The U.S. Navy has been confronted with budget cuts and constraints during recent years. This reduction in budget compels the U.S. Navy to limit the number of manpower and personnel to control costs. Reducing the total ownership cost (TOC) has become a major topic of interest for the Navy as plans are made for current and future fleets. According to the U.S. Government Accountability Office (GAO, 2003), manpower is the most influential component of determining the life cycle cost of a ship. The vast majority of the TOC is comprised of operating and support (O&S) costs which account for approximately 65 percent of the TOC. Manpower and personnel costs account for approximately 50 percent of O&S costs. This research focused on tradeoff analysis and cost estimation between manpower and new technology implementation. Utilizing concepts from System Dynamics Modeling (SDM), System Dynamics Causal Loop diagrams (CLD) were built to identify major factors when implementing new technology, and then stocks and flows diagrams were developed to estimate manpower cost associated with new technology implementation. The SDM base model reflected an 18 months period for technology implementation, and then compared different technology implementation for different scenarios. This model had been tested by the public data from Department of the Navy (DoN) Budget estimates. The objective of this research was to develop a SDM to estimate manpower cost and technology tradeoff analysis associated with different technology implementations. This research will assist Navy decision makers and program managers when objectively considering the impacts of iii technology selection on manpower and associated TOC, and will provide managers with a better understanding of hidden costs associated with new technology adoption. Recommendations were made for future study in manpower cost estimation of ship systems. In future studies, one particular type of data should be located to test the model for a specific manpower configuration.

Realistically, organizational and/or system efficiency performance is dynamic, non-linear, and a function of multiple interactions in production. However, in the efficiency literature, system performance is frequently evaluated considering linear combinations of the input/output variables, without explicitly taking into account the interactions and feedback mechanisms that explain the causes of efficiency behavior, the dynamic nature of production, and non-linear combinations of the input/output variables. Consequently, policy decisions based on these results may be sub-optimized because the non-linear relationships among variables, causal relationships, and feedback mechanisms are ignored. This research takes the initial steps of evaluating system efficiency performance in a dynamic environment, by relating the factors that effect system efficiency performance to the policies that govern it. First, this research extends the concepts of the static production axioms into a dynamic realm, where inputs are not instantaneously converted into outputs. The relationships of these new dynamic production axioms to the basic behaviors associated with system dynamics structures are explored. Second, this research introduces a methodological approach that combines system dynamics modeling with the measurement of productive efficiency. System dynamics is a modeling paradigm that evaluates system policies by exploring the causal relationships of the important elements within the system. This paradigm is coupled with the fundamental assumptions of production theory in order to evaluate the productive efficiency of a production system operating within a dynamic and non-linear environment. As a result, a subsystem within the system dynamics model is introduced that computes efficiency scores based on the fundamental notions of productive efficiency. The framework's ability to combine prescriptive and descriptive modeling characteristics, as well as dynamic and combinatorial complexity, can potentially have a greater impact on policy decisions and how they affect system efficiency performance. Finally, the utility of these concepts is demonstrated in an implementation case study. This methodology generates a prescriptive dynamical production frontier which defines the optimal production resources required to satisfy system requirements. Additionally, the dynamical production frontier allows for analysis for comparisons between options during a transient period, insight into possible unintended consequences, and the ability to forecast optimal times for introducing system or process improvements. / Ph. D.

