Functional technology foresight. A novel methodology to identify emerging technologies

Why functions?

In order to examine a technology or a product, particularly in the perspective of Technology Foresight, it is important to specify which perspective is adopted.

Before addressing societal needs, let us first study an easier case, i.e. consumer products. Let us consider for example a sailing ship. The marketing staff will work on the values that could motivate the potential users: freedom, adventure, contact with nature and the like, which in turn will imply the need for safety, comfort, speed, acceleration, stability. In the same way the technical personnel will design the hull, the sails etc., using specifications, drawings, computations and choosing materials and components. As another example, consider a washing machine. The marketing staff will work on the values that could motivate the potential users: health, cleanliness, integrity, self-image, social acceptance, which in turn will imply the need for efficient hygienic treatments, reliability, low energy consumption. In the same way the technical personnel will concentrate on the engine, the drum, etc.

These worlds are described using different languages: the language of technical specifications, on one hand, and the language of daily life, on the other hand. Functions constitute precisely the link between the two worlds, the world of technical structures and the one of user needs. Indeed, one definition of function is “the teleological connection between a human’s goal and the measurable effects of the artefact” [1]. Another useful definition is “the result of the user’s interpretative process about the product’s physical behaviours” [2].

Functions can be described in a language which is based on verb + object pairs (with further modal qualifications, if needed), is independent on specific technological solutions, is subject to different levels of abstraction and allow decomposition. Thus in the case of ship, all user needs can be captured and translated into the overall ship functions: to float, to navigate, to (be easy to) control and manoeuvre, all of which can be further decomposed and in turn imply and can be obtained through a series of technical solutions for the boat structure. It is possible to break down secondary functions into more detailed sub functions, and associate them to geometrical or mechanical features of the product so that a deep understanding of each part is gained, as well as of the interaction with the external environment [3].

Recent developments in the literature have offered extremely powerful linguistic resources (“functional bases”) that can be used, with some initial training, to develop precise and complete functional descriptions. Functional descriptions can therefore be obtained at the desired level of abstraction, for whatever technology or product (so far, only for entities located above the nanoscale), in a language that is fully consistent with physical descriptions [4–6]. These developments support the promise that Functional Analysis, which has been hitherto confined into a highly specialized and idiosyncratic academic and professional community, is instead largely used to address a variety of issues. This paper builds upon this literature to introduce Functional Analysis into the field of Technology Foresight.

Functional representations

Rather than a single methodology, Functional Analysis (FA) is a paradigm within the engineering design discipline, which has established a core of fundamental ideas over sixty years ago and evolved since then along various directions, both at academic and industrial level. The approach has now entered its maturity phase, even though its applications are still discontinuous.¹

A detailed illustration of the theoretical aspects of Functional Analysis would be out of the scope of the paper. In the following we will just sketch some of the relevant entities that characterize the functional paradigm and the derived methodologies.

In the classical approach by Pahl and Beitz [7], functions are conceived as “operation on flows” and represented using a pair of words: a verb, describing an action (operation), and a noun, describing the object of that action. The objects are conventionally called flows and classified according to three categories: material, energy and signal. The functional decomposition of a product is therefore given by an ordered list of (verb + noun) pairs.

Clearly, the goodness of such decomposition heavily depends on the goodness of the “vocabulary” used, i.e., on the correct choice of the functional actions and objects. Many research efforts have therefore been focused on the development of standardized, suitable taxonomies and catalogues of all possible functions and flows. It is not an easy task: the functional language should be at the same time general (in order to abstract from specific technical realizations) but complete (in order to encompass all possible solutions); it should be precise (in order to avoid loss of information) but concise (in order not to sink in irrelevant details). Furthermore, the language must be clear, easy to understand also by non experts and easy to use.

As mentioned above, after several decades of research and practice, recent developments in the literature offer restricted vocabularies [9] or large comprehensive vocabularies [5], upon which it is possible to build functional representations that satisfy these requisites. Although there have been critical remarks and extensions [10, 11], this line of research is established. As we have argued elsewhere, it is a promising direction for reconceptualising several chapters in the theory of technology and technological change [12].

