application of quality tools
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Application of Quality Tools
Nilesh Surana 10DM_095
Nishant Singh Gahlot 10DM-097
Priya Kanodia 10DM-107
Swati Goyal 10FN-113
Sabeeka Rizvi 10FN-135
Soumya Kaushal 10FN-137
Many companies in the world are gradually promoting quality as the central customer value and regard it as a key concept of company strategy in order to achieve the competitive edge. Quality improvement decisions are viewed as the catalyst for substantial technological developments being made in the manufacturing sector.
Quality Costs are a measure of the costs specifically associated with the achievement or non-achievement of product or service quality –including all product or service requirements established by the company and its contract with customers and society.
Measuring and reporting the quality cost is the first step in a quality management program. Quality costs allow us to identify the soft targets to which improvement efforts can be applied.
Quality Tools The various tools are used to check the quality of the product to define weather the
product is a quality one or not and to take the further necessary actions to bring the process under control.
Cause and effect diagram
PepsiCo entered India in 1989 and has grown to become the country’s largest selling food and Beverage Company. One of the largest multinational investors in the country, PepsiCo has established a business which aims to serve the long term dynamic needs of consumers in India.
PepsiCo nourishes consumers with a range of products from treats to healthy eats that deliver joy as well as nutrition and always, good taste. PepsiCo India’s expansive portfolio includes iconic refreshment beverages Pepsi, 7 UP, Mirinda and Mountain Dew, in addition to low calorie options such as Diet Pepsi, hydrating and nutritional beverages such as Aquafina drinking water, isotonic sports drinks - Gatorade, Tropicana 100% fruit juices, and juice based drinks – Tropicana Nectars, Tropicana Twister and Slice, non-carbonated beverage and a new innovation Nimbooz by 7Up. Local brands – Lehar Evervess Soda, Dukes Lemonade and Mangola add to the diverse range of brands.
PepsiCo’s foods company, Frito-Lay, is the leader in the branded salty snack market and all Frito Lay products are free of trans-fat and MSG. It manufactures Lay’s Potato Chips, Cheetos extruded snacks, Uncle Chipps and traditional snacks under the Kurkure and Lehar brands and the recently launched ‘Aliva’ savoury crackers. The company’s high fibre breakfast cereal, Quaker Oats, and low fat and roasted snack options enhance the healthful choices available to consumers. Frito Lay’s core products, Lay’s, Kurkure, Uncle Chipps and Cheetos are cooked in Rice Bran Oil to significantly reduce saturated fats and all of its products contain voluntary nutritional labeling on their packets.
The group has built an expansive beverage and foods business. To support its operations, PepsiCo has 36 bottling plants in India, of which 13 are company owned and 23 are franchisee owned. In addition to this, PepsiCo’s Frito Lay foods division has 3 state-of-the-art plants. PepsiCo’s business is based on its sustainability vision of making tomorrow better than today. PepsiCo’s commitment to living by this vision every day is visible in its contribution to the country, consumers and farmers.
Purpose: To determine the spread or variation of a set of data points in a graphical form.
Variation: It is always a desire to produce things that are equal to their design values. For example if we are producing a number of cylindrical objects having a certain diameter value, we wish that every part produced will have the same diameter value. But this is not always the case. We will always have variation in the values of the diameter between each produced part. Variation is everywhere. It is found in the output of any process: manufacturing, service, or administrative. But variation is not all bad. One characteristic of variation is that it always displays a pattern, a distribution. These patterns can tell us a great deal about the process itself and the causes of problems found in the process. Histograms help us identify and interpret these patterns.
Histograms: A histogram is a tool for summarizing, analyzing, and displaying data. It provides the user with a graphical representation of the amount of variation found in a set of data. Histograms sort observations or data points, which are measurable data, into categories and describes the frequency of the data found in each category.
Constructing a Histogram: The following are the steps followed in the construction of a histogram: The following are the steps followed in the construction of a histogram:
Data collection: To ensure good results, a minimum of 50 data points, or samples, need to be collected
Calculate the range of the sample data: The range is the difference between the largest and smallest data points. Range = Largest point - smallest point. Data points need to be divided on the X axis into classes, look at Figure 1above. This is a very important step because how you select the class scale will determine the effectiveness of the histogram in interpreting the variation found in the data set. Table 1 below lists some of the rules of thumb for determining the number of classes to use with respect to the number of collected data points.
