7.1 PUGH CONCEPT SELECTION METHOD

Invented by Stuart Pugh, both the Pugh method and Pugh Concept Selection is a quantitative technique used to rank the multidimensional options of an option set. It is frequently used in engineering for making design decisions but can also be used to rank investment options, vendor options, product options or any other set of multidimensional entities. A basic decision matrix (also called Pugh method) consists of establishing a set of criteria upon which the potential options can be decomposed, scored, and summed to gain a total score which can then be ranked. Importantly, the criteria are not weighted to allow a quick selection process. The advantage of this approach to decision making is that subjective opinions about one alternative versus another can be made more objective. Another advantage of this method is that sensitivity studies can be performed. An example of this might be to see how much your opinion would have to change in order for a lower ranked alternative to out rank a competing alternative

CONCEPT SELECTION (EVALUATION)

Concept selection is one of the most critical decision-making exercises in a product development. To make decisions effective, one must basically carry out two steps:

v  Minimize the possibility of misrepresenting a solution that may be effective.

Eg : engineer is not familiar with the technology

v  Fully consider the different ramifications of a decision.

Eg : not considering the costumer’s need may lead to the product failing in the marketplace.

v  Design Evaluations

-  occurs at all phase of product evaluation, from concept to detailed design phases.

v  Quality of Information

Low quality of information - how well each alternative design would meet criterion cannot be fully understood.

High quality of information -  the alternative solutions is well understood.

v  Technology Readiness Assessment

-          if a technology is to be used as part of a product design, it must be mature enough that its use is a design issue, not a research issue.

MECHANISM OF THE PUGH’S CONCEPT:

It is implemented by establishing an evaluation team, and setting up a matrix of evaluation criteria versus alternative embodiments. This is the scoring matrix usually associated with the QFD method and is a form of prioritization matrix. Usually, the options are scored relative to criteria using a symbolic approach. These get converted into scores and combined in the matrix to yield scores for each option. Comparison of the scores generated gives insight into the best alternatives.

1.                  Comparing alternative concepts

2.                  Scores concepts relative to one another

3.                  Iterative evaluation method

4.         Comparing result of a design team which performed independently

CONCEPT SELECTION (PUGH’S METHOD)

Based on the Decision-Matrix (Pugh’s method), this method is very effective for comparing concepts that are not refined enough for direct comparison with the engineering requirement. The method is an iterative evaluation that tests the completeness and understanding of requirements, quickly identifies the strongest concept. It is most effective if each member of the design team performs it independently. The results of the comparison will usually lead to repetition of the method, with iteration continued until the team reaches a consensus.

Steps to design Pugh Matrix

1.     Develop criteria for comparison

• Examine customer requirements.
• Generate a set of engineering requirements and targets.

2.      Select alternatives to be compared

·         The alternatives are the different ideas developed during concept generation. All concepts should be compared at the same level of generalization.

3.      Generate scores

·         Comparison usually measured by each of the customer requirements. If the problem is to redesign an existing product, then the existing product can be used as the datum.

4.      Compute the total score

• Four scores will be generated, the number of plus scores, minus scores, the overall total and the weighted total.
• The overall total is the number of plus scores- the number of minus scores.
• The weighted total is the scores times their respective weighting factors, added up.

5.      Variations on scoring

                  For example a seven level scale could be used for a finer scoring system where:

• +3 meets criterion extremely better than datum
• +2 meets criterion much better than datum
• +1 meets criterion better than datum
•   0  meets criterion as well as datum
• -1  meets criterion not as well as datum
• -2  meets criterion much worse then the datum
• -3  meets criterion far worse than the datum

General Format for a Pugh Matrix:

     

		Concepts
Criterion	Wt	(Step 2)
(Step 1)	:	Generate score (step 3)
	:
:		:
:		:
Total +
Total -
Overall Total		Generate totals (step 4)
Weighted Total

                                               

 Example of Pugh Matrix

 This example is looking was looking at alternatives for buying a cellphone here in the US in early 2007. Based on what what’s filled in so far, the Nokia 6682 with T-Mobile is the best choice. But if that doesn’t feel like the right decision, or things change, it’s a signal to spend more time refining the criteria and weights.

The basic steps of the Pugh Concept Selection Process are

1. Brainstorm alternatives, list them across columns of sheet. Make one alternative the “default” — often it’s the “do-nothing” or status quo choice. This choice is rated zero for all criteria.
2. Brainstorm criteria and characteristics important to the customer. List them down rows of sheet.
3. Begin filling in 1, 0, or -1 ratings in the main area of sheet, based on whether that alternative is better, equivalent, or worse than the status quo for that criteria.
4. If some criteria are more important than others, adjust the weights. If some products are much better than others, adjust the rating weights in the main area of the sheet. Don’t go overboard with this.
5. Look at what the spreadsheet tells you is the best choice. Do you and the group feel good about that decision? If so, you’re done.
6. If not, look again at steps 1-5 — do you have a complete set of criteria, or was something important to the decision missed? Are the weights you’ve assigned close enough?

