Technologie de rupture

Résumé

Une technologie de rupture (dite aussi rupture d'innovation ou technologique rupture) est une innovation technologique qui porte sur un produit ou un service et qui finit par remplacer une technologie dominante sur un marché. Cette disparition de la technologie existante se fera bien que la technologie de rupture soit radicalement différente et qu’elle soit souvent moins performante à l’origine selon les critères traditionnels de mesure. Une technologie de rupture survient et domine un marché déjà existant soit en remplissant une fonction que la technologie traditionnelle ne pouvait pas remplir pour une application particulière (comme ce fut le cas des petites disquettes initialement plus chères et de capacité réduite développées pour les ordinateurs portables) ou bien en augmentant progressivement les parts de marché au fur et à mesure que les performances augmentent, jusqu’à remplacer ceux qui étaient établis sur le marché (comme ce fut le cas avec la photographie numérique).

Source officielle

https://fr.wikipedia.org/wiki/Technologie_de_rupture

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (100)

Publications associées

Chargement

Personnes associées (41)

Personnes associées

Chargement

Unités associées (13)

Unités associées

Chargement

Concepts associés (10)

Concepts associés

Chargement

Cours associés (37)

Cours associés

Chargement

Séances de cours associées (70)

Séances de cours associées

Chargement

Fluid Dynamics and Outliers

Bernard André Vincent Calisti

The current craze for unicorns and other hyper growth companies brings a novel interest in what makes the exceptional performance of a firm. Literature about technological innovation explains the bond between the performance level reached by a company and its ability to promote radical innovations. But if the theory of disruption enables to validate various levels of innovation, it faces difficulties to define more accurately the drivers of hyper growth. This thesis comprises five main moments to better understand what drives fast growing young technological companies. To survey this question we have defined one single scientific field, namely microfluidics, focusing on active clusters and spinoffs from the same parent in this sector. The first phase aims at highlighting evolutionary prospects about scientific enhancements in microfluidics leading to successful innovations. It shows that there is no innovation âdue to succeedâ and that technical innovations do not make progress as linearly as usual technological trees found in academic literature could suggest. That leads to a kind of rewriting in the evolution of inkjet technology. The second phase intends to explore performance among groups of spinoffs from the same parent. We consider the 2 European clusters in microfluidics. It appears that all these firms are developing, but they do not reach the same degree of performance. From far, yet, the differences in performance do not clearly appear. To explain variations, we study each spinoffâs growth during the 5 first years after launching before comparing with the growth during the 5 first years of the mother company. We have to look in details to the European clusters, to see also technology variants. A fractal approach is thus enabling to understand that performance of firms is like hidden in technology layers. The third moment elaborates on the relationship between a superior technology and an outstanding performance level. In the case of spin off performance we are not at a stage where we could put the phenomenon under equation! That is why it was appropriate to survey the scientific and technological clusters in Europe from a case study perspective. It appears then that seemingly similarly disrupting technologies do not end up capturing similar value. In fact, slight differences in the technologies used are leading to massive differences in performance. We discover in the end that one single variable is rooting Outstanding Performance. In a fourth phase we restate the initial question to better understand what makes Outliers and Outstanding Performance. Where we show that exceptional value capture is coming from exceptionally fitting of the technology considered to a very specific application. Outstanding Performance is thus attached to an extraordinary EBITDA. The last moment of the dissertation is devoted to ultimately check findings. The whole checking process eventually led to the conclusion that outliers and outstanding performance are permanently referring to a kind of âuniquenessâ embedded in the various cases of exceptional value capture. Such a unique feature, qualifying outliers among technological startups, makes by itself a research topic. As more and more attention is given to specific statistical populations, not only could this research contribute to improve the knowledge about hyper growth companies but it could also impact methods in statistics; and beyond methods: statistics themselves as a discipline.

EPFL2017

Chargement

It is well established that firms are exposed to the risk of supply chain disruptions. Many firms build risk mitigation strategies in order to increase the resilience of their supply chains. In this dissertation we present three essays that study operational risk mitigation levers such as inventory, reserve capacity and dual sourcing. The first essay outlines an application in the pharmaceutical industry where the risk mitigation levers inventory, dual sourcing and agility capacity are analyzed. We study the relationship between these three levers by modeling a drug manufacturing firm that is exposed to disruptions in its supply chain. The firm determines optimal inventory levels for assumed dual sourcing and agility capacity. We quantify the decrease in inventory levels in the presence of dual sourcing and agility capacity. Furthermore, using an example, we analyze inventory, dual sourcing and agility capacity decisions jointly. It turns out that inventory and agility capacity can be substitutes as long as no dual source is available. Once the dual source is available, agility capacity and dual sourcing appear to be substitutes. We further show that for long disruption times, the optimal dual source production rate may decrease in the disruption time. Within our modeling framework, we introduce an operational metric as a measure for resilience under deterministic demand. In the second essay we study the joint role of inventory and reserve capacity in mitigating supply disruptions. A reserve capacity can be used for production in a reactive fashion when a disruption occurs. We first determine optimal inventory levels and reserve capacity production rates jointly under stochastic customer demand. This allows us to fully characterize three main risk mitigation strategies: inventory strategy, reserve capacity strategy, and mixed strategy. Furthermore, we provide structural insights of optimal risk mitigation strategies. We then combine our analytical results with the well-established (Q,R) policy to perform numerical experiments using Conditional Value at Risk (CVaR) as a resilience measure. Our results suggest that the resilience measure depends on both the reorder point R and the batch size Q. Comparing the resilience measure with the service level, we find that a high service level and high resilience can be conflicting objectives. The third essay is concerned with the role of inventory in mitigating disruptions in two-echelon supply chains under stochastic demand. The research problem is to determine the optimal backup inventories for a serial, assembly and distribution supply chain respectively. For the three supply chain types, disruptions at each production site are described by an infinite-state discrete-time Markov process. We derive structural insights on the optimal inventory levels for the serial supply chain. For the assembly and distribution supply chain we show that under mild assumptions an early commitment to finished goods inventories is optimal. This finding is different from optimal safety inventory policies where often a delayed differentiation in assembly supply chains or risk pooling in distribution supply chains is optimal.

EPFL2016

Chargement

, ,

Service-level requirements play a crucial role in eliminating stock-outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and customer demand and causing the manufacturer to adapt the initial strategy in response to disruptions. Such deviations from optimality frequently result in unexpected (and potentially very high) costs and are complex to manage. On the one hand, the manufacturer can use a robust or distributionally robust approach to prepare for the worst-case disruption, ensuring that the realized cost will be lower than the estimated cost with high probability. On the other hand, this solution may lead to overly conservative production schedules. In this article, we take a different approach and develop a bilevel stochastic optimization model with chance constraints, which allows us to make the production more predictable in the event of disruptions by driving costs and optimal schedules closer to the benchmark for each scenario considered. We introduce doubly probabilistic service-level requirements to account for two interdependent layers of uncertainty, that is, production disruptions and distributional uncertainties in customer demand. This allows us to make high-quality production decisions with only a limited understanding of the demand pattern. Approximating the problem for a numerical solution, we guarantee tight optimality gaps for high service levels and propose an efficient solution scheme, combining robust scenario reduction with a customized Benders decomposition procedure. In the managerial section, we use the Omegax Swatch MoonSwatches example to demonstrate that a desirable doubly probabilistic service level can be attained for disruptions with a drop in demand. In the case of disruptions followed by a peak in demand, one can tighten the optimality gaps if the service level is reduced.

WILEY2022

Chargement

Fluid Dynamics and Outliers

Bernard André Vincent Calisti

EPFL2017

EPFL2016