Table 1. Rules of thumb for class selection.
No# of classes
0-50 5-751-99 8-10100-250 10-15over 250 15-20
Calculate the size of the class interval. The class interval is the width of each class on the X axis. It is calculated by the following formula: Class interval = Range / Number of classes. Determine the class boundary. They are the largest and smallest data points that can be included in each class. Calculate the number of data points (frequency) that are in each class. A tally sheet is usually used to find the frequency of data points in each interval. Draw the Histogram, as in Figure 1, and plot the data.
Purpose: Flow Charts provide a visual illustration of the sequence of operations required to complete a task.
Flow Charts: Flow charting is the first step we take in understanding a process. Whether this process is an administrative or a manufacturing one, flow charts provide a visual illustration, a picture of the steps the
process undergoes to complete it's task. From this picture we can see how this process and the elements comprising it, fit into the overall picture of the business. Every process will require input(s) to complete it's task, and will provide output(s) when the task is completed. For example, an injection molding machine requires inputs in the form of raw material and proper machine settings to be able to produce a proper part. The output of the injection molding process is the finished part. (See above picture) Flow charts can be drawn in many styles. They can be drawn by using pictures, engineering symbols, or just squares and rectangles. Also, flow charts can be used to describe a single process, parts of a process, or a set of processes. There is no right or wrong way to draw a flow chart. The true test of a flow chart is how well those who create and use it can understand it.
Construction of the Flow Chart (General Guide Lines):
* Involving the right people in making the flow chart. This includes people who actually do the work of the process, supervisors, suppliers to the process, and customers to the process.
* Flow charts usually require more time to construct than expected. Therefore, enough time must be allotted to the group members to finish their work.
* Asking questions is the key to the flow charting process. The more questions everyone asks the better. Here is a list of helpful questions to ask when constructing a flow chart:
1. What is the first thing that happens? 2. What is the next thing that happens? 3. Where does the output(s) of this operation go? 4. Where does the input(s) to the process come from? 5. How does the input(s) get to the process? 6. Is their anything else that must be done at this point?
A Special Benefit of Flow Charting: People involved in constructing a flow chart begin to better understand the process. They begin to identify areas for improving the process. They also begin to realize how the process and all the people involved, including them, fit into the overall process or business. Because of their involvement, they become enthusiastic supporters to quality and process improvement. Finally, they all begin to understand the process in the same terms.
Purpose: To identify correlations that might exist between a quality characteristic and a factor that might be driving it.
Scatter Diagrams: A scatter diagram is a nonmathematical or graphical approach for identifying relationships between a performance measure and factors that might be driving it. This graphical approach is quick, easy to communicate to others, and generally easy to interpret. Figure 1 below shows a commonly used structure for a scatter diagram. The data needed to construct the scatter diagram must be collected in pairs (X,Y). Almost always the performance characteristic, Y, is plotted on the vertical axis, while the suspected correlated factor, X, is plotted on the horizontal axis. The point of intersection between the two axes is the average of each of the sets of data (i.e. the average of all the X's and the average of all the Y's). The collected data is not for only observing the quality characteristic under investigation but also observing other factors or causes that might have an impact on the quality characteristic. For example if we are taking measurements of the surface finish of a machined part, we will want to take measurements of other factors such as feed rate and tool condition that could have an effect on our surface finish quality characteristic.
Constructing the Scatter Diagram:
Step 1: Select the two items you wish to study. The results of the cause-and-effect diagram could be very helpful in determining which items to select. For example the two items could be an effect and a related cause.Step 2: Collect the data. The more data you have more accurate your conclusions will be. Always remember that the type of data needed to construct the scatter diagram is paired.Step 3: Draw the axis of the scatter diagram. Remember that the performance characteristic in on the Y axis, and the suspected correlated factor on the X axis. The point of intersection of the two axes is the average of each of the sets of data. You can also make the origin point (0,0) your intersection point.Step 4: Plot each set of paired data onto the graph (i.e. (Xo,Yo), (X1,Y1),(X2,Y2),......,(Xn,Yn), where n is the number of samples taken.
