China has continued the economic reform and open door policy over 30 years with many great achievements, such as the second largest GDP, the largest import and export economy with the largest infrastructural investment in the world. On the other hand, the conflicts and risks the firms especially for small and medium sized manufacturing enterprises (SMEs) have faced are extremely serious and more acute due to the economy growth and increasing social wealth, especially in Yangtze River Delta, in the general context of ever increasing cost such as labour, land and higher customers’ expectations such as the quality of product. These serious problems are challenges for the competitiveness of SMEs in Yangtze River Delta. This research aims to investigate and improve the competitiveness of SMEs by the main variables such as enterprise’s resources, product’s competitive issues and innovation activities related barriers. To achieve the aim, the research employed a mixed method of quantitative and qualitative approaches to build the competitiveness’s belief network model by Bayesian Belief Networks and analyze the factors of the most important variables by the SPSS software. Secondly, 36 entrepreneurs of small and medium sized manufacturing enterprises in Yangtze River Delta have been carefully selected to participate in the questionnaire survey and face to face interviews. All participants are entrepreneurs who have run enterprise for at least three years. Five kinds of resources, competitive issues and innovation have been identified as the variables of competitiveness. The findings of research are mainly related to the three aspects which are general view of variables; barriers to innovation activity and importance of variables for improving the competitiveness; and the factor analysis of quality management practices. Firstly, the general condition of financial resource is the worst in resource sector of SMEs; Dependability is the best performance in competitive issues of SMEs; Lack of finance is generally identified the biggest barrier to innovation of SMEs. Secondly, the Physical resource in resource sector and Quality in competitive issues sector are the most important variables for improving the competitiveness of SMEs after BBN assessment; Lack of technical experts is the most serious barrier when the SMEs are really focusing on the innovation according to the BBN assessments. Thirdly, the factor analyses have identified the key independent factors explaining the quality management practices in these SMEs. Finally, these findings can help the SMEs build variables’ impact tables based on the outputs from the conditional assessment of BBNs to make more efficient and effective decisions when they try to improve the enterprise competitiveness, with detailed recommendations. At the same time, the importance and factors of good quality management practices have also been argued to help the entrepreneurs improve the quality performance and their enterprise competitiveness.

This thesis explores the causal relationships between tax incentives, research and development (R&D) and productivity. Using R&D survey data from the United Kingdom (UK) Office for National Statistics and administrative data on corporation tax returns from HM Revenue and Customs, I first conduct empirical analyses of tax incentive policies for R&D, and then estimate the elasticity of output with respect to firms' own R&D efforts as well as external R&D performed by neighboring firms in technology and product space. In the first two chapters which focus on tax incentive policies and their evaluation, I am able to identify the policy effect of interest by exploiting two significant reforms in the UK in 2002 and 2008. I find that tax incentives had a positive and significant stimulating effect on businesses' R&D spending. I argue that the availability of a quasi-experimental set up helps in better identifying the policy impact. The production function estimation exercise in the third chapter shows that double counting of R&D human resources and materials in the production function causes the elasticity of output with respect to the firms' own R&D to be substantially underestimated. I also find that the R&D done in multi-unit enterprise groups is productive for the production facilities which themselves do not perform R&D. The Jaffe (1986) and Bloom et al. (2013) measures of external R&D, which account for closeness of firms in technology and product space can be constructed and included in the production function in the spirit of Griliches (1979). I find that the point estimate for the elasticity of output with respect to firms' own R&D is around 3 percent and statistically significant. Evidence is mixed regarding the productivity effects of R&D carried out by competitors in the product market or neighboring firms in technology space. The detailed data sets used in this study offer valuable resources for empirical work on R&D and productivity.