                                          

CONCLUSION

·         The feasibility of the concepts is based on the design team’s knowledge. It is often necessary to augment this knowledge with research and development of simple models.

·         The decision matrix (Pugh’s method) provides means of comparing and evaluating concepts. The method gives insight into strong and weak areas of the concepts.

·         In order for a technology to be used in a product, it must be ready.

Assignment Chapter 16

Assignment 16.1

Year = 1985	Year = 2007
$35 000	-
C= 100ft³/min	C= 1000 ft³/min

Year: 2007- 1985 = 22 years | cost increase 5% per year

5/100 x 22 x $35 000 = $38 500

Cost = $38 500 + 35 000 = $ 73 500

Assignment 16.3

Cost of tooling, CS = $600 and CH  = $7500

Cost of tool setup, SS = $100 and SH = $60

Cost to make one part, CPS = $3.40 and CPH = $0.80

Parts are made in batches, b (lots) of 500 units

The break-even point is the sales or production volume at which sales and costs balance:

CH + [QBEP/b] SH + CPH QBEP = CS + [QBEP/b] + SS + CPS QBEP

Break even point:

QBEP = CH – CS / [(SS – SH) / b] + (CPS - CPH)]

            = 7500 – 600 / [(100 – 60) / 500] + (3.4 – 0.8)]

            = 2574.63

            = 2575 units

Since the total production is expected to be 5000 units, the best decision is to use hard tooling if the time required to make to tools and prepare the production machines is compatible with the product development schedule. The break-even point which is 2575 units gives the total production at which the hard tooling approach becomes more cost effective than soft tooling

Assignment 16.4

Total Fixed Cost

= 950 000 + 180 000 + 50 000 + 120 000 + 60 000 + 10 000 + 90 000 + 120 000 + 100 000

= $ 1680 000

Total Variable Cost

=2150 000 + 60 000 + 70 000 + 30 000

=$ 2310 000

Total Cost = 1680 000 + 2310 000 = $3990 000

Manufacturing Cost per Unit = $ 3990 000

Total Manufacturing Cost = $ 239 400 000

115/100 x 3990 000 = $ 4588 500 = Selling Price.

Assignment 16.5

A jewel case = 20 gram ($ 2.20/lb)

OH = 40% G & A = 15% Profit = 10%

Estimated sell price

1 lb = 454 gram

1 gram = 2.2x10¯³ lb

(2.2x10¯³) x 20 x 2.20 = $0.097/unit

$20/1400= $0.014 per unit.

Total cost = 0.097 + 0.014 = $0.111

Cost/Hour = $0.111 x 1400 = $155.4

40/100 x 155.4 = $62.16

15/100 x 155.4 = $23.31

Total = 155.4 + 62.16 + 23.31 = $240.87

Profit = 110/100 x 240.87 = $264.96

$264.96/1400 unit = $0.19 per unit.

Assignment 8.8 Design a fingernail cutter

Assignment Failure mode and effects analysis (FMEA)

Failure mode and effects analysis

A FMEA is often the first step of a system reliability study. It involves reviewing as many components, assemblies, and subsystems as possible to identify failure modes, and their causes and effects. For each component, the failure modes and their resulting effects on the rest of the system are recorded in a specific FMEA worksheet. There are numerous variations of such worksheets. A FMEA is mainly a qualitative analysis. An FMEA is an inductive reasoning (forward logic) single point of failure analysis and is a core task in reliability engineering, safety engineering and quality engineering. Quality engineering is especially concerned with the "Process" (Manufacturing and Assembly) type of FMEA. Failure modes and effects analysis (FMEA) is a step-by-step approach for identifying all possible failures in a design, a manufacturing or assembly process, or a product or service. “Failure modes” means the ways, or modes, in which something might fail. Failures are any errors or defects, especially ones that affect the customer, and can be potential or actual. “Effects analysis” refers to studying the consequences of those failures. Failures are prioritized according to how serious their consequences are, how frequently they occur and how easily they can be detected. The purpose of the FMEA is to take actions to eliminate or reduce failures, starting with the highest-priority ones. Failure modes and effects analysis also documents current knowledge and actions about the risks of failures, for use in continuous improvement. FMEA is used during design to prevent failures. Later it’s used for control, before and during ongoing operation of the process. Ideally, FMEA begins during the earliest conceptual stages of design and continues throughout the life of the product or service. A successful FMEA activity helps to identify potential failure modes based on experience with similar products and processes - or based on common physics of failure logic. It is widely used in development and manufacturing industries in various phases of the product life cycle.