Interpreting the Results: Once all the data points have been plotted onto the scatter diagram, you are ready to determine whether their exists a relation between the two selected items or not. When a strong relationship is present, the change in one item will automatically cause a change in the other. If no relationship can be detected, the change in one item will not effect the other item. Their are three basic types of relationships that can be detected to on a scatter diagram:
Pareto Charts Overview
The purpose of the Pareto chart is to prioritize problems - to decide what problems must be addressed. No company has enough resources to tackle every problem, so they must prioritize. The purpose of this report is to inform the reader of the Pareto principles and how it can be applied to the manufacturing environment. I will try to answer commonly asked questions regarding the Pareto process.
The Pareto concept was developed by the Italian economist Vilfredo Pareto describing the frequency distribution of any given characteristic of a population. Also called the 20-80 rule, he determined that a small percentage of any given group (20%) account for a high amount of a certain characteristic (80%). For instance in the BYU Law parking lot, there are basically 9 different colors of cars: white, blue, red, black, tan, silver, brown, yellow, and green. I decided to illustrate the Pareto principle by the different colors of cars. All colors are not created equally. Out of the nine different colors, three (red, blue, and white) make up about 64% of the cars. The same principle can be applied to all aspects of manufacturing. The Pareto chart is especially helpful in improving manufacturing processes.
Where do I begin?
The important thing is to begin. Start somewhere. Choose a process that is not producing the yields you would like it to produce. Are there an excess amount of parts that you are having to rework or scrap? Why? What are the reasons for scrapping those parts? Make a list of the causes of the problem. Don't worry about narrowing the list because it will narrow itself. Keep track of the number of scrapped or reworked parts and what the reason is. In the example above, I wanted to know what color of car was most popular. I could have also counted cars by their brand, or their type (compact, subcompact, etc.) It doesn't really matter. As the data begins to come in, then you can make more decisions.
What do I do next?
After you have collected a sufficient amount of data, chart what you have. In my example, I used the number of cars. This is a great method. However, you may want to keep track of the cost of each product failure. For example, if you have a milling operation, and scratching the surface of the product causes the most reworks, but it only costs $2 to correct, whereas milling an uneven surface, while occurring less often, is actually more expensive to correct. That is something that you must
decide, whether to go by cost or total numbers. If you don't know which to choose, then do both.
There may be times where different products come off the same production line. In this instance, it may be important to keep charts on the different products. For instance, there may be 10 scraps out of 10,000 of product A; whereas with product B, there may be 8 scraps out of 100. If you were to group the whole process together, the problem with product B might not be discovered. Be aware of grouping products together that come from the same process.
Now you should have an idea of what problems are most troublesome. There should be two or three problems that stick out. Focus on those problems causing the greatest number of reworks or those problems which are the costliest.
After you feel you have improved the process, do another Pareto chart and find out if your improvements worked. If they did, great! and your Pareto chart shows some new problems that must be worked on. Continually improve on the problem that is causing the greatest harm.
One important part of process improvement is continuously striving to obtain more information about the process and it's output. Cause-and-effect diagrams allow us to do not just that, but also can lead us to the root cause, or causes, of problems. It is a tool that enables the user to set down systematically a graphical representation of the trail that leads ultimately to the root cause of a quality concern or problem.
First developed in 1943 by Mr. Ishikawa at the University of Tokyo, the cause-and-effect diagram, also known as the fishbone diagram, relates the symptom or problem under question to the factors or causes driving it. It accomplishes this through a hierarchical relationship between the effect, the main causes of this effect, and their subsequent relationship to the sub causes. While a cause-and-effect diagram can be developed by an individual, it is best when used by a team. A cause-and-effect diagram consists primarily of two sides. The right side, effect side, lists the problem or the quality concern under question. While the left side, cause side, lists the
primary causes of the problem. The right hand side can also include a desired effect
the user wishes to achieve. The picture below is an example of a eause-and-effect diagram. The symptom or Quality Concern is the "Black Spots" that are occurring in an injection molding process. The main causes that could give rise to the problem are the machine, the raw material, the method or process procedures, and the human element. Each of these main causes is composed of sub causes that influence or effect the quality concern under question. The selection of the headings for the main causes could be generic, such as Man or Method, or they can be specific such as Training or Wrong Speed. The important thing is to continually define and relate causes to each other.
