Productivité | EPFL Graph Search

En économie, la productivité entend mesurer le degré de contribution d'un ou de plusieurs facteurs de production (facteurs matériels consommés ou facteurs immatériels mis en œuvre) à la variation du résultat final dégagé par un processus de transformation. La productivité est en lien avec les notions de , d'efficacité, et d'efficience. La productivité a un rôle clé dans la compréhension de la façon dont les actions humaines (à l'échelon micro ou macro) contribuent au progrès, au développement et à la croissance économique. Historiquement, les gains de productivité les plus forts ont coïncidé avec les périodes de très forte croissance économique. Perception de la productivité Jean Fourastié, dans son ouvrage La productivité, note que « la notion de productivité est à peu près la seule notion admise à la fois par les théories économiques marxistes ou libérales ». Selon cet auteur, c'est depuis un très petit nombre d'années que la productivité est apparue so

Study of physical phenomena of selective laser melting towards increased productivity

Christoph Van Gestel

This thesis presents an experimentally focused study of three of the key physical phenomena of the Selective Laser Melting (SLM) process. The SLM process increasingly gains momentum in industrial applications. As awareness of the process and its capabilities for the manufacturing of high performance objects increases, some of the current limitations of the process become more apparent. The SLM process is not a fully mature manufacturing process and there are still significant gains to be made in terms of material understanding, process optimization, final part prediction and quality control. The experimental study of the physical aspects contributes to the material understanding on a fundamental level but also provides the tools for facilitating future research. A link to practical applications is presented in a case study on evaluating the influence of beam movement patterns and on the identification of process instabilities for quality control. A broad literature survey covers the state of the art on improving the SLM process. The three physical phenomena which are the subject of this study are: the interaction of laser beam light with the material to be processed, the transport of heat within the material and the behavior of the molten material. These three phenomena take place under conditions which are unlike conventional production processes, in terms of energy intensity, thermal gradients and liquid (metal) dynamics. The laser-material interaction is characterized by the absorptance. An integrating sphere setup is integrated in an SLM machine to measure the absorptance of the material in the pristine and final state. Also the absorptance evolution is characterized. Results are available for Maraging 300 steel powder, silver powder and AlSi10Mg powder. The influence of surface oxidation and surface roughness is discussed. The heat transport within the powder bed is characterized by diffusion. The thermal diffusivity and thermal conductivity are measured using the flash method with a laser as the energy source. The implementation uses a special laser beam intensity profile and data processing by correlation with numerical simulations. Thermal diffusivity and conductivity values are included for Maraging 300 steel, silver and titanium. The results confirm the large difference between bulk and powder conductivity and the limited influence of the material type. The melt pool behavior is studied using an integrated coaxial vision system. This system uses a combination of sensors to monitor the radiation which is emitted by the melt pool and its surroundings. The implementation presents optimized timing and data processing protocols. The relation between the melt pool properties and the process stability is explained. A procedure is also developed for obtaining temperature field profiles of the melt pool.

EPFL2015

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

At the Origins of Learning: Absorbing Knowledge Flows from within the Team

Charles Chadi Ayoubi, Michele Pezzoni, Fabiana Visentin

Empirical studies document a positive effect of collaboration on team productivity. However, little has been done to assess how knowledge flows among team members. Our study addresses this issue by exploring unique rich data on a Swiss funding program promoting research team collaboration. We find that being involved in an established collaboration and team size foster the probability of an individual learning from the other team members. We also find that team members with limited experience are more likely to learn from experienced peers. Moreover, there is an inverted U-shaped effect of cognitive distance on the probability of learning from other team members.

Elsevier2017

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022

Study of physical phenomena of selective laser melting towards increased productivity

Christoph Van Gestel

EPFL2015