Step 3: Idea selection 68

The idea generation phase usually gives rise to several options. Hence, this divergent activity must be followed by a convergent idea selection task (Rietzschel et al. 2006). The idea selection, sometimes called Opportunity Analysis stage (e.g. Cagan and Vogel 2001; Koen et al. 2002; Achiche et al. 2013), constitutes the decision-making phase of the Product Planning that allows choosing the alternatives to be further developed.

Both benefits and feasibility limits of identified ideas are assessed in this step in order to support comparison and final choice of the most promising one.

The main output of this task is a New Value Proposition (NVP), i.e. the list of product attributes and related performance that should be used as a guide for designing and developing new products.

This step includes the following activities:

1. Benefits’ analysis: such activity is aimed at assessing the potential success of

ideas (or sub-group of ideas) identified in the previous phase.

2. Costs’ analysis: this activity is aimed at assessing the feasibility of new

identified ideas, according to various points of view (e.g. economic investment, requested time, requested skills, etc.).

3. Product profile’s choice: this activity is aimed at selecting the most promising

idea, comparing benefits and costs of new product profiles.

Step implementation

1. Benefits’ analysis

In order to select the most promising product idea, mere subjective assessments should be avoided. The analysis of benefits can be carried out through a repeatable and reliable tool introduced in the second Chapter, i.e. VAM (Borgianni et al. 2013). It allows estimating the potential market appraisal of a new artefact through a balance of its functionalities with respect to the alternatives existing in the market. The metrics have been defined through an induction process from a large collection of successful innovations and market failures. Therefore, this tool is completely independent from subjective assessments.

The tool requires as input product attributes classified according to the four differentiation moves and functional features (described in the previous step) and gives as output success percentages ranging from 0 to 100% of each product profile. The VAM formula is:

𝑉𝐴𝑀(%) = 1

1 + 𝑒!!×100      (1)

Where z can be obtained through the following formula:

𝑧 = −3,19 + 3,44× 𝑈𝐹 𝐶𝑟𝑒𝑎𝑡𝑒 + 1,32× 𝐻𝐹 𝐶𝑟𝑒𝑎𝑡𝑒 + 2,87× 𝑅𝐸𝑆 𝐶𝑟𝑒𝑎𝑡𝑒 + 0,97× 𝑈𝐹 𝑅𝑎𝑖𝑠𝑒 + 1,75× 𝐻𝐹 𝑅𝑎𝑖𝑠𝑒 + 0,41× 𝑅𝐸𝑆 𝑅𝑎𝑖𝑠𝑒 − 0,84× 𝑈𝐹 𝑅𝑒𝑑𝑢𝑐𝑒 − 0,27× 𝐻𝐹 𝑅𝑒𝑑𝑢𝑐𝑒 − 1,78× 𝑅𝐸𝑆 𝑅𝑒𝑑𝑢𝑐𝑒 − 0,46× 𝑈𝐹 𝐸𝑙𝑖𝑚𝑖𝑛𝑎𝑡𝑒 − 9,49× 𝐻𝐹 𝐸𝑙𝑖𝑚𝑖𝑛𝑎𝑡𝑒

− 1,65× 𝑅𝐸𝑆 𝐸𝑙𝑖𝑚𝑖𝑛𝑎𝑡𝑒        (2)

Each term characterized by functional feature/differentiation move concerns the number of product attributes classified in that manner. For instance, in formula (2)

UF⁄Create will be equal to 3 if product profile would include three radical new attributes

related to the delivery of useful functions. In order to use this tool, it is important that all the attributes are expressed in a positive manner because, as seen above, the raise of performance improves the customer satisfaction and vice versa.

If VAM results in the range 0-50% the product is expected to fail, vice versa the range 50-100% suggests a commercial success. However, VAM is affected by uncertainty because of the nature of the approach used to develop this tool. Each weight of formula (2) varies according to a normal distribution characterized by a given mean value and a standard deviation. Therefore, the author introduced an approach to assess the uncertainty range:

1. Pick n (e.g. 5000) random weights from each normal distribution; 2. Calculate n VAM using the collected weights;

3. Supposing a normal distribution of obtained data, identify the mean value and standard deviation of VAM data. Latter index will be useful to consider the uncertainties on the mean value of VAM.

Obviously these calculations required an automatic system and a Java app (see Appendix C for the code) has been developed. A screenshot of the tool is shown in Figure 3.10. The app requires as input the number of attributes classified according to the two taxonomies and automatically calculate the VAM and the uncertainty on that value.

According to the ideal properties collected in Chapter 2, VAM allows to satisfy the following ones:

• it supports the selection of most beneficial product idea; • it is quick and ease to use;

• it has been implemented by the author in a computer application;

• it has been improved in terms of reliability, integrating a method to assess the uncertainty of the outputs;

• it is independent from inputs subjectivity; • it focuses on product attributes.

