Hello and welcome to the sixth lesson of the Certified Six Sigma Green Belt Course offered by Simplilearn. This lesson will cover the details of the control phase. The Control Phase is the last phase in the Six Sigma cycle. Control often leads back to the Define phase where new projects might be initiated. Let us explore the objectives of the lesson in the next screen.

After completing this lesson, you will be able to describe Statistical Process Control (SPC) and explain control charts. Next, you will be able to discuss control plan strategies and develop a control plan. Finally, you will be able to describe Total Productive Maintenance (TPM) and discuss visual factory. Let us start with the first topic in the following screen.

In this topic, we will discuss Statistical Process Control (SPC) in detail. Let us start with an introduction to Statistical Process Control in the following screen.

Walter A. Shewhart developed Statistical Process Control in 1924. SPC aids in the visual monitoring of a process and controlling its parameters by placing statistical measures around the process outputs or input variables, for example, control charts. SPC heavily depends on data collection. The following are the benefits of SPC. SPC separates the special and common causes of variability. We will learn about this in detail in the forthcoming slides. SPC quickly recognizes the unexpected changes in the process output. Since the data is displayed visually, it is easy to identify the changes in the variables. Processes in general do not have an output which will give a straight line when represented graphically. Hence, there will be some variation in the process which could be within the specification limit of the customer. For variables where specification limits are not known, SPC helps to identify the stable zone so that the process capability can be calculated. SPC also provides useful external information for the continuous improvement of the process and helps in monitoring the process online. Note that the concept of Process Control can also be used in the Measure Phase, when you check for data stability. In the next screen, let us discuss the common cause variation and special cause variation.

The common cause variation is the variation that can be usually seen in the process. Some of the examples are minute variations in raw materials, variation due to manual interventions in manual processes, response type from machines or systems, etc. These are situations which invariably the organization has to go through. However, these variations have to be within the process limit. The special cause variation is the variation that cannot be normally seen in the process. Some of the examples for special cause variation are machine or system crash, delay in supply of raw materials, and huge variations in the raw materials. To learn about the features of common cause variation and special cause variation, click the respective tabs. Common cause variation is a part of the process and the organization is aware of - its presence. The frequency of its occurrence in the process is very high due to which it can be easily predicted. The common cause variations are repetitive. Identifying such causes and removing them from the process will require huge investment and time. The common cause of variation will be within the tolerance or specification limits. Special cause variations are not part of the process, hence the occurrence of the special cause variation affects the regular process and defects are produced. The frequency of its occurrence is less and is hence unpredictable. The special cause variations are non-repetitive. Identifying the special cause variation is comparatively easy, as the process might have shifted from the regular process. And the investment required for the removal of the special cause is less. Special cause variation affects the flow of the process due to which the defects appear.

Let us compare common cause variation and special cause variation in this screen. The frequency of occurrence of common cause variation is high, whereas that of special cause variation is less. Unlike special cause variation, the common cause variation is predictable. Common cause variation is part of the process, whereas special cause variation is not. High investment is needed to remove common cause variation, on the other hand, the investment is relatively less to remove special cause variation. The causes for the common cause of variation are repetitive and the causes for the special cause of variation are not. The identification of common cause variation is difficult when compared to special cause variation. We will learn about rational subgrouping in the next screen.

Rational subgrouping refers to the selection of subgroups or samples in a way that if assignable causes are present, chance for differences between subgroups will be maximized, and chance for difference due to assignable causes within a subgroup will be minimized. There are two general approaches for constructing rational subgroups. Click the tabs to learn more about each approach. In the first approach, the sample consists of units produced at the same time, that is, they are consecutive units. The Primary purpose of this type is to detect process shifts. A process shift happens over time and is reflected in the variation of the output variables. In the second approach, the sample consists of units that are representative of all units produced since the last sample taken. Sampling is random for all process output over the sampling interval. This type of approach is often used to make decisions about the acceptance of a product. This is also effective in detecting the shifts of the output variable to an out-of-control state and back into an in-control state between samples. It is important to be careful because you can often make any process appear to be in statistical control by stretching the interval between observations in the sample.

Let us learn about control charts and analysis in this screen. Control charts are useful for tracking process statistics over time and detecting the presence of special causes. A process is in control when most of the points fall within the bounds of the control limits, and the points do not display any nonrandom patterns. Since the data is depicted visually in a control chart, it is easy to find the differences between common cause and special cause. Let us learn about setting the control limits in the next screen.

A standard control chart uses control limits at three standard deviations of the mean (?mean) (pronounce as sigma mean) from the data’s grand average (X-double bar, or average of the sample averages or ? [pronounced as myoo]). The probability of an out-of-control point when the process has not changed is only 0.27%. 99.73% of the normal data lies within three standard deviations from the mean. Hence, the rest of 0.27% is outside the three standard deviations. However, if the control limits are set at two standard deviations, it increases the chance of type I or alpha errors. Type one error is rejecting the product when it is not defective. If control limits are set at four standard deviations, it increases the chance of type II or beta errors in which a defective product is accepted by the quality assurance team. A Control chart should take into consideration both the error types when setting the control limits. Note that Walter Shewhart had set three Sigma limits on control charts with the belief that when the process goes beyond these limits, the process needs correction. In the next screen, let us look at some of the common rules for control chart analysis.

