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The economic Rationale behind the Adoption of Some Digital Technologies

15 November 2018 at 11:30 AM - 1:00 PM

Room 205b, Campus on Viale Romania, 32

Speaker: Luca Beltrametti , Università di Genova


In recent years there has been a lot of hype about the impact of the new digital technologies on productivity and growth. Even if many different terms have been devised to refer to the ongoing process (“Industrie 4.0” in Germany1, “Industrial internet2” or “Second machine age3” in the US), the common idea behind these definitions is that the availability of low cost sensors, low cost and widespread internet connections, an almost infinite number of IP addresses, low cost data storage and analytics, artificial intelligence, simulation, 3D printing… enable a radically new production paradigm that has a dramatic impact on the economy. 3D printing (additive manufacturing) received a special enthusiastic attention4 that is know a bit fading but indeed the these set of technologies5 deserve a special focus. In this narrative, for the first time since many decades, manufacturing would be at the epicenter of innovation. The potential for such enabling technologies would be particularly important in Europe where the manufacturing sector accounts for about 2/3 of the total R&D expenditure (Veugelers e Batsaikhan, 2017 and CSC 2011); in Italy the figure is even bigger (70%). Unfortunately however, up to now the aggregate figures about factor productivity and wages are disappointing (Byrne et al 2016, Fernald, 2015, Syverson, 2016) and also the news from the field are not good: for instance, in a very recent report McKinsey (2018, p.1) acknowledges that “while our earlier research has found that fewer than one-third of organizational transformations succeed at improving a company’s performance and sustaining those gains, the latest results find that the success rate of digital transformations is even lower … Only 16 percent of respondents say their organizations’ digital transformations have successfully improved performance and also equipped them to sustain changes in the long term”. In my opinion, two positions are today possible: i) to argue that there has been too much ado about a relatively small thing (Gordon, 2017) or ii) to argue that the big aggregate effects on factor productivity and wages did not occur yet but are coming in the next future (Bessen, 2015). According to this latter view, the delivering of such productivity increases is long because a complex learning by doing process has to take place, new skills have to be acquired by workers, new organization patterns have to be implemented and gradual process of revamping (retrofitting) of the existing machines can path the way to a more radical change in the capital stock. The evidences one can collect in manufacturing firms that under this perspective, a closer scrutiny of what’s going on in special niches and advanced manufacturing plants is of particular interest since it conveys information about what could be the real drivers of future productivity and growth. Indeed, the heralded radical macro effects of the digital transformation have been poorly discussed in terms of the microeconomic processes that could lead to the macro effects. This paper provides a preliminary overview of these microeconomic mechanisms that are actually displaying a significant effect on firms; some of them are interesting not just because of their (perspective) quantitative impact on the economy but also because of their novelty from the point of view of the economic theory. The paper first discusses (Par. 1.1) how more data and artificial intelligence lead to better decisions and more efficient economic processes taking the perspective of production costs; machine learning implemented by interconnected industrial capital stock determines new types of economies of scale. Then (Par. 1.2) a focus is made on how the digitalization of design, together with simulation and 3D printing enable the production of more efficient products; the very nature of 3D printing generates a new form of economies of scope. Second, the paper discusses (Par 2.1) how such technologies not only provide more information but also affect its distribution: new profitable business models are therefore enabled. Digitalization determines both situations in which the asymmetry in information distribution increases and situations in which decreases and situations in which the extent of such an asymmetry increases: in both cases I expect a shift towards “lease” in the “buy or lease” decision. The chapter closes (Par 2.2) with a short comment on the new business models that are believed to emerge in the next future because of the joint effect of digitalization and the development of 3D printing for final parts. This paper does not present any formal model but discusses some intuitive implications of digitalization, offers a few examples that come from a direct experience in the plant floor of several manufacturing firms and aims at providing a tentative bridge between the narrative of the consultancy firms and the academic one. The issues associated with the implications of digitalization on employement and skill requirements are not discussed here.