Data

3.3.2.1 Collection of data

The data that was obtained from the Car magazine publication is the primary data used in this research to develop the parametric cost model. A total ofeighty (80) vehicle models were selected from the car magazine and used for developing the parametric cost model such that the final database is equally representative of all the model engine power for the power ranges

involved. Sixty (60) of the vehicle models were used to build the parametric cost model whilst an additional fifteen (15) vehicle models were used for the testing of the parametric cost model.A further five vehicle models were used to test the parametric cost model beyond the parameter limits. Before I proceed in detailing the kind of information that was used to build the database, perhaps it is imperative at this stage to take a step backwards and put things in perspective.

3.3.2.2 Assumptions and simplifications made to the data

In essence what has been donein the study is that the motor vehicle data obtained from the car magazine was used to simulate an engineering system which is still in the “development phase”, viz. in the feasibility study stage. What we know about feasibility studies is that at this stage of the lifecycle of any system, very little information is known about the system. Complete system specificationsare not yet available as the system has not yet been fully developed; only performance parameters of the envisaged system are known at this point and are not sufficient to produce detailed cost estimates. On the other hand, the data contained in the car magazine is for a fully developed system. However, to simulate a condition that closely resembles that of a feasibility study, many of the quantitative vehicle specifications available in the car magazine were left out. Only those specifications that were deemed to be typical of the kind of information available during feasibility study stages were included in the parametric cost model database as the key performance parameters.

3.3.2.3 Selection criteria of key parameters

The criteria used to select these key parameters were based on the experience of the author in dealing with feasibility studies. The information available is usually just basic information about the overall performance characteristics of the system such as the conveyor design throughput in tons per hour, running length in metres of the conveyor, overall conveyor lift in metres etc. So for our parametric cost model, the criterion was to select the most basic information about the vehicle but yet which still had some effect on the performance of the vehicle.

In Table 3.1 below I present all the quantitative specifications presented in the car magazine.

The specifications I chose that I believe are representative of the kind of information that would be available during a feasibility study stage are highlighted in yellow.

Table 3.1: Motor Vehicle Specifications Published in the Car magazine

• Vehicle acceleration from 0 – 100 km/h,

• Engine capacity, and

• Engine torque.

A process of elimination and engineering logic was used in deciding on the key parameters given the available data as follows:

• Engine Capacity – likely.It is related to the engine maximum power because the bigger the capacity the more the power produced.

• Engine max power – most likely.To the author this represents the design throughput in the conveyor belt example.

• Power/mass ratio – most likely. However, this information is not provided formost vehicle models listed. Moreover, it is the opinion of the author that this parameter would not be known during the feasibility stages.

• Engine max torque – most likely. It is related to engine power and it is also commonly referred to when talking about vehicle performance.

• Amount of transmission gears –likely. However, according to the author this kind of information would only be available after the detail design phase.

• Acceleration from 0 – 100 km/h. Most definitely–This is the most referenced performance parameter in any vehicle performance conversation.

• Maximum speed. Most likely –This is also referenceda lot in vehicle performance discussions.

• Fuel consumption – likely. Big engines produce big power and consume more fuel.

However, this parameter is unlikely to be available at feasibility stage.

• Number of airbags –not likely.

• CO₂ emissions – not likely.

Although many of these parameters are related, only a few would be known in the early stages of the development of the system viz., Engine Power, Engine Torque, Engine Capacity, Acceleration, and Maximum Speed. However, these parameters were tested for statistical significance before including them in the final parametric cost model, the details to follow in the results chapter. The test that was used to test the statistical significance of these parameters is contained in the next subsection 3.3.3.

In addition, Khan and Manarvi[25] in their research paper titled “Selecting a Spots Car through Data Mining of Critical Feature” were able to come to the conclusion that the vehicle parameters that have an influence on superior performance, which correspond to my designation above are Retail Price, Engine Capacity, Engine Power, and Top speed. This is piece of information is used as a guiding principle to indicate that we are headed in the right direction.

3.3.2.4 Characteristics of the selected data

This section describes the characteristics of the data obtained and used in developing the parametric cost model. The full database of motor vehicle models is contained in Appendix A.

Please note that the different vehicle models are referred to as systems; the exact description of the vehicle model has no significance on the outcomes of the research. The aim of this section is to discuss the method followed when selecting the data i.e. the criteria used to select the vehicle models and rationale behind the criteria.The criteria that was used in selecting the models was based on the model’s maximum engine power output. The vehicle models were selected in such a manner that at the end there will be equal number of vehicle models in an interval of 30 kW starting from the lowest power vehicle model to the highest in the database.

The rationale forusing this criterion was to ensure that that all vehicle models are represented equally in the database as far as the engine power is concerned. Emphasis is placed on engine power by the author as he believes it is the single most-weighing parameter on the retail price of a vehicle in the database of the three selected key parameters.The resulting histograms are presented in Figure 3.1 to 3.5 below.

0 2 4 6 8 10 12

51 - 80 81 - 110 111 - 140 141 - 170 171 - 200 201 - 230

Frequency of Occurence

Engine Power [kW]

Histogram - Engine Power

Figure 3.1: Engine Power Histogram

Figure 3.2: Maximum Speed Histogram 0

2 4 6 8 10 12 14 16

141 - 160 161 - 180 181 - 200 201 -220 221 - 240 241 - 260 > 261

Frequency of Occurence

Maximum Speed [km/h]

Histogram - Maximum Speed

Figure 3.3: Acceleration Capacity Histogram

0 2 4 6 8 10 12 14 16 18

1001 - 1649 1650 - 2049 2050 - 2449 2450 - 3049 3050 - 3999

Frequency of Occurence

Engine Capacity [cc]

Histogram - Engine Capacity

Figure 3.4: Engine Capacity Histogram 0

2 4 6 8 10 12 14 16 18 20

4.0 - 6.0 6.1 - 8.0 8.01 - 10.0 10.1 - 12.0 12.1 - 14.0 14.1 - 16.0

Frequency of Occurence

Acceleration 0 - 100 km/h [s]

Histogram - Acceleration

0

The distribution of the data for Maximum Speed, Engine Torque, and Engine Capacity in Figure 3.2, 3.4 &3.5 above does not resemble any particular pattern and there does not seem to be any bias or skew in the distribution. In Figure 3.3 for Acceleration, the distribution is a normal one. It can be concluded that there is a good representation of all key parameter values in the database.

The table below contains a summary of all the basic statistical data for each parameter. The data is important in understanding the limits of input parameters of the parametric cost model.

Table 3.2: Basic Database Statistics