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This thesis is about How External Uncertainties Affect the Innovation Process. Written during the spring term of 2020 by Simon Algotsson and Johan Öhlander. The thesis main goal is to generate knowledge about the properties and sources of external uncertainties and create an understanding of how they can come to affect an innovation process. This research encourages organizations that are planning to participate, or currently resides in an innovation process to give it a read. Anyone who seeks a deeper understanding of the impact of external uncertainties may use our findings as a source of inspiration. The research question we have answered is: How do external uncertainties affect the innovation process? As the title and research question shows, innovation and uncertainties are the two most common denominators in this work. Presented in the theoretical framework is previous research done concerning the innovation process, and what it consists of. As well as how other researchers describe different types of uncertainties. We have also constructed our own model of how external uncertainties can give rise to internal uncertainties. For this type of research, a qualitative method has been selected, since it enabled us to go in-depth in one specific innovation process. We have conducted two interviews with the CEO and CFO of a company referred to as Gamma. They have both shared their own perspectives of the innovation process their company has gone through. The data collected from the interview has been transcribed separately and is later presented in the empirical evidence. The final sections of this thesis include the analysis and the conclusion. In these chapters we draw parallels between the research presented in the theoretical framework and Gamma's innovation process. The process we have investigated for this thesis encountered several uncertainties, both internal and external. In the analysis we present the authors own model of how external uncertainties came to affect Gamma's innovation process. The conclusion discusses the significant findings of the research such as how Gamma's innovation turned into a 'black hole' for the profits generated by the company.

The tire is one of the most important components of the vehicle. It has many functions, such as supporting the load of the vehicle, transmitting the forces which drive, brake and guide the vehicle, and acting as the secondary suspension to absorb the effect of road irregularities before transmitting the forces to the vehicle suspension. A tire is a complex reinforced rubber composite air container. The structure of the tire is very complex. It consists of several layers of synthetic polymer, many flexible filaments of high modulus cord, and glass fiber, which are bonded to a matrix of low modulus polymeric material. As the tire is the only component of the vehicle which makes contact with the road surface, almost all forces and moments acting on the vehicle must be transferred by the tire. To predict the dynamics of the vehicle, we need to know these forces and moments generated at the tire contact patch. Therefore, tire models that accurately describe this dynamic behavior are needed for vehicle dynamic simulation. Many researchers developed tire models for vehicle dynamic simulations; however, most of the development in tire modeling has been limited to deterministic steady-state on-road tire models. The research conducted in this study is concerned with the development of semi-empirical transient tire models for on-road and off-road vehicle simulations. The semi-empirical transient tire model is developed based on existed tire models, analytical tire structure mechanics analysis, and experimental data collected by various researchers. The tire models were developed for vehicle traction, handling and ride analysis. The theoretical mechanics analysis of the tire model focused on the determination of tire and terrain deformation. Then, the results are used together with empirical data to calculate the force response and the moment response. Moreover, the influence of parametric uncertainties in tire parameters on the tire-terrain interaction is investigated. The parametric uncertainties are quantified and propagated through the tire models using a polynomial chaos theory with a collocation approach. To illustrate the capabilities of the tire models developed, both deterministic and stochastic tire models are simulated for various scenarios and maneuvers. Numerically simulated results are analyzed from the perspective of vehicle dynamics. Such an analysis can be used in tire and vehicle development and design. / Ph. D.

