exp>USL 0%   Obs>USL 0% Figure 4.10 Lathe data in Table 14.2 of Christensen et. al.

To better understand the meaning of the capability index, a portion of the table presented by (Montgomery 2013) is shown in Table 4.2. The second column of the table shows the process fallout in ppm when the mean is such that $$C_{Pl}$$ or $$C_{Pu}$$ is equal to the PCR shown in the first column. The third column shows the process fallout in ppm when the process is centered between the two spec limits so that $$C_P$$ is equal to the PCR. The fourth and fifth columns show the process fallout in ppm if the process mean shifted left or right by 1.5$$\sigma$$ after the PCR had been established.

Table 4.2 Capability Indices and Process Fallout in ppm

PCR One-sided spec Two-sided specs One-sided spec Two-sided specs
0.50 66,807 133,613 500,000 500,000
1.00 1,350 2,700 66,807 66,807
1.50 4 7 1,350 1.350
2.00 0.0009 0.0018 4 4

In this table it can be seen that a $$C_{p_l}=1.50$$ or $$C_{p_u}=1.50$$ (when there is only a lower or upper specification limit) would result in only 4 ppm (or a proportion of 0.000004) out of specifications. If $$C_{p}=1.50$$ and the process is centered between upper and lower specification limits, then only 7 ppm (or a proportion of 0.000007) would be out of the specification limits. As shown in the right two columns of the table, even if the process mean shifted 1.5$$\sigma$$ to the left or right after $$C_{p}$$ was shown to be 1.50, there would still be only 1,350 ppm (or 0.00134 proportion) nonconforming. This would normally be an acceptable quality level (AQL).

Table 4.2 makes it clear why Ford motor company began in 1980 requiring that their suppliers demonstrate their processes were in a state of statistical control and had a capability index of 1.5 or greater. That way Ford could be guaranteed an acceptable quality level (AQL) for incoming components from their suppliers, without the need for acceptance sampling.

The validity of the PCR $$C_p$$ is dependent on the following.

1. The quality characteristic measured has a normal distribution.

2. The process is in a state of control.

3. For two sided specification limits, the process is centered.

The control chart and output of the $$\verb!process.capability!$$ function serve to check these. The histogram of the Lathe data in Figure 4.7 appears to justify the normality assumption. Normal probability plots would also be useful for this purpose.

When Attribute control charts are used in a Phase I study, $$C_p$$ and $$C_{pk}$$ are unnecessary. When using a $$p$$ or $$np$$ chart, the final estimate $$\overline{p}$$ is a direct estimate of the process fallout. For $$c$$ and $$u$$ charts, the average count represents the number of defects that can be expected for an inspection unit.

## 4.5 OC and ARL Characteristics of Shewhart Control Charts

The operating characteristic OC for a control chart (sometimes called $$\beta$$) is the probability that the next subgroup statistic will fall between the control limits and not signal the presence of an assignable cause. The OC can be plotted versus the hypothetical process mean for the next subgroup, similar to the OC curves for sampling plans discussed in Chapters 2 and 3.

### 4.5.1 OC and ARL for Variables Charts

The function $$\verb!ocCurves!$$ in the R package $$\verb!qcc!$$ can make OC curves for $$\overline{X}$$-control charts created with $$\verb!qcc!$$. In this case $\begin{equation} OC=\beta=P(LCL \le \overline{X} \le UCL) \tag{4.8} \end{equation}$

For example, the R code below demonstrates using the $$\verb!ocCurves!$$ function to create the OC curve based on the revised control chart for the Coil resistance data discussed in Section 4.2.1.

Coilm <- read.table("Coil.csv", header=TRUE,sep=",",na.strings="NA",dec=".",strip.white=TRUE)
library(qcc)
pc<-qcc(Coilm, type="xbar",plot=FALSE)
beta <- ocCurves(pc, nsigmas=3)

Figure 4.11 shows the resulting OC curves for future $$\overline{X}$$ control charts for monitoring the process with subgroups sizes of 5, 1, 10, 15, and 20 (the first number is the subgroup size for the control chart stored in $$\verb!pc!$$). It can be seen that if the process shift for the next subgroup is small (i.e., less that .5 $$\sigma$$), then the probability of falling within the control limits is very high. If the process shift is larger, the probability of falling within the control limits (and not detecting the assignable cause) decreases rapidly as the size of the subgroup increases. Figure 4.11 OC curves for Coil data

Figure 4.12 gives a more detailed view. In this figure, it can be seen that when the subgroup size is $$n=5$$ and the process mean shifts to the left or right by 1 standard deviation, there is about an 80% chance that the next mean plotted will fall within the control limits and the assignable cause will not be detected. On the other hand, if the process mean shifts by 2 standard deviations, there is less that a 10% chance that it will not be detected. On the right side of the figure, it can be seen that the chance of not detecting a 1 or 2 standard deviation shift in the mean is much higher when the subgroup size is $$n=1$$, . Figure 4.12 OC curves for Coil data

If the mean shifts by $$k\sigma$$ just before the $$i$$th subgroup mean is plotted on the $$\overline{X}$$ chart, then the probability that the first mean that falls out of the control limits is for the ($$i+m$$)th subgroup is:

$\begin{equation} [\beta(\mu+k\sigma)]^{m-1}(1-\beta(\mu+k\sigma)). \tag{4.9} \end{equation}$

This is a Geometric probability with parameter $$\beta(\mu+k\sigma)$$. Therefore, the expected number of subgroups before an assignable is discovered, or the average run length (ARL), is given by

$\begin{equation} ARL=\frac{1}{(1-\beta(\mu+k\sigma))}, \end{equation}$

the expected value of the Geometric random variable.

