SystemEngineering

Week 7/8: Preliminary Design

Relevant Lecture: Week 7: Design Criteria, Week 8 Tradeoff Analysis

Introduction

Once conceptual design is complete the next phase of the systems engineering process is preliminary design. Preliminary design is focussed on verification and optimisation of the conceptual design. This stage of the design process involves a significant analysis and benchmarking of various designs.

The key to benchmarking and tradeoff analysis is to develop system-wide quantitative cost criteria that measure the relative merit of different options. Quality Function Deployment (QFD) and the associated House of Quality (HoQ) is a powerful tool for system-wide comparisons. The House of Quality tool depends on the development of a set of Technical Performance Measures (TPMs) that capture the various requirements of the system weighted by importance. By tracking customer requirements through to TPMs in a properly weighted matrix the customer needs are represented by properly weighted design attributes and performance measures. In this manner, the ‘voice of the customer’ is reflected in the ultimate design.

In a full design, the QFD is used for system optimisation where various design parameter changes are analysed and the resulting design points optimised using the cost criteria. Doing analysis of different design options is too difficult in a short tutorial session, so instead we will use the House of Quality in a benchmarking study – another important use of this tool. Here we will evaluate a range of existing products against the House of Quality and evaluate the relative scores of the different products. This is an important first step in any preliminary design since it provides key insight into the key competitor designs and how they solved the problem.

The Wattle course page has an MS Excel template that you might want to use for this process. There is an empty template as well as a partly filled template for this workshop’s activity.

Click here to download the Excel template for HoQ.

Click here to download the Excel template for the Bicycle Suspension example.

Activity 1: Select a Bicycle Suspension

In this activity, you will compare six different bicycle suspensions and decide which of them best addresses your client’s needs (see Figure 1).

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Figure 1: Stakeholder requirements and importance. (Ulrich and Eppinger)

Enter numbers in a range 1-5 into these columns, where 5 means “addresses the need very well”, 4 means “addresses the need well”, 3 means “addresses the need partly”, 2 means “addresses the need poorly”, and 1 means “does not address the need

In the absence of a real benchmarking study, a reasonable assumption is that there are existing solutions that address all needs well. This corresponds to entering “4” into all rows of the “Benchmark solution” column. Do this, and agree with your team on which needs you wish to carry through unchanged, which ones you wish to emphasize and which ones you wish to de-emphasize. Enter corresponding numbers into the “Planned design” column.

The spreadsheet will compute the “Relative weight” column accordingly.

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Figure 2: Requirements correlations (Ulrich and Eppinger)

The spreadsheet will now compute the “Absolute importance” (sums to an arbitrary value across all design attributes/TPMs) resp. the normalized “Relative importance” (sums to one across all design attributes/TPMs) rows at the bottom of the HoQ.

Activity 2: Understanding Technical Performance Metrics

Consider the Design Criteria DC6 “Maximum Travel”. Look at the data in Figure 3 for the existing front forks.

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Figure 3: Design criteria (Ulrich and Eppinger)

Your goal is to design a TPM using this data along with your expectation of what is possible.

There are fields in the spreadsheet where this information can be entered. Rows 28-30 coloured blue. The spreadsheet does nothing with this data – it is purely information for the engineer.

A Technical Performance Metric can be generated by setting control points for an associated graph. The TPM is a function that assigns a value between zero and one to the measurement of the design criteria.

The standard TPM uses the min, and max and provides a range in which the design scores the maximum value of 1.

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In this course, we will use TPMs that are piecewise linear. This is good enough to capture design tradeoffs in real systems. There are lots of situations where the design may meet targets and the TPM is 1. This is the expected, once you have met design targets it is not necessary to continue to optimise the design. Indeed, a typical design will end up with many targets met and just a few TPM active on the sloped sections where the tradeoffs in the design are active.

Open the second sheet of the spreadsheet “TPM graph test”. On this page, you can visualise different TPMs. You can change the values in the orange boxes C6:C9. Have a play at changing the numbers in the box now and see how the graph changes

This is a design choice in which you encode the system tradeoffs into a quantitative metric. You need to think about whether you are designing for an expensive shock with extra travel or happy to have a lower-performance shock with less travel. In the first case you would want to skew the graph towards the upper end, in the other you skew the graph to the lower end. A good rough guide is to have a flat area scoring 1 across the main range of the benchmark metrics. Then you roll off on either side to model how much it costs to underperform or limit how much energy you put into making the criteria larger.