L'examen de la filière EVHT (Electrolyse de Vapeur d'eau à Haute Température) montre qu'elle est en train de passer de la phase de recherche à la phase de développement. Il devient maintenant nécessaire de prouver et si possible d'améliorer la compétitivité de cette technologie. Pour cela nous avons fait ressortir le besoin de posséder un outil capable de faire le lien entre les producteurs d'hydrogène, propriétaires d'usine, et les fabricants d'empilements de cellules d'électrolyse. Pour répondre à cet objectif principal, deux contraintes fortes sont identifiées : être insérable dans un schéma usine (logiciel de simulation de procédé), et être représentatif de la technologie de cellule et du stack utilisée. La modélisation d'un objet dans un logiciel de simulation de procédé implique généralement une représentation très simplifiée de celui-ci. Pour pouvoir satisfaire ces contraintes, nous avons bâti une chaîne de modèles partant des modèles d'électrodes et aboutissant finalement à une modélisation de procédé représentative de la technologie EVHT utilisée. Le travail et la valeur ajoutée de cette thèse sont focalisés sur cette démarche d'optimisation énergétique globale et locale, qui permet, à chaque échelle, une analyse adaptée des phénomènes principaux se déroulant dans chaque objet et le chiffrage de l'impact énergétique et économique de la technologie utilisée. Cette démarche permet d'aboutir à un outil capable de réaliser une optimisation technico-économique poussée sur une unité de production EVHT / HTSE field (High Temperature Steam Electrolysis) is moving from the research phase to development phase. It?s now necessary to prove and to possibly improve the technology competitiveness. Therefore we need a tool able to allow communication between hydrogen producers and electrolysis cell stack designers. Designers seek where their efforts have to focus, for example by searching what are the operating best conditions for HTSE (voltage, temperature). On the contrary, the producer wants to choose the most suitable stack for its needs and under the best conditions: hydrogen has to be produced at the lowest price. Two main constraints have been identified to reach this objective: the tool has to be inserted into a process simulation software and needs to be representative of the cell and stack used technology. These constraints are antagonistic. Making an object model in a process simulation usually involves a highly simplified representation of it. To meet these constraints, we have built a model chain starting from the electrode models and leading to a representative model of the HTSE technology used process. Work and added value of this thesis mainly concern a global and local energy optimization approach. Our model allows at each scale an appropriate analysis of the main phenomena occurring in each object and a quantification of the energy and economic impacts of the technology used. This approach leads to a tool able to achieve the technical and economic optimization of a HTSE production unit.

Avec l’essor du développement durable, des instruments de gestion ont été déployés dans les entreprises afin d’aborder cette problématique. Sur le climat en particulier, où le but est de réduire les émissions de gaz à effet de serre, les marchés du carbone, la fiscalité carbone et les normes d’interdiction constituent les instruments classiques de politique publique. A leurs côtés, la comptabilité carbone, plus confidentielle, a été développée pour rendre visible et estimer ces émissions afin de permettre d’enclencher des actions de réduction. Toutefois ses effets sont actuellement largement méconnus : comment les acteurs en entreprise se sont-ils saisis de cet instrument ? Nous adoptons dans cette thèse une perspective gestionnaire pour nous intéresser aux enjeux d’action collective qui entourent cet objet. Nous questionnons alors la performativité de la comptabilité carbone en proposant d’une part d’analyser le modèle implicite qu’elle véhicule et en étudiant d’autre part ses implications managériales. Pour cela, notre méthodologie repose sur une démarche qualitative et propose notamment des études de cas. En déconstruisant l’objet comptabilité carbone, nous montrons que les différents outils développés sous ce terme générique présentent en réalité des identités différentes, relevant ainsi soit d’une logique ingénierique, soit financière. Les implications managériales qui en découlent sont diverses : si la comptabilité carbone s’inscrit parfois dans une stratégie orientée vers la réduction d’émissions (sous certaines conditions dont nous proposons un modèle explicatif), d’autres effets induits existent (développement d’une bureaucratie, exhortation à la transparence, etc.). Cette thèse présente finalement des contributions théoriques (performativité), empiriques (réflexivité pour les entreprises et l’ADEME), et méthodologiques (analyse de la performativité par les instruments de gestion). / Since sustainable development has spread, management tools have been developed in companies in order to tackle this problem. For climate change, the goal is to reduce greenhouse gas emissions. To do that, carbon markets, carbon taxation and limits of emissions are traditional policy tools. Less known, carbon accounting has been developed in order to assess greenhouse gas emissions, make it visible and therefore to make it possible to reduce them. However carbon accounting’s effects remain mostly unknown. In this thesis, I address collective action problem around this tool in a managerial perspective. Therefore I tackle carbon accounting's performativity by analyzing hidden model which is embedded in it and by studying its managerial effects. My methodology rests upon a qualitative research by using case studies specifically. I explain first that there are different logics behind the common term “carbon accounting”: an engineering and a financial one. Managerial effects are also varied: a strategy oriented toward the emission reduction is sometimes settled but mostly other effects are created (claim for more and more transparency, etc.). Finally I propose theoretical contributions (performativity), empirical ones (ADEME and companies’ reflections) and methodological ones (performativity analysis through managerial tools).