Constructing the Cause-and-Effect Diagram:
Step 1: Select the team members. It is preferable that the team members are knowledgeable about the quality concern under question. One of the team members is selected as the team leader (Facilitator). His job is to listen to the ideas presented by the other team members, capture those thoughts in simple words and write them on the chart. Also to keep the team members focused on the problem under investigation.
Step 2: Assuming that the quality concern has been identified, write this concern as a problem statement on the right hand side of the page, and draw a box around it with an arrow running to it. This quality concern is now the effect.
Step 3: Brain-storming. The team members generate ideas as to what is causing the effect. Main causes as well as sub causes are identified. As indicated earlier, the team could choose generic or specific headings for describing the main causes.
Step 4: This step could be combined with step 3. Identify, for each main cause, its related sub-causes that might effect our quality concern or problem (our Effect). Always check to see if all the factors contributing to the problem have been identified. Start by asking why the problem exists. If a cause is identified, repeat the question again. When no other causes can be identified, you have likely identified all the possible causes to the problem.
Step 5: Focus on one or two causes for which an improvement action(s) can be developed using other quality tools such as Pareto charts, check sheets, and other gathering and analysis tools. This will depend on the team members themselves on where and how to start. Agreement between the team members must be based on consensus.
Check sheets allow the user to collect data from a process in an easy, systematic, and organized manner.
Before we can talk about check sheets we need to understand what we mean by data collection. Process improvement actions are always based on information obtained from data collected from the actual process. This collected data needs to be accurate and relevant to the quality problem being analyzed if we wish for our information about this problem to also be accurate. Information is based on data. There are three primary steps that need to be taken before any data can be collected. The first is to establish a purpose for collecting this data. This is based on the quality problem that is going to be investigated. Second, we need to define the type of data that is going to be collected. Data can be collected in two ways: Measurable data such as length, size, weight, time,...etc., and countable data such as the number of defects. Which type of data to use again depends on the quality problem being
investigated. The third step is to determine who is going to collect that data and when it should be collected. Usually one can use statistical guides on when to take samples, or data points, from a process. As for who is going to collect the data, what is important is that the person collecting the data understands the purpose of collecting this data and his role in the data collecting process.
Check sheets are some of the most common tools used for collecting data. They allow the data to be collected in an easy, systematic, and organized manner. Also, data collected using check sheets can be used as input data for other quality tools such as Pareto diagrams. There are four main types of check sheets used for data collection (custom check sheets can also be designed to fit specific needs):
1.Defective Item Check Sheet:
This type of check sheet is used to identify what types of problems or defects are occurring in the process. Usually these check sheets will have a list of the defects or problems that may occur in the process. When each sample is taken, a mark is placed in the appropriate column whenever a defect or a problem has been identified. The type of data used in the defective item check sheets is countable data. Table 1 below shows an example of a defective item check sheet for the wave solder manufacturing process.
Table 1. Wave Solder Defect Count.
Defect Type Insufficient Solder Cold Solder Solder
Bridge Blow Holes Excessive Solder
Frequency xxxxxxx Xx xxx xxxxxxxxxxxxxx xxTotal 7 2 3 14 2
2. Defective Location Check Sheet:
These type of check sheets are used to identify the location of the defect on the product. They are used when the external appearance of the product is important. Usually this type of check sheet consists of a picture of the product. On this picture, marks can be made to indicate were defects are occurring on the surface of the product.
3. Defective Cause Check Sheet:
This type of check sheet tries to identify causes of a problem or a defect. More than one variable is monitored when collecting data for this type of check sheets. For example, we could be collecting data about the type of machine, operator, date, and time on the same check sheet. Table 2 below is an example of this type of check
sheets. As we can see most of the error is occurring at machine 2 and at the afternoon shift. This could suggest that machine 2 has problems when it is run in the afternoon shift.
