<p>After decades of power generating units increasing in size, there is currently a growing focus on distributed generation, power generation close to energy loads. Investments in large-scale units have been driven by economy of scale, but recent technological improvements on small generating plants have made it possible to exploit the benefits of local power generation to a larger extent than previously. Distributed generation can improve power system efficiency because heat can be recovered from thermal units to supply heat and thermally activated cooling, and because small-scale renewables have a promising end-user market. Further benefits of distributed generation include improved reliability, deferral of often controversial and costly grid investments and reduction of grid losses. The new appeal of small-scale power generation means that there is a need for new tools to analyze distributed generation, both from a system perspective and from the perspective of potential developers. In this thesis, the focus is on the value of power generation for end-users. The thesis identifies how an end-user can find optimal distributed generation systems and investment strategies under a variety of economic and regulatory scenarios. The final part of the thesis extends the analysis with a bottom-up model of how the economics of distributed generation for a representative set of building types can transfer to technology diffusion in a market.</p><p>Four separate research papers make up the thesis. In the first paper, Optimal Investment Strategies in Decentralized Renewable Power Generation under Uncertainty, a method for evaluation of investments in renewable power units under price uncertainty is presented. It is assumed the developer has a building with an electricity load and a renewable power resource. The case study compares a set of wind power systems with different capacity and finds that capacity depends on the electricity price and that there under uncertain prices can be a significant value in postponing investment until larger projects are profitable. In the second paper, Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis, a Monte Carlo simulation program to find the value and risk characteristics of combined heat and power units is presented. Using historical price data to estimate price process parameters, it is shown that uncertain prices should not be a barrier for investment, since on-site generators can adapt to uncertain prices and reduce the total energy cost risks. In, Optimizing Distributed Generation Systems for Commercial Buildings, which uses a mixed integer linear program, distributed generation portfolios that maximize profitability are tailored to a building's energy load. Distributed generation with heat recovery and thermally activated cooling are found profitable in an office and a health care building, using current generator data and energy tariffs from California. With the fourth paper, Distributed Energy Resources Market Diffusion Model, the analysis is taken a step further to predict distributed generation market diffusion. Market penetration is assumed to depend on economic attractiveness and knowledge and trust in the technologies. A case study based on the U.S. commercial sector depicts a large market for reciprocating engines and microturbines, with the West and Northeast regions driving market diffusion. Technology research and outreach programs can speed up and change the path of capacity expansion.</p><p>The thesis presents three different models for analyzing investments in distributed generation, all of which have benefits and disadvantages. Choice of model depends on the specific application, but the different approaches can be used on the same problem to analyze it from different viewpoints. The cases in the thesis indicate that distributed generation can reduce expected energy costs while at the same time improve cost predictability. Further, the thesis identifies several important factors and potential barriers to distributed generation adoption. Analyzing distributed generation from the end-user perspective is important also for policy makers, because of the importance of estimating how the market will react to potential policy measures. The thesis shows that small-scale generating capacity has the potential to increase in the near future. Further research should increase the understanding of economic and environmental issues related to distributed generation, while policy makers should aim to construct and implement measures that make it attractive for end-users to invest in efficient local generating capacity.</p>

Supply chains have expanded rapidly over the decades, with the aim to increase productivity, lower costs and fulfil demands in emerging markets. The increasing complexity in a supply chain hinders visibility and consequently reduces one’s control over the process. Cases of disruption such as the ones faced by Ericsson and Enron, have shown that a risk event occurring at one point of the supply chain can greatly affect other members, when the disruption is not properly controlled. Supply chain management thus faces a pressing need to maintain the expected yields of the system in risk situations. To achieve that, we need to both identify potential risks and evaluate their impacts, and at the same time design risk mitigation policies to locate and relocate resources to deal with risk events. This dissertation aims to analyse how supply chain risks could be effectively managed. This is done firstly by positioning the research agenda in supply chain risk management (SCRM). Then, methods for effective management of supply chain risk are identified and analysed. In order to find these, we develop a research framework in which the supply chain system is divided into subsystems based on the operations of make, source and deliver; as well as on material, financial and information flows. Furthermore, research questions are raised in order to understand the impact of risks on supply chains, to identify the performance measures for monitoring supply chains, and to determine risk mitigation strategies for improving system performances. This dissertation includes a bibliometric analysis of relevant literature of SCRM published in recent years. Based