With businesses operating in an environment of uncertainty, questions were raised around the role of rolling forecasts in this environment and how the current financial processes have changed as a result of the uncertainty. There are currently debates regarding the role of budgeting and other planning methods, one of which is rolling forecasts. One school of thought believes that budgets are no longer relevant in current times and should be replaced with newer methods, whereas the second school of thought believes that budgets are still current and relevant. Having experienced a global financial crisis, the current time can be described as one of uncertainty. Banks were one of the industries hardest hit by this crisis. This research investigates whether the financial planning processes in banks has changed to align itself with an environment of uncertainty. It further explores whether banks have adopted rolling forecasts in their planning processes. Data was collected using expert interviews. The sample included 8 respondents across 3 different banks. The data was then analysed using content analysis. The outcome allowed for visibility into the changes made by banks in their financial planning processes. Insights into the reasons why rolling forecasts were not used was also obtained. Uncertainty in the environment resulted in banks making changes to their financial planning processes. It also emerged that none of the respondents use rolling forecasts. However some of the respondents use forecasting models which are based on some principles of rolling forecast. This research contributes to the budgeting and planning discipline. It contributes by exploring how the budget process is changing to align itself with the changing environment. It also contributes by providing insights into why rolling forecasts are not adopted. / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

This study considers an expanded meaning of "uncertainty" as it affects decision-makers. The definition adopted is based on a decision-maker who is uncertain, i.e. aware of the insufficiency of her knowledge for the purpose of rationally determining which option to choose. A taxonomy of uncertainties is developed from this definition. The first stage is a Generalized Decision Model, which expands on a standard decision model often assumed in technical works by allowing uncertainty over components of the model that are assumed to be perfectly known in the standard model. These additional potential "subjects" of uncertainty include the feasibility of options, the authority of the decision-maker to effect a choice, membership of and probability distributions over the set of possible future states of the world, and considerations about how the consequences are to be valued. The taxonomy also describes possible "sources" of uncertainty, dividing them into characteristics of the world (e.g. variability), evidence the decision-maker has (e.g. ambiguity or imprecision), or characteristics of the decision-maker himself. Other important ways in which uncertainties can vary is whether they are hard (irreducible in principle) or soft, whether a decision is unique or repeatable, and the role time has in the decision and in the resolving of the uncertainties. A finding of this work is that many uncertainties in addition to the uncertainty in the standard decision model over the future state of the world can keep a procedure for implementing rational choice from being decisive, thus requiring another (nonrational) process to complete the selection of an option. Other insights: (1) Deciding is only part of being rational, and in many instances is not the most important part. (2) Uncertainty may complicate decision-making, but is by no means always bad for the decision-maker. (3) Rationality is inescapably subjective in any implementation. (4) True "decision under certainty" does not exist. (5) Uncertainties vary sufficiently that no single treatment can be prescribed; it is hoped that this work contributes to a survey of the territory of uncertainty that facilitates Smithson's (1988) "suburbanization" or subdivision into smaller tracts to be developed individually.

We propose a classification framework for the operations management literature that has looked at pricing and ordering in supply chains when supply and/or demand are uncertain. We then focus on developing three new models for managing supply and demand uncertainties in supply chains. In the first model, we study a two period sourcing problem of a firm under two sets of contracts. The contracts differ in terms of acquisition costs and the level of risk that they impose on the firm. We provide the conditions where the optimum solution is unique and also explore the behaviour of the optimum solution analytically and numerically. One application of our model is in the agribusiness supply chain and we provide numerical examples based on data from the almond industry in California. In the second model we look at a joint ordering, pricing and capacity planning problem. We characterize the optimum policy both in single and multi-period cases. In addition, we study the impact of fixed production costs on the optimum policy. The third model is devoted to coordination between a buyer and a supplier where there is a possibility of improving the supplier by both players. We analyze the problem under a Stackelberg game setting where the buyer is the leader. We show that the buyer either tries to amplify the investment of the supplier by order inflation or assumes all the investment costs. We investigate the behaviour of the optimum solution under different strategies. / Thesis / Doctor of Philosophy (PhD)