Table 2. Defect cause check sheet.
Machine 1 Machine 2Operator A Morning X X
Afternoon XX XXXXXX Operator B Morning X XX
Afternoon XX XXXXXXXXX X= Number of times the supervisor is called per day.
4. Checkup Confirmation Check Sheet:
This type of check sheet is used to ensure that proper procedures are being followed. These check sheets usually will have a list of tasks that need to be accomplished before the action can be taken. Examples of checkup confirmation check sheets are final inspection, machine maintenance, operation checks, and service performance check sheets.
To ensure that the process is in control and to monitor process variation on a continuous basis.
Statistical Process Control:
The development and use of statistics and statistical theories about distributions and how they vary has become the corner stone of process improvement. More known as statistical process control, SPC, this method allows the user to continuously monitor, analyze, and control the process. It is based on the understanding of variation and how it effects the output of any process. Variation is the amount of deviation from a design nominal value. Not every product that is produced will exactly match it's design nominal values. That's why we have tolerances on the nominal values to judge whether a product is acceptable or not. But the closer we are to the nominal value the better the product is. Control charts is one SPC tool that enables us to monitor and control process variation.
Common and Special Cause Variation:
Variation in a process can be caused by, or related to, two types of causes. They are common or system causes and special or local causes. Special causes are problems that arise in a periodic fashion. They are somewhat unpredictable and can be dealt with at the machine or operator level. Examples of special causes are operator error, broken tools, and machine setting drift. But this type of variation is not critical and only represents of small fraction of the variation found in the process.
Common causes are problems inherent in the system itself. They are always present and effect the output of the process. Examples of common causes of variation are poor training, inappropriate production methods, and poor workstation design. As we can see, common causes of variation are more critical on the manufacturing process than special causes. In fact Dr. Deming suggests that about 80 to 85% of all the problems encountered in production processes are caused by common causes, while only 15 to 20% are caused by special causes.
Developed in the mid 1920's by Walter Shewhart of Bell labs, this SPC tool has become a major contributor to the quality improvement process. It allows the use to monitor and control process variation. It also allow the user to make the proper corrective actions to eliminate the sources of variation. Even though they require the user to have some statistical background, the are relatively easy to construct. There are two basic types of control charts, the average and range control charts. The first deals with how close the process is to the nominal design value, while the range chart indicates the amount of spread or variability around the nominal design value. A control chart has basically three line: the upper control limit UCL, the center line CL, and the lower control limit LCL. These lines are computed from samples taken from the production line. Each sample represents a point on the control chart. A minimum of 25 points is required for a control chart to be accurate. To better understand the use of a control chart and how it is constructed we present the following example:
Construction of a Control Chart:
A manufacturing process produces gasket material blanks. In order to better understand the manufacturing process, it was suggested to the manufacturing engineer to construct a control chart. After meeting with his team, he decides to construct a control chart for the thickness of the gasket material blanks. The team decides to take a sample size of n=4 every have hour and measure the thickness of each gasket material blank. The average of the four thicknesses is then measured as well as the range for the four parts. Table 1 below has a list of the X-bar, or average, of each sample and it's range.
The average= Sum of the thickness for each measured part/ number of parts. The range= The biggest value in the sample - the lowest value in the sample.
Table 1. The X-bar And Range of Each Sample.
8am 8:30am 9am 9:30a
m 10am 10:30am 11am 11:30a
m 12pm 12:30pm 1pm
X-bar 1.25 0.95 0.8 1.0 1.15 0.95 0.95 1.2 1.05 1.0 0.9
range 0.5 0.45 0.55 0.00 0.25 0.5 0.4 0.65 0.1 0.5 0.35
1:30pm 2pm 2:30p
m 3pm 3:30pm 4pm 4:30p
m 5pm 5:30pm 6pm 6:30p
mX-bar 1.15 0.85 1.0 1.25 0.95 1.05 1.0 1.3 1.15 1.0 1.05
range 0.45 0.5 0.6 0.5 0.55 0.3 0.5 0.35 0.55 0.4 0.4
The next step is to construct the three lines of each of the average and range control charts:
For the Range chart: The center line = The average of all the sample ranges (R-bar)= 0.425. UCL= D4*(R-bar)= 2.283*0.425=0.97LCL=D3*(R-bar)= Zero *0.425= Zero.
