Step 1: Information gathering 46

The aim of the first step is to collect information about existing products or services, allowing the analysis of as-is scenario in a reference market. This preliminary activity is fundamental to understand the current offer and plan differentiation strategies based on customer value. Such an exploration supports even another demand, i.e. avoiding the development of existing solutions (especially in case of exclusive rights protected by patents).

In order to satisfy these objectives, the information gathering phase requires performing the following activities:

1. Data mining: such activity is aimed at extracting and collecting all the

information related to the current offer in a reference market. This task allows identifying competing factors and related performances offered by competitors and/or alternative products that satisfy analogous needs.

2. Definition of the industrial standard: this task is aimed at identifying the main

previous activity. In addition, it can support the identification of further product and/or service categories not considered during the first task.

3. Representation of the as-is scenario: this activity is aimed at depicting the data

collected and clustered in the previous tasks in graphical form. The drawn value curves that show attributes and performances offered by industrial standard(s) constitute the strategic framework that supports the planning of differentiation strategies.

Step implementation

1. Data mining

The data mining task is an essential activity (Pahl and Beitz 2007) that allows collecting information about a certain product or service, competitors’ offers and alternative goods (that satisfy analogue needs). Even if most of the information are collected in a preliminary phase, this task can be repeated several times during Product Planning because further information can be required carrying on with the process.

This activity can be carried out involving customers and/or taking in consideration several information sources that nowadays can be mostly found on the web. However, taking part to fairs, workshops, visiting stores, consulting experts and testing existing products or services can provide useful information too.

The literature search highlighted no rules to collect data in the Product Planning phase and, according to the author’s research team experience, each company has its own approach. However, in order to accelerate this task the main types of sources (and some illustrative websites) have been summarized in Table 3.2 and a possible roadmap is shown in Figure 3.2.

Type of sources Illustrative sources Illustrative websites Technical

sources

Thesis, lecture notes, documents made by experts and/or enthusiasts, encyclopaedias, etc. www.wikipedia.org www.madehow.com www.slideshare.net www.openthesis.org etc. Technical- commercial sources

Catalogues, companies’ websites, resellers’ websites, fairs’ websites, online auction, (virtual) leaflets, price comparison shopping sites, etc.

www.ebay.com www.amazon.com

www.pricegrabber.com etc. Scientific

sources papers, workshops, etc. Books, scientific journals, conference

www.scholar.google.com www.scirus.com www.scopus.com etc. Patents Innovation patents, design patents, utility

models www.espacenet.com www.uspto.gov www.wipo.int/patentscope www.google.com/patents etc. Reviews made by customers and/or experts

Video reviews, blogs, forums, online communities, social networks, FAQs, etc.

www.youtube.com www.ciao.com www.amazon.com etc. Table 3.2 list of main available sources that can be analysed during the Product Planning phase. The Table provides some illustrative websites too

Figure 3.2 possible roadmap to support the data mining activity. For each task the useful sources are showed on the right and main outputs are inserted under each block

In case the type of product is new to the firm a preliminary search has to be carried out in order to understand how the product works and its main features. After that, competitors have to be identified. Usually companies already know the actors that operate in the reference market, anyway some hints to identify competitors can be:

• searching on Internet (e.g. focusing on resellers’ websites, online auction, etc.); • participating in trade fair (or looking at fairs’ websites);

• asking to stakeholders (e.g. commercial agents, technicians, customers, etc.) which brands they know and/or prefer;

• going to distribution channels (if product is sold in stores), looking at marketed brands and/or asking to assistants, which are the best (or most sold) products.

Besides the analysis of competitors’ offer, products/services that satisfy the same needs in a different manner can be analysed in order to create a complete picture of the as-is scenario.

The information that has to be highlighted from collected sources concerns competing factors and related performances currently offered. According to the approach showed in the previous section all technical features have to be expressed in terms of product attributes. For instance, Table 3.3 shows this abstraction process, starting from the specifications of a dishwasher identified on a company’s website.