The benchmarks provide a powerful design tool in this process. A reasonable starting point is to choose control 2 to be the lowest benchmark and control 3 to be the largest benchmark. Then choose control 1 about 10% of the range below control 2, and choose control 4 about 10% of the range higher than the largest benchmark.

This encodes the existing competition without applying any design tradeoff. As long as your design is comparable to existing designs then the metric sits at a unit cost of 1. Only if you start to design outside of expectations does the metric influence tradeoff decisions.

In order to apply some influence on the design it is common to introduce a Target. The target may come from design concept, or from management, or design team discussion. In this case, noting that there are several benchmarks in the mid 40mm travel range, you may set a target around 45mm to be competitive with a certain group of products in the overall range. Then you may choose to push Control 2 higher to penalise short travel designs.

See how this penalises the shorter travel designs.

In some cases, there is a specific target. It is going to be impossible to design a product to have precisely a given unit cost, however, it is still important to be able to reflect the specific target in the TPM. In this case you can set control 2 and control 3 equal.

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Now think about reasonable values for Control 1 and Control 4 for this case. The closer to the target, the more aggressively you are assigning that specific target in the tradeoff analysis.

Another common situation is that there is no maximum value. For example, as long as the designed Shocks can withstand a sufficient period of time in the spray chamber then that is good enough. There is no upper limit required.

In this case, the right-hand side of the graph goes to infinity at value 1. In the excel code, this is difficult to implement so I suggest you just double the target value for controls 3 and 4.

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For the reverse case where there is no minimum – usually, this is associated with a measure that cannot go negative anyway. In this case, we set control 1 and control 2 to zero

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If there is negative data then you need to do a similar trick to the no maximum case but in the negative axis.

The last case to be discussed is where the values of the design criteria are drawn from a finite set. For example, design criteria 11 considers the headset size and lists them against each design. A TPM for this design criteria assigns a value between 0 and 1 to each of the finite possibilities.

Metric 11 from bicycle design TPM
(1, 1.125) 0.5
(1, 1.125, 1.250) 1
N/A 0

The only tricky part is to fit this idea into the spreadsheet format. Of course, you could just hand-enter the appropriate TPM and overwrite the spreadsheet function – there is no requirement to use the control point mapping. But if you want to use the spreadsheet function, this can be done by mapping the finite set of options into a number. For example, assign the number of headset sizes to each category.

Metric Number of standard headset sizes TPM
N/A 0 0
  1 0.25
(1, 1.125) 2 0.75
(1, 1.125, 1.250) 3 1

Again – I am assigning a TPM to each category – however, this time I am deliberately choosing values that allow a simple linear interpolation. Choosing the control points as follows instantiates this TPM.

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Although this is a bit of a hack it works quite well. One of the very common cases is if the design criteria is a Boolean parameter (taking only two possibilities) map those options to {0,1} and use

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Activity 3: Evaluate concept designs

Consider the 5 concepts drawn from benchmark data: ST Tritrack, Mainray 2, Rox Tahx Quadra, Rox Tahx T1 21, Tonka Pro, and Gunhill Head Shox.

You will see that in the metric space below the relative importance of the metric and benchmark information has been entered. Your goal is to generate Technical Performance Metrics (TPMs) for each design criteria and to evaluate the 5 concepts.

First, you need to go along the TPM metric column and fill in a target or target range for every metric. Do this based on your desire to improve certain aspects of the design or not. You can see which requirements corelate to which design criteria, and those where you want to improve the design, the targets should be set to push the design. In other design criteria, meeting the industry benchmark should be sufficient.

Once this is complete, then you would use this House of Quality to evaluate your own design. However, a good first step is to evaluate the benchmark designed against your design criteria. After all, if one of the benchmark designs scores more highly than your design, you would not be well placed in the market.

Go ahead and score the various concepts by entering the values of the metrics into the columns. The QFD score is in the blue box on the left of the score. Figure 3 contains the performance of six different bicycle suspension designs against the TPMs. Use this data to score the concepts against every TPM.

You should get a score for each concept in the light blue box in column C. Since everything is normalised, the score should be between 0 and 1.

Activity 4: Interpreting the outcomes

Any optimisation tool is only a tool. The data generated by this process still needs to be interpreted. In your small groups discuss what you can learn from the House of Quality. Consider the final scores obtained from the House of Quality. Are there significant differences between the various products? How would you be able to test the significance of a difference in the design?

What would you do with this information in a design process?

Learning Outcomes

  1. Identify, analyse and objectively resolve design trade-offs
  2. Understand different types of requirements, constraints, and assumptions encountered during systems design

Additional Reading