When to use FMEA

·         When a process, product or service is being designed or redesigned, after quality function deployment.

·         When an existing process, product or service is being applied in a new way.

·         Before developing control plans for a new or modified process.

·         When improvement goals are planned for an existing process, product or service.

·         When analyzing failures of an existing process, product or service.

·         Periodically throughout the life of the process, product or service

FMEA Procedure

1.      Assemble a cross-functional team of people with diverse knowledge about the process, product or service and customer needs. Functions often included are: design, manufacturing, quality, testing, reliability, maintenance, purchasing (and suppliers), sales, marketing (and customers) and customer service.

2.      Identify the scope of the FMEA. Is it for concept, system, design, process or service? What are the boundaries? How detailed should we be? Use flowcharts to identify the scope and to make sure every team member understands it in detail. (From here on, we’ll use the word “scope” to mean the system, design, process or service that is the subject of your FMEA.)

3.      Fill in the identifying information at the top of your FMEA form. Figure 1 shows a typical format. The remaining steps ask for information that will go into the columns of the form.

4.      Identify the functions of your scope. Ask, “What is the purpose of this system, design, process or service? What do our customers expect it to do?” Name it with a verb followed by a noun. Usually you will break the scope into separate subsystems, items, parts, assemblies or process steps and identify the function of each.

4.      For each function, identify all the ways failure could happen. These are potential failure modes. If necessary, go back and rewrite the function with more detail to be sure the failure modes show a loss of that function.

5.      For each failure mode, identify all the consequences on the system, related systems, process, related processes, product, service, customer or regulations. These are potential effects of failure. Ask, “What does the customer experience because of this failure? What happens when this failure occurs?”

6.      Determine how serious each effect is. This is the severity rating, or S. Severity is usually rated on a scale from 1 to 10, where 1 is insignificant and 10 is catastrophic. If a failure mode has more than one effect, write on the FMEA table only the highest severity rating for that failure mode.

7.      For each failure mode, determine all the potential root causes. Use tools classified as cause analysis tool, as well as the best knowledge and experience of the team. List all possible causes for each failure mode on the FMEA form.

8.      For each cause, determine the occurrence rating, or O. This rating estimates the probability of failure occurring for that reason during the lifetime of your scope. Occurrence is usually rated on a scale from 1 to 10, where 1 is extremely unlikely and 10 is inevitable. On the FMEA table, list the occurrence rating for each cause.

9.      For each cause, identify current process controls. These are tests, procedures or mechanisms that you now have in place to keep failures from reaching the customer. These controls might prevent the cause from happening, reduce the likelihood that it will happen or detect failure after the cause has already happened but before the customer is affected.

10.  For each control, determine the detection rating, or D. This rating estimates how well the controls can detect either the cause or its failure mode after they have happened but before the customer is affected. Detection is usually rated on a scale from 1 to 10, where 1 means the control is absolutely certain to detect the problem and 10 means the control is certain not to detect the problem (or no control exists). On the FMEA table, list the detection rating for each cause.

11.  (Optional for most industries) Is this failure mode associated with a critical characteristic? (Critical characteristics are measurements or indicators that reflect safety or compliance with government regulations and need special controls.) If so, a column labeled “Classification” receives a Y or N to show whether special controls are needed. Usually, critical characteristics have a severity of 9 or 10 and occurrence and detection ratings above 3.

12.  Calculate the risk priority number, or RPN, which equals S × O × D. Also calculate Criticality by multiplying severity by occurrence, S × O. These numbers provide guidance for ranking potential failures in the order they should be addressed.

13.  Identify recommended actions. These actions may be design or process changes to lower severity or occurrence. They may be additional controls to improve detection. Also note who is responsible for the actions and target completion dates.

14.  As actions are completed, note results and the date on the FMEA form. Also, note new S, O or D ratings and new RPNs.

Probability (P)

In this step it is necessary to look at the cause of a failure mode and the likelihood of occurrence. This can be done by analysis, calculations / FEM, looking at similar items or processes and the failure modes that have been documented for them in the past. A failure cause is looked upon as a design weakness. All the potential causes for a failure mode should be identified and documented. This should be in technical terms. Examples of causes are: Human errors in handling, Manufacturing induced faults, Fatigue, Creep, Abrasive wear, erroneous algorithms, excessive voltage or improper operating conditions or use (depending on the used ground rules). A failure mode is given an Probability Ranking.

