The University of Central Florida has many successful measures to reflect on as it celebrates its 50th year in 2013. It is the university with the 2nd largest student population in the U. S. and its overall ranking in the U.S. News & World Report has improved 4 years in a row. However, with respect to research, the federally funded research and development for the University of Central Florida (UCF) has remained flat. In addition, when compared to other schools, its portion of those federal research dollars is small. This thesis lays the groundwork for developing a model for improving the federally financed academic research and development. A systems approach using the balanced scorecard methodology was used to develop causal loop relationships between the many factors that influence the federal funding process. Measures are proposed that link back to the objectives and mission of the university. One particular measure found in the literature was refined to improve its integration into this model. The resulting work provides a framework with specific measures that can be incorporated at the university to improve their share of the federally financed research and development. Although developed for UCF this work could be applied to any university that desires to improve their standing in the federal financed academic research and development market.

Investment in new technologies is considered by firms as a solution to improve their productivity, product and service quality and their competitive advantages in the global market. Unfortunately, not all technology adoption projects have met their intended objectives. The complexity of technology adoption along with little consideration of the long term cost of the technology, are among the factors that challenge companies while adopting a new technology. Companies often make new technology adoption decision without enough attention to the total cost of the technology over its lifecycle. Sometimes poor decision making while adopting a new technology can result in substantial recurring loss impacts. Therefore, estimating the total cost of the technology is an important step in justifying the technology adoption. Total Ownership Cost (TOC) is a wildly-accepted financial metric which can be applied to study the costs associated with the new technology throughout its lifecycle. TOC helps companies analyze not only the acquisition and procurement cost of the technology, but also other cost components occurring over the technology usage and service stage. The point is that, technology adoption cost estimation is a complex process involving consideration of various aspects such as the maintenance cost, technology upgrade cost and the cost related to the human-resource. Assessing the association between the technology characteristics (technology upgrades over its life cycle, compatibility with other systems, technology life span, etc) and the TOC encompasses a high degree of complexity. The complexity exists because there are many factors affecting the cost over time. Sometimes decisions made today can have long lasting impact on the system costs and there is a lag between the time the decision is taken and when outcomes occur. iv An original contribution of this dissertation is development of a System Dynamics (SD) model to estimate the TOC associated with the new technology adoption. The SD model creates casual linkage and relationships among various aspects of the technology adoption process and allows decision makers to explore the impact of their decisions on the total cost that the technology brings into the company. The SD model presented in this dissertation composes of seven sub-models including (1) technology implementation efforts, (2) workforce training, (3) technology-related workforce hiring process, (4) preventive and corrective maintenance process, (5) technology upgrade, (6) impact of technology on system performance and (7) total ownership cost sub model. A case study of Enterprise Resource Planning (ERP) system adoption has been used to show the application of the SD model. The results of the model show that maintenance, upgrade and workforce hiring costs are among the major cost components in the ERP adoption case study presented in Chapter 4. The simulation SD model developed in this dissertation supports trade-off analysis and provides a tool for technology scenarios evaluation. The SD model presented here can be extended to provide a basis for developing a decision support system for technology evaluation.