on the co-citation analysis, we identify the changing interest in SCRM, from performance-focused individual issues in the early years to integrated system issues with management perspective in recent years. We also identify the growing importance of information issues in SCRM. However, there is a relative lack of research into risk mitigation focusing on information flows in the literature. This dissertation also develops a conceptual model for analysing supply chain risk. The adoption of tools from the established field of reliability engineering provides a systematic yet robust process for risk analysis in supply chains. We have found that the potential use of a stand-alone tool of Failure Modes and Effect Analysis (FMEA) or a hybrid application of Fault Tree Analysis (FTA) and Analytical Hierarchy Process (AHP), will be most appropriate in SCRM. Apart from above mentioned studies, this dissertation then includes three manuscripts respectively investigating the risk mitigation policies in SCRM. First, we suggest a dynamic pricing policy when facing supply yield risk, such as price postponement, where price is determined only after receiving the delivery information. This postponed pricing, can improve the balance between supply and demand, especially when the delivery quantity is small, demand has a low uncertainty and there is a wide range when demand is sensible to price change. In another paper, a system dynamics model is developed to investigate the dispersion of disruption on the supply chain operation as well as along the network. Based on this simulation model, policies are tested to observe their influence to the performance of the supply chain. The study results support the benefit of a dual-sourcing strategy. Furthermore, information sharing, appropriate order splitting and time to react would further improve the supply chain performance when disruption strikes. In the last paper, we study how capacity should be expanded when a new product is introduced into the market. The major risk here is due to a quick capacity expansion with large investments which could be difficult to recover. Using the Bass diffusion model to describe demand development, we study how capacity expansion, together with sales plan could affect the economics of the system. Using sales information for the forecast, delaying the sales and adding initial inventories, should create a better scheme of cash flows. This dissertation contributes in several ways to the research field of SCRM. It plots research advancements which provide further directions of research in SCRM. In conjunction with the conceptual model, simulations and mathematical modelling, we have also provided suggestions for how a better and more robust supply chain could be designed and managed. The diversified modelling approaches and risk issues should also enrich the literature and stimulate future study in SCRM.

<p>Diss. Stockholm : Handelshögsk.</p>

<p>Koi and goldfish have been released into rivers in South Africa since the 1800&rsquo / s for food and sport fish and have since spread extensively. These fish are present in most of the river systems in South Africa and pose an additional threat the indigenous cyprinids in the Western Cape. Monogenean parasites of the genus Gyrodactylus are of particular concern, as their unique biology renders them a possible threat. Gyrodactylus kherulensis and G. kobayashii were identified from koi and goldfish respectively imported from Asia, Europe and locally bred fish. Morphometrics and the use of statistical classifiers, which includes univariate (ANOVA and Kruskal-Wallis), bivariate (Pearson&rsquo / s correlation) and multivariate (Principal Component Analysis) placed the two species within their respective groups. There was some intraspecific variation among the different populations collected from the various locations, especially in the hamulus and ventral bar features, but the marginal hooklets, however, remained static for both helminth species.</p>

Safety analysis is a process involving several techniques.The purpose of this thesis is to test and develop methodssuitable for the safety analysis of railway risks and railwaysafety measures. Safety analysis is a process comprisingproblem identification, risk estimation, valuation of safetyand economic analysis. The main steps are described in separatechapters, each of which includes a discussion of the methodsand a review of previous research, followed by the contributionof this author. Although the safety analysis proceduredescribed can be used for analysing railway safety, it has suchgeneral foundations that it can be used wherever safety isimportant and wherever safety measures are evaluated. Itcombines cost benefit analysis with criteria for thedistribution and the absolute levels of risk. Risks are estimated with both statistical and risk analysismethods. Historical data on railway accidents are analysed andstatistical models fitted to describe trends in accident ratesand consequences. A risk analysis model is developed usingfault tree and event tree techniques, together with Monte Carlosimulation, to calculate risks for passenger train derailments.The results are compared with the statistical analysis ofhistorical data. People's valuation of safety in different contexts isanalysed, with relative values estimated in awillingness-to-pay study. A combination of focus groups andindividual questionnaires is used. Two different methods areused to estimate the value of safety and the results arecompared. Comparisons are also made with other studies. Different approaches for safety analysis and methods foreconomic analysis of safety are reviewed. Cost-benefit analysisas a decision criterion is discussed and a study on theeconomic effectsof a traffic control system is presented. There are several results of the work. Historical data showsa decrease in the accident rate. The average consequence ofeach accident has not changed over time. The risk analysismodel produces comparable results and enables analysis ofvarious safety measures. The valuation study shows that peopleprefer the prevention of small-scale accidents over theprevention of larger, catastrophic accidents. There are onlysmall differences in the valuation of safety in differentcontexts.