The aim of this master thesis work was to investigate how the uncertainties in nuclear data for 238U affects the uncertainty of keff in criticality simulations for nuclear fuel storages. This was performed by using the Total Monte Carlo (TMC) method which allows propagation of nuclear data uncertainties from basic nuclear physics to reactor parameters, such as keff. The TMC approach relies on simulations with hundreds of calculations of keff with different random nuclear data libraries for 238U for each calculation. The result is a probability distribution for keff where the standard deviation for the distribution represents a spread in keff due to statistical and nuclear data uncertainties. Simulations were performed with MCNP for a nuclear fuel storage representing two different cases:  Normal Case and Worst Case. Normal Case represents a scenario during normal conditions and Worst Case represents accident conditions where optimal moderation occurs. In order to validate the MCNP calculations and the libraries produced with TMC, criticality benchmarks were used. The calculated mean value of keff for the criticality benchmark simulations with random libraries produced with TMC obtained a good agreement with the experimental keff for the benchmarks. This indicates that the libraries used in this this work were of good quality. The TMC method´s drawback is the long calculation time, therefore the new method, fast TMC, was tested.  Both fast TMC and original TMC were applied to the Normal Case. The two methods obtained similar results, indicating that fast TMC is a good option in order to reduce the computational time. The computer time using fast TMC was found to be significantly faster compared with original TMC in this work. The 238U nuclear data uncertainty was obtained to be 209 pcm for the Normal Case, both for original and fast TMC. For the Worst Case simulation the 238U nuclear data uncertainty was obtained to be 672 pcm with fast TMC. These results show the importance of handling uncertainties in nuclear data in order to improve the knowledge about the uncertainties for criticality calculations of keff. / Nukleära databibliotek innehåller all nödvändig information för att till exempel kunna simulera en reaktor eller en bränslebassäng för kärnbränsle. Dessa bibliotek är centrala vid beräkningar av olika reaktorparametrar som krävs för en säker kärnkraftsproduktion. En viktig reaktorparameter är multiplikationskonstanten (keff) som anger reaktiviteten för ett system. Ett kritiskt system (keff = 1) innebär att en kedjereaktion av kärnklyvningar kan upprätthållas. Detta tillstånd erfordras i en reaktor för att möjliggöra elproduktion. I en bränslebassäng där använt kärnbränsle förvaras är det viktigt att systemet är underkritiskt (keff < 1). Olika reaktorkoder används för att utföra dessa beräkningar av keff, vars resultat används i processen för att designa säkra bränsleförråd för kärnbränsle. Dagens nukleära databibliotek innehåller osäkerheter som i sin tur beror på osäkerheter i de modellparametrar som används vid framställningen av biblioteken.  Ofta är dessa nukleära data osäkerheter okända, vilket ger upphov till okända osäkerheter vid beräkning av keff. Vattenfall Nuclear Fuel AB undersöker idag möjligheten att öka anrikningen på bränslet för att minska antalet behövda bränsleknippen för en viss energimängd.  Varje bränsleknippe blir då mer reaktiv och i och med det minskar marginalen till kriticitet i bränslebassängen. Därmed är osäkerheterna för nukleära data viktiga i processen för att kunna beräkna den maximalt tillåtna anrikningen för bränslet. För att undersöka hur stora dessa osäkerheter är, användes en relativ ny metod TMC (Total Monte Carlo) som propagerar osäkerheter i nukleära data till olika reaktorparametrar (t.ex. keff) i en enda simuleringsprocess.  TMC metoden användes för att undersöka hur osäkerheterna i nukleära data för 238U påverkar beräkningar av keff för en bränslebassäng med använt kärnbränsle. Beräkningar utfördes för en bränslebassäng under normala driftförhållanden samt för en olyckshändelse då optimal moderering förekommer. Resultaten visade på att standardavvikelsen för nukleära data för 238U var 209 pcm vid normala driftförhållanden och 672 pcm för fallet med optimal moderering. Den ursprungliga TMC metoden är en tidskrävande metod och nyligen har en snabbare variant av TMC utvecklats. Denna nya metod applicerades också på bränslebassängen under normala driftförhållanden och resultaten jämfördes. Resultaten visade att båda metoderna beräknade samma nukleära dataosäkerhet för 238U och genom att använda den snabba TMC metoden, minskade beräkningstiden betydligt jämfört med att använda den ursprungliga TMC metoden.