Ideality and predictivity

The idea of applying the analysis of functions to Foresight is not new. An influential tradition in engineering literature has claimed that a finite number of evolutionary trends can be identified on the basis of a massive analysis of past technologies, as documented in patents. The TRIZ methodology, developed by Russian scholars and popularized by its founder Altshuller [13], has suggested that it is possible to anticipate technological evolution by applying a small number of such general evolutionary principles to existing technologies [14].

The TRIZ literature offers a rich analysis of evolutionary trends aimed at increasing product ideality. If applied to an industry, these trends offer a technological counterpart of traditional economic models of technology evolution, such as S-shaped curves or the Industry Life Cycle model. This allows a combination of various dimensions in a predictive model, as shown in Fig. 1.

According to the S-curve model, the technology is invented at point 0 in time. Initially (phase 1), the development proceeds slowly due to a huge number of implementation problems to be solved and to the lack of profits (actually the technology absorbs money in terms of investment in R&D). During the growth stage (2) the advantages of the technology become clear and therefore a profitable market develops. By now, many problems have been solved and new investments help performances to improve even more. In the maturity stage (3), profitability and diffusion are at their highest; on the other hand the process of innovation slows down again, proceeding only through minor improvements and approaching the saturation. In the decline stage (4) it is not possible to improve the technology further; eventually it will be ousted by a radically new solution.

The crucial prediction of this model is that there is a point in time (point A in Fig. 2) in which it will be more profitable to switch from the old to the new technology, because there will be another point in time (point B) in which the entire market will switch to the new technology as well. Needless to say, the model has limited predictive power on when points A and B will materialize for a given technology. While this model is considered appropriate at theoretical level, its utilization in practice is severely limited by the sheer difficulty in identifying the appropriate level of analysis and predict accurately the switching points.

We suggest that Functional Foresight is an answer to this issue. Focusing on functions, rather than on technology and performance, it complements standard S-curve based analyses and helps to make more abstract and general predictions on the evolution of technology.

This is an important achievement, one that other frameworks find very difficult to reach. In fact, models focusing on the technical aspects only, and TRIZ-based approaches are no different in this respect, applying a restricted set of principles and ignoring the possibility that other forces can be at work.

Furthermore, TRIZ and similar performance-oriented methods require the description of technology to be fully available in engineering language, requiring strong technical background in all participants to sessions and raising the barriers to entry. On the contrary, following recent developments on functional bases and languages, it is now possible to build up complete functional descriptions starting from natural language.

The key to predictivity, as it will be shown below, is the ability of our approach to locate the evolutionary trends and the associated switching points not in the space of technology (or the structure space), but in the abstract space of functions. The condition for this analysis is that the function space is described accurately and completely, something we have demonstrated feasible, at least using a constructive proof approach [15].

Combining functional analysis with bibliometric tools

Scientific literature is one of the main sources for Foresight exercises. Its use has been profitably coupled with text mining techniques [16]. Kostoff and Schaller [17] review the methods based on co-occurrences and co-citations from bibliometric data. More recently, these techniques have been greatly enhanced by the power of computational linguistics tools applied to web resources (for an interesting example but on a different subject, see [17]).

Patents have also been intensely used as sources of information for Technology Foresight. On one hand, patent bibliometrics, since the pioneering contribution of Narin [18] has been used to identify trends and technology clusters, using both statistical analysis and citation analysis, particularly on non-patent references [19–21]. In this direction, patent analysis is carried out alongside bibliometric analysis [22]. On the other hand, patent network analysis has been recommended as a tool to identify latent structures in emerging technological areas [23–25]. As Verspagen [23] has noted, however, the existing literature lacks a perspective on the “inner dynamics” of the engineering dimension of technological paradigms and the relations with scientific developments [23]. Following the approach of Hummon and Doreain [26], he uses graph-theoretic properties of citation networks to highlight turning points and cumulativeness in engineering developments.

Interestingly, patents have not been utilized to extract one of the most important information content they contain - the functional description of the invention.