After decades of power generating units increasing in size, there is currently a growing focus on distributed generation, power generation close to energy loads. Investments in large-scale units have been driven by economy of scale, but recent technological improvements on small generating plants have made it possible to exploit the benefits of local power generation to a larger extent than previously. Distributed generation can improve power system efficiency because heat can be recovered from thermal units to supply heat and thermally activated cooling, and because small-scale renewables have a promising end-user market. Further benefits of distributed generation include improved reliability, deferral of often controversial and costly grid investments and reduction of grid losses. The new appeal of small-scale power generation means that there is a need for new tools to analyze distributed generation, both from a system perspective and from the perspective of potential developers. In this thesis, the focus is on the value of power generation for end-users. The thesis identifies how an end-user can find optimal distributed generation systems and investment strategies under a variety of economic and regulatory scenarios. The final part of the thesis extends the analysis with a bottom-up model of how the economics of distributed generation for a representative set of building types can transfer to technology diffusion in a market. Four separate research papers make up the thesis. In the first paper, Optimal Investment Strategies in Decentralized Renewable Power Generation under Uncertainty, a method for evaluation of investments in renewable power units under price uncertainty is presented. It is assumed the developer has a building with an electricity load and a renewable power resource. The case study compares a set of wind power systems with different capacity and finds that capacity depends on the electricity price and that there under uncertain prices can be a significant value in postponing investment until larger projects are profitable. In the second paper, Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis, a Monte Carlo simulation program to find the value and risk characteristics of combined heat and power units is presented. Using historical price data to estimate price process parameters, it is shown that uncertain prices should not be a barrier for investment, since on-site generators can adapt to uncertain prices and reduce the total energy cost risks. In, Optimizing Distributed Generation Systems for Commercial Buildings, which uses a mixed integer linear program, distributed generation portfolios that maximize profitability are tailored to a building's energy load. Distributed generation with heat recovery and thermally activated cooling are found profitable in an office and a health care building, using current generator data and energy tariffs from California. With the fourth paper, Distributed Energy Resources Market Diffusion Model, the analysis is taken a step further to predict distributed generation market diffusion. Market penetration is assumed to depend on economic attractiveness and knowledge and trust in the technologies. A case study based on the U.S. commercial sector depicts a large market for reciprocating engines and microturbines, with the West and Northeast regions driving market diffusion. Technology research and outreach programs can speed up and change the path of capacity expansion. The thesis presents three different models for analyzing investments in distributed generation, all of which have benefits and disadvantages. Choice of model depends on the specific application, but the different approaches can be used on the same problem to analyze it from different viewpoints. The cases in the thesis indicate that distributed generation can reduce expected energy costs while at the same time improve cost predictability. Further, the thesis identifies several important factors and potential barriers to distributed generation adoption. Analyzing distributed generation from the end-user perspective is important also for policy makers, because of the importance of estimating how the market will react to potential policy measures. The thesis shows that small-scale generating capacity has the potential to increase in the near future. Further research should increase the understanding of economic and environmental issues related to distributed generation, while policy makers should aim to construct and implement measures that make it attractive for end-users to invest in efficient local generating capacity.

We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility. As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss for the Ericsson B stock data during the period 1999 to 2004.

Most of the city of Uppsala rests on postglacial clay with a varying content of sulphur. The sulphur occurs naturally in the clay in reduced form as sulphide and the clay is for that reason usually called “sulphide clay”. Excavation during construction activities gives rise to large amounts of excavation material, of which the sulphide clay is a considerable part. When the clay is exposed to air and water, an oxidation of the sulphides occurs which may produce an acid leachate and the mobilisation of heavy metals bound in the clay or underlying material. The aim of the work has been to improve our understanding of the behaviour of sulphide clays and produce a basis for risk assessment in the future handling of excavation material with high sulphide content. After sampling had been carried out in two places, batch tests were performed to examine the long-term acidification potential of clays with different calcium carbonate content. The tests were performed with continuous air supply and during the experiment leachate water was analysed with respect to pH, alkalinity, dissolved sulphate and mobilised metals. The results from the laboratory study showed that a clay with a high calcium carbonate content and thus a high net neutralisation potential (NNP) will consume the generated acid and prevent against a lowering of the pH. In clay lacking calcium carbonate, the pH dropped significantly and caused a mobilisation of chiefly Cd, Mn, Co, Ni, Ca and As. Regardless of the changes in pH over time, a large production of sulphur was observed as a result of the sulphide oxidation. In a risk assessment, it is suggested that the NNP should be determined by methods agreed upon to facilitate future comparisons and because different methods may give different results. A clay with a NNP&lt;5 kg CaCO3/ton should be further analysed with respect to metal content to establish the leachate generation potential and estimate the future environmental influence of the excavation material. / Uppsala stad vilar till stor del på postglacial lera med varierande svavelhalt. Svavlet finns naturligt i leran i reducerad form som sulfid och leran kallas därför vanligtvis för ”sulfidlera”. Vid schaktningsarbete i samband med byggnation uppkommer stora mängder schaktmassor varav en betydande del är sulfidlera. Då leran exponeras för luft och nederbörd sker en oxidation av sulfiderna som kan ge upphov till surt lakvatten och läckage av tungmetaller bundna i leran eller underliggande material. Syftet med arbetet har varit att öka kunskaperna om hur sulfidleror beter sig och ta fram ett underlag för riskbedömning i samband med den framtida hanteringen av sulfidhaltiga schaktmassor i Uppsala. Efter genomförd provtagning på två platser utfördes skaktest för att undersöka den långsiktiga försurningspotentialen hos lera med varierande kalkinnehåll. Testerna utfördes med kontinuerlig tillförsel av luft och löpande under försökets gång togs lakvatten ut för analys avseende pH, alkalinitet, löst sulfat och utlakade metaller. Resultaten från den laborativa studien visade att en kalkhaltig lera med hög nettoneutralisationspotential (NNP) kan förbruka den bildade syran och därigenom genereras ingen pH-sänkning. I lera som saknar kalk sjönk pH kraftigt och föranledde läckage av framförallt Cd, Mn, Co, Ni, Ca och As. Oavsett utvecklingen av pH observerades en stor utlakning av svavel till följd av oxidationen av sulfider. I en riskbedömning föreslås att lerans NNP bestäms enligt överenskomna metoder för att underlätta framtida jämförelser och för att olika tekniker kan ge olika resultat. En lera med NNP-värde &lt; 5 kg CaCO3/ton bör analyseras vidare avseende metallinnehåll för att fastställa den potentiella utlakningen och bedöma schaktmassans framtida naturpåverkan.

Real-world problems, especially those that involve natural systems, are complex and composed of many nondeterministic components having non-linear coupling. It turns out that in dealing with such systems, one has to face a high degree of uncertainty and tolerate imprecision. Classical system models based on numerical analysis, crisp logic or binary logic have characteristics of precision and categoricity and classified as hard computing approach. In contrast soft computing approaches like probabilistic reasoning, fuzzy logic, artificial neural nets etc have characteristics of approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost of computation, effective communication and high Machine Intelligence Quotient (MIQ). Proposed thesis has tried to explore use of different soft computing approaches to handle uncertainty in environmental risk management. The work has been divided into three parts consisting five papers. In the first part of this thesis different uncertainty propagation methods have been investigated. The first methodology is generalized fuzzy &#945;-cut based on the concept of transformation method. A case study of uncertainty analysis of pollutant transport in the subsurface has been used to show the utility of this approach. This approach shows superiority over conventional methods of uncertainty modelling. A Second method is proposed to manage uncertainty and variability together in risk models. The new hybrid approach combining probabilistic and fuzzy set theory is called Fuzzy Latin Hypercube Sampling (FLHS). An important property of this method is its ability to separate randomness and imprecision to increase the quality of information. A fuzzified statistical summary of the model results gives indices of sensitivity and uncertainty that relate the effects of variability and uncertainty of input variables to model predictions. The feasibility of the method is validated to analyze total variance in the calculation of incremental lifetime risks due to polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) for the residents living in the surroundings of a municipal solid waste incinerator (MSWI) in Basque Country, Spain. The second part of this thesis deals with the use of artificial intelligence technique for generating environmental indices. The first paper focused on the development of a Hazzard Index (HI) using persistence, bioaccumulation and toxicity properties of a large number of organic and inorganic pollutants. For deriving this index, Self-Organizing Maps (SOM) has been used which provided a hazard ranking for each compound. Subsequently, an Integral Risk Index was developed taking into account the HI and the concentrations of all pollutants in soil samples collected in the target area. Finally, a risk map was elaborated by representing the spatial distribution of the Integral Risk Index with a Geographic Information System (GIS). The second paper is an improvement of the first work. New approach called Neuro-Probabilistic HI was developed by combining SOM and Monte-Carlo analysis. It considers uncertainty associated with contaminants characteristic values. This new index seems to be an adequate tool to be taken into account in risk assessment processes. In both study, the methods have been validated through its implementation in the industrial chemical / petrochemical area of Tarragona. The third part of this thesis deals with decision-making framework for environmental risk management. In this study, an integrated fuzzy relation analysis (IFRA) model is proposed for risk assessment involving multiple criteria. The fuzzy risk-analysis model is proposed to comprehensively evaluate all risks associated with contaminated systems resulting from more than one toxic chemical. The model is an integrated view on uncertainty techniques based on multi-valued mappings, fuzzy relations and fuzzy analytical hierarchical process. Integration of system simulation and risk analysis using fuzzy approach allowed to incorporate system modelling uncertainty and subjective risk criteria. In this study, it has been shown that a broad integration of fuzzy system simulation and fuzzy risk analysis is possible. In conclusion, this study has broadly demonstrated the usefulness of soft computing approaches in environmental risk analysis. The proposed methods could significantly advance practice of risk analysis by effectively addressing critical issues of uncertainty propagation problem. / Los problemas del mundo real, especialmente aquellos que implican sistemas naturales, son complejos y se componen de muchos componentes indeterminados, que muestran en muchos casos una relación no lineal. Los modelos convencionales basados en técnicas analíticas que se utilizan actualmente para conocer y predecir el comportamiento de dichos sistemas pueden ser muy complicados e inflexibles cuando se quiere hacer frente a la imprecisión y la complejidad del sistema en un mundo real. El tratamiento de dichos sistemas, supone el enfrentarse a un elevado nivel de incertidumbre así como considerar la imprecisión. Los modelos clásicos basados en análisis numéricos, lógica de valores exactos o binarios, se caracterizan por su precisión y categorización y son clasificados como una aproximación al hard computing. Por el contrario, el soft computing tal como la lógica de razonamiento probabilístico, las redes neuronales artificiales, etc., tienen la característica de aproximación y disponibilidad. Aunque en la hard computing, la imprecisión y la incertidumbre son propiedades no deseadas, en el soft computing la tolerancia en la imprecisión y la incerteza se aprovechan para alcanzar tratabilidad, bajos costes de computación, una comunicación efectiva y un elevado Machine Intelligence Quotient (MIQ). La tesis propuesta intenta explorar el uso de las diferentes aproximaciones en la informática blanda para manipular la incertidumbre en la gestión del riesgo medioambiental. El trabajo se ha dividido en tres secciones que forman parte de cinco artículos. En la primera parte de esta tesis, se han investigado diferentes métodos de propagación de la incertidumbre. El primer método es el generalizado fuzzy &#945;-cut, el cual está basada en el método de transformación. Para demostrar la utilidad de esta aproximación, se ha utilizado un caso de estudio de análisis de incertidumbre en el transporte de la contaminación en suelo. Esta aproximación muestra una superioridad frente a los métodos convencionales de modelación de la incertidumbre. La segunda metodología propuesta trabaja conjuntamente la variabilidad y la incertidumbre en los modelos de evaluación de riesgo. Para ello, se ha elaborado una nueva aproximación híbrida denominada Fuzzy Latin Hypercube Sampling (FLHS), que combina los conjuntos de la teoría de probabilidad con la teoría de los conjuntos difusos. Una propiedad importante de esta teoría es su capacidad para separarse los aleatoriedad y imprecisión, lo que supone la obtención de una mayor calidad de la información. El resumen estadístico fuzzificado de los resultados del modelo generan índices de sensitividad e incertidumbre que relacionan los efectos de la variabilidad e incertidumbre de los parámetros de modelo con las predicciones de los modelos. La viabilidad del método se llevó a cabo mediante la aplicación de un caso a estudio donde se analizó la varianza total en la cálculo del incremento del riesgo sobre el tiempo de vida de los habitantes que habitan en los alrededores de una incineradora de residuos sólidos urbanos en Tarragona, España, debido a las emisiones de dioxinas y furanos (PCDD/Fs). La segunda parte