In 2001, the Government of India announced a program for the establishment of Agricultural Export Zones (AEZ). This is the first national program in India to adopt a regional approach to promoting exports of agricultural products, with the combined action of the central and state governments. The gherkin AEZ, covering eight districts of Karnataka, was notified in November 2001. Gherkin is an export-oriented vegetable that is being produced and exported from Karnataka since the late 1980s. Exploration of data on gherkin exports and cultivation costs, for the period 2001-06, revealed the following trends, namely, the share of value of total exports from the gherkin AEZ in India’s gherkin exports has declined from 81% to about 54%; overall (bulk + bottled) gherkin export quantities and value from the AEZ have grown with an annual rate of 18% and 21% respectively; unit price of AEZ bulk gherkins in 200506 is about 4% higher than its unit price in 2001-02 while the unit price of AEZ bottled gherkins is lower by about 9%; and the cost of gherkin cultivation has increased by about 27%. The aim of this research is to apply a stakeholder-based planning framework for enhancing the performance of the Gherkin AEZ in Karnataka. To meet the research aim, the following objectives were defined: 1.Forecasting the primary performance indicators of the gherkin AEZ, in the medium term, given the current policy environment 2.Conducting stakeholder analysis, identifying system structures that influence the gherkin AEZ’s performance and the interventions preferred by stakeholders to enhance gherkin AEZ performance 3.Exploring probable impacts of the identified interventions on gherkin AEZ’s competitiveness 4.Developing policy suggestions and a planning framework for enhancing the performance of the Gherkin AEZ Towards meeting the research objectives, the following approach has been used. One, data has been collected on AEZ performance indicators. Evolution of those variables, given the prevailing policy environment, is forecast using the Seasonal Auto Regressive Integrated Moving Average (SARIMA) methodology. Second, System elements of the gherkin AEZ are identified and organized as a system dynamics model. Stakeholder analysis of the Gherkin AEZ has been done to identify stakeholder groups. Preferred interventions for improvement of AEZ performance are collected from most stakeholders, and the impact of key interventions on system elements is qualitatively assessed. Considering the large number of system elements and the interrelationships between them, a multiple criteria decision making technique, the analytic network process, is used to describe the decision making environment and prioritize the system elements (factors and subfactors influencing AEZ performance, stakeholder objectives and interventions). Third, the probable impact of preferred interventions on competitiveness of gherkin cultivation in the AEZ is assessed using the Policy Analysis Matrix. Fourth, the analysis and learnings are integrated to suggest policy interventions and develop a planning framework for improved performance of the gherkin AEZ. The 3-year SARIMA models, to forecast the AEZ’s performance indicators, indicate a continued growth in the quantities of gherkin exports from the AEZ with a higher rate of growth in exports of bottled gherkins. The forecast also indicate a continued fall in the unit prices of both bottled and bulk gherkins. Stakeholder analysis and System dynamic models indicate the presence of several stakeholders with multiple objectives, multiple factors which influenced AEZ performance, and several preferred interventions. In this multiple criteria decision making environment, the system elements along with dependencies shown in the system dynamics model, the interventions collected from stakeholders (alternatives), and stakeholder objectives derived from stakeholder analysis, are organized to develop an Analytic Network Process (ANP) model. The cluster priorities from the ANP model indicate that global trade conditions are the most important determinant of AEZ performance. The limit matrix indicates that the element of tastes and preferences of world consumers obtains the highest priority followed by price of other countries’ gherkins, and sanitary and phytosanitary conditions as applicable to Gherkins. The probable impacts of the identified interventions on competitiveness of gherkin cultivation in the AEZ are assessed by applying the policy analysis matrix (PAM). Synthesis of sectoral performance data and factors, results of the system dynamics, ANP and PAM analysis resulted in the development of the planning framework for the gherkin AEZ. Overall, the results and analyses demonstrate that the AEZ program should focus on increasing demand, enhancing prices of AEZ gherkins and reducing cultivation costs. Towards achieving this, recommended policy interventions have been prioritized and participating stakeholders identified. This work represents an application of the multiple criteria methodology of the ANP to agricultural regional planning. The system dynamic methodology has been integrated with ANP to identify the important elements in the gherkin AEZ. This thesis on agricultural exports planning has collected and used micro data (price, product form and grade data) to identify and analyze sectoral issues.

Over the past decades, cost-effectiveness principle or cost-benefit analysis has been employed oftentimes as a typical assessment tool for the expansion of drinking water utility. With changing public awareness of the inherent linkages between climate change, population growth and economic development, the addition of global change impact in the assessment regime has altered the landscape of traditional evaluation matrix. Nowadays, urban drinking water infrastructure requires careful long-term expansion planning to reduce the risk from global change impact with respect to greenhouse gas (GHG) emissions, economic boom and recession, as well as water demand variation associated with population growth and migration. Meanwhile, accurate prediction of municipal water demand is critically important to water utility in a fast growing urban region for the purpose of drinking water system planning, design and water utility asset management. A system analysis under global change impact due to the population dynamics, water resources conservation, and environmental management policies should be carried out to search for sustainable solutions temporally and spatially with different scales under uncertainties. This study is aimed to develop an innovative, interdisciplinary, and insightful modeling framework to deal with global change issues as a whole based on a real-world drinking water infrastructure system expansion program in Manatee County, Florida. Four intertwined components within the drinking water infrastructure system planning were investigated and integrated, which consists of water demand analysis, GHG emission potential, system optimization for infrastructure expansion, and nested minimax-regret (NMMR) decision analysis under uncertainties. In the water demand analysis, a new system dynamics model was developed to reflect the intrinsic relationship between water demand and changing socioeconomic iv environment. This system dynamics model is based on a coupled modeling structure that takes the interactions among economic and social dimensions into account offering a satisfactory platform. In the evaluation of GHG emission potential, a life cycle assessment (LCA) is conducted to estimate the carbon footprint for all expansion alternatives for water supply. The result of this LCA study provides an extra dimension for decision makers to extract more effective adaptation strategies. Both water demand forecasting and GHG emission potential were deemed as the input information for system optimization when all alternatives are taken into account simultaneously. In the system optimization for infrastructure expansion, a multiobjective optimization model was formulated for providing the multitemporal optimal facility expansion strategies. With the aid of a multi-stage planning methodology over the partitioned time horizon, such a systems analysis has resulted in a full-scale screening and sequencing with respect to multiple competing objectives across a suite of management strategies. In the decision analysis under uncertainty, such a system optimization model was further developed as a unique NMMR programming model due to the uncertainties imposed by the real-world problem. The proposed NMMR algorithm was successfully applied for solving the real-world problem with a limited scale for the purpose of demonstration.