A crucial tool we suggest is Functional Bibliometric Analysis (FBA). It is commonly understood that scientific publications and patents can be scanned in order to identify promising technologies. We apply state-of-the-art semantic software technologies to the tracking of promising technologies not only by using keywords and descriptors, but by searching for meaningful clusters of keywords whose meaning is given by functional lemmas, extracted from full scale functional maps. As it will be shown, this is more powerful than conventional scanning using keywords based on technology.

This should not come as a surprise, given that functions are the most efficient way to capture the key features of human artefacts, but is nevertheless particularly relevant in the context of patent analysis, where sometimes companies hide sensible information behind vague or generic technical descriptions to better protect their competitive advantage.

A risk of traditional queries based on a list of keywords is that the perimeter of the search is fixed by the knowledge of the expert selecting the keywords themselves, and therefore it is difficult to find less known, unexpected or divergent solutions. Moreover, with standard investigations it is not straightforward to disentangle what is really new with respect to the past in the technologies that have been found out.

On the contrary, by contrasting the set of more recent patents with the whole patent corpus, the FBA software tools are able to self-detect, among the unstructured information, those technical solutions that are really new and not just matching a given query criterion.

In addition to finding the positive marks of technologies, the functional semantic extraction is also able to let emerge their potentially harmful or unwanted consequences, another aspect that is not easy to determine otherwise, since not explicit in patents and often overlooked in publications in favour of technical promises and expectations.

The gathered information about advantages and disadvantages can be combined with the study of S-curves and with a full-scale failure analysis to provide precise indications about the potential of development for a given technology.

The advantages of functional technology foresight

A first advantage of the use of Functional Analysis in the context of Technology Foresight is the abstract and universal nature of functional descriptions. Let us consider one of the most common objects of daily life, a drinking glass or cup. It has two main functions, the first being of course to contain liquids. Such function is however shared with other containers such as bottles, barrels and the like; what distinguishes a glass from bigger recipients is the ability to set the amount of contained liquid to a reasonable quantity (usually one or two mouthfuls), in order to be held and brought to mouth without effort.

The so-called secondary or auxiliary functions will also tell what is necessary to make a good glass or cup. These functions are not indispensable as such, but their implementation would make the difference between a successful product and an unsuccessful one. It does not really matter whether the cup is made of glass, plastic or wood, neither does its colour or shape. What matters for a good glass is to be stable while standing on a table and while pouring liquid, to be easy to grasp, to have a good match with human hands and lips, and to properly guide the liquid into the mouth when drinking. The functional requirements will then suggest the right material or the right shape to use, and rule out the wrong ones. It is not a coincidence for example that almost all cups are roundly shaped: a cup with a square section is not practical to grasp, it does not match the lips’ shape and water tends to spill out at the edges. In other words, functions capture the user needs (to drink without nuisances) and translate them into the proper technical specifications.

Let us now remark the generalizing power of such representation. Any object that fulfils the two main functions is, almost by definition, a drinking tool. Two abstract functions describe the essence of a glass and six (considering the four secondary functions) the essence of a good, successful glass.

Even distinctive aspects of specific glasses can be described using few additional functions (e.g., wine glasses use their shape to keep the aroma and direct it to the nose, and to isolate the liquid from the body’s heat). Due to its power of abstraction, Functional Analysis is the appropriate methodology to deal with almost all dimensions of technological innovation. As an example, the study of functions proved to be extremely useful in generating new ideas and concepts and in guiding the innovation process.

A second advantage of the approach is that it provides a unified point of view for describing human artefacts. This is done by creating a shared language between technologists (both from academia and from enterprises) and stakeholders (users, commercial partners, policy makers and so on), two communities which otherwise would hardly understand each other. Engineers and scientists would pursue better performances and talk about technical details, while stakeholders would focus on satisfying needs or solving problems, but would not be able to define them rigorously enough, to examine the implications of different technological options, or to propose implementations. This is an enormous advantage in the context of societal challenges, for which the gap in understanding each other between society and technology may be large.