For X-bar or average chart: The center line (X-double bar)= is the average of all the averages of the 25 sample points.= 1.043UCL= X-double bar + A2*(R-bar).= 1.043 + (0.729*0.425)=1.486LCL = X-double bar -A2(R-bar) = 1.05 - (0.729*0.425)=0.60
Interpretation of the X-bar and Range charts: Variation reveals it self through recognizable patterns on the control charts. A process is said to be under the influence of common causes only if all the data points lie within the upper and lower control limits. If a point falls out side the control limits then the process is said to be out of control or under the influence of special causes. Remember that special causes are sporadic and in most cases we can eliminate what is causing them. Process improvement can only start with the process being in control. Then we look at the control charts to see if any pattern or trend exists in the graph. Control charts can detect several basic patterns that can occur in a process. These patterns are:
Runs: A series of consecutive points on the control chart that fall on one side of the central Line. This indicates that the mean or average of the process has shifted.Trends: A series of points continues to rise or fall in one direction. This is an indication that an abnormal condition is operating within the process. An example of causes of trends is tool or equipment wear.Cycles: A series of points displaying a similar or repeatable pattern.Jumps: Occurs when a large shift has occurred between two consecutive points.Hugging: A series of points are very close to the central line or the two control limits.
Application of various quality tools in the bottling of cold drink at the bottling plant of Pepsico India.
The data collected from the Greater Noida plant of Pepsico. India for the last month is as follows
Defects No. Of Defects in a month
Flat drink 4166.666667
faulty bottle 720
not proper mix 4166.666667
extra heated slice 1666.666667
extra inflated PET bottles 360
Considering this data and understanding the process flow in a cold drink bottling company we have applied the 7 Quality tools to understand the various process which if improved can result in a better quality of product.
The complete process which is followed in a cold drink industry is as shown in the below diagram.
The various quality tools applied are
1. Histogram2. Pareto Analysis3. Scatter Diagram4. Fish Bone Diagram5. Check Sheet6. Process Flow Chart7. Statistical Process Control
The results obtained are as follows:
In the histogram, the data for the faults and the frequency of their occurrence was taken into consideration
Defects No. Of Defects in a month
Flat drink 4166.666667
faulty bottle 720
not proper mix 4166.666667
extra heated slice 1666.666667
extra inflated PET bottles 360
No. Of Defects in a month
No. Of Defects in a month
2. Pareto Analysis:
Defects % Defects Cummulative Frequency
Flat drink 37.605295 37.60529483
not proper mix 37.605295 75.21058965
extra heated slice 15.042118 90.25270758
faulty bottle 6.4981949 96.75090253
extra inflated PET bottles 3.2490975 100
f(x) = − 24.0922369741124 ln(x) + 43.0682771157422R² = 0.846773986835247
% DefectsLogarithmic (% Defects)
3. Scatter Diagram:
0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.50
f(x) = − 666.666666666667 x + 4216R² = 0.326924309176242
4. Fish Bone Diagram:
5. Check Sheet:
Defects 1 2 3 4 5 Total
Flat drink 205 212 208 209 208 1042
faulty bottle IIIIIIIIII IIIIII IIIIIIII IIIIIII IIIII 36
not proper mix 200 205 201 211 209 1026
extra heated slice 80 85 84 82 81 412
extra inflated PET bottles II IIII IIIIIIII IIIII IIIIII 25
6. Process Flow Chart:
7. Statistical Process Control Chart:
1 2 3 4 5200
The conclusions we came up with after the application of the various quality tools on the complete process of bottling of beverages at the Pepsico India’s plant are as follows
Quality Tools play a very important part in maintaining the quality of goods being produced
It assists in finding the cause of defects.
Helps in reducing the frequency of defects.
Better project planning.
Pepsico should concentrate on reducing the number of defects caused due to the following processes: