Traditional prototyping

Prototyping and testing work is usually done during product development for three main reasons: evaluate and validate design choices, mitigate uncertainty through real life assessment and learn by experimentation (Otto & Wood, 2001; Thomke, 2008; Ulrich & Eppinger, 2012). In the aerospace industry, evaluating and validating choices generally aims at demonstrating performance and compliance with design objectives and regulatory authority. Mitigating uncertainty consists in bringing several bodies of knowledge into interaction so each one’s influence is disclosed and taken into account as soon as possible. Learning by experimentation, on the short term, supports design convergence. On the long term, it spearheads the core organisational learning system at the root of set-based design methodologies as developed in chapter 2 and beyond.

Physical prototypes are generally built to examine design problems, including the evaluation and refinement of solutions, as well as measuring one or more of its core qualities of role, implementation, and look and feel (Houde & Hill, 1997). These qualities can be defined as follows:

- Role: The function and how it corresponds to the user’s needs;

- Implementation: The constituent parts and the logic through which the product function is performed;

- Look and feel: The sensory experience of the user.

Figure 4.3: Three core qualities of a prototype. Adapted from (Houde & Hill, 1997)

Prototypes are also used during the PDP for four other main objectives: learning, communication, systems integration and milestones (Lazzari & Raimondo, 2001; Otto & Wood, 2001; Thomke, 2008; Ulrich & Eppinger, 2012). It is useful therefore to consider the prototype as an approximation of the product along one or more dimensions of interest (Blomkvist & Holmlid, 2012; Camburn et al., 2013; Dunlap et al., 2014; Hammon et al., 2014; Hannah, Michaelraj, &

Summers, 2008). For example, a prototype can be classified according to the degree to which it approaches reality. This level of fidelity can determine the prototype’s ability to detect unanticipated phenomena, which can be difficult with non-physical prototypes e.g. digital mock-ups (DMU), computational models, etc. (Gerber, 2009; Häggman, Honda, & Yang, 2013).

Ulrich and Eppinger (2012) note that some experimental prototypes are built and tested early in the design process, prior to the definition of the detailed part geometry, see Fig.2.3. These prototypes are thus based on lower maturity documentation created following the concept generation. Furthermore, they are usually built without using mass production infrastructure or tooling (Clark & Fujimoto, 1991). These prototypes are named looks-like and works-like models and serve the purpose of design concepts instantiation and evaluation. Prototypes at the testing and refinement phases are more mature approximations of the product and they effectively disclose a part of its actual behaviour and signal necessary changes, while considering the level of approximation.

The amount an organisation can learn from a physical prototype directly depends on its level of approximation. Table 4.2 presents three categories of prototype developed during the PDP,

namely alpha, beta and preproduction. These categories are defined by the prototype’s main objectives, its similarities with the production product, and its relevant deviations.

Table 4.2: Properties of typical physical prototypes categories. Adapted from (Ulrich & Eppinger, 2012)

Main objectives Similarities Deviations Alpha prototypes Assess whether the product works

as intended Geometry, material Production

The objectives listed in the table deal primarily with assessing the concept performance and validating the supply and manufacturing processes. These include testing activities preceding the serial production. It should be noted that prototyping and testing activities are also carried out in the aerospace industry for certification issues, mature technologies introduction as well as for investigation of failures in the field. Prototyping and testing is therefore performed for the main part during the PDP but is not excluded from happening well earlier or well later during the product lifecycle. Figure 4.4 exemplifies aerospace industry practices by graphically showing the numerous physical prototypes that can be built and tested during the development of a complex aerospace system.

Figure 4.4: Experimental builds during the development of a complex aerospace system

The graph allows to visualize data as extracted from a legacy development and test information system and plotted using a graph editor. Each circular cloud on the left side of the exhibit represents the cluster of experimental builds for one development program or technology demonstration. It is possible to see that the number of physical prototypes that are built and tested varies according to the nature of the development program (NPI, derivation, TRL, etc.) and also, the extent to which the new product reuses existing proven technology, modules and components.

Besides the progressive level of fidelity selected while prototyping, it is not always easy to find an optimal approach to prototyping during a development project, especially when considering the variety of strategies and techniques that may exist to improve the outcome of the activity (Christie et al., 2012). Systematic methods for planning and executing design prototyping activities exist in the literature with the aim of improving the overall development cost and performance. For example, Camburn et al. propose a phased approach to systems prototyping which consists of the partitioning, search and implementation phases (Camburn et al., 2017;

Camburn et al., 2015a; Camburn, Jensen, Crawford, Otto, & Wood, 2015b). Partitioning defines the aspect from which the complex system is prototyped and tested i.e. by function, subsystem or domain e.g. usability, manufacturability, sustainability, etc. Search is performed either through an iterative testing of overlapping design concepts or the parallel testing of multiple concepts at a single point in time. These are equivalent to exploration strategies found in point-based vs. set-based design (Ward et al., 1995). Implementation is the actual prototype execution. Key prototyping techniques are synthesized from the literature (heuristics) to define three

“conceptually distinct cost reduction techniques” i.e. scaling, isolation and abstraction.

Mathematical equations are also proposed to assess the expected performance of iteration versus parallel testing on one side and, on the other side, assess the reduced prototyping cost that results from considering a scaling factor, an isolation factor, an abstraction factor or an eventual combination of these. The effectiveness of multiplying prototypes early in the design effort to later iterate on higher fidelity models is demonstrated in controlled studies using the proposed metrics (Camburn et al., 2015a) as well as a graphical representation of the design topological space exploration (Camburn et al., 2017). As reviewed in section 2.1 and 4.1, multiplying prototypes by favouring low fidelity, virtual prototyping, requirement relaxation and rapid prototyping is similarly believed in the lean paradigm to reduce development time and cost and to increase flexibility, development performance and a company competitive advantage on the long run (Hoppmann et al., 2011; Ward & Sobek, 2014). It is therefore vital for a company to carefully study the various strategies, dimensions and techniques while planning for prototypes during their development projects, especially when considering the cost of building prototypes (virtual or physical).