Functional Analysis offers a well-structured bridging language that can translate the terminology and expressions of one world into the ones used in the other. Indeed, our experience is that after having built the proper functional representation for a technology, the dialogue between the two parties becomes much more concrete, fluid and creative. Participants do not need to be educated in engineering to enter into discussions of technology, but after an initial (relatively short) training they can start discussing options at the appropriate level of abstractions.

Third, a distinctive property of functional language is to facilitate the identification of technology trends, or the long-term directions of technology in specific fields, and of technology options, or the bifurcations that may take place along long-term directions. With functional forecasting, it is possible to apply general trends (such as those identified in the TRIZ literature [13, 14] to a specific technology, and to study functional variants.

By focusing on abstract functions, rather than on specific solutions, such approach allows a systematic comparison between current technology and the frontier, as will be discussed below. In the same way, it allows a proper comparison of products, even in the case of products that are very far from a technical point of view. It goes without saying that the possibility of such comparison is very useful in Technology Foresight, where one has to assess the potential of competing technologies, and the focus on other dimensions such as product performance could be misleading and potentially dangerous, as extensively discussed in [23]. In doing so, a functional view greatly increases the flexibility of decision makers, by clarifying the implications of all technology options and limiting the commitment to given solutions.

In addition, a functional view is very useful to identify what is missing in the technological perspective. As a large literature in STS has shown very often, what is missing, is a deep consideration of changes in work practices, skill deployment, or complementary skills, induced by technological change. Oudshoorn [27] has recently shown how the introduction of telecare in health systems has failed to take into proper account the professional skills of doctors and nurses for the new delivery system. It is our contention that a higher level, functional view would have helped to identify missing elements of the technology, as the case study below will show in detail.

Finally, functions naturally embed the information about user needs, along with the information about physical realization, and are therefore more suitable to point out potential failures of developing technologies in meeting the market or societal expectations [28]. In the same way the study of functions allows to highlight the presence of hidden negative functions that can undermine the potential of certain otherwise successful technologies (see [29] for a comprehensive application of failure analysis techniques to Foresight and for a case study in the textile industry). The possibility to spot hidden failures in advance is a powerful antidote to contrast the effects of hype, excessive expectations and even of vested interests in the decision making process.

Introduction

The functional approach and the tools described in the previous section have been applied to the analysis of the medical device industry, a rapidly growing sector characterized by high technology content, often at the frontier of scientific research, and therefore rich in promises as well as in uncertainties about future developments.

The study was performed as part of an innovative Technology Foresight exercise commissioned by the Regional Government in Tuscany, Italy, and carried out in 2010–2011, publicly presented in 2011 and published in 2013 [30]. The main goals of the investigation were to evaluate the emerging technologies, to identify the most promising products, and to anticipate the scientific and societal inputs that will influence the evolution of the medical device industry. The information thus gathered will help the Regional government to focus and prioritize its investments into R&D support.

The study was performed combining three different approaches, so that the results could be cross-checked. First, traditional methods of analysis have been used, such as expert panels [31], consensus building, SWOT tables and roadmaps, mainly to circumscribe and define with precision the objects of interest and the boundaries of the investigation. Second, Functional Analysis has been used to re-structure the gathered information, producing full-scale functional maps of all involved technologies. Using the functional maps, a number of technology trends have been identified, translated into roadmaps, and discussed extensively with stakeholders. Finally, Functional Bibliometric Analysis (FBA) was used to identify the relevant patent and publications literature on future trends.

From technology-based segmentation to functional segmentation

On each of them a quite conventional technological analysis has been carried out, following a structured questionnaire. A panel of five senior experts (from academia, industry and consulting) was hired to support and guide the analysis, while the results were discussed with a large group of stakeholders, including approximately forty among managers, start up entrepreneurs, patent consultants, trade association representatives, public health care officials, and hospital managers.

Starting from this material, technologies have been decomposed following functional criteria and these have been used to produce a functional segmentation (that is a structured reorganization following functional principles). Combining the technological approach and the functional decomposition/segmentation resulted in a large full scale functional map, initially realised on paper, eventually crafted as a digital product and shared among experts and stakeholders (Fig. 3).