With the advances in the information and computing technologies, the ways the manufacturing enterprise systems are being managed are changing. More integration and adoption of the system perspective push further towards a more flattened enterprise. This, in addition to the varying levels of aggregation and details and the presence of the continuous and discrete types of behavior, created serious challenges for the use of the existing simulation tools for simulating the modern manufacturing enterprise system. The commonly used discrete event simulation (DES) techniques face difficulties in modeling such integrated systems due to increased model complexity, the lack of data at the aggregate management levels, and the unsuitability of DES to model the financial sectors of the enterprise. System dynamics (SD) has been effective in providing the needs of top management levels but unsuccessful in offering the needed granularity at the detailed operational levels of the manufacturing system. On the other hand the existing hybrid continuous-discrete tools are based on certain assumptions that do not fit the requirements of the common decision making situations in the business systems. This research has identified a need for new simulation modeling approaches that responds to the changing business environments towards more integration and flattened enterprise systems. These tools should be able to develop comprehensive models that are inexpensive, scalable, and able to accommodate the continuous and discrete modes of behavior, the stochastic and deterministic natures of the various business units, and the detail complexity and dynamic complexity perspectives in decision making. The research proposes and develops a framework to combine and synchronize the SD and DES simulation paradigms to simulate the manufacturing enterprise system. The new approach can respond to the identified requirements in simulating the modern manufacturing enterprise systems. It is directed toward building comprehensive simulation models that can accommodate all management levels while explicitly recognizing the differences between them in terms of scope and frequency of decision making as well as the levels of details preferred and used at each level. This SDDES framework maintains the integrity of the two simulation paradigms and can use existing/legacy simulation models without requiring learning new simulation or computer programming skills. The new framework uses a modular structure by which the SD and DES models are treated as members of a comprehensive simulation. A new synchronization mechanism that that maintains the integrity of the two simulation paradigms and is not event-driven is utilized to coordinate the interactions between the simulation modules. It avoids having one simulation paradigm dominating the other. For communication and model management purposes the SDDES formalism provides a generic format to describe, specify, and document the simulation modules and the information sharing processes. The SDDES controller which is the communication manager, implements the synchronization mechanism and manages the simulation run ensuring correct exchange of data in terms of timeliness and format, between the modules. It also offers the user interface through which users interact with the simulation modules.

In today's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect to each other and the environment cause these systems to behave in a highly nonlinear dynamic manner. Ripple effects that have a huge, negative impact on the behavior of the supply chain (SC) are called instabilities. They can produce oscillations in demand forecasts, inventory levels, and employment rates and, cause unpredictability in revenues and profits. Instabilities amplify risk, raise the cost of capital, and lower profits. To reduce these negative impacts, modern enterprise managers must be able to change policies and plans quickly when those consequences can be detrimental. This research proposes the development of a methodology that, based on the concepts of asymptotic stability and accumulated deviations from equilibrium (ADE) convergence, can be used to stabilize a great variety of supply chains at the aggregate levels of decision making that correspond to strategic and tactical decision levels. The general applicability and simplicity of this method make it an effective tool for practitioners specializing in the stability analysis of systems with complex dynamics, especially those with oscillatory behavior. This methodology captures the dynamics of the supply chain by using system dynamics (SD) modeling. SD was the chosen technique because it can capture the complex relationships, feedback processes, and multiple time delays that are typical of systems in which oscillations are present. If the behavior of the supply chain shows instability patterns, such as ripple effects, the methodology solves an optimization problem to find a stabilization policy to remove instability or minimize its impact. The policy optimization problem relies upon a theorem which states that ADE convergence of a particular state variable of the system, such as inventory, implies asymptotic stability for that variable. The stabilization based on the ADE requires neither linearization of the system nor direct knowledge of the internal structure of the model. Moreover, the ADE concept can be incorporated easily in any SD modeling language. The optimization algorithm combines the advantage of particle swarm optimization (PSO) to determine good regions of the search space with the advantage of local optimization to quickly find the optimal point within those regions. The local search uses a Powell hill-climbing (PHC) algorithm as an improved procedure to the solution obtained from the PSO algorithm, which assures a fast convergence of the ADE. The experiments showed that solutions generated by this hybrid optimization algorithm were robust. A framework built on the premises of this methodology can contribute to the analysis of planning strategies to design robust supply chains. These improved supply chains can then effectively cope with significant changes and disturbances, providing companies with the corresponding cost savings.

Carbon sequestration is paramount to reducing climate change. Grasslands, representing 40% of all terrestrial area, can serve as a primary sequestration location if optimal management strategies can be realized. This study used system dynamics modeling to examine the temporal dynamics of carbon stocks and flows in response to grass species composition, grazing intensity, and temperature and precipitation changes at the landscape level. While there are other biogeochemical models in existence, they are either meant to model large areas, including globally, or are meant to be at a farm level and have limited plot sizes, limiting the options for rangeland managers to test management strategies in larger areas. The aims included conducting a field study of the rangeland, create an initial model; evaluate how the model responded to grazing, temperature, and precipitation changes; and compare the model outcomes to prior work to test the behavior of the model as the start of validation. This thesis used four plant functional groups (C3 and C4 grasses, forbs, and legumes) as the base groups for the model. C4 grasses were not found in in the field study but served to test whether the model detected changes in sequestration when grassland composition is changed. The results demonstrated an approach of using functional groups in system dynamics modeling to optimize carbon sequestration while accounting for diverse management strategies, as has been seen in other biogeochemical models. The model was aligned with prior field research in terms of carbon sequestration levels. The model was able to note differences in grazing regimes, temperature, and precipitation changes in terms of carbon sequestration. Grazing scenarios showed that while increased grazing impacted aboveground litter, it had little impact on sequestration; there was only a 4% increase in carbon with no grazing, Changes in temperature, up to 3°C, were predicted to increase carbon sequestration by 16% from 0.442 to 0.514 kg*m-2*day-1 while decreases in precipitation, both alone and in combination with increasing temperatures, was predicted to decrease sequestration up to 44%. This has to do with the grassland composition, ii especially as this was a C3 dominated grassland which grows in the winter and early spring and required more water but lower temperatures for growth. Future research should continue model validation, test additional functional groups like shrubs, implement more soil carbon pools and flows and add a nitrogen component to the model.

The world shipping freight market for the transportation of bulk cargo - liquid and dry - is characterised by the somewhat erratic and extreme fluctuations exhibited by many of the measures that can be used to describe the "state" of the market. Such 'measures' include the spot freight rates - the cost to the shipper of chartering a unit of shipping capacity 'on the spot' (that is, not reserving the vessel in advance), - the proportion of chartered tonnage at any time taken on a period (time) basis as opposed to a voyage (single) basis, and the rate at which orders for new vessels reach the shipyards. These factors are extremely variable, and have exhibited a similar form of behaviour for a large number of years. This present thesis describes an attempt to investigate the behaviour of the freight market (bulk cargo); the aim of the investigation being to identify the structure of the market system and to construct a System Dynamics model which describes the complex relationships in the market and which can be used to explore questions relating to present or future market behaviour. The construction of such a model is described, together with an exhaustive chapter on the analysis of the model, covering loop-analysis, the behaviour of the model under various test inputs, and the senstivity of the model to significant changes in parameter values. The final chapter considers the use of the model, and, in particular, describes its use in an investigation of the feasibility or desirability of introducing tonnagestabilization schemes into the market. Such schemes aim to prevent the freight rates from falling to unacceptably low values, by implementing a scheme whereby tonnage is withdrawn from operation and laid-up: the decrease in the supply of tonnage then has a positive influence on the freight rates. It is concluded that the problems involved in designing such schemes preclude the schemes from implementation. The appendices cover the various data used in the determination of market relationships, as well as the computer listing, in Fortran, of the market model. Also included in an appendix is a description of the application of System Dynamics to a hypothetical company. The tcompany model' allows for the examination of chartering policies and lay-up policies under a variety of conditions of market demand.

The aim of this thesis is to explore the diversification of a major oil company into the. petrochemical business and then through thorough analysis to recommend the appropriate corporate strategies to be followed by the petrochemical subsidiary of such a company in the 1980's and the 1990's. The petrochemical industry has undergone great changes during the last decade. In the early 1970's it entered a new era of maturity, however due to the misplannings of the late 1960's extending to the early 1970's the industry was suddenly faced with significant overcapacity which has persisted to the present date and is expected to last well into the 1980's. The 1974 oil crisis caused a further decline in the growth of demand, hence exacerbating the situation. During the seventies the industry has had to operate under increasing material prices, unlike the past, which when coupled with the problem of overcapacity and the resulting deterioration of prices, has caused considerable decline in the financial ability of the companies to finance their capital expenditure programmes through internal cash generation (which was the case in the industry's 'golden era'). This situation is threatening the long term viability and survival of the petrochemical businesses. A System Dynamics model for a hypothetical petrochemical subsidiary of a major oil company has been constructed which embodies all the policies inherent in such a system. The dynamic behaviour of the model closely resembles that expected from the real system such as the declining financial ability, which is mostly due to the inflationary conditions. Through thorough analysis, the impact of varying inflation level on the performance of the system was explored, and the need for adopting suitable accounting policies which would take account of the replacement costs of assets, during periods of high inflation, was proposed. The adoption of a number of policies led to a certain degree of improvement in the financial performance of the system, and these are recommended concerning the corporate strategy of the company for the next two decades. Finally it was discovered that due to the low level of growth of demand (compared to the past), the large economic sizes of the petrochemical plants and the market share consensus, the companies will have to go into joint ventures in the future.