Extensive digitization is currently underway in the railway sector, which has resulted in several benefits and improvements, but also challenges. The increased use of digital technologies increases the risks of vulnerabilities and susceptibility to cyberattacks. The effects of a cyber attack can have significant consequences on operations such as financial losses and damaged reputations, or in the worst-case scenario, devastating consequences where the lives of passengers are endangered. With the ongoing digitalization of the railways and the growing concern for cybersecurity, stakeholders in the sector have identified the need to systematically understand the risks of digitization related to cybersecurity and safety. Therefore, this study aims to identify and communicate these risks using system dynamics modeling. This study evaluated how actors in the railway sector reason about risks in the railway, how safety and cybersecurity are related, and new risks associated with digitalization and cybersecurity that have not been mentioned in the literature before. A qualitative study was conducted to answer the research question. Ten actors from different parts of the railway value chain were interviewed, and secondary data was collected from articles and reports within the area of cybersecurity and the railways. The results revealed a connection between cybersecurity and safety which could be seen through the chain of consequences that can arise from a cyberattack and in the event of loss of data availability and integrity. Based on this, core elements of the system and the relationships between them could be identified, from which the causal loop diagram (CLD) was constructed. New risks that were identified were the safety culture that permeates the railway industry, unclear areas of responsibility that are a result of deregulation in the Swedish railway sector, and competitiveness. Insights from the system dynamic model identified a reinforcing loop telling a causal story that shows that low cybersecurity priorities could lead to decreased safety on the railway. This further demonstrates the usefulness of identifying and communicating risks using system dynamics modeling. / En omfattande digitalisering pågår just nu inom järnvägssektorn vilket dels har resulterat i en mängd fördelar och förbättringar, men också utmaningar. Den ökade användningen av digitala teknologier ökar risker för sårbarheter samt mottagligheten av cyberattacker. Effekterna av en cyberattack kan ha betydande konsekvenser på verksamheten så som ekonomiska förluster och skadat rykte, eller också i värsta fall förödande konsekvenser där passagerarnas liv sätts i fara. I och med den pågående digitaliseringen av järnvägen och den ökade oron för cybersäkerhet har intressenter inom sektorn identifierat behovet av att på ett systematisk sätt förstå riskerna med digitalisering relaterade till cybersäkerhet och säkerhet (safety). Denna studie syftar därför till att identifiera och kommunicera dessa risker genom att använda systemdynamisk modellering. Studien utvärderade hur aktörer i järnvägens värdekedja resonerade kring risker i järnvägen, hur säkerhet och cybersäkerhet var relaterat, samt vad det finns för nya risker relaterade till digitaliseringen och cybersäkerhet som inte nämnts i litteraturen tidigare. En kvalitativ studie genomfördes för att svara på frågeställningen. Tio aktörer från olika delar av järnvägssektorns värdekedja intervjuades, och sekundärdata samlades in från artiklar och rapporter inom cybersäkerhet och järnvägen. Resultaten visade att det finns en koppling mellan cybersäkerhet och säkerhet (safety) som syns i den kedja av konsekvenser som kan uppstå från en cyberattack vid förlust av datas tillgänglighet och integritet. Utifrån detta kunde nyckelelement i systemet samt relationerna mellan dessa identifieras, och baserat på detta konstruerades vidare ett causal loop diagram (CLD). Nya risker som identifierades var säkerhetskulturen som genomsyrar järnvägsbranschen, oklara ansvarsområden som är ett resultat av den svenska järnvägens avreglering, samt konkurrenskraft. Insikter från den systemdynamiska modellen identifierade en förstärkande loop som berättar en orsakshistoria som visar att låg prioritering av cybersäkerhet kan leda till minskad säkerhet på järnvägen. Vidare demonstrerar detta nyttan av att identifiera och kommunicera risker med hjälp av systemdynamsik modellering.