Figure 3.10 screenshot of the tool developed to calculate VAM. The labels CR, RA, RE, EL, UF, HF, RES stand for Create, Raise, Reduce, Eliminate, Useful Functions, Harmful Functions and Resources respectively

2. Cost’s analysis

Feasibility of a product can depend from several factors. In addition, it is very difficult to assess costs in early phases of NPD process. Therefore, a flexible tool, namely Profile Cost Matrix (PCM), has been developed in order to guide this activity according to specific companies needs and priorities like: expected economic investment, requested time to design and develop the product, reputation risks, etc.

As shown in Figure 3.11 in PCM companies’ demands are prioritised assigning a weight, wj (e.g. from 0 up to 5), and attributes (with their related strategic moves) are linked

to company’s demands through correlation factors, aij (e.g. Null=0, N; Low=1, L;

Medium=3, M; High=9, H). High correlation means that a certain action made on a product attribute could have a great impact on a specific need. In other words it entails a reduction of feasibility of product idea.

Company’s Needs/Risks

Need 1 Need 2 … Need m

Weight w1 w2 … wm Attribute’s Cost

Pr od uc t At tr ib u te s - Di ff er en ti at io n mo ve s Attribute-move 1 a11 a12 … a1m Cost A1 Attribute-move 2 a21 a22 … a2m Cost A2 … … … … … … Attribute-move n an1 an2 … anm Cost An

The cost of each attribute (Ai) can be calculated through the following formula: 𝐶𝑜𝑠𝑡  𝐴!= 𝑤!𝑎!!+ 𝑤!𝑎!!+ ⋯ + 𝑤!𝑎!"= 𝑤!𝑎!"      

!

!!!

(3)

Hence, the feasibility of a new idea can be obtained summing up the costs of the attributes related to that product profile (Pk):

𝐶𝑜𝑠𝑡  𝑃!= 𝐶𝑜𝑠𝑡  𝐴! !_!      (4)  

1. Product profile’s choice

After identifying both potential benefits and costs of new product profiles, the final step of the process concerns the selection of most promising one.

As advanced by Miles’ Value Engineering (Miles 1949), firms usually want to maximize the ratio between the profitability of the delivered products and the costs pertaining its design and development. Therefore, the ratio between VAMs and feasibility costs (5), namely Value Index (VI), can be used to rank different product profiles.

𝑉𝐼 =𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 𝐶𝑜𝑠𝑡𝑠 =

𝑉𝐴𝑀 𝐶𝑜𝑠𝑡  𝑃      (5)

Using this approach the selection task could be computerised and the output would be independent from subjective assessments.

However, it could be useful to have a wide picture of alternative profiles in order to make a well-founded decision. Therefore, a benefits-costs (success-costs) chart can be used to represents both potential success and feasibility of alternative ideas (Figure 3.12).

The chart can be divided into four areas:

1. low cost-low success: here the feasibility is high but potential success is less than

50%;

2. low cost-high success: this is the most promising area because the feasibility of

profiles is high and ideas are expected to achieve a commercial success; 3. high cost-high success: in this area high success require high costs;

4. high cost-low success: this is the less promising area because both feasibility and

potential success are low.

The minimum cost can be added on the left side of x-axis, while the maximum one on the right side. In this way all the other costs can be distributed on the span. To simplify this process smallest and biggest values can be assumed equal to 0 and 100, respectively, and other values can be normalized as it follows:

𝐶𝑜𝑠𝑡! % =

𝐶𝑜𝑠𝑡!− 𝐶𝑜𝑠𝑡!"#

𝐶𝑜𝑠𝑡!"#− 𝐶𝑜𝑠𝑡!"# ×100      (6)

Observing the chart, companies can decide which product profile they want to develop, according to their propensity to risk and available resources.

In the end of the process the selected product profile, i.e. the list of attributes and related differentiation moves, will constitute the target objectives of the following design phases.

Partial results

The results obtained through the execution of the final step of the method are summarized as it follows:

1. Benefits of product profiles: potential success of new product profiles;

2. Costs of product profiles: potential cost (feasibility) of new product profiles;

3. Selected product profile: most promising product profile that company want to

develop in the following phases of the design process.

According to ideal properties collected in Chapter 2, general approach and techniques illustrated in this step satisfy the need of supporting the selection of most beneficial product idea. In addition, proposed tools are quick and ease to use, they can potentially involve multidisciplinary teams, they allow to formally schematize identified ideas and they focus on product attributes. Eventually, an existing tool (VAM) has been implemented in a computer application, improving both quickness of use and reliability (adding an analysis of uncertainties of output data).