1. Explain what is QFD, (Quality Function Deployment)?
A key element in TQM in each approach is customer involvement. Customer involvement is the fuel that powers Quality Function Deployment.
QFD (quality function deployment) is defined as a method for developing a design quality aiming at satisfying the consumer and then translating the consumer's demand into design targets and major quality assurance points to be used throughout the production phase. QFD is a way to assure the design quality while the product is still in the design stage. From this definition, QFD can be seen as a process where the consumer’s voice is valued to carry through the whole process of production and services.
In a sense, with QFD the customer—the potential user of the product—becomes part of the team that designs the product. It is a system that guides designers and planners to focus on the attributes of a product which are the most important to the customer. It involves:
1. Identifying customer needs known in QFD-speak as the “Voice of the Customer” (VOC).
2. Identifying the product attributes that will most satisfy the VOC.
3. Establishing product development and testing targets and priorities that will result in a product or service that satisfies the VOC.
The four phases of QFD are:
1. Product concept planning. It starts with customers and market research with leads to product plans, ideas, sketches, concept models, and marketing plans.
2. Product development and specification. It would lead to the development to prototypes and tests.
3. Manufacturing processes and production tools. They are designed based on the product and component specifications.
4. Production of product. It starts after the pilot has been resolved.
2. Explain the WHATs in a QFD matrix
The premise of QFD is that before any product or service is designed, the producer should have a good understanding of his potential customers’ needs in order to improve the likelihood that the product or service will be a market success. WHATs is defined as the customer needs, they are what the customers would like to see or have corrected in the product.
The QFD team must work diligently to determine what potential customers would like to see in terms of attributes and features of the product and perhaps what they don’t like about our current product.
Once the cross-functional QFD team has assembled sufficient information on what characteristics, attributes and features customers say they need, the information must be distilled into something useful. Typically the problem is that the inputs invariably cover the spectrum from some really good ideas and nuggets of information to some that are trivial or frivolous, and the volume of information so great that the designers are unable to cope with it.
The data must be sorted into a prioritized set of the most important customer needs. At this point we will call on some QFD Tools, the first of which is the Affinity Diagram. Refining a large collection of data into something that represents the essence of the VOC is done through the analysis techniques of the affinity diagram, and QFD team discussion.
Affinity Diagram
Affinity diagrams are used to promote creative thinking. They can be very helpful in breaking down barriers created by past failures. This is a critical element in achieving continual improvement. Affinity diagrams give structure to the creative process by organizing ideas in a way that allows them to be discussed, improved, and interacted with by all the participants.
Tree Diagram
The next tool to be used is the Tree Diagram. Tree diagrams can be used for countless purposes. It is used here simply to refine the affinity diagram results to make the list the customer needs, or WHATs that will be placed in the HOQ. Although a tree diagram could go all the way down into the nuts and bolts of a new design, the objective here is not to design the new product, but to list the items to be addressed by the design team once the entire HOQ is completed.
House of Quality should be created by a team of people with first-hand knowledge of both company capabilities and the expectations of the employee. Effective use of QFD requires team participation and discipline inherent in the practice of QFD, which has proven to be an excellent team-building experience.
A key element in TQM in each approach is customer involvement. Customer involvement is the fuel that powers Quality Function Deployment.
QFD (quality function deployment) is defined as a method for developing a design quality aiming at satisfying the consumer and then translating the consumer's demand into design targets and major quality assurance points to be used throughout the production phase. QFD is a way to assure the design quality while the product is still in the design stage. From this definition, QFD can be seen as a process where the consumer’s voice is valued to carry through the whole process of production and services.
In a sense, with QFD the customer—the potential user of the product—becomes part of the team that designs the product. It is a system that guides designers and planners to focus on the attributes of a product which are the most important to the customer. It involves:
1. Identifying customer needs known in QFD-speak as the “Voice of the Customer” (VOC).
2. Identifying the product attributes that will most satisfy the VOC.
3. Establishing product development and testing targets and priorities that will result in a product or service that satisfies the VOC.
The four phases of QFD are:
1. Product concept planning. It starts with customers and market research with leads to product plans, ideas, sketches, concept models, and marketing plans.
2. Product development and specification. It would lead to the development to prototypes and tests.
3. Manufacturing processes and production tools. They are designed based on the product and component specifications.
4. Production of product. It starts after the pilot has been resolved.
2. Explain the WHATs in a QFD matrix
The premise of QFD is that before any product or service is designed, the producer should have a good understanding of his potential customers’ needs in order to improve the likelihood that the product or service will be a market success. WHATs is defined as the customer needs, they are what the customers would like to see or have corrected in the product.
The QFD team must work diligently to determine what potential customers would like to see in terms of attributes and features of the product and perhaps what they don’t like about our current product.
Once the cross-functional QFD team has assembled sufficient information on what characteristics, attributes and features customers say they need, the information must be distilled into something useful. Typically the problem is that the inputs invariably cover the spectrum from some really good ideas and nuggets of information to some that are trivial or frivolous, and the volume of information so great that the designers are unable to cope with it.