The map clusters technologies around broad functions, and offers for each technology a descriptive label based on technology/product and market/application. The labelling is important in order to keep the abstract functional description within a concrete context. At the same time each technology is placed within a broader and more abstract context, described by functional verbs.

Identifying technology trends in the functional map

A full-scale functional map is a powerful cognitive support for a number of creative activities.

In the field of product innovation we experimented with using creative problem solving heuristics (such as analogy, crossover, recombination, and the like) directly within the functional map in order to identify new product ideas (see for example [32, 33]).

Here, in the context of foresight we adopted a structured approach: we projected all dimensions of functions in the future, by changing systematically the objects or the modalities involved in the functional description. This resulted in a large number of potential evolutionary trends, which we submitted to thorough technical evaluation. Finally, we discussed these trends with stakeholders, with surprisingly good results in terms of clarity of perspective and precision of suggestions.

For example, consider the trend towards integration between diagnostic and therapeutic functions in the same device. The same pattern emerges clearly (even visually) when comparing two temporally shifted functional segmentations such as that in Fig. 3, noticing the increased positioning of new devices across the two areas.

It was possible to project this trend into the future by examining the respective functions, decomposing them into elementary sub-functions, specifying the nature of state variables changed by the action, and measuring the physical variables involved. Thus for example if the therapeutic function involves the activation of a targeted laser ray in a small region of a tissue, what was investigated was the compatibility of this function (in terms of precision, guidance, tolerance, thermal impact, biochemical reactions) with the diagnostic function (in terms of bandwidth of the signal, noise, quality of image or sound).

To the surprise of most actors involved, including senior experts, this method in a few cases delivered solutions that were not yet clear to them, but were at a very preliminary stage in exploratory research. In other words, it was possible to foresee the emergence of certain technical solutions just as the natural following steps of some existing evolutionary trends, and a later investigation found out that such solutions were actually under study by research groups around the world.

Needless to say, technical solutions that deliver the next step in a trend in act in a certain sector have a greater chance of success and greater potential, while products that innovate only minor aspects but do not improve over the present evolutionary stage are far less competitive.

Another important implication of the methodology was that technological views of actors involved, particularly those from industry, were challenged.

Therefore technology trends were not obtained by extrapolating current activities in R&D, but combining expert and stakeholder view with insights generated through an abstract representation, which opened options and challenged established views.

Involving stakeholders

Does Functional Foresight lead to technological determinism? Definitely not. Functions are described in physical terms, but reflect the teleological relation between the behaviour of an artefact and the needs of a user. In our own definition, a function is “the result of the user’s interpretative process about the product’s physical behaviours” [2]. The fact that we insist on the use of physical variables [5] is linked to the need for a complete and rigorous description, which is needed for a scientific foundation of Functional Analysis. But functions do not exist outside the user perspective, at least a potential one. Functions are inherently normative [10, 12], implying a perspective of relation between a behaviour describable in physical terms and a user.

Based on these firm methodological grounds, we submitted the functional maps and the trends to several stakeholders, both in group meetings and personal interviews. The user perspective added a number of interesting issues, leading to clarification of potential problems in technological trends, sometimes pointing to possible bifurcations.

For example, the trend towards integration between diagnostic and therapeutic functions in the same medical device was discussed in terms of the required changes in the skills of doctors, with deep implications in terms of patients’ attitudes (trust), administrative procedures, doctors’ status, liability and insurance issues, and the like. From the user perspective, there will be great advantages (in the same session the disease is identified and cured), but also sources of anxiety (there is no longer a time interval between diagnosis and therapy, which could be used by the patient and her family to make an informed decision, or to take a second opinion). The identification of these problems in functional terms, as opposed to extrapolation of existing devices (with associated conflict of interests of industry involved) helped to frame the discussion in a transparent way, reducing the usual suspect of vested interests from the industry side, or manipulation from hospitals.