The aim of my thesis was to analyze and identify main types of risks and uncertainties, which can be faced by international companies. I also described main ways and methods of avoidance and minimisation of such risky situations. AB Inbev was chosen as an example since I was working in the branch of this company located in Prague, Czech Republic. This explains my concentration mainly on the market for beverages.

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Mechanical systems always operate under some degree of uncertainty, which can be due to the inherent properties of the system parameters, to random inputs or external excitations, to poorly known parameters in the interface between different systems, or to inadequate knowledge of the dynamic process. Also, mechanical systems are large and highly nonlinear, while the magnitude of uncertainties may be very large. This dissertation addresses the critical need for understanding of the stochastic nature of mechanical system, especially vehicle and terramechanics systems, and need for developing efficient computational tools to model mechanical systems in the presence of parametric and external uncertainty. This dissertation investigates the influence of parametric and external uncertainties on vehicle dynamics and terramechanics. The uncertainties studied include parametric uncertainties, stochastic external excitations, and random variables between vehicle-terrain and vehicle-soil/snow interface. The methodology developed has been illustrated on a stochastic vehicle-terrain interaction model, a stochastic vehicle-soil interaction model, two stochastic tire-snow interaction models, and two stochastic tire-force relations. The uncertainties are quantified and propagated through vehicle and terramechanics systems using a polynomial chaos approach. Algorithms which can predict the geometry of the contact patch and the interfacial forces and torques on the vehicle-soil interfaces are developed. All stochastic models and algorithms are simulated for various scenarios and maneuvers. Numerical results are analyzed from the computational effort point of view, or from the angle of vehicle dynamics and terramechanics, and provide a deeper understanding of the evolution of stochastic vehicle and terramechanics systems. They can also be used in guiding vehicle design and development. This dissertation represents a pioneer study on stochastic vehicle dynamics and terramechanics. Moreover, the methodology developed is not limited to such systems. Any mechanical system with uncertainties can be treated using the polynomial chaos approach presented, considering their specific characteristics. / Ph. D.

L’optimisation est un sujet très important dans tous les domaines. Cependant, parmi toutes les applications de l’optimisation, il est difficile de trouver des exemples de systèmes à optimiser qui ne comprennent pas un certain niveau d'incertitude sur les valeurs de quelques paramètres. Le thème central de cette thèse est donc le traitement des différents aspects de l’optimisation en présence d’incertitudes. Nous commençons par présenter un bref état de l’art des méthodes permettant de prendre en compte les incertitudes dans l’optimisation. Cette revue de la littérature a permis de constater une lacune concernant la caractérisation des propriétés probabilistes du point d’optimum de fonctions dépendant de paramètres aléatoires. Donc, la première contribution de cette thèse est le développement de deux méthodes pour approcher la fonction densité de probabilité (FDP) d’un tel point : la méthode basée sur la Simulation de Monte Carlo et la méthode de projection en dimension finie basée sur l’Approximation par polynômes de chaos. Les résultats numériques ont montré que celle-ci est adaptée à l’approximation de la FDP du point optimal du processus d'optimisation dans les situations étudiées. Il a été montré que la méthode numérique est capable d’approcher aussi des moments d'ordre élevé du point optimal, tels que l’aplatissement et l’asymétrie. Ensuite, nous passons au traitement de contraintes probabilistes en utilisant l’optimisation fiabiliste. Dans ce sujet, une nouvelle méthode basée sur des coefficients de sécurité est développée. Les exemples montrent que le principal avantage de cette méthode est son coût de calcul qui est très proche de celui de l’optimisation déterministe conventionnelle, ce qui permet son couplage avec un algorithme d’optimisation globale arbitraire. / The optimization is a very important tool in several domains. However, among its applications, it is hard to find examples of systems to be optimized that do not possess a certain uncertainty level on its parameters. The main goal of this thesis is the treatment of different aspects of the optimization under uncertainty. We present a brief review of the literature on this topic, which shows the lack of methods able to characterize the probabilistic properties of the optimum point of functions that depend on random parameters. Thus, the first main contribution of this thesis is the development of two methods to eliminate this lack: the first is based on Monte Carlo Simulation (MCS) (considered as the reference result) and the second is based on the polynomial chaos expansion (PCE). The validation of the PCE based method was pursued by comparing its results to those provided by the MCS method. The numerical analysis shows that the PCE method is able to approximate the probability density function of the optimal point in all the problems solved. It was also showed that it is able to approximate even high order statistical moments such as the kurtosis and the asymmetry. The second main contribution of this thesis is on the treatment of probabilistic constraints using the reliability based design optimization (RBDO). Here, a new RBDO method based on safety factors was developed. The numerical examples showed that the main advantage of such method is its computational cost, which is very close to the one of the standard deterministic optimization. This fact makes it possible to couple the new method with global optimization algorithms.