An Out-of-Control (OOC) (Pronounce as: o-o-c) condition is indicated if one of the following is true: If one point is outside the Control Limits (either above the UCL or below the LCL), then p(f) (pronounced as p of f) equals 0.27 percent. If eight consecutive points are above the center line (CL) or consecutively below the CL, then p(f) (pronounced as p of f) equals (0.5)8 (pronounced as 0.5 to the power 8) which is equal to 0.39 percent. If six to eight points are consecutively increasing or consecutively decreasing, then p(f) (pronounced as p of f) equals (0.5)6 (pronounced as 0.5 to the power 6) or (0.5)8 (pronounced as 0.5 to the power 8) which is equal to 1.6 percent to 0.39 percent. If two out of three points are within 1 ?mean (pronounced as sigma mean) of either the UCL or the LCL ( that is, within the outside 1/3rd (pronounced as one third) of the distance between the control limits and the CL, then p(f) (pronounced as p of f) is (3!/(2!1!)(0.023)2(0.477) (pronounced as 3 factorial divided by 2 factorial and 1 factorial whole multiplied by product of 0.023 squared and 0.477) which is equal to 0.08 percent for one side. There are different ways to choose an appropriate control chart depending on the type of data, such as continuous or discrete. First, let us understand how to choose an appropriate control chart for continuous data in the following screen.

There are two ways in which sampling of continuous data can be done. First is through individual data points, which means that one sample is pulled at a fixed time interval or frequency. For this type of data, you can use Individual and Moving Range or ImR (Pronounce as: i-m-r) type of control chart. This chart depicts the Variability of individual characteristics over time. The second way of sampling of continuous data is subgroups. If sampling is done by taking periodic grouped data, then two types of control charts can be created. If complete data points are available, where the sample size n is between 2 and 9, then we can create X bar and R chart or x bar r chart, where X bar is the mean and R is the range of the data. If the standard deviation of the data points is available and n is greater than or equal to ten, then x bar and s chart or x bar s chart should be created, where x bar is the mean and s is the standard deviation of the data points. Next, let us understand how to choose an appropriate control chart for discrete data in the following screen.

Discrete data can be divided into defectives and defects. np charts are used for defectives that have a constant subgroup size and is represented as number of units rejected. A p chart is used for defectives which have a varying subgroup size and is represented as percentage of units rejected. A c chart is used for defects that have a constant subgroup size and is represented as number of defects. A u chart is used for defects that have a varying subgroup size and is represented as average number of defects per opportunity. In the next screen, we will discuss the principles of X bar chart.

X bar means average, that is, the average of each subgroup of the data. The data is divided into subgroups and average is calculated for each subgroup. The subgroup average data will be plotted on the X bar chart. Some of the principles of the X bar R and X bar s chart are as follows. X bar R and X bar s charts are each two separate charts of the same subgrouped data. One chart is the x bar and the other is the R chart or S chart. An X bar chart is the plot of the means of the subgrouped data. This chart shows inter-subgroup variation or between-subgroup variation. R chart is the plot of the value of subgroup range, where the R Chart shows intra-subgroup variation and S chart is the plot of the standard deviation of the subgroup range. Both these types of charts are the most sensitive charts to track and identify assignable causes of variation. In X bar control charts, the control limits are calculated based on mean of means, range or standard deviation, and other factors that we will discuss in later slides. The value of the control limits calculated is very close to three standard deviations. Also note that you can plot Xbar R and Xbar S charts with any type of data. In the next screen, we will learn to define UCL and LCL in an X bar R chart.

In the expressions shown on the screen, X doublebar is the grand average and R bar is the average of the range. The Upper Control Limit or UCL and Lower Control Limit or LCL for an X bar R chart are calculated by using the following expressions: The upper control limit for X Bar equals X Doublebar plus A2 multiplied by R Bar Similarly, the lower control limit for X Bar equals X Doublebar minus A2 multiplied by R Bar Next, you can calculate control limits for range using the expressions, which are also shown in the lower part of the image. The Upper control limit for R chart equals D4 multiplied by R Bar Similarly, the lower control limit for R chart equals D3 multiplied by R Bar Here, A2, D3, and D4 are values from the control chart table. In the next screen, we will learn to define UCL and LCL in an X bar s chart.

S stands for standard deviation of each subgroup data. The data is divided into subgroups and the standard deviation is calculated for each subgroup. This data also shows the inter-subgroup variation or variation within the group accurately. In an S chart, the standard deviation of each subgroup is plotted. The Upper Control Limit or UCL and Lower Control Limit or LCL for X bar s chart are calculated by using the following expressions: The Upper control limit equals X double bar plus A three multiplied by s bar. Similarly, the lower control limit equals x double bar minus A three multiplied by s bar. Next, calculate control limits for range using the following expressions. Upper control limit equals B4 multiplied by s Bar Similarly, lower control limit equals B3 multiplied by s Bar Here, values for A3, B3, and B4 are constant and are taken from the control chart table. X bar and S charts are used to track process variation where the subgroup sample size is 9 or more. In the next screen, let us look at an example of Xbar R and subgroup data.