Rating	Meaning
A	Extremely Unlikely (Virtually impossible or No known occurrences on similar products or processes, with many running hours)
B	Remote (relatively few failures)
C	Occasional (occasional failures)
D	Reasonably Possible (repeated failures)
E	Frequent (failure is almost inevitable)

 

 

Severity (S)

Determine the Severity for the worst case scenario adverse end effect (state). It is convenient to write these effects down in terms of what the user might see or experience in terms of functional failures. Examples of these end effects are: full loss of function x, degraded performance, functions in reversed mode, too late functioning, erratic functioning, etc. Each end effect is given a Severity number (S) from, say, I (no effect) to VI (catastrophic), based on cost and/or loss of life or quality of life. These numbers prioritize the failure modes (together with probability and detectability). Below a typical classification is given. Other classifications are possible. See also hazard analysis.

Rating	Meaning
I	No relevant effect on reliability or safety
II	Very minor, no damage, no injuries, only results in a maintenance action (only noticed by discriminating customers)
III	Minor, low damage, light injuries (affects very little of the system, noticed by average customer)
IV	Moderate, moderate damage, injuries possible (most customers are annoyed, mostly financial damage)
V	Critical (causes a loss of primary function; Loss of all safety Margins, 1 failure away from a catastrophe, severe damage, severe injuries, max 1 possible death )
VI	Catastrophic (product becomes inoperative; the failure may result complete unsafe operation and possible multiple deaths)

 

 

 

 

Detection (D)

The means or method by which a failure is detected, isolated by operator and/or maintainer and the time it may take. This is important for maintainability control (Availability of the system) and it is especially important for multiple failure scenarios. This may involve dormant failure modes (e.g. No direct system effect, while a redundant system / item automatic takes over or when the failure only is problematic during specific mission or system states) or latent failures (e.g. deterioration failure mechanisms, like a metal growing crack, but not a critical length). It should be made clear how the failure mode or cause can be discovered by an operator under normal system operation or if it can be discovered by the maintenance crew by some diagnostic action or automatic built in system test. A dormancy and/or latency period may be entered.

Rating	Meaning
1	Certain - fault will be caught on test
2	Almost certain
3	High
4	Moderate
5	Low
6	Fault is undetected by Operators or Maintainers

Calculation of the Risk Priority Number 

 

To be able to judge objectively the current state of a process or product and the effect of actions during the course of a Failure Mode and Effects Analysis, it is possible to calculate the risk priority number (RPN) of an FMEA automatically.

This is possible as soon as you have determined the valuations for causes and effects of defects and entered them in the system.

In FMEA the Risk Priority Number(RPN) is calculated by using following formula:

RPN = Severity of the Effect X Probability of Occurrence of the cause X Probability of the Detection

Benefits/Advantages

1.     It provides a documented method for selecting a design with a high probability of successful operation and safety.

2.     A documented uniform method of assessing potential failure mechanisms, failure modes and their impact on system operation, resulting in a list of failure modes ranked according to the seriousness of their system impact and likelihood of occurrence.

3.     Early identification of single failure points (SFPS) and system interface problems, which may be critical to mission success and/or safety. They also provide a method of verifying that switching between redundant elements is not jeopardized by postulated single failures.

4.     An effective method for evaluating the effect of proposed changes to the design and/or operational procedures on mission success and safety.

5.     A basis for in-flight troubleshooting procedures and for locating performance monitoring and fault-detection devices.

6.     Criteria for early planning of tests

7.     Improve the quality, reliability and safety of a product/process

8.     Improve company image and competitiveness

9.     Increase user satisfaction

10. Reduce system development time and cost

11. Collect information to reduce future failures, capture engineering knowledge

12. Reduce the potential for warranty concerns

13. Early identification and elimination of potential failure modes

14. Emphasize problem prevention

15. Minimize late changes and associated cost

16. Catalyst for teamwork and idea exchange between functions

17. Reduce the possibility of same kind of failure in future

18. Reduce impact on company profit margin

19. Improve production yield

Limitations

If used as a top-down tool, FMEA may only identify major failure modes in a system. Fault tree analysis (FTA) is better suited for "top-down" analysis. When used as a "bottom-up" tool FMEA can augment or complement FTA and identify many more causes and failure modes resulting in top-level symptoms. It is not able to discover complex failure modes involving multiple failures within a subsystem, or to report expected failure intervals of particular failure modes up to the upper level subsystem or system.

Additionally, the multiplication of the severity, occurrence and detection rankings may result in rank reversals, where a less serious failure mode receives a higher RPN than a more serious failure mode. The reason for this is that the rankings are ordinal scale numbers, and multiplication is not defined for ordinal numbers. The ordinal rankings only say that one ranking is better or worse than another, but not by how much. For instance, a ranking of "2" may not be twice as severe as a ranking of "1," or an "8" may not be twice as severe as a "4," but multiplication treats them as though they are.