Dans un environnement économique décrit comme turbulent, la flexibilité est perçue comme un facteur clé de succès que les entreprises devraient maximiser. Pourtant, la notion de flexibilité est souvent présentée comme un concept relatif au domaine étudié. Celle-ci reste par conséquent un concept difficile à appréhender de façon globale et est souvent limitée à une fonction analytique malgré les nombreux travaux dont elle a fait l’objet depuis le début du XXe siècle. La complexité provient essentiellement de la multiplicité des domaines d’application, en témoignent les travaux portants sur la classification des différents types de flexibilités et de l’absence d’approche homogène pour traiter ces problématiques. L’enjeu, pour développer une gestion de la flexibilité au sein des niveaux stratégique, tactique et opérationnel, est alors d’intégrer cette dimension au processus décisionnel. Aujourd’hui, seules certaines décisions ont pour objet la flexibilité alors que tout processus de décision, par le changement d’état qu’il opère, modifie la topologie de l’espace des situations atteignables et par conséquent la capacité du système à être flexible. En vue d’établir une véritable politique de couverture du risque efficiente en univers incertain pour les entreprises, notre recherche s’est attachée à proposer une unité de gestion commune pour la flexibilité que nous avons nommée « potentialité ». Nous avons ainsi défini un cadre conceptuel sur lequel repose une méthodologie permettant de concevoir un système à flexibilité souhaitée. Nos travaux ouvrent des perspectives de recherches importantes sur deux axes majeurs. Le premier consiste à formaliser l’expression du besoin en termes de flexibilité dans le cadre de la méthodologie proposée. Le second, concerne le développement d’une politique de flexibilité basée sur la gestion d’un portefeuille de potentialités à l’instar d’une gestion de stock. / In an economic environment described as turbulent, flexibility is perceived as a key success factor which firms should maximize. Nevertheless, notion of flexibility is often presented as a concept relative to field studies. Therefore, flexibility remains a difficult concept to take on board in a global manner. Despite the fact that there are many studies on the subject since the beginning of XXth century, flexibility is often limited to an analytical function. Complexity results essentially from fields applications’ multiplicity and on lack of homogeneous approach to handle these problems as shown in carrying works on different flexibility types classifications. The aim in developing a flexibility management within strategic, tactical and operational levels, is to integrate this dimension into decisionmaking process. In today’s business, only few decisions process have for object flexibility while every decision making process, by the change of state that it operates, modifies space typology to reachable situations and consequently, modifies as well system capacity to be flexible. To establish a real policy for efficient risk cover in uncertain universe for today’s bussinesses, our research attempted to propose a unit of common management for flexibility, which we named "potentiality". Therefore, we defined a conceptual framework in which a methodology allows designing a “wished flexibility” system. Our works open on perspectives researches based on two major axes. The first one consists in formalizing expression of need in terms of flexibility within the proposed methodology framework. The second axe concerns developing a flexibility policy based on a potentialities portfolio management following stock management example.