This dissertation studies firms' strategic interactions in anticipation of random service disruption following technology failure. In particular it is aimed at understanding how contracting decisions between a vendor and one or multiple clients affect the firms' subsequent decisions to ensure disruption response and recovery are managed as efficiently as possible. This dissertation consists of three studies that were written as standalone papers seeking to contribute to the literature on contract design and technology management in operations management. Together, the three studies justify the importance of structuring the right incentives to mitigate disruption risks. In the first study we contribute to this literature by means of an analytical model which we use to examine how a client and vendor should balance investments in response capacity when both parties' efforts are critical in resolving disruption and each may have different risk preferences. We study the difference in the client's optimal expected utility between a case in which investment in response capacity is observable and a case in which it is not and refer to the difference in outcomes between the two cases as the cost of complexity. Firstly, we show that the cost of complexity to the client is decreasing in the risk aversion of vendor but increasing in her own risk aversion. Secondly, we find that a larger difference in risk aversion between a client and vendor leads to underinvestment in system uptime in case the client's investment is observable, yet the opposite happens when the client’s investment is not observable. In the second study we further examine the context of the first study through a controlled experiment. We examine how differences in risk aversion and access to information on a contracting partner’s risk preferences interact in affecting contracting and investment decisions between the client and vendor. Comparing subject decisions with the conditionally optimal benchmarks we arrive at two observations that highlight possible heuristic decision biases. Firstly, subjects tend to set and hold on to an inefficiently high investment level even though it is theoretically optimal to adjust decisions under changing differences in risk preferences. Secondly, subjects tend to set and hold on to a penalty that is too high when interacting with more risk averse vendors and too low in case the vendor is equally risk averse. Furthermore, cognitive feedback on the vendor’s risk aversion appears to have counterproductive effects on subject’s performance in the experiment, suggesting cognitive overload can have a reinforcing effect on the heuristic decision biases observed. In the third study we construct a new analytical model to examine the effect of contract design on a provider's response capacity allocation in a setting where multiple clients may be disrupted and available response capacity is limited. The results show that while clients may be incentivized to identify and report network disruptions, competition for scarce emergency resources and the required investment in understanding their own exposure may incentivize clients to deliberately miscommunicate with the vendor.

One of the major demand related risks for companies that produce consumer electronics goods is change in consumer preferences over time as reflected in the weights they attach to the attributes of products. This contributes to the difficulty of predicting whether consumers will purchase a new product or not and the accuracy of such forecasts can have significant ramifications for companies’ strategies, profitability and even their chances of survival. Knowledge of attribute-weights and accurate forecasts of new products can give companies better insights during the product development stages, inform go-no-go decisions on whether to launch a developed product and also support decisions on whether a recently launched product should be withdrawn or not due to poor early stage sales. Despite the important implications of change in attribute-weights, no research has investigated the extent to which such changes occur and impact on the accuracy of forecasts of the future market share of these products. Prior to the current research, it was assumed that the weights are constant over time – even when the nature of the attributes was assumed to change. To investigate these concerns choice based conjoint (CBC) was applied to data gathered in a longitudinal survey of consumer choices relating a range of consumer electronic products, where innovation has different rates and the product life cycles are various. This allowed an assessment of the extent to which the weights of attributes of choice-based conjoint models change over a six months period for consumer durable products and the degree to which this variability is dependent on the nature of the product. It demonstrates that the change in weights is greater for products that have high technological complexity and shorter lifecycles and also links the changeability of weights to the characteristics of potential consumers. The results of thesis demonstrate that the assumption of constant weights can potentially lead to inaccurate market share forecast for high-tech, short life-cycle products that are launched several months after the choice-based modelling has been conducted.