The data must be sorted into a prioritized set of the most important customer needs. At this point we will call on some QFD Tools, the first of which is the Affinity Diagram. Refining a large collection of data into something that represents the essence of the VOC is done through the analysis techniques of the affinity diagram, and QFD team discussion.
Affinity Diagram
Affinity diagrams are used to promote creative thinking. They can be very helpful in breaking down barriers created by past failures. This is a critical element in achieving continual improvement. Affinity diagrams give structure to the creative process by organizing ideas in a way that allows them to be discussed, improved, and interacted with by all the participants.
Tree Diagram
The next tool to be used is the Tree Diagram. Tree diagrams can be used for countless purposes. It is used here simply to refine the affinity diagram results to make the list the customer needs, or WHATs that will be placed in the HOQ. Although a tree diagram could go all the way down into the nuts and bolts of a new design, the objective here is not to design the new product, but to list the items to be addressed by the design team once the entire HOQ is completed.
House of Quality should be created by a team of people with first-hand knowledge of both company capabilities and the expectations of the employee. Effective use of QFD requires team participation and discipline inherent in the practice of QFD, which has proven to be an excellent team-building experience.
3. Explain the HOWs in a QFD matrix
The Technical Requirements room of the HOQ states how the company intends to respond to each of the Customer Needs. It is sometimes referred to as the voice of the company.
We must state at the outset that the technical requirements are not the design specifications of the product or service. Rather, they are characteristics and features of a product that is perceived as meeting the customer needs. They are measurable in terms of satisfactory achievement.
The technical requirements are generated by the QFD team through discussion and consultation with the Customer Needs and Planning matrices used as guidance. The team may use affinity or tree diagrams to develop, sort, and rank the requirements, similar to the Customer Needs development process. The difference here is that the input is from within the company rather than from external customers.
Often, Customer Requirements are very subjective statements of the benefits which a Customer is seeking from a product. Though the team members may think they are in near total agreement about the meaning of a particular statement, the individuals on a team are often interpreting the statements in very different ways. This difference of interpretation often does not become evident until the team attempts to define ways to satisfy the Requirements.
The process of translating Customer Requirements into Design Measures is a way to force the team to define, using measurable and actionable statements, exactly what each Requirement means in the language of the organization. For example, let's say that a Customer Requirement for a software package is "Is easy to use". The programmers would be left to their own interpretation as to whether their software would really satisfy their Customers.
Technical Requirements that needs to be placed in the HOWs room of the HOQ are finally selected by the QFD team.
4.Explain the 1, or 3, or 9 interrelationship values in a QFD matrix
QFD team’s technical requirements (HOWs) in the HOQ, the next step is to examine how they relate to the WHATs of the Customer Needs. The results will be shown in the Interrelationships matrix, which links the HOWs and the WHATs. At each intersection cell of the interrelationship matrix the team must assess the degree of relationship between the WHAT and the corresponding HOW. This is usually done using scales of significance of 1 to 5 or 1 to 9, with the higher number indicating a stronger relationship. In this case, the matrix have to be completed using the following interrelationship values:
9- Strongest relationship
3- Medium relationship
1- Weak relationship
Blank cell indicates No relationship
There is a rule of thumb in QFD that only about 15% of the interrelationship cells will show a relationship between WHATs and HOWs. There is, however, one firm rule with the interrelationship matrix every row and every column must have at least one entry. An empty column means that the HOW (a technical requirement) is not delivering value to the customer-needs.
Relationships between Lists indicate how the two lists are related to each other. They are generally used to prioritize one List based upon the priorities of another list. Relationships can be defined by answering a particular question for each cell in a Matrix.
For example, the Relationships between Customer Requirements and Design Measures might be defined by asking "To what degree does this Measure predict the Customer's Satisfaction with this Requirement". By asking this same question consistently for each Measure and Requirement combination, a set of Relationships will be defined in the Matrix which will help to determine which Measures are most important to control in order to achieve a desired level of Customer Satisfaction. With greater frequency, teams are defining relationships using advanced methods such as the Analytic Hierarchy Process to establish scales with an infinite number of levels.
5. Explain how you calculate the technical priorities in the design target matrix
To determine the relative importance, or priorities, of each of the stated Technical Requirements (HOWs) in meeting the Customer Needs (WANTs), the QFD team simply multiplies each of the interrelationship ratings of the technical requirement
(0, 1, 3, or 9) from the Interrelationship matrix, times the corresponding customer needs.
Overall Weighting value in the Planning matrix; and then sums the columns. Moreover, some QFD users translate the priority values into a percentage scale. This is done, of course, by dividing the individual technical priority values by the sum of all the priority values, and multiplying by 100.
% Total priority = (Technical Requirement Priority, Ʃ Technical Priorities) * 100
The rest of the % of Total Priority values are calculated and placed in a row just below the Technical Priorities.
This information is used by the organization as guidance for the appropriate deployment of resources for the project.
6. Define statistical process control
Statistical process control (SPC) is a statistical method of separating variation resulting from special causes from variation resulting from natural causes in order to eliminate the special causes and to establish and maintain consistency in the process, enabling process improvement.