Technical features Product attributes Notes

Dimensions (cm): 82x59x57 Minimal amount of space

Another attribute can be related to the compatibility with kitchen cabinets Energy efficiency class:

A+++ Energy saving

This attribute could be split into ethics and cheapness demands

Standard cycle water consumption (lt): 11

Minimal consumption of water

This attribute could be split into ethics and cheapness demands

Number of programmes : 8 Versatility of use

Each program can be expressed as the possibility of satisfying specific needs. Place settings : 13 Maximum capacity

Sound Power (dB(A)) : 49 Noiselessness

Salt refill indicator light Ease of managing salt refill

Table 3.3 illustrative conversion of product features into product attributes for a dishwasher. Some notes to provide clarification, if necessary, have been provided

It is important to express all the attributes in a positive manner (as shown in Table 3.3), i.e. the raise of performance has to improve the customer satisfaction and vice versa. Indeed, it allows having a homogeneous set of attributes that simplify the analysis of as-is scenario and the following differentiation strategy. For instance, according to Table 3.3, if designer uses a set of attributes that includes “noise” (instead of “noiselessness”), “occupied space” (instead of “minimal amount of space”), “versatility of use” and “ease of managing salt refill” the raise of attributes’ performances produces different effects for the first two and the last two factors, i.e. the reduction and increase of customer satisfaction, respectively.

Three kinds of performance can be identified according to the type of attribute: • quantitative (e.g. cheapness, quickness, etc.);

• qualitative (e.g. aesthetics, ease of use, etc.);

• present/absent (e.g. water resistant, possibility of use in the dark, etc.).

It is quite easy to identify information about quantitative and present/absent attributes using most of sources collected in Table 3.2, on the other hand the performance of qualitative attributes are quite difficult to identify/assess. The most useful sources to gather information about this latter type of attributes are the reviews made by costumers and/or experts. However, they can state subjective experiences that are not generalizable.

Some hints to find and conveniently assess these sources are presented here in the following:

• Consumer and online-shopping portals (e.g. www.ciao.com, www.amazon.com) allow identifying both comments and “statistics” about existing product attributes (see Figure 3.3). These “statistics” are based on one or more customer reviews. Hence, the first information that has to be checked is the number of reviews, in order to understand if data can be generalizable.

• Consumer forums provide lots of information about product attributes and perceived performances. However, only widely shared visions have to be collected, neglecting comments based on specific needs or that are radically different from the others. Sometimes these feedbacks can be also used to identify new opportunities (e.g. developing a product for disabled persons, professionals, etc.).

• Frequently Asked Questions (FAQs), available on several technical-commercial sources (see for instance Figure 3.4), allow understanding the main demands of customers. According to these questions and related answers, the user can infer if and how some product attributes are offered. For instance, if a question is: “Why my dishwasher does not clean dishes completely?” and the answer is: “It can happen with persistent dirt, you should manually remove it”, the cleaning efficacy of the product is surely not high.

Figure 3.3 screenshot of a web page of www.ciao.com. It shows 24 reviews about a smartphone, providing both an average assessment of the product and of 5 attributes/features (as highlighted in red boxes)

Figure 3.4 screenshot of a FAQs web page (source www.whirlpoolindia.com)

In addition, this kind of sources can be used to understand if currently offered performances outstrip customers’ demands or barely satisfy their expectations. For instance, customers that agree with the statement: “the product provides too many functionalities” allow understanding that the number of functionalities (or the versatility of the product) should be reduced. Vice versa if customers claim that a product is too much noisy, the noiselessness could be improved in new products.