Establish one sigma process limits for the data set shown. Use the table of control chart constants for the values of A2, D3, and D4. Consider the table shown here. The data is in subgroups with five samples in each subgroup. Subgroup is represented by SG and sample data is represented by X. The table for control chart constant values is also given here. In the Minitab application, go to stat, then control charts, followed by variable chart for subgroups, and then choose x bar R. Click the answer button given on the screen to learn how to analyze and construct the x bar r chart, using this data. X bar is the sample data in each sub group. Therefore, x bar for SG1 is the mean of X1 to X5. Range is the difference between the maximum and minimum of X1 to X5. The central line in the x bar chart is the average of all x bars. The Central line in an R chart is the average of all the ranges calculated. As depicted on the graph of the x bar and R chart, SG 6 is the point of change in the process from below the centerline to above. The process can be said to be in control as no point is outside the control limits of the graph. The dots connected on the X bar and R chart are the subgroups. Notice how the points 6 and 7 in the sample mean area of the X-bar chart are more than halfway toward the UCL from the Center Line or CL. It is important to check if they violate rule number 4, which is, 2 out of 3 points are within 1 sigma of the UCL. In this case, 1 sigma is (46.49-45.13)/3 = 0.45 (Pronounce as: forty six point four nine minus forty five point one three whole divided by 3, equals zero point four five) or (0.58*2.36)/3 = 0.46 (Pronounce as: zero point five eight multiplied by two point three six whole divided by three equals zero point four six), where 0.58 is the A2 factor. The threshold for being within 1 sigma of the UCL is 46.0. Point 6 (which has a mean value of 45.9) is not above 46.0. Therefore, rule 4 is not violated. Next, notice how points 10 and 11 on the R chart are also more than halfway toward the UCL. Calculate a similar threshold, (2.11*2.36)-(((2.11*2.36)-2.36)/3)= 4.1 (Pronounce as: two point one one multiplied by two point three six minus two point one one multiplied by two point three six whole minus two point three six whole divided by three equals four point one) where 2.11 is the D4 factor. Both SG 10 and SG 11 at 4.2 are above 4.1, hence rule #4 is violated here and point 11 is indicating that the process is out of control. In this case, something significant has changed and a special cause is evident.

In this screen, we will understand Xbar S and subgroup data with an example. The data shown here is in subgroups with 10 samples in each subgroup. In the X bar chart, subgroup is represented by SG and sample data is represented by X. The table for control chart constant values is also given here. Find out if the process is in control using this data. In the Minitab application, go to stat, then control charts, followed by variable chart for subgroups, and then choose x bar s. Click the answer button given on the screen to learn how to analyze and construct the x bar s chart, using this data. As depicted on the graph, the Xbar chart point SG 10 is the variation of the point from the mean. The dots connected on the X bar and S chart are the subgroups. In the Xbar chart, the points 4, 10, and 23 have more variation from the center. These points can be analyzed further. The points are within the limits and hence the process is in control.

Let us discuss the ImR chart principles in this screen. ImR Charts are two separate charts of the same data. An I chart is a plot of the individual data points and an MR chart is a plot of the moving range of the previous individuals. ImR charts are sensitive to trends, cycles, and patterns. ImR charts are also sensitive to normality. The data must be normal for the ImR chart to give you credible and valid results. ImR charts are used when subgroup variation is zero or no subgroups exist. ImR charts are best used with data points from destructive testing, batch processing, or summary data from a time period (day, week, month for example). Control limits of an ImR chart are calculated using the same method as an X bar R chart. Let us look at an example of ImR and individual data in the next screen.

At Nutri Worldwide Inc, the QC department measures the strength of its milk cartons to assure quality packaging. The test is done every one hour and the data is given here. Using this data, find out if the process is in control. Since the data is individual data, an ImR chart will be used here. This is an example of a destructive test. However, if several samples are tested, they can grouped, and X-bar & R charts can be used. In the Minitab application, go to stat, then control charts, followed by variable chart for individuals and choose I-MR. Click the answer tab to learn how to analyze and construct an ImR chart. Moving range is the absolute value of difference between the last two data points. As per the table, MR of the second data point is the difference of 2.38 and 2.06, which is 0.32. Similarly, the complete table is formed. The resultant graph is also shown here. The following can be analyzed from the graph: In an I chart, point number 16 is closer to the upper limit. Further analysis must be done to find out the reasons. No points are out of control in this process.