Although the $$\verb!qcc!$$ package does not have a function to plot the ARL curve for a control chart, the $$\verb!ocCurves!$$ function can store the OC curves for the $$\overline{X}$$ chart. In the R code below, $$\verb!pc!$$ is a matrix created by the $$\verb!ocCurves!$$ function. The row labels for the matrix are the process shift values shown on the x-axis of Figure 4.8. The column labels are the hypothetical number of values in the subgroups (i.e., $$n=$$ 5, $$n=$$ 1, $$n=$$ 10, $$n=$$ 15, and $$n=$$ 20). In the body of the matrix are the OC=$$\beta$$ values for each combination of the process shift and subgroup size. By setting $$\verb!ARL5=1/(1-beta[ ,1])!$$ and $$\verb!ARL1=1/(1-beta[ ,2])!$$ the average run lengths are computed as a function of the OC values in the matrix and plotted as shown in Figure 4.13.

library(qcc)
pc<-qcc(Coilm, type="xbar",plot=FALSE)
nsigma = seq(0, 5, length=101)
beta <- ocCurves(pc, nsigmas=3)
ARL5=1/(1-beta[ ,1])
ARL1=1/(1-beta[ ,2])
plot(nsigma, ARL1, type='l',,lty=3,ylim=c(0,20),ylab='ARL')
lines(nsigma, ARL5, type='l',lty=1)
text(1.2,5,'n=5')
text(2.1,10,'n=1') Figure 4.13 ARL curves for Coil data with hypothetical subgroup sizes of 1 and 5

In this figure, it can be seen that if the subgroup size is $$n=$$ 5, and the process mean shifts by 1 standard deviation, it will take 5 (rounded up to an integer) subgroups on the average before before the $$\overline{X}$$ chart shows an out of control signal. If the subgroup size is reduced to $$n=$$ 1, it will take many more subgroups on the average (over 40) before an out of control signal is displayed.

### 4.5.2 OC and ARL for Attribute Charts

The $$\verb!ocCurves!$$ function in the $$\verb!qcc!$$ package can also make and plot OC curves for $$p$$ and $$c$$ type attribute control charts. For these charts, $$\beta$$ is computed using the Binomial or Poisson distribution. For example, the R code below makes the OC curve for the $$p$$ chart shown in Figure 4.4 for the orange juice can data. The result is shown in Figure 4.14.

data(orangejuice)
beta <- with(orangejuice,
ocCurves(qcc(D[trial], sizes=size[trial], type="p", plot=FALSE)))
print(round(beta, digits=4)) Figure 4.14 OC curves for Orange juice can data, subgroup size=50

The center line is $$\overline{p}=0.231$$ in Figure 4.4, but in Figure 4.11 it can be seen that the probability of the next subgroup of 50 falling within the control limits of the $$p$$ chart is $$\ge 0.90$$ for any value of $$p$$ between 0.15 and about 0.30. So the $$p$$ chart is not very sensitive to detecting small changes in the proportion nonconforming.

ARL curves can be made for the $$p$$ chart in a way similar to the last example by copying the OC values from the matrix $$\verb!beta!$$ created by the $$\verb!ocCurves!$$ function. OC curves for $$c$$ charts can be made by changing $$\verb!type="p"!$$ to $$\verb!type="c"!$$.

## 4.6 Summary

Shewhart control charts can be used when the data is collected in rational subgroups. The pooled estimate of the variance within subgroups becomes the estimated process variance and is used to calculate the control limits. This calculation assumes the subgroup means are normally distributed. However, due to the Central Limit Theorem, averages tend to be normally distributed and this is a reasonable assumption.

If the subgroups were formed so that assignable causes occur within the subgroups, then the control limits for the control charts will be wide and few if any assignable cause will be detected. If a control chart is able to detect assignable causes, it indicates that the variability within the subgroups is due mainly common causes.

The cause for out of control points are generally easier discover when using Shewhart variable control charts (like $$\overline{X}-R$$ or $$\overline{X}-s$$) than Shewhart attribute charts (like $$p$$ charts, $$np$$ charts $$c$$ charts or $$u$$ charts). This is important in Phase I studies where one of the purposes is to establish an OCAP containing a list of possible assignable causes.