Statistical process control procedures can help you monitor process behavior. Arguably the most successful SPC tool is the control chart.
A control chart helps you record data and lets you see when an unusual event. Control charts attempt to distinguish between two types of process variation: Common cause variation, which is intrinsic to the process and will always, be present. Special cause variation, which stems from external sources and indicates that the process is out of statistical control.
Various tests can help determine when an out-of-control event has occurred. However, as more tests are employed, the probability of a false alarm also increases
With real-time SPC we can:
- Dramatically reduce variability and scrap
- Scientifically improve productivity
- Reduce costs
- Uncover hidden process personalities
- Instantly react to process changes
- Make real-time decisions on the shop floor
7. Explain control charts for variables, with a simple mathematical example
The general approach to on-line quality control is straightforward: We simply extract samples of a certain size from the ongoing production process. We then produce line charts of the variability in those samples and consider their closeness to target specifications. If a trend emerges in those lines, or if samples fall outside pre-specified limits, we declare the process to be out of control and take action to find the cause of the problem.
In all production processes, we need to monitor the extent to which our products meet specifications. In the most general terms, there are two "enemies" of product quality:
- Deviations from target specifications
- Excessive variability around target specifications
Consider an example using x -charts and R -charts. These charts are individual, directly related graphs plotting the mean (average) of samples (x) over time and the variation in each sample (R) over time. The basic steps for developing a control chart for data with measured values are these:
1. Determine sampling procedure. Sample size may depend on the kind of product, production rate, measurement expense, and likely ability to reveal changes in the process.
2. Collect initial data of 100 or so individual data points in k subgroups of n measurements.
3. Calculate the mean (average) values of the data in each subgroup x.
4. Calculate the data range for each subgroup (R).
5. Calculate the average of the subgroup averages x. This is the process average and will be the centerline for the x -chart.
6. Calculate the average of the subgroup ranges R. This will be the centerline for the R -chart.
7. Calculate the process upper and lower control limits, UCL and LCL represent the limits of the process averages and are drawn as dashed lines on the control charts.
8. Draw the control chart to fit the calculated values.
9. Plot the data on the chart.
The following charts are mathematical examples of X-chart and R-chart from the text book:
8. Explain control charts for attributes, with a simple mathematical example
When a process is stable and in control, it displays common cause variation, variation that is inherent to the process. A process is in control when based on past experience it can be predicted how the process will vary (within limits) in the future. If the process is unstable, the process displays special cause variation, non-random variation from external factors.
Attributes data are concerned not with measurement but with something that can be counted. For example, the number of defects is attributes data. Whereas the X – charts and R -charts are used for certain kinds of variables data, where measurement is involved, the p -chart is used for certain attributes data.
Actually, the p -chart is used when the data are the fraction defective of some set of process output. It may also be shown as percentage defective. The points plotted on a p -chart are the fraction (or percentage) of defective pieces found in the sample of n pieces.
C-Chart used when identifying the total count of defects per unit (c) that occurred during the sampling period, the c-chart allows the practitioner to assign each sample more than one defect. This chart is used when the number of samples of each sampling period is essentially the same.
Used when each unit can be considered pass or fail – no matter the number of defects – a p-chart shows the number of tracked failures (np) divided by the number of total units (n).
9. Explain how we can use control charts for continual quality improvement
Control charts of all types are fundamental tools for continual improvement. They provide alerts when special causes are at work in the process, and they prompt investigation and correction.
When the initial special causes have been removed and the data stay between the control limits ( within ±3σ ), work can begin on process improvement. As process improvements are implemented, the control charts will either ratify the improvement or reveal that the anticipated results were not achieved.
Whether the anticipated results were achieved is virtually impossible to know unless the process is under control. This is because there are special causes affecting the process; hence, one never knows whether the change made to the process was responsible for any subsequent shift in the data or if it was caused by something else entirely.
However, once the process is in statistical control, any change you put into it can be linked directly to any shift in the subsequent data. You find out quickly what works and what doesn’t. Keep the favorable changes, and discard the others.
Knowing which control chart to use in a given situation will assure accurate monitoring of process stability. It will eliminate erroneous results and wasted effort, focusing attention on the true opportunities for meaningful improvement.
10. Explain the way control charts could be used for quality improvements
The control charts is supposed to detect the presence of special causes of variation. In its basic form, the control chart is a plot of some function of process measurements against time.
The points that are plotted on the graph are compared to a pair of control limits. A point that exceeds the control limits signals an alarm. An alarm signaled by a control chart may indicate that special causes of variation are present, and some action should be taken, ranging from taking a re-check sample to the stopping of a production line in order to trace and eliminate these causes.
On the other hand, an alarm may be a false one, when in practice no change has occurred in the process. The design of control charts is a compromise between the risks of not detecting real changes and of false alarms.
When a process is out of control, these charts can help managers to understand the cause of the problem and eliminate them by finding solution. Once the problem is solved and the process is improved, it is considered “under control”. Those considerations can be applied to processes that can be considered out of control or under control without intermediate ways.
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