Let us look at the IT or ITES industry example of the ImR chart in this screen. The data given was used to study the number of calls handled per hour in call center operations. This data was studied using ImR charts to check if the process is in control. The data, ImR chart, and analysis are given on the screen. The following can be analyzed using the given data. In an I chart, all the points are closer to the mean value. Hence, the process is well within control. In an MR chart, there are a few points closer to LCL. The process variation can be analyzed further. However, no point is outside the control limits. In the next screen, we will learn about control charts for attribute data.

The type of control chart that needs to be selected depends on two factors. First, the sample size and second, the data-type which is known, such as defects or defectives. Based on these parameters, there are four types of control charts for Attribute data. If the sample size is consistent and the data for data type defectives is provided, the best chart that can be used will be the np chart. If the sample size is consistent and the data type available changes from defectives to defects, the control chart used will be the c chart. Suppose the sample size is inconsistent, the control charts used will be p chart for data type defectives and u chart for data type defects. Control limits may be constant, such as X-bar and R charts - for np and c charts, or they may vary depending on sample size for p and u charts. Let us discuss the np chart principles in the following screen.

An np chart is used to measure the non-conforming proportions or number of defectives within a standardized group size. Some of the principles of the np chart are as follows. The expectation is that each of these groups must have the same proportion. The chart follows the binomial distribution. For an np chart, large subgroups are required, at least 50. In addition, the subgroup size must be constant. When the subgroup size is the same, there’s no need to calculate p and then np in order to plot data points on a control chart, as np will act as c, equaling the number of nonconformities in each group. Control limits will be constant in an np chart. Click the button given on the screen for some important formulas of the np chart. In order to calculate np, one collects data for n each time and the number of defectives. The proportion of p equals D divided by n. And np equals n multiplied by D by n, where D stands for defectives. The control limits are calculated by finding the average number of defectives (np-bar) and adding or subtracting 3 standard deviations. The standard deviation for np is the square root of np-bar multiplied by the remainder proportion (that is, 1—p-bar).

Let us understand np chart and uniform subgroup size with an example in this screen. The sourcing department at Nutri Worldwide Inc. measures 125 purchase orders daily and records the number of entry errors in them. The tabulated data is given here. Find out if the order entry process is in control. Since the data has a constant subgroup size of orders processed and are all defectives, you can use an np chart. In this example, assume that there’s only one error per order possible, that is, the number of errors equals the number of bad orders or the number of forms with errors, which is the number of defectives. Otherwise, you will need a c chart when errors are the same as defects. Click the button given on the screen to learn how to analyze and construct an np chart using this example. In the table shown here, np is the number of defective items in any subgroup. np bar is the mean of all such np values. The np points are now plotted against the upper control limit and lower control limit. Analysis of this chart shows that point number 12 is beyond the control limit of three standard deviations. To find out the reason, an analysis must be done and corrective measures must be taken if required. Point number 12 is therefore out of control in this process.

Let us learn about the principles of p chart in this screen. p chart is used to measure the non-conforming proportion or defectives. The principles of np chart and p chart are similar. Some of the principles of the p chart are as follows: The expectation is that the same proportion exists in each subgroup. A p chart also follows binomial distribution. The subgroup size should at least be fifty; however, it does not have to be constant. Control limits may vary from subgroup to subgroup based on the subgroup size. Control limits are three standard deviations from the mean. The control limits for a p chart are calculated by adding or subtracting 3 standard deviations to the average proportion (p-bar). The standard deviation is the square root of the proportion multiplied by the remainder proportion (that is, 1—p-bar), the whole divided by the number of samples in the subgroup. When n changes, the control limits change. Let us understand the p chart and varying subgroup size with an example in the next screen.

The sourcing department in Nutri Worldwide Inc measures the number of entry errors on a daily basis. The data has been tabulated and is presented here. Find out if the order entry process is in control. Since the data has varying subgroup size and the errors are defectives and not defects, a p-chart will be used. Click the answer button to analyze and construct the P Chart using this example. The proportion of defectives has been calculated by dividing the number of errors by the subgroup size. P bar is the mean of all P values calculated. Note that the control limits are not constant as the control limits are a function of subgroup size. Let us analyze this p chart. In the p chart, point number 12 is outside the control limit of three standard deviations or sigma level. This point should be investigated for any special cause variation. Hence, point number 12 is out of control in this process.

In this screen, we will discuss the principles of a c chart. c chart is another type of control chart for attributes. To form a c chart, you measure the number of occurrences of non-conforming defects. Some of the principles of a c chart are as follows: A C chart follows a Poisson distribution given by c ?=? (pronounced as c-bar equals lambda). It is used when the sample size is fixed or the area of opportunity is constant, for example in a unit or invoice. A C chart is used to identify attribute data for the sample. For example, how many orders were passed versus failed. The Poisson distribution is related to the average number of defects in a given period. You can calculate the probability of certain levels of defects based on that average (lambda or c-bar). However, the area of opportunity (or number of opportunities) has to be constant from one subgroup to another. For example, equal number of hours in a working day. In a c chart, each count is a subgroup of samples and the control limits will be constant. One should have at least 20 or more subgroups for analysis using C-charts. The control limits are calculated by using the average level of defects (c-bar) plus or minus 3 times the square root of that average. Let us look at an example of the c chart in the next screen.

The table shown here lists the final inspection grades of tinted glasses on the number of white specs. This is calculated based on the number of “white specs” on each glass pane. The Product is priced as per its grade. The higher the number of white specs, lesser is the quality grade of the product. White specs are defects, not defectives, and are measured over a constant sample area; so in this example, a c-chart will be used. Using the data in the table, analyze if the process is in control. Click the answer button to analyze the data in the table and construct a c chart using this example. 34.1: Once you build a control chart for the data, you can observe that there are two data points which are outside the upper control limit of 36. There are also 4 data points which are outside the lower control limit of 8. Points 2, 3, 4, 12, 13, 16, and 17 are out of control in this process. Points 2, 3, 17 are outside the upper control limit of 36 and points 4, 12, 13, 16 are outside the lower control limit of 8. Now, evaluate whether points 7 and 9 break rule number 4, which is 2 out of 3 points within 1 sigma of the LCL. The threshold is 8.236+sqrt (22.45) = 13 (pronounced as “eight point two three six plus square root of twenty two point four five equals thirteen). Points 7 and 9 (10, 11 defects respectively) are below the threshold; therefore rule 4 is violated and the process is also out of control at point 9. Points 18 and 19 are under the UCL. You can calculate and find out the threshold is about 31. Both points are above that number, so we would say the process is still out of control at point 19. We would want to find the special cause that is creating excessive specks. In this c chart, the process is not stable and many points go beyond 3 sigma control levels. Analysis must be done to find the reason and take corrective action. In this example, there are different out of control points. One of the reasons for variability is that people are over-reacting to the data points. A correction perhaps was made at point 2, since there was not much of a change with point 3. There was another correction and the LCL was overshot in point 4. Therefore, then it was corrected along with the other two points that were going up. However, as indicated by point 9, which is too low, another correction must have been made. Too close to the top on the next point means another correction. Similarly, correction must have been done due to the LCL overshot. This indicates that the process is not in control. The overall process is less than 3-sigma levels and hence needs to be analyzed to identify improvement areas.

In this screen, we will discuss the principles of a u chart. A u chart is also used to measure the number of non-conforming defects. The principles of a c chart and u chart are similar. Some of the principles of a u chart are as follows: Similar to the c chart, a u chart follows a Poisson distribution. It is also used to identify attribute data for the sample. Unlike the C-chart, the U-chart is used to measure defects when the sample size is not fixed. In case of the u chart, the control limits of the process may vary. Each count is a subgroup of samples and one should have 20 or more subgroups for analysis using U charts. Similar to the c-chart, a “u-chart” will follow the Poisson distribution. In this, the probability of a defect is related to the average rate of defects (lambda or defects per opportunity). Unlike the c-chart, the area of opportunity (or number of opportunities) does not have to be equal in each subgroup. You can audit different numbers of forms. For example, the number of operational hours may be different for a piece of equipment each day. You can calculate the control limit as u bar plus and minus 3 standard deviations. Standard deviation is the square root of an average defect per opportunity (that is, u-bar divided by a). Since the u chart follows Poisson distribution, the probability of certain number of defects is known. Let us look at an example of the u chart in the next screen.

The plastics operation team counts defects after a logical “run”. Each run is undetermined in length. Once started, the run continues until all material is used up. Looking at the table of data, is it possible to identify if the process is in control? Since the count of defects has a varying area of opportunity and the length of runs is not constant, a u chart will be used.. Click the answer button to analyze the data in the table and construct a u chart using this example. Once plotted within the UCL and LCL, you can identify that only one point, point number 18, is outside of the UCL. Analysis must be done to find the reason and corrective action must be taken if necessary. Hence the process is out of control. The exact data points for plotting are calculated by dividing the count of defects by the number of units produced. This provides the number of defects per unit of production.

Let us proceed to the next topic of this lesson. In this topic, we will discuss a control plan in detail. Let us start with a control plan and its uses in the following screen.

A control plan is a written summary description of the system for controlling a process. It describes actions required to maintain the “desired state” of the process and minimize the process and product variation. It is a living document, which evolves and changes with the process and product requirements. It is also considered a knowledge-transfer document. A control plan can be created for a process, a step in the process, or even a piece of equipment which is used in the process. A control plan provides a single point of reference for understanding process characteristics, specifications, and Standard Operation Procedures also known as SOP (Pronounce as: s-o-p) for the process. It enables assignment of responsibility for each activity within the process, which in turn ensures the process is executed smoothly and is sustainable in the long run. The next screen will describe the control plan strategy.

A good control plan needs to be based on a well thought out strategy. A good control plan strategy should minimize the need of tampering with the process. It should also clearly state the actions to be taken for out-of-control conditions. It should raise appropriate alarms to indicate the need for Kaizen activities. A control plan should describe the training requirements to ensure that everyone on the team is familiar with the standard operating procedures. In case of an equipment control plan, it should also include details on maintenance schedule requirements. In essence, a good control plan should clearly describe what actions are to be taken, when to take them, and who should take them. This in turn provides a documented approach to be followed in case of variations and thereby reduces the “fire fighting” syndrome. Let us define what needs to be controlled in the next screen.

In order to define a strong control plan, it is important to define what needs to be controlled. Each process consists of the basic formula - Y as a factor of X. In order to control the process, you need to monitor and control both X and Y. The X factors are called the Key Performance Input Variables, also known as KPIV. The output Y is called Key Performance Output Variable, also known as KPOV. A control plan controls the KPIV to ensure the desired state for the KPOV. Both the input and output variables need to be controlled and monitored respectively. Merely monitoring the KPOV, the output, is not an effective way to control a process and will not result in an efficient process. In the next screen, let us learn how to identify KPIVs (pronounced as K-P-I-Vees).

The KPIVs (pronounced as K-P-I-Vees) or the inputs to the process can be identified using various sources like Failure Mode and Effects Analysis or FMEA, Cause and Effect Matrix or Diagram and Cause Verification matrix, Multi-vari studies, Regression Analysis, and Design of Experiments or DOE (Pronounce as: d-o-e). These techniques have also been covered in this course. Let us proceed to the next screen to learn about control plan tools.

Once you have the variables and the control plan in place, you can use various tools to control the process. Some popular tools to develop and execute control plans include Control charts, Measurement System Analysis (MSA), Error proofing, Standard Operating Procedures (SOP), and Preventive Maintenance (PM). Click each tool to know more. Control charts are useful for tracking process statistics over time and detecting the presence of special causes. Measurement System Analysis is a technique that identifies measurement error (variation) and sources of that error in order to reduce the variation. Error proofing is a common Lean technique and is also known as Poka-Yoke (Pronounce as: poh-kah yoh-keh). It involves implementation of fail-safe mechanisms within a process to prevent it from creating defects. It is based on the principle that it is better to prevents defects in the first place rather than correcting them later. An SOP is a written document or instruction listing all the steps and activities of a process or procedure in detail. The goal of the SOP document is to provide clear information to the user of the process so that there is no ambiguity or gaps in the way the process is executed. Preventive Maintenance refers to inclusion of Preventive Maintenance as part of documented scheduled maintenance of the process or equipment. This is an important element of process control and ensures the system functions smoothly. PM often requires a prompting mechanism that triggers the PM activity.

Let us learn about developing a control plan in this screen. In order to define a control plan, it is important to start with the basic understanding of the process. After understanding the process, you should form a multi-functional team that includes representation from most important functional areas of the process. This team will be responsible for controlling the process. You can use multiple tools to develop a control plan, such as process flow diagram, FMEA, special characteristics including critical and significant characteristics, control plans or lessons learned from similar parts or processes, technical documentation, validation plan results, optimization methods, and team knowledge of the process to understand and document. We will continue this concept in the next screen.

To define the control plan in detail you should ask the following questions: What do you want to control? How often do you need to measure the process? Do you have an effective measurement system? What is the cost of sampling? How much shift can you tolerate? Who needs to see the data? What type of tool or chart is necessary? Who will generate the data? Who will control the process? What are the system requirements for auditing and maintenance? Answers to these questions should be included in the Control Plan. These answers will provide information on how the process will be controlled and who does what as part of process controlling. In the next screen, we will learn about choosing the right level of control.

While developing the control plan, it is important to identify the level of control that should be built into the process. While it is good to have the highest level of control through techniques like Process Design for Six Sigma (PDFSS), Poka-Yoke (Pronounce as: poh-kah yoh-keh), Statistical Process Control (SPC), and Operational Method Sheets, (OMS) it is also important to note that these sophisticated techniques will take a structured process to be implemented. However, once implemented, the amount of effort expended in controlling the process will decrease considerably. For simpler and standard processes, techniques like verbal or written instructions should be sufficient. For a large or critical process, the amount of control should be increased. Let us look at an example of a transactional control pan in the next screen.

A sample transactional control plan is given on the screen. Apart from the standard fields like Document Number, Revision Number, etc., a Control Plan includes the following sections: Process Step Characteristic or Parameter CTQ or CL Specification or Requirement Measurement Method Sample Size Frequency Who Measures Where recorded Decision Rules or Corrective Action, and Reference Number We will learn about each column or section of this control plan in the next screen.

