Value-based metrics

Value-based approaches are an extension of those techniques and tools outlined above into the cost, engineering management and systems domain, where Value Driven Design (VDD) is a process activity which takes place iteratively, across all levels of the organisation, as stated by Eres et al. (2014). This VDD methodology provides early multidimensional value information, in order to:

• Enable the selection of early concepts and designs representing the highest value contribution;

• Enable system optimisation at the highest integration level (in terms of the value proposition);

• Promote the development of high quality and high value driven requirements.

A number of research papers have dealt with this domain, focusing on larger aircraft concepts, different technologies, and incorporating the approaches as discussed in the previous section. These papers can be loosely collected into those that deal with value-based life-cycle evaluation methodologies which have been developed by Georgia Institute of Technology; Value-Based Multi-Disciplinary Optimisation (MDO) techniques developed by Massachusetts Institute of Technology (MIT); with VDD undertaken at the Value Driven Design Institute Illinois.

The major contributor to this area is research undertaken by MIT, where a different approach was undertaken which relied more on financial modelling techniques extended to the operational domain; market uncertainty, business risk, development and manufacturing costs and aircraft demand. Papers by March et al.

(2009), Markish & Willcox (2002, 2003), Peoples & Willcox (2004), Willcox (2005, 2002), Willcox and Wakayama (2003) optimise these parameters using stochastic and dynamic programming approaches to investigate performance, cost and revenue for single and family of aircraft cases studies. Research by Collopy (2009) and Collopy &

Hollingsworth (2011) provides a useful insight into VDD involving value modelling theory, aerospace value models, and guidelines for constructing these models.

2.5.3.1 Cost bottom-up and top-down methods

Curran et al. (2004, 2005) provides a very good account of cost estimating techniques including analogous, bottom up, neural networks, fuzzy logic and parametric costing methods. Parametric cost estimates utilise Cost Estimate Relationships (CERs) and associated mathematical algorithms (or logic) to derive cost estimates. This approach is commonly used within the aerospace industry which typically involves linear regression analysis for CER development. This is further applied in Roskam (2002), as discussed below. These CERs are derived using a methodology as shown by Figure 11.

Figure 11. Methodology for developing parametric cost models

Curran et al. (2004)

2.5.3.2 Causal cost modelling approaches

Castagne et al. (2008) states that “ideally, any facilitating costing methodology should be able to operate and interface at various levels and during all stages of the life-cycle”. This has been a fundamental consideration in the development of an approach referred to as Genetic Causal Costing. This is conceptualised in Figure 12 where the causal definition of cost to design dependencies is seen in the context of product families. The model adopts the scientific principle of categorisation whilst also incorporating the requirement of utilising causal relations. Although this cost modelling approach has been successfully applied to airframe manufacturing, it is noted that it could be also applied throughout the aircraft development life-cycle including aircraft operations.

Figure 12. Conceptual illustration of causal cost modelling approach

Curran et al. (2004)

2.5.3.3 Systems life cycle costing

Fabrycki and Blanchard (1991) outlines a Cost Breakdown Structure (CBS) also known as a cost tree framework which can be used to support lifecycle cost analysis.

This CBS is provided as a means to facilitate the initial breakdown of costs (top-down) and the subsequent estimation of costs on a functional basis (bottom-up). The CBS includes all costs and is intended to aid in the overall visibility of costs. Fabrycki and Blanchard (1991) state that the CBS is tailored to specific requirements, with the cost categories varying in terms of the depth of coverage and the system being evaluated.

In these case studies, the system evaluated comprises the alternate fuel or propulsion system modification of a small aircraft. Therefore, it is a requirement that the CBS shall cover of the modification development lifecycle from research and development through to disposal. An example of a general top-level CBS is presented in Figure 13.

Figure 13. Excerpt - General cost breakdown structure – intentionally cropped

Fabrycki and Blanchard (1991)

2.5.3.4 Aircraft cost estimation methods

Roskam (2002) provides a general methodology for aircraft cost estimation using linear regression analysis of existing designs. This approach is based on a thorough parametric analysis of general categories of aircraft, such as twin-engine commuter category aircraft, the application of regression analysis to estimate various costs associated with Research, Development, Test and Evaluation (RDTE), manufacturing and operating costs (direct and indirect). It also provides an account of aircraft design optimisation and design to cost and associated constraints. However, this reference does not provide methodology that could be applied to evaluate new technologies apart from general notes and guidance in relation to configuration selection, drag prediction, loads prediction, laminar flow and range prediction.

Nevertheless, this reference is a value comparative data resource from which to derive baseline data to validate the methods and data outputs from this proposed research.

Gudmundsson, (2014) provides another parametric costing methodology based on the Development and Procurement Costs Aircraft (DAPCA-IV) model to estimate development costs associated with General Aviation (GA) aircraft and Business Aircraft. This costing methodology establishes special cost estimating relationships which are a set of parametric equations that predict aircraft acquisition costs using only basic information like empty weight and maximum airspeed. For this reason, the DAPCA-IV model can only be used to estimate cost for RDTE and workforce estimation. It should be noted that the DAPCA-IV model was based on cost structures associated with military aircraft. Therefore, the modifications to this method presented in this reference are based on the “Eastlake” model by Eastlake & Blackwell (2000) which accounts for GA and business aircraft as described above. Like the models presented in Roskam (2002) the basis of these cost models are parametric and statistically based, and therefore do not take account new technologies integration.

However, like Roskam (2002) the cost models, particularly those for business aircraft provide validation data for the methods proposed in this research.