Os paradigmas de mercado e produção têm-se alterado sensivelmente nos últimos cinquenta anos. Nesse novo contexto, para que as empresas tenham um desempenho competitivo, deve-se considerar fatores como integração, qualidade da informação e incertezas do ambiente. Um dos objetivos principais deste trabalho é analisar as inter-relações entre esses três fatores citados, no âmbito do Planejamento e Controle da Produção (PCP), bem como avaliar seu impacto no desempenho do PCP e da empresa. O desempenho do PCP foi avaliado considerando-se o nível de reprogramações, ou seja, o nível de modificações na programação da produção, e considerando-se o atendimento das metas definidas para os indicadores dessa função. Essa pesquisa qualitativa e descritiva foi feito por meio da metodologia de estudo de casos múltiplos. Como resultados, foram observadas evidências da existência de relações positivas entre os constructos analisados, ou seja, integração, incerteza, qualidade da informação e desempenho. Além disso, foram identificados diferentes mecanismos de integração utilizados nas empresas, e foram observadas diferentes causas para as reprogramações. O segundo objetivo principal deste trabalho é o desenvolvimento de um modelo dinâmico para controle da produção de múltiplos produtos, capaz de responder às incertezas que afetam estabilidade dos sistemas produtivos. O objeto da modelagem foi um sistema com fluxo job shop destinado à produção de embalagens de polipropileno, pertencente a uma empresa dos setores têxtil e petroquímico. O modelo foi desenvolvido com base na Modelagem de Sistemas Dinâmicos e na Teoria de Controle. Para obtenção do equacionamento matemático, utilizou-se a metodologia dos Grafos de Ligação. A implementação e simulação do modelo foi realizada com o auxílio do módulo Simulink®, do software Matlab®. O objetivo de controle consiste no ajuste da frequência de operação das máquinas de forma a atender as demandas previstas e, simultaneamente, manter os níveis estabelecidos de estoque em processo. No presente trabalho, focou-se na análise da resposta do sistema com controle no regime transiente, com os estoques iniciais nulos. Os melhores resultados foram observados com a utilização de um controlador híbrido, que estabelece uma produção constante em um período inicial e passa posteriormente a atuar como um controlador proporcional. O modelo dinâmico desenvolvido neste trabalho é coerente com resultados obtidos na pesquisa qualitativa e com prática do PCP, pois está alinhado aos indicadores de desempenho desta função, promove a melhoria da qualidade das informações disponíveis ao planejamento e é capaz de responder a incertezas que afetam o fluxo de produção. / The production paradigm has considerably changed over the last fifty years. In this new context, factors such as integration, information quality and ability to respond to uncertainties must be pursued by companies that want to remain competitive. Thus, one of the main objectives of this research is to analyze the relationships among these three factors mentioned, in the level of the Production Planning and Control (PPC) function, as well as to assess their impact on the performance of the PPC function and on the overall business performance. The performance of the PPC function was measured by means of the rescheduling frequency, i.e., the frequency with which the production activities have to be rescheduled, and by means of the degree of accomplishment of organizational goals, that is, the extent to which the PPC metrics are accomplished. This qualitative and descriptive research was carried out as a multiple case study. Evidences of the existence of a positive relationship among the analyzed constructs, i.e., integration, uncertainty, information quality and performance, were found. In addition, different integration mechanisms and different rescheduling causes were observed on the studied cases. The second main objective of this work is the development of a dynamic model for the production control of multi-product systems, i.e, a model that could respond to the environmental and internal uncertainties that affect the production flow. A job shop production system of propylene bags from a manufacturing company of the textile and petrochemical industry was modeled. The model was developed using Control Theory, System Dynamics modeling, and, more specifically, the Bond Graph technique. The mathematical formulation of the system was derived from the Bond Graphs. System implementation and simulation was performed with the aid of Simulink® and Matlab® software. The control objective was to adjust the operation frequency of the machines to attend the required demand while simultaneously keeping the work in process at the desired levels. In this current work, the focus of the analysis was placed on the transient response of the controlled system, with initial inventory levels set to zero. The best results were achieved with a hybrid controller, which leads the machines to operate at constant frequency in the initial period and, later on, starts to perform as a proportional controller. The proposed dynamic model is compatible with the findings of the qualitative research and with the PPC practice, since it is aligned with the PPC metrics, it improves the quality of information available for planning and it can respond to the uncertainties that may affect the production flow.

