PREDICT-OP

This research project describes how predictive maintenance of military equipment may impact State-industry relations. Three theoretical perspectives are mobilized: the economic sociology, the sociology of organizations and the network analysis. Considering that the creation of a new market (such as the predictive maintenance market) is a highly uncertain process, the project puts forward three sets of hypotheses. First, it assumes that the role of public authorities is crucial in stabilizing the provision of predictive maintenance services. Through its digital transformation plan announced in 2018, but also through its supplier selection practices, the French Ministry of the Armed Forces contributes directly to build the market. To better understand the new rules of the game, we believe that defence companies will try to mobilize their relational resources as a priority. Secondly, we are interested in analysing power relations on the market. The big data technologies open up the possibility of a transformation of competition in the military equipment maintenance sector. To face the pressures of challengers (new comers on the market, especially start-ups or large IT service groups), we think that insiders (large defence companies) will rely primarily on legal “weapons”. Indeed, their long experience in contractual negotiations with the State constitutes an important organizational resource, which allows them to better anticipate new issues related to the ownership, exchange and analysis of digital data. Finally, our third hypothesis concerns the effects of the international circulation of industrial maintenance models. While we expect insiders to strengthen their market position in the short run, we assume that their activities will nevertheless undergo a major reorganization, particularly following the global institutionalization of certain technological solutions (the cloud, etc.). This is why we want to understand the extent of these changes, but also the long-term effects on the State's regulatory capacity. From an empirical point of view, we will collect data using three methods: interview, network analysis and observation. The project will also analyse secondary data on the use of big data analytics in industrial maintenance (civil and military) and will produce framework data on defence policies (France, Great Britain, United States). At the operational level, this project will help to raise awareness among defence institutions about the risks associated with the first (trial and error) developments in the creation of a predictive maintenance market. These risks will be mainly captured from an organisational and contractual perspective.