Additive Manufacturing (AM) has demonstrated great potential to advance product design and manufacturing, and has showed higher flexibility than conventional manufacturing techniques for the production of small volume, complex and customised components. In an economy focused on the need to develop customised and hi-tech products, there is increasing interest in establishing AM technologies as a more efficient production approach for high value products such as aerospace and biomedical products. Nevertheless, the use of AM processes, for even small to medium volume production faces a number of issues in the current state of the technology. AM production is normally used for making parts with complex geometry which implicates the assessment of numerous processing options or choices; the wrong choice of process parameters can result in poor surface quality, onerous manufacturing time and energy waste, and thus increased production costs and resources. A few commonly used AM processes require the presence of cellular support structures for the production of overhanging parts. Depending on the object complexity their removal can be impossible or very time (and resources) consuming. Currently, there is a lack of tools to advise the AM operator on the optimal choice of process parameters. This prevents the diffusion of AM as an efficient production process for enterprises, and as affordable access to democratic product development for individual users. Research in literature has focused mainly on the optimisation of single criteria for AM production. An integrated predictive modelling and optimisation technique has not yet been well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no robust methods for the optimal design of complex cellular support structures, and most of the software commercially available today does not provide adequate guidance on how to optimally orientate the part into the machine bed, or which particular combination of cellular structures need to be used as support. The choice of wrong support and orientation can degenerate into structure collapse during an AM process such as Selective Laser Melting (SLM), due to the high thermal stress in the junctions between fillets of different cells. Another issue of AM production is the limited parts’ surface quality typically generated by the discrete deposition and fusion of material. This research has focused on the formation of surface morphology of AM parts. Analysis of SLM parts showed that roughness measured was different from that predicted through a classic model based on pure geometrical consideration on the stair step profile. Experiments also revealed the presence of partially bonded particles on the surface; an explanation of this phenomenon has been proposed. Results have been integrated into a novel mathematical model for the prediction of surface roughness of SLM parts. The model formulated correctly describes the observed trend of the experimental data, and thus provides an accurate prediction of surface roughness. This thesis aims to deliver an effective computational methodology for the multi- objective optimisation of the main building conditions that affect process efficiency of AM production. For this purpose, mathematical models have been formulated for the determination of parts’ surface quality, manufacturing time and energy consumption, and for the design of optimal cellular support structures. All the predictive models have been used to evaluate multiple performance and costs objectives; all the objectives are typically contrasting; and all greatly affected by the part’s build orientation. A multi-objective optimisation technique has been developed to visualise and identify optimal trade-offs between all the contrastive objectives for the most efficient AM production. Hence, this thesis has delivered a decision support system to assist the operator in the "process planning" stage, in order to achieve optimal efficiency and sustainability in AM production through maximum material, time and energy savings.

The development of information technologies and the reduction of trade barriers have fostered the international fragmentation of production and the expansion of knowledge networks. Globalization has stimulated an unprecedented economic growth across the globe, shifting the balance in the world economy, with a decline of developed countries and the rise of emerging economies. The response of rms in mature economies to global competition is an increased engagement in internationalization and innovation strategies. In this thesis we investigate rst how trade protectionism might not be an e cient instrument to prevent the negative e ects of international competition, nding mixed e ect of EU anti-dumping measures on Chinese products, with temporary bene t for domestic producers, but a negative impact on importers and long-run perverse e ect on productivity. Second, we analyse the role of innovation in fostering the international performance of rms. Our results show that R&D investment, innovation and outsourced R&D improve the export performance of European rms, exporting more products and accessing new and more di cult foreign markets. Only by investing in innovation European rms will be able to positively internalise the externalities linked to globalization, increasing human capital and the stock of knowledge, boosting productivity and creating new value-added jobs.