Besides the analysis of as-is scenario, data mining activity can even support the identification of new opportunities in terms of not widespread product attributes, completely new technologies and/or solutions that are typical of different industrial field. The sources that mainly allow identifying this kind of information are scientific works (e.g. scientific papers, books, etc.) and patents. Even if it is not the main goal of this task, these data could be particularly useful during the following phases of product development process. Therefore, the author decided to provide a quick overlook of main literature techniques that support the analysis of new technologies. Here in the following diffused methods are briefly described:

• Laws of technical system evolution: they are the most diffused evolution trends for technical systems discovered by Altshuller (1984) analysing thousands patents. These low can be used to forecast new technologies (Cascini et al. 2011);

• Delphi method: it is an intuitive forecasting method, which involves a panel of experts who anonymously reply to questionnaires about new technological opportunities and subsequently give feedback of collected solutions. After this first round the process repeats itself more times. The goal is to identify an opportunity that is closer to expert consensus (see Dalkey and Helmer 1963 for more details).

• Biomimetics (or biomimicry): it concerns the analysis of natural systems with the aim of identifying new technologies inspired by solutions (Rosa et al. 2011; Baldussu and Cascini 2015). The search can be facilitated by available databases (e.g. www.asknature.it);

• Patent analysis: the search of information in patent databases allows to identify useful technologies currently applied in fields that are different from the analysed one and technological trends (Campbell 1983). Text mining (Yoon and Park 2004; Cascini and Russo 2007) and statistical (Basberg 1987; Lee et al. 2012) tools can accelerate this kind of analysis.

As a whole, in the end of this first activity the gathered information should include: • existing technical features (that have to be converted into product attributes); • existing product attributes (i.e. features already expressed in terms of benefits); • offered performances (qualitative, quantitative and present/absent);

• attributes with offered performances that outstrip customers demand; • attributes with offered performances that barely satisfy customers;

• attributes that are not widespread and do not constitute competing factors yet; • new technologies and technical solutions (useful for the Conceptual Design

phase).

This activity cannot generally support the identification of radically new ideas, however in some cases customers (e.g. Von Hippel 2005) can provide useful hints to support the planning of a differentiation strategy.

2. Definition of the industrial standard

Such task is aimed at supporting the identification of main product categories in a reference market and allows considering alternative product or services that satisfy analogue needs (e.g. pen and paper are an alternative to word processors). This activity can be carried out together with the first task and it can be useful to address the search of information. Indeed, designer can focus on specific product categories reducing the exploration space.

In order to support this task a flow diagram, namely Industrial Standard Chart, has been developed (Figure 3.5).

According to the type of product that a company want to develop a reference market generally exist. However, sometimes companies can even identify completely new market spaces. In this case, different products or services that satisfy similar needs can be used as reference industrial standards. For instance, the first bulb could be compared with candles, or the first television could be compared with the radio market. Rarely, the product cannot be compared with other goods or services, however, if it happens, the main objective is understanding if an actual need exists and if possible competitors could appear from different industrial field.

Often companies already have one or more products in a certain market and their objective is the development of a new product’s model. In this case they can decide to consider only their product(s) or perform a competitors analysis. However, the first choice is not recommended, unless the companies have a monopoly or are government institutions, because useful information can be lost.

Figure 3.5 industrial standard chart. It supports the identification of the standard(s) in a reference market. Dotted arrows suggest elective tasks

Because of the strong growth of emerging countries, in most of the markets two main categories of products can be identified:

• budget: cheap products with generally low quality performances; • premium: quite expensive products with high quality levels.

In order to understand if these two categories exist in a certain market, the main factor that has to be analysed is the price (i.e. the cheapness attribute). If a considerable gap between products’ prices can be observed, it can be assumed that the above mentioned standards have been identified. However, there is also another factor that support this task, i.e. the analysis of distribution channels. Indeed, for instance, a product distributed through large retailers and another one sold in boutique can respectively belong to budget and premium categories.

As seen above, there could be different products that satisfy the same main useful function, requiring low investments but providing a low efficiency level too. In this case there are two different product types that provide the same benefits and can be assimilated to budget/premium categories.

Eventually, if in a certain market domain only one category of product can be identified and the offered performance are comparable, the industrial standard can coincide with the average offer. However, if there are significant differences among various offers, they should be analysed separately.

