The Mediterranean has long been recognised as an area that is particularly sensitive to climate change. It is also an area that has been impacted by human activity for millennia. Disentangling climatic and anthropogenic influences on the history of vegetation change in the Mediterranean remains an important challenge. As a contribution to this ongoing debate, this thesis explores the late Holocene environment of part of the coast in Central Italy using a multiproxy approach to investigate the archives of change preserved in dune slack deposits. Distinguishing between natural and anthropogenic signals presents a real challenge in most environmental reconstruction work; however, due to the extensive archaeological research carried out at Castelporziano, it is possible to examine human-environmental interactions in some detail. In order to understand these interactions part of the thesis examines how management has affected recent environmental changes and the current vegetation and whether there is a legacy of Roman landuse at the Castelporziano estate. The key findings of the thesis showed that dune slacks are suitable for palaeoenvironmental reconstruction with proxies such as plant macrofossils, ostracods, molluscs and bryozoans statoblasts. However, the pH and seasonality of the slacks meant proxies such as pollen were badly preserved or absent, and diatoms did not preserve due to the high levels of carbonate on site. Overall the results show the impact of the Romans on site in terms of localised eutrophication and increased fires, but with abandonment, came the formation of wet woodlands.

The purpose of this work was to study the ability of the Slacks-Based Model of Data Envelopment Analysis in the prediction of corporate failure of non-manufacturing companies as compared to Altman’s Z’’ score model. This research looks at non-manufacturing firms specifically and attempts to classify companies