de la tesis consistió en la utilización de las técnicas de la inteligencia artificial para la generación de índices medioambientales. En el primer artículo se desarrolló un Índice de Peligrosidad a partir de los valores de persistencia, bioacumulación y toxicidad de un elevado número de contaminantes orgánicos e inorgánicos. Para su elaboración, se utilizaron los Mapas de Auto-Organizativos (SOM), que proporcionaron un ranking de peligrosidad para cada compuesto. A continuación, se elaboró un Índice de Riesgo Integral teniendo en cuenta el Índice de peligrosidad y las concentraciones de cada uno de los contaminantes en las muestras de suelo recogidas en la zona de estudio. Finalmente, se elaboró un mapa de la distribución espacial del Índice de Riesgo Integral mediante la representación en un Sistema de Información Geográfico (SIG). El segundo artículo es un mejoramiento del primer trabajo. En este estudio, se creó un método híbrido de los Mapas Auto-organizativos con los métodos probabilísticos, obteniéndose de esta forma un Índice de Riesgo Integrado. Mediante la combinación de SOM y el análisis de Monte-Carlo se desarrolló una nueva aproximación llamada Índice de Peligrosidad Neuro-Probabilística. Este nuevo índice es una herramienta adecuada para ser utilizada en los procesos de análisis. En ambos artículos, la viabilidad de los métodos han sido validados a través de su aplicación en el área de la industria química y petroquímica de Tarragona (Cataluña, España). El tercer apartado de esta tesis está enfocado en la elaboración de una estructura metodológica de un sistema de ayuda en la toma de decisiones para la gestión del riesgo medioambiental. En este estudio, se presenta un modelo integrado de análisis de fuzzy (IFRA) para la evaluación del riesgo cuyo resultado depende de múltiples criterios. El modelo es una visión integrada de las técnicas de incertidumbre basadas en diseños de valoraciones múltiples, relaciones fuzzy y procesos analíticos jerárquicos inciertos. La integración de la simulación del sistema y el análisis del riesgo utilizando aproximaciones inciertas permitieron incorporar la incertidumbre procedente del modelo junto con la incertidumbre procedente de la subjetividad de los criterios. En este estudio, se ha demostrado que es posible crear una amplia integración entre la simulación de un sistema incierto y de un análisis de riesgo incierto. En conclusión, este trabajo demuestra ampliamente la utilidad de aproximación Soft Computing en el análisis de riesgos ambientales. Los métodos propuestos podría avanzar significativamente la práctica de análisis de riesgos de abordar eficazmente el problema de propagación de incertidumbre.

This report is the result of a master thesis with a focus on risk analysis of production location decisions. The project is a part of “PROLOC-manufacturing footprint during the product’s life cycle”. The main aim of this thesis is to point out how current applicable risk analysis techniques evaluate the risks involved in production location decisions and then underline the most important risks involved in production location decisions and elicit strengths and weaknesses of these methods.A systematic review of literature with a focus on journal papers of risk analysis and production fields is conducted by using the content analysis and coding technique. The current risk analysis techniques identified are failure mode and effects analysis (FMEA), life cycle cost (LCC) analysis, and system based techniques like multiobjective analysis, decision tree analysis, and analytic hierarchy process (AHP). In addition two identified frameworks of foreign direct investment (FDI) and international production are the research fields that have contributed extensively in identifying various risks of production location decisions.Having reviewed the literature, it is realized that majority of companies take a short sighted vision in choosing production location and consider just cost based issues like cheaper raw material and low labour cost in some countries and simply ignore uncertainties that can be sources of political, economic, social, competitive, and seismic risks. Low cost countries are usually situated in politically instable areas that can cause long production halts or expropriation. Political risk is mainly identified in FDI literature and is usually triggered by a political turmoil, coup d’état, or revolution. On the other hand cheap labour does not necessarily mean decrease in costs and might bring about quality issues and damage company prestige among customers which results in time and monetary loss. Currency exchange and inflation in costs often causes the initial forecast and cost analysis go wrong. Supply risks are because of disruption of ties with raw material or part suppliers in home country and might result in risk of misuse by new suppliers or partners. Also the seismic risk is introduced as a separate category of risks of production location decisions which can be considered a matter of more investigation and requires further research.The study also presents a review of strengths and weaknesses of existing risk analysis techniques of production location decisions. The lack of consistency, vagueness of information, unfamiliarity with design to cost concept are among the major weaknesses of risk analysis techniques of production location decisions. The study concludes with the fact that just considering the cost oriented factors like cheap labour and raw material by production companies exposed them to various risk and might make the whole investment in vain. Suggestions for further study on techniques and risks of production location decisions are also proposed.