Racing with (or without) the machine: Robot adoption and FDI driven transformation in the automotive industry

Résumé:

During the last decade, the interest and the anxiety towards the next industrial revolution and the impact of its enabling technologies on the economy have increased substantially. However, while some authors argue that the disruptiveness of new technologies makes the Fourth Industrial Revolution (4IR) different from the past ones – the ‘this time is different’ argument, others have been more cautious. This second group of maintain that technological changes, ‘even this time’, are going to be more incremental than disruptive. This incremental argument reflects businesses’ emphasis on retrofitting production technologies and systems, and the importance of integrating new technologies into processes and products. From this perspective the 4IR story is highly heterogenous, both across sectoral value chains and countries.

Purpose. The paper contributes to this emerging literature by focusing on two main patterns of transformation in the automotive industry. Specifically, we provide new evidence of the ways in which FDIs have reshaped the automotive global value chain and, secondly, how FDIs have driven changes in production technologies – i.e. robotization. In doing that, we introduce a new tool – i.e. FDI smiling curve – and new econometric evidence of the relationship linking FDI and robotization in the automotive industry, taking into account ‘FDI directionality’. With the increasing modularization and outsourcing of production – the so called ‘second unbundling’, the industrial organisation of the global automotive industry has changed dramatically. The rise of mega suppliers contributed in creating strong oligopolies which coexist with big automotive companies (the final assemblers) that detain the power downstream the chain. Power concentration and increasing disintegration are two forces reflected also in the increasing automotive related FDIs. We propose to observe the restructuring of the automotive value chains from the FDIs perspective. Using fDi market data, we illustrate cross-country differences in the distribution of FDIs along the main segments of the value chain in the automotive industry. A global FDIs smiling curve will be presented and confronted with smiling curves that pinpoint trade directionality: North-North, North-emerging countries, North-developing countries, based on selected countries . The automotive industry has been also affected by the development of new digital production technologies (DPTs) and advancements in automated systems – in particular industrial robots, IoT systems and cobots. Industrial robots are characterised by an increasing variety and variability of tasks they can perform. From mass production, to lean systems and to agile manufacturing systems; the increasing modularization and the use of reconfigurable processes are just some of the elements needed to reach customization. We analyse the role played by industrial robots in this paradigm shift by focusing on the relationship between FDIs and robotization.

Methodology. The paper innovatively combines two datasets the International Federation of Robotics (IFR) dataset and fDi market dataset. We take the middle part of the value chain, concerning manufacturing production processes, and analyse to which extent FDIs are driving the adoption of industrial robots. Taking advantage of the high level of disaggregation of the data mentioned we construct an ad hoc panel dataset that incorporates the application of industrial robots in four subsectors of the automotive industry and FDIs in the same sub-sectors. Considering 22 countries we conducted the analysis on two levels; the first, more disaggregated, is a micro analysis of the impact of FDIs in the four subsectors mentioned, controlling for GDP, manufacturing value added as share of GDP and technological investments related to the automotive sector . The second level is less disaggregated, we consider two sub-sectors Automotive OEM and Automotive Components; we run regression controlling also for a measure for each country export competitiveness (using UNcomtrade data) and for different specifications of FDIs.

Findings & Practical Implications. Finally, we discuss the results of the FDI smiling curve and FDI-driven robotization analyses, with a focus on the heterogenous impact of FDIs on robot adoption. Disaggregated descriptive statistics will be used to show the different features and links of FDIs’ sub-sectors and robot applications. Indeed, investments are different both in their nature and in their implications. Heterogeneity of countries impose to look differently at industrialised countries which have a presence in almost all segments of the automotive value chain and have targeted industrial policies for the automotive industry and, on the other hand, different emerging and developing countries in which the local production system is more disarticulated. In relation to this second group of countries, and considering the importance of domestic/regional value chains in developing industrial ecosystems, we formulate hypotheses around other factors driving robotization more than FDIs, for example the existence of local production systems and targeted technology policies.