The industrial standard chart provide two further hints (highlighted by dotted line in Figure 3.5) to extend the search including alternative not substitutive products (e.g. cars vs. plans) and different product that could be mixed with the analysed one (e.g. bookshop and coffee shop), as suggested by the BOS (Kim and Mauborgne 2005). These further data can support the differentiation strategy, but they have not to be necessarily collected and/or analysed in depth.

More details about the use of the industrial standard chart can be found in the next Chapter that illustrate two case studies, in which this tool has been applied.

Hence, the information about competitors’ offer (and/or alternative product/services), gathered during the first activity of the process, can be clustered using the identified standards. It allows to order the collected data, understand the segmentation of the reference market and start planning differentiation strategies.

3. Representation of the as-is scenario

Once the information about competitors’ offers has been collected and industrial standards have been identified, a useful representation of gathered data can be obtained through the employment of a widely diffused technique: the Strategy Canvas (Kim and Mauborgne 2005).

This tool is the most appropriate technique, identified in the literature that satisfies the needs of proposed general approach. Indeed, Strategy Canvas allows to formally schematizing both current offer and new product ideas in the Product Planning phase and it satisfies most of the ideal properties collected in Chapter 2:

• it turned out to be quick and easy to use (integrating some practical guidelines); • it can be easily drawn and modified using commercial software (e.g. Excel); • it has been developed to support both competitors’ analysis and graphic

• it can be considered a reliable tool because it has been widely tested in several industrial fields. In addition, the integration of some practical guidelines (introduced by the author) allows to make the tool more repeatable;

• the employment of collected information leaves no room for personal judgment; • it can be potentially used by multidisciplinary team and customers can be

involved (even if no literature examples have been found);

• it focuses on product attributes and curves provide a quick image of general product ideas.

This graphical tool consists in a two dimensional map where in the abscissa there are product attributes (sometimes called competing factors), while in the ordinate the performance at which each product attribute is delivered to the customer is reported. This tool allows representing both the as-is scenario and new offers that will be ideated in the second step of Product Planning. The curves drawn in Strategy Canvas are usually called Value Curves and they represent the so-called product profiles. In Figure 3.6 an example of Strategy canvas is shown.

Figure 3.6 Strategy canvas. In the abscissa there are the product attributes (competing factors), while in the ordinate the performance at which each product attribute is delivered to the customer is reported. Such graphical representation allows depicting as-is scenario and supports differentiation strategy

No rules have been identified in the literature to support the use of Strategy Canvas. Indeed, curves are generally drawn in a qualitative way in order to provide a quick picture of the as-is scenario (average curves) and new offers. Therefore, the author has introduced some practical guidelines with the aim of:

• making the use of the tool more repeatable;

• simplifying the analysis of the curves, limiting the number of intersections; • making qualitative and quantitative attributes comparable.

Collected information can be managed implementing the following steps: • divide the ordinate into five levels: null, low, medium, high, very high/ideal; • link the qualitative attributes to these performances’ levels;

• analyse the quantitative performance and try to normalize data in a range 0-1 (where 1 represents the maximum performance that is, or could be, obtained with existing, or future, technologies);

• move data normalized in the previous point to the nearest level (assuming null=0; low=0,25; medium=0,5; high=0,75; very high/ideal=1). For instance, a normalized performance of 0,45 will be assumed as 0,5 (medium) and a performance of 0,8 will be moved to 0,75 (high);

• link present/absent attributes respectively to ideal and null performances. In order to have a Strategy Canvas that can be easily read the number of curves cannot be too high. Hence, if more than 3-5 curves have to be drawn, more than one Strategy Canvas should be used. For instance, a Strategy Canvas could be used to compare the main competitors and another one to shows the offer of alternative products. In addition, if some curves coincide or are very similar can be simplified in a unique representative curve. The industrial standards should support this simplification process too. For instance, the curves belonging to the budget and/or premium categories can be drawn with a unique average curve, if they have similar shape (i.e. similar level of performance). When simplification cannot be done, but it is required, only main competitors and/or alternative products have to be considered in the Strategy Canvas. All the other neglected data could be anyway used to