Os paradigmas de mercado e produção têm-se alterado sensivelmente nos últimos cinquenta anos. Nesse novo contexto, para que as empresas tenham um desempenho competitivo, deve-se considerar fatores como integração, qualidade da informação e incertezas do ambiente. Um dos objetivos principais deste trabalho é analisar as inter-relações entre esses três fatores citados, no âmbito do Planejamento e Controle da Produção (PCP), bem como avaliar seu impacto no desempenho do PCP e da empresa. O desempenho do PCP foi avaliado considerando-se o nível de reprogramações, ou seja, o nível de modificações na programação da produção, e considerando-se o atendimento das metas definidas para os indicadores dessa função. Essa pesquisa qualitativa e descritiva foi feito por meio da metodologia de estudo de casos múltiplos. Como resultados, foram observadas evidências da existência de relações positivas entre os constructos analisados, ou seja, integração, incerteza, qualidade da informação e desempenho. Além disso, foram identificados diferentes mecanismos de integração utilizados nas empresas, e foram observadas diferentes causas para as reprogramações. O segundo objetivo principal deste trabalho é o desenvolvimento de um modelo dinâmico para controle da produção de múltiplos produtos, capaz de responder às incertezas que afetam estabilidade dos sistemas produtivos. O objeto da modelagem foi um sistema com fluxo job shop destinado à produção de embalagens de polipropileno, pertencente a uma empresa dos setores têxtil e petroquímico. O modelo foi desenvolvido com base na Modelagem de Sistemas Dinâmicos e na Teoria de Controle. Para obtenção do equacionamento matemático, utilizou-se a metodologia dos Grafos de Ligação. A implementação e simulação do modelo foi realizada com o auxílio do módulo Simulink®, do software Matlab®. O objetivo de controle consiste no ajuste da frequência de operação das máquinas de forma a atender as demandas previstas e, simultaneamente, manter os níveis estabelecidos de estoque em processo. No presente trabalho, focou-se na análise da resposta do sistema com controle no regime transiente, com os estoques iniciais nulos. Os melhores resultados foram observados com a utilização de um controlador híbrido, que estabelece uma produção constante em um período inicial e passa posteriormente a atuar como um controlador proporcional. O modelo dinâmico desenvolvido neste trabalho é coerente com resultados obtidos na pesquisa qualitativa e com prática do PCP, pois está alinhado aos indicadores de desempenho desta função, promove a melhoria da qualidade das informações disponíveis ao planejamento e é capaz de responder a incertezas que afetam o fluxo de produção. / The production paradigm has considerably changed over the last fifty years. In this new context, factors such as integration, information quality and ability to respond to uncertainties must be pursued by companies that want to remain competitive. Thus, one of the main objectives of this research is to analyze the relationships among these three factors mentioned, in the level of the Production Planning and Control (PPC) function, as well as to assess their impact on the performance of the PPC function and on the overall business performance. The performance of the PPC function was measured by means of the rescheduling frequency, i.e., the frequency with which the production activities have to be rescheduled, and by means of the degree of accomplishment of organizational goals, that is, the extent to which the PPC metrics are accomplished. This qualitative and descriptive research was carried out as a multiple case study. Evidences of the existence of a positive relationship among the analyzed constructs, i.e., integration, uncertainty, information quality and performance, were found. In addition, different integration mechanisms and different rescheduling causes were observed on the studied cases. The second main objective of this work is the development of a dynamic model for the production control of multi-product systems, i.e, a model that could respond to the environmental and internal uncertainties that affect the production flow. A job shop production system of propylene bags from a manufacturing company of the textile and petrochemical industry was modeled. The model was developed using Control Theory, System Dynamics modeling, and, more specifically, the Bond Graph technique. The mathematical formulation of the system was derived from the Bond Graphs. System implementation and simulation was performed with the aid of Simulink® and Matlab® software. The control objective was to adjust the operation frequency of the machines to attend the required demand while simultaneously keeping the work in process at the desired levels. In this current work, the focus of the analysis was placed on the transient response of the controlled system, with initial inventory levels set to zero. The best results were achieved with a hybrid controller, which leads the machines to operate at constant frequency in the initial period and, later on, starts to perform as a proportional controller. The proposed dynamic model is compatible with the findings of the qualitative research and with the PPC practice, since it is aligned with the PPC metrics, it improves the quality of information available for planning and it can respond to the uncertainties that may affect the production flow.