As another example, within the broad trend of elimination of human involvement, we discussed the concept of rehabilitative robotics. This concept, borrowed from robotics and mechatronics, implies the substitution of the hands of the physiotherapist for automatic operations actuated by robots (usually exoskeletons) and controlled by sophisticated software that controls for physiological parameters, the neurological activity and the muscular reaction of patients, and other factors. Here there are deep social implications. On the one hand, patients will be asked to trust machines instead of humans. The extent to which this delegation of responsibility is perceived with anxiety has been discussed extensively. On the other hand, professional skills of physiotherapists will be challenged. In principle, there might be a new division of labour between humans and devices, with the former concentrating on the high end, sophisticated operations.

Again, working on functional descriptions helped to discuss these issues more thoroughly and dispassionately. These insights were also challenged to regional authorities for health care (a regional responsibility in the Italian institutional framework), which included them into technology assessment activities and into the drafting of health plans.

On the basis of these analyses and discussions we ended up, for a subset of technologies, with a full fledged roadmap. The roadmap includes not only the traditional dimensions of technology, product and market/application, as discussed in the literature (e.g. [17, 24]). It includes explicitly a user dimension (needs) and an environmental dimension (events), aimed at examining broad societal changes, or regulatory activities. An example of roadmap is given in Fig. 4.

Thus the Functional Foresight methodology is not only more powerful and flexible from the technology side, but also more productive on the stakeholder side. It is a candidate for addressing societal challenges by making the technology-society conversation more productive.

Cross-checking

Although the functional investigation had already delivered striking insights that gathered the consensus of both experts and stakeholders, the Foresight exercise included also a further cross-check of the results through other methodologies, such as the analysis of S-curves.

The same additional data were also used to integrate the results coming from the Functional Bibliometric Analysis that will be presented in the next subsection.

Of the four parameters that can be used to construct S-curves (see “Big data meets domain knowledge: the functional bibliometric analysis” section), we decided to study the number of inventions over time, since the other three parameters (profitability, performance and level of invention) are not as easy to determine or assess. On the contrary the number of inventions is directly related to the number of patents and scientific papers, which are both already gathered in fully accessible and exhaustive databases. In particular, the study of patents has the highest likelihood of providing complete and accurate results.

Two cases of S-curves for technologies at different maturity stages are shown in Fig. 5.

Apart from the indications that such diagrams give on their own, their relevance increase when put in correlation with other sources of information. It is interesting to note for example that diagrams such as the case a) in Fig. 5, representing emerging technologies with great potential for innovation and profitability, are more often associated to areas of the functional segmentation (see Fig. 3) where fewer solutions are present to satisfy the user’s needs expressed by each function. Here in particular we are referring to the painless colonoscopy technologies where we located both soft robots (worm-like endoscopy by EraEndoscopy²) and Capsules for endoscopy (Apreda et al. 2016 Technology Foresight based on functional analysis, submitted), namely eatable capsules provided with micro video devices, aimed at making pictures of the organs). In 2010, such technology (both capsules and soft robots) was in the early stage but very promising with respect to the standard rigid colonoscopy air inflation and rigid tubing negatively affected the patient during the analysis.

As an additional example, we enquired experts and stakeholders about technologies associated with diagrams such as the case b in Fig. 5), representing apparently declining solutions. In the case of Fig. 5b we are referring to HIFU technology, namely High intensity focused ultrasound. Such technology focuses ultrasound waves in a targeted way against tumoral areas but not damaging surrounding organs.³ In 2010 we found evidences that the HIFU was associated with a series of drawbacks. Therefore we consulted external medical doctors who pointed out the existence of collateral damages or other factors limiting the further success of the technology. Nowadays HIFU is limited to a very small subset of tumours and medical applications than the high number claimed in 2010 (please see the evolution history in Wikipedia page from 2010 to now).

Nowadays, when we recently contacted again the experts (June 2016), they explained how very recent research solutions solved some of the problems that blocked the HIFU development, thus probably shifting the technology over a new S-curve.

Finally, many of the outcomes of this stage of the investigation were confirmed by the subsequent Functional Bibliometric Analysis, which extracted from the literature the positive and negative keywords associated to the various technologies. For a more detailed description of the above-mentioned cases please refer to (Apreda et al. 2016 Technology Foresight based on functional analysis, submitted).