If characteristics that are important to the customer can be measured and variables control charts can be used, then demonstrating that the process is in control with a capability index $$C_p\ge$$ 1.50 or $$C_{pk}\ge$$ 1.50 will guarantee that production from the process will contain less that 1,350ppm nonconforming or defective items. This is the reason (Deming 1986) asserted that no inspection by the customer (or next process step) will be necessary in this situation.

## 4.7 Exercises

1. Run all the code examples in the chapter, as you read.

2. Create an OC curve and an ARL curve for the $$\overline{X}$$-chart you create using the data from Table 14.2 in (Christensen, Betz, and Stein 2013).

3. Use the $$\verb!qcc!$$ function to plot the $$p$$ chart created using the data from Table 14.1 in (Christensen, Betz, and Stein 2013). Why do the control limits vary from subgroup to subgroup?

4. If variables control charts (such as $$\overline{X}-R$$ or $$\overline{X}-s$$) were used and the process was shown to be in control with a $$C_{pk}=1.00$$, then

1. What proportion nonconforming or out of the specification limits would be expected? (i.e., show how the values in Table 4.2 were obtained)

2. If an attribute $$p$$-chart were used instead of the $$\overline{X}-R$$ or $$\overline{X}-s$$ charts, then what subgroup size would be necessary to show the same in control proportion nonconforming as the variables charts in a) with $$C_{pk}=1.00$$?

1. Consider the data in Table 4.3 taken from the Automotive Industry Action Committee (TaskSubcommittee 1992).

Table 4.3 Bend Clip Diameters

Subgroup D D D D
1 .65 .70 .65 .65
2 .75 .85 .75 .85
3 .75 .80 .80 .70
4 .60 .70 .70 .75
5 .70 .75 .65 .85
6 .60 .75 .75 .85
7 .75 .80 .65 .75
8 .60 .70 .80 .75
9 .65 .80 .85 .85
10 .60 .70 .60 .80
11 .80 .75 .90 .50
12 .85 .75 .85 .65
13 .70 .70 .75 .75
14 .65 .70 .85 .75
15 .90 .80 .80 .75
16 .75 .80 .75 .80
17 .75 .70 .85 .70
18 .75 .70 .60 .70
19 .65 .65 .85 .65
20 .60 .60 .65 .60
21 .50 .55 .65 .80
22 .60 .80 .65 .65
23 .80 .65 .75 .65
24 .65 .60 .65 .60
25 .65 .70 .70 .60
1. Make an $$R$$-chart for the data. If any points are out of the control limits on the R-chart, assume that assignable causes were discovered and added to the OCAP.
2. Remove the subgroups of data you found out of control on the $$R$$ chart, and construct $$\overline{X}$$ and $$R$$ charts with the remaining data.
3. Check for out of control signals on the $$\overline{X}$$ chart (use the Western Electric Rules in addition to checking points out of the control limits).
4. Assume that assignable causes were discovered for any out of control points found in subgroups 18 to 25 only (since it was discovered that out of spec raw materials were used while making those subgroups).
5. Eliminate any out of control subgroups you discovered between 18 to 25, and then make $$\overline{X}$$ and $$R$$ charts with the remaining data.
6. If there are no out of control signals in the control charts you created, then calculate the appropriate capability index and a graph similar to Figure 4.7.
7. Is the process capable of meeting customer requirements? If not, what is the first thing you might suggest to improve it.

### References

Borror, C.M., and C.W. Champ. 2001. “Phase 1 Control Charts for Independent Bernoulli Data.” Quality and Reliability Engineering International 17: 391–96.

Burke, S. E., and R. T. Silvestrini. 2017. The Certified Quality Engineer Handbook 4th Ed. Milwaukee, Wisconsin: ASQ Quality Press.

Christensen, C., K.M. Betz, and M.S. Stein. 2013. The Certified Quality Process Analyst Handbook. 2nd ed. Milwaukee, Wisconsin: ASQ Quality Press.

Deming, W.E. 1986. Out of the Crisis. Cambridge, Mass.: MIT Center for Advance Engineering Study.

Joiner, B. L. 1994. Fourth Generation Management - the New Business Consciousness. New York, NY: McGraw-Hill, Inc.

Lawson, J. 2015. Design and Analysis of Experiments with R. Boca Raton, Florida: Chapman; Hall/CRC.

Mitra, A. 1998. Fundamentals of Quality Control and Improvement. Upper Saddle River, New Jersey: Prentice Hall.

Montgomery, D.C. 2013. Introduction to Statistical Quality Control. 7th ed. Hoboken, New Jersey: John Wiley & Sons.

Shewhart, W.A. 1931. Economic Control of Quality of Manufactured Product. New York: D. Van Nostrand.

TaskSubcommittee, A.I.A.G. 1992. Statistical Process Control (Spc) Reference Manual. Troy Michigan: Automotive Industry Action Group.

Wickham, H., and G. Grolemund. 2017. R for Data Science-Import, Tidy, Transform, Visualize and Model Data. Sebastopol, CA: O’Reiilly Media Inc.