Pas de résumé en français / We live in the information age. Data has become an essential asset for most everyday situations and business interactions. The need to share data, to generate information, and create new knowledge from that data is common to all fields of research and all economic activity. Managing data is a critical, and sometimes costly, process. When not properly defined, data might become incomplete, inconsistent or, even worse, unusable. Requirements for data evolve and we must define new data or update existing data over the entire data lifecycle. Evolving data requirements is an important issue and a technological challenge as it is not possible to define, in advance, information structures that meet requirements you do not yet know. Specifying information requirements is particularly challenging in domains such as manufacturing where information exchange involves many actors and sharing across multiple functions and software applications. As a result, it becomes hard to find a common information structure for representing data. The challenge is even bigger when a temporal aspect has to be considered since it requires the ability to extend the information structure dynamically over time. One area within the manufacturing domain that we have identified with these characteristics is Product Lifecycle Management (PLM). PLM involves many global actors using a myriad of software applications that perform a series of product management functions that can last from weeks to decades. Because the mechanism to extend models is static by its nature, requiring numerous updates of the initial information model, this operation is expensive in cost and time, and requires and understanding of the entire initial model to ensure correct extensions are developed. This research presents an alternative based on dynamic customization of information models in the context of PLM, by leveraging existing PLM standards and frameworks, and using emerging semantic web technologies such as OWL, SPARQL and SPIN. Following a state of the art in Chapter 2, Chapter 3 defines technical requirements used to evaluate existing PLM standards and frameworks. Based on the analysis of this evaluation, Chapter 4 presents new framework components for defining dynamically customizable information models for PLM. In chapter 5 these components are integrated together into a framework, and a use case demonstrates the efficiency of the framework. Chapter 6 concludes the research and introduces ideas for future research.

Aujourd’hui, le processus de conception de produits fait face à une mondialisation des marchés, conduit par des équipes géographiquement distribuées. Ces équipes sont ainsi amenées à travailler ensemble afin de concevoir ces produits nouveaux. Les activités de conception ont donc évolué au fil du temps pour pouvoir constamment répondre aux nouvelles contraintes industrielles, de la même manière que les processus de fabrication se sont adaptés aux marchés. Ainsi, afin de faciliter les phases de travail en commun, de nouvelles stratégies de gestion de la collaboration, notamment à travers de nouveaux systèmes d’information, sont mises en place. Ces systèmes d’information sont nombreux et prennent différentes formes, ce qui rend souvent difficiles la sélection et le pilotage de ces derniers. Or, pour les équipes projet, la gestion de ces nouveaux outils informatiques fait partie intégrante des éléments clés du processus de conception de produits. Ainsi, dans le cadre de nos travaux, nous nous focalisons sur l’aide au pilotage de l’ingénierie collaborative en mode projet pour la conception et le développement de produits matériels techniques. Notre objectif est d’aider le chef projet à mieux gérer son projet en utilisant au moment adéquat l’outil d’aide à la collaboration le plus adapté. Dans nos travaux, nous avons souligné la nécessité d’utiliser un indicateur quantitatif de pilotage de la conception collaborative. Cet indicateur apporte ainsi une approche complémentaire de l’évaluation de la pertinence de la collaboration en cours, en prenant en compte son impact sur l’évolution du projet. Le calcul de cet indicateur s’appuie sur une métrique spécifique et concerne l’évolution de la complétude de la donnée CAO. De plus, nous avons montré que ce nouvel indicateur peut être intégré à une approche organisationnelle de type PLM afin de faciliter le stockage des données et le calcul de la complétude, cette dernière étant liée aux outils utilisés et aux jalons projet. / Today, product design process is facing a market globalization led by distributed teams. Moreover, the international market context in which the companies evolves, leads them to work in large multi-disciplinary collaborative teams using in collaborative practices. In this context, product design process is led by the integration and optimization of stakeholders’ collaboration. Thus, to facilitate collaboration steps, new management strategies are defined and new information systems can be used. These information systems are numerous and take various forms, leading to difficulties for companies to select one of them and manage them. However, to the design teams, the management and the choice of those are key elements of the product design process.Toward this ends, in the present thesis, we focalize our research on the topic of collaborative design project management. Our objective is to assist the project leader to better manage her or his product design project using optimal collaborative tool all along the design project. We have underlined the necessity to use quantitative and non-intrusive indicator during the management of collaborative design phases in order to subjective evaluation. The tracking of this indicator is performed in parallel to the existing approaches of the evaluation of the suitability of the collaboration. It defines the impact of the collaboration steps on the design project evolution. The computation of this indicator is based on a precise metric which details the completeness of the CAD model based on the used collaborative tools and the project milestones. Moreover, we have showed that this new indicator can be integrated to an organizational approach, as a PLM, to facilitate data storage and completeness computation.