To better design structures and machines, understanding of flaws and failures is essential. The body of this work has addressed numerous facets of fatigue crack propagation. The affect of crack closure, testing errors, and data scatter are a few important components of crack growth developed and investigated. It was found that the widely accepted compliance-offset technique for closure measurement may be sensitive to increases in load ratio. Opening load uncertainty was calculated to be on the order of 5%. The application of practical regression techniques and the use of DKeff were used to characterize closureree crack growth data to develop a single intrinsic da/dN curve. The best form of regression was found to be a multi-linear fit. A strip-yield model requiring the intrinsic curve was used to successfully predict crack growth at other load ratios. Uncertainties with a strong dependence on crack mouth displacement were found for da/dN, DK, and a.To better design structures and machines, understanding of flaws and failures is essential. The body of this work has addressed numerous facets of fatigue crack propagation. The affect of crack closure, testing errors, and data scatter are a few important components of crack growth developed and investigated. It was found that the widely accepted compliance-offset technique for closure measurement may be sensitive to increases in load ratio. Opening load uncertainty was calculated to be on the order of 5%. The application of practical regression techniques and the use of DKeff were used to characterize closureree crack growth data to develop a single intrinsic da/dN curve. The best form of regression was found to be a multi-linear fit. A strip-yield model requiring the intrinsic curve was used to successfully predict crack growth at other load ratios. Uncertainties with a strong dependence on crack mouth displacement were found for da/dN, deltaK, and a.

In order to identify ways to improve cost estimation, especially early in design, cost estimation needs to be viewed and represented as a process. An important activity within the cost estimation process is assessing the cost risk of a system. A decision-support tool that assesses cost risk should represent the impact of subsystem or system-level uncertainty and provide mechanisms to help select among competing designs. In order to address these problems, a generic cost estimation process was developed. It is based on an extensive review of the cost estimation literature. Also, a hierarchicial product structure, model-based approach and tool to estimate system-level cost risk was developed. This tool provides a link between cost models and cost elements for each component, mechanisms for determining the impact of risk on the cost of the design, and outputs used for selecting among alternative competing designs.

With limited resources and time available for a typical design project, it is difficult to decide how to allocate these resources and time to produce an optimum design. Also, the question arises, ?Given the design process, available resources, and available time, will the design meet the program goals?? Uncertainty analyses of design processes addresses these issues and could substantially improve design quality, cost, and cycle time. Research to examine uncertainty in the design process employs previous experience in experimental, model, and manufacturing uncertainty, in an innovative approach for analyzing the entire design process. This research was initiated with a pilot project, a 4-bar-slider mechanism. Two new theories for the research have arisen from this pilot project. First, design optimization techniques could be used to compare steps of the design process and to help determine the overall uncertainty of the final manufactured product. Second, manufacturing uncertainty can be included as an additional random uncertainty in the analysis of the final manufactured product. While more research needs to be completed to test, apply, and expand on these theories, the pilot project has been a positive step forward. It has already produced two proposals with one funded and one awaiting a decision. This research, although in its beginning stages, could substantially improve the design process.

Engineering models both for analysis and experimental data reduction include variables that have uncertainties associated with them. Analyzing these models without considering the uncertainties may provide misleading results. In this paper, several methods for evaluating uncertainty are summarized. In particular, second-order uncertainty analysis method is illustrated using Taylor series expansion. It is the intent of this paper to compare the first-order and second-order propagation methods, Monte Carlo simulation methods and sequential perturbation uncertainty analysis methods and investigate the situation that second-order propagation method is necessary through examples studies by MathCad worksheet.

Test results from a rocket turbine test model, called the Oxidizer Technology Turbine Rig (OTTR), are discussed in this paper. The turbine was designed to support the development of advanced turbines for future liquid rocket engines. It is a highly loaded single stage liquid oxygen pump drive turbine which uses inlet and exit volutes to provide optimum performance in a compact configuration. The system design creates high pressure and temperature gradients as well as high Mach number flow. These factors make it especially difficult to accurately measure the flowfield. Test issues such as probe calibration; probe interference, rake blockage, and averaging techniques were discussed in a previous paper. Test results including inlet volute, exit volute (both circular and square), and diffuser static pressure distributions, stator airfoil static pressure distributions, total and static pressure drops through the system, and overall performance parameters at the turbine aerodynamic design point and off-design point are presented here. This thesis will mainly focus on the information of both aerodynamic design point and off-design point of inlet volute, exit volute and diffuser for both circular exit volute and square exit volute.

A zero dimensional single zone model was developed to determine the crank resoled heat release rate at various injection timings (15°-60° BTDC) and the associated uncertainties from a pilot ignited natural gas engine. The uncertainty analysis examines the percentage contribution from various sources of error, including cylinder pressure measurements, intake manifold pressure measurements, and the impact of assumptions such as constant versus temperature dependent specific heat ratios. In particular, uncertainty percentage contributions and uncertainty magnification factors were used to quantify and compare the uncertainties in heat release rates using temperature dependent specific heat ratio correlations to constant specific heat ratio assumption. It is demonstrated that the error associated with the constant specific heat ratio assumption contributes to about 40 percent error (full scale value) in the net heat release estimates in comparison to using temperature dependent specific heat ratio correlations.

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