The objective of this thesis is to investigate and describe components of pricing system which influence the dynamic performance of a price leader manufacturing firm in an oligopoly capital intensive industry producing an identical intermediate product (s). The synthetic fibre industry is chosen as an illustrative case upon which the discussion is built. However, this work could generally be applied to a wide variety of organizations and situations. After discussing the suitability of system dynamics to the formulation of long-run pricing strategy, a model of the pricing system is constructed by using this technique. The behaviour of this system is examined in terms of feedback loops. That is to illustrate how the characteristics of these loops and the interaction among them affect the dynamic behaviour of the system, and how this behaviour can be improved via changing the components of these loops and/or their structures. The improved system is simulated under different external disturbances, certain parameter changes, and different pricing control policies. The simulation shows that the design of a set of robust pricing policies makes the system insensitive to external disturbance and error in parameters. It also shows that the ability of the firm to attain its growth and profitability objectives is affected by the chosen control pricing policies. Some potential applications of the model, particularly, as planning and training tools are highlighted. It is concluded that System Dynamics is an appropriate approach to the formulation of the long-run pricing strategies.

Textile waste amounts and its utilization are pressing problems in the EU today, with further expected worsening as legislations come into play, increasing collection rates. The EU strives towards a more sustainable future, however this future might entail initial disimprovement, as consumption and disposal behaviour are further expected to increase. The study showed by mapping the future material flow that potential scenarios are severe, in waste volumes and it is unclear how they will be handled and utilized. It is of highest importance to prepare to mitigate them through levers that enable preferred circular flows towards a more sustainable future. Reusing clothing might be such a lever that promises to partially substitute the demand for new products. Through quantitative research and a systemic and dynamic analysis, this study concludes that waste management remains an even more acute problem, whilst slowing consumption through reuse and maintenance of existing products might be a (small) step in the right direction.

Bovine respiratory disease (BRD) is a significant health problem for cattle producers in terms of economic cost and animal welfare. In the United States (US), it is one of the leading causes of sickness and death in beef calves prior to weaning. Although much research has been conducted to develop vaccines for prevention and antibiotics for treatment, the morbidity and mortality of BRD in beef calves prior to weaning has not improved over the years. The identification of risk factors associated with BRD is an area of focus which might ultimately allow producers to minimize morbidity and mortality from BRD. Little research has been performed to understand factors contributing to the risk of BRD in beef calves prior to weaning. BRD affects the beef cattle industry through losses due to mortality, prevention cost, treatment cost, or morbidity effect on productivity. Currently, the economic losses due to BRD for beef calves prior to weaning is not available. Price paid for feeder cattle is a major factor influencing the income of producers. The effect of BRD is a complicated problem since the parameters associated with the cost of BRD in beef cow-calf production are variable and interrelated. To better understand the economic effect of BRD in beef calves prior to weaning, concepts of uncertainty, variability, stochasticity, nonlinearity, and feedback might be involved during the process of assessing risk. The objectives of this dissertation are the following: 1) to test if calf sex, birth weight, and age of dam are associated with BRD of beef calves prior to weaning in different age periods; 2) to identify factors affecting the national market price of beef feeder cattle in the US and how the prices change over time; 3) to investigate the prevention and treatment cost of BRD in beef calves prior to weaning; 4) to estimate the economic cost of BRD in US beef calves prior to weaning; and 5) to understand the effect of BRD occurrence or absence on the national net income of the US beef cow-calf industry.