Le diagnostic et la prise en charge des infections ostéo-articulaires (IOA) sont souvent complexes occasionnant une perte osseuse irréversible. La variabilité intra et inter-patient en terme de présentation clinique rend impossible le recours à une description systématique ou à une analyse statistique pour le diagnostic et l'étude de cette pathologie. Le développement d'IOA résulte d'interactions complexes entre les mécanismes cellulaires et moléculaires du tissu osseux et les bactéries. L'objectif de cette thèse est de modéliser l'IOA afin de simuler le comportement du système suite à des interactions au niveau cellulaire et moléculaire en utilisant l'approche de modélisation à base d'agents. Nous avons utilisé une méthode basée sur l'analyse bibliographique pour extraire les caractéristiques du modèle et les utiliser pour deux aspects. Le premier consiste en l'élaboration de la structure du modèle en identifiant les agents et les interactions, et le deuxième concerne l'estimation quantitative des différents paramètres du modèle. La réponse du système BJI aux différentes tailles d’inoculum bactérien a été simulée par la variation de différents paramètres. L'évolution des agents simulés a ensuite été analysée en utilisant une modélisant par des systèmes dynamiques non linéaires et une méthodologie "Datadriven", grâce auxquelles nous avons décrit le système d'IOA et identifié des relations plausibles entre les agents. Le modèle a réussi à présenter la dynamique des bactéries, des cellules immunitaires innées et des cellules osseuses au cours de la première étape de l'IOA et pour différentes tailles d'inoculum bactérien. La simulation a mis en évidence les conséquences sur le tissu osseux résultant du processus de remodelage osseux au cours de l'IOA. Ces résultats peuvent être considérés comme une base pour une analyse plus approfondie et pour la proposition de différentes hypothèses et scénarios de simulation qui pourraient être étudiés dans ce laboratoire virtuel. / Bone and joint infections are one of the most challenging bone pathologies that associated with irreversible bone loss and long costly treatment. The high intra and inter patient's variability in terms of clinical presentation makes it impossible to rely on the systematic description or classical statistical analysis for its diagnosis or studying. The development of BJI encompasses a complex interplay between the cellular and molecular mechanisms of the host bone tissue and the infecting bacteria. The objective of this thesis is to provide a novel computational modeling framework that simulates the behavior resulting from the interactions on the cellular and molecular levels to explore the BJI dynamics qualitatively and comprehensively, using an agent-based modeling approach. We relied on a meta-analysis-like method to extract the quantitative and qualitative data from the literature and used it for two aspects. First, elaborating the structure of the model by identifying the agents and the interactions, and second estimating quantitatively the different parameters of the model. The BJI system’s response to different microbial inoculum sizes was simulated with respect to the variation of several critical parameters. The simulation output data was then analyzed using a data-driven methodology and system dynamics approach, through which we summarized the BJI complex system and identified plausible relationships between the agents using differential equations. The BJI model succeeded in imitating the dynamics of bacteria, the innate immune cells, and the bone cells during the first stage of BJI and for different inoculum size in a compatible way. The simulation displayed the damage in bone tissue as a result of the variation in bone remodeling process during BJI. These findings can be considered as a foundation for further analysis and for the proposition of different hypotheses and simulation scenarios that could be investigated through this BJI model as a virtual lab.

The degradation of natural resources as a result of consumption to support the economic growth of humans society represents one of the greatest sustainability challenges. In order to allow economic growth to continue in a sustainable way, it has to be decoupled from the consumption and destruction of natural resources. This thesis focuses on an innovative manufacturing technology called additive manufacturing (AM) and its potential to become a more efficient and cleaner manufacturing alternative. The thesis also investigates the benefits of accessing the technology through the result-oriented Product-Service Systems (PSS) approach. The outcome of the study is the quantification of raw materials and energy consumption. The scope of study is the application of AM in the scale model kit industry. The methods used are the life cycle inventory study and the system dynamics modeling. The result shows that AM has higher efficiency in terms of raw material usage, however it also has higher energy consumption in comparison to the more traditional manufacturing techniques. The result-oriented PSS approach is shown to be able to reduce the amount of manufacturing equipment needed, thus reducing the energy and raw materials used to produce the equipment, but does not completely decouple economic growth from the consumption of natural resources.