Click each section of the transactional control plan to learn more. Process Step The first section in the control plan is the Process Step. This section is used to highlight the name of the process and distinguish a process from a process step or a piece of equipment. Control plans, when prepared for a complete set of processes and equipment in the system, also help in standardization within the various processes of the system. Characteristic or Parameter The Characteristic or Parameter section identifies the KPIV or KPOV to be measured to ensure the process is in control. The metric to be measured is determined through team discussions. These discussions should involve the key project team members. Once defined, this parameter can be standardized across similar processes or equipment to measure the performance of the process. In this sample, the Metric used to control the Purchase Order process is the time needed to enter each purchase order in the system. CTQ The parameters defined in the previous section are often part of the CTQ parameters of the process, as identified in the Define phase. These parameters help in measuring the impact on process performance and have known to impact process performance negatively if not measured and controlled. Specification or Requirement The next section is used to define the specification or requirement of the process, including the target goal of the process. This goal maps to the Metric defined in the earlier step. The goal for the process should be determined through team discussions, understanding the technology, and the history of the process. The goal should start with the current specifications unless changed through team discussions. In this sample, the target goal is to complete entry of each Purchase Order in 3 days. Measurement Method The measurement method section defines the tool or gauge that will be used for measurement of the metric. While defining which method to be used for measurement, one should consider the following factors: availability of the equipment for the process, calibration and MSA needs of the equipment, training needs on the tool or method, supporting Manufacturing Performance Index (MPI), and operational blueprint requirements. The final method or tool chosen should be able to capture the most accurate measurement of the metric. In this sample, the tool used to capture the time of entry of each purchase order is the server time stamp in the access database. These stamps will be used to calculate the total time used for completely entering the purchase order in the system. Sample Size The sample size refers to the number of data entries that will be used to calculate the metric. In this sample, all the data entries in the access database will be used for calculation. Frequency The frequency section defines the frequency at which the metric will be captured and analyzed. In this sample, the data will be analyzed on a weekly basis. As the same process is measured regularly, it is assessed over time and the process requirements themselves might be modified. Who Measures The next section defines who will measure the metric based on the frequency defined earlier. The person selection will be based on the need of the process, location of the person and the process or equipment, and the skills of the person. How the person reacts to the metric generated is critical to the success of the control plan. In this sample, the purchase order administrator will be measuring the process. Where Recorded The where recorded section is used to indicate where the metric will be recorded. This can be done through control sheets like charts, plots, log, or check sheets. Control methods should be customized for different floor or functional applications. These methods must use as much quantitative information as possible to provide objective information. Paper charting may be preferable as a starting point when compared to using a computer. In this sample, the metric will also be recorded in the access database. Decision Rule or Corrective Action The decision rule or corrective action section identifies the actions to be taken for the out of control specification situation or situations. This column should include references to process support documentation like corrective procedures, troubleshooting maps, etc. In this sample, the decision rule first requires the need to analyze the reason for the length of time entry, followed by a corrective action, if required, to control the length. Let us look at the last section of the control plan, reference number, in the next screen. Reference Number The reference number section is used to facilitate access to documented or corrected procedures against each corrective measure identified in the previous section.

Let us take a look at a sample control plan of the manufacturing sector in this screen. Notice the difference in specification requirements, measurement methods, and corrective actions identified from a process control plan discussed in the earlier screen. Note that there are specifications for the Part Dimension and inputs (Cavity Pressure and Coolant Flow). The coolant flow spec is indicated by color coding on the gage. However, it is a spec based on discrete data (attribute data) of green or yellow or red. It is recorded on a check sheet. There is no control chart. Assume the color zones are based on control limits due to the note that says an adjustment is made. A check sheet will provide only rule number 1 analysis for out-of-control (yellow or red is out-of-control). Also note the spec limits are on the coolant flow and determine the process capability. Perhaps the coolant flow is too variable and it is not critical for part dimension control and the spec limits can be wider. From the way the document is written, it is not clear how closely the operator needs to monitor the coolant flow, and how often he or she can expect to make adjustments. The less capable the process is at controlling flow, the more adjustments the operator will be making. Apparently, there has been some analysis that says coolant flow should be corrected when it is in the yellow zone. However, in order to replace the gage, you need to know what the Six Sigma analysis has determined, if the required limits on the coolant flow are in order to produce parts that meet the dimensional spec, what is to be done when a new gage is installed, how will someone know where to put the green or yellow or red zones, and if there is a reference procedure. In the next screen, let us take a look at a sample control plan of an IT or ITES sector.

A sample control plan for Code Review Process in IT/ITES is shown on the screen. It is recommended to take a moment to go through the contents in this control plan for better understanding. Let us proceed to the next topic of this lesson in the following screen.

In this topic, we will discuss Lean tools for process control in detail. Let us learn about Total Productive Maintenance or TPM in the following screen.

Total Productive Maintenance or TPM is a method commonly used in manufacturing industries. The aim of this method is to eliminate deficiencies from machines and equipment to minimize or remove the defects and decrease or avoid the downtime. TPM also emphasizes maintenance and improvement of the process, system, and environment. TPM increases the operational efficiency of equipment. It is the key operational activity for managing quality. Let us look at the elements of TPM in the next screen.

The 5 core elements of Total Productive Maintenance are shown here. Maintenance excellence: This refers to the maintenance of machines and equipment by greasing, cleaning, general inspection, and minimum preventive maintenance, which can be taken care of by the production operators. Conduct planned maintenance: This refers to developing and executing planned maintenance activities based on factors like time, cost, and productivity. System Operating Procedures or SOP are maintained and established for each equipment. Equipment improvement: Measures to improve the performance and efficiency of the equipment are taken using different methods, such as 5S, 5 Why analysis, or Kaizen activities. Education and training: A systematic training must be provided to all the employees to maintain the equipment and increase productivity. Equipment design excellence: The new equipment is designed and the equipment that requires less maintenance and that is easily maintained will be identified. In the next screen, let us discuss the uses of TPM to control the improved process.

TPM plays an important role in controlling the improved process. The main uses are described here. Spare parts management: TPM helps in maintaining and storing spare parts of the equipment using the 5S method. Measures for downtime: TPM collects the data for downtime and conducts Root Cause Analysis (RCA). It also applies the countermeasures which reduce the mean time to repair. Support and guidance for operators: TPM maintains SOPs and trains the employees to maintain the equipment. It provides constant support and guidance to the employees on maintenance and management of the equipment. Let us learn about visual factory in the following screen.

Visual factory is a term used to describe a Lean Production environment where charts and signs are used to display the information. It refers to managing the factory by vision. Highways and airports are examples, where an individual can understand the directions without external assistance. The purpose of a visual factory is to make the immediate status of activities clear to all the employees. It also enables all the involved people to understand how the plants are working individually. By using and implementing visual factory techniques, faster and evident results will be visible. Visual factory makes the work area self-explanatory at a glance. It helps is displaying real time information and data to the workforce. Visual factory also helps in standardizing the process and eliminating waste from it. Let us look at the elements of visual factory in the next screen.

The 3 core elements of visual factory are mentioned here. Each of these will be explained in the forthcoming screens. The three core elements are organizing and standardizing work place, sharing information, and visual controls. Organizing and Standardizing work place includes 5S, specified regions and areas, and color coding. Sharing information includes the three minute management approach and signaling system. Visual controls include Control board, SOP, Control chart, and Control plan. Let us discuss organizing and standardizing work place in the next screen.

Visual factory uses standard procedure to display the things for people to understand the system at a glance. 5S, specified regions and areas, and colour coding are the ways to organize and standardize the work place. Click each type to learn more. 5S: 5S is one of the well-known and frequently used methods in visual factory to organize and standardize the work place. The five S stands for Sort, Set in order, Shine, Standardize, and Sustain. Specified regions or areas: In visual factory, the regions are defined for a specific purpose like storing, walkway, etc., and the areas will be defined along with the storage for the parts. The walkway areas are displayed with the footsteps. Stairways are given defined with some colours; machine movement areas and storing areas for all the parts are also mentioned. Colour coding: In visual factory, colour coding is one of the methods of organizing the work place. The colours are defined for specific activities so that the information can be communicated by the use of the colour. For example, material shortage, breakdown, or other issues have a colour to follow, usually red.

Let us learn about sharing information in this screen. In visual factory, information sharing is one of the important elements that help in information exchange in the process. Information can be shared using the Three-minute management approach and Signaling system. Click each type to know more. Three-minute management approach: This approach refers to setting up information centers in work areas. Using graphics, the issues, progress, and information will be communicated to the workforce in three minutes. The flow chart and work plans are also used in the information center to easily communicate with the employees. Signaling system: This can be visual or audio or both, and signals the status of the process, sub process, or machines. The signaling system could be using the stack lights or ‘andon’ to indicate the status. Andon refers to the system to notify the quality or process issue to management and other workers.

Let us learn about visual controls in this screen. Visual controls are also highly used in visual factory to manage the factory by vision. The types of visual controls that can be used are control board, SOP, control chart, and control plan. Click each type to learn more. Control board: It helps people to read the complete process at a glance. It helps them to analyse how the process is working. It also displays the daily targets and the actual performance on a daily basis. It also helps in identifying the bottleneck of the process and removing it. SOP: Standard Operating Procedures are the set of rules and regulations that has to be mandatorily followed in a particular process. Control chart: Control charts provide information on process performance. This helps to understand if the process is in control and to sustain the improvements made. Control plan: Control plans refer to the plans that are displayed to know the performance of the process on a timely basis. The process will be kept under control through these control plans.

The benefits of visual factory are discussed in this screen. Using visual factory benefits the organization in the following ways: It clearly displays the real time scenario and keeps the workforce well informed of the process. The issues and bottlenecks are aided immediately and support is highlighted when required. Visual factory also improves the performance of the process, monitors, maintains and controls the inventory.

Following is the quiz section to check your understanding of the lesson.

Let us summarize what we have learned in this lesson. Statistical process control aids in the visual monitoring of a process and controlling its parameters by placing statistical measures around the process outputs or input variables. Control charts are useful for tracking process statistics over time and detecting the presence of special causes. A good control plan should clearly describe what actions are to be taken, when to take them, and who should take them, thereby reducing the “fire fighting” syndrome. After understanding the process, a multi-functional team must be formed who will be responsible for controlling the process. Multiple tools and techniques can be used. Total Productive Maintenance is a method commonly used in manufacturing industries. It is the key operational activity for managing quality. Visual factory is a term used to describe a Lean production environment where charts and signs are used to display information.

With this, we have come to the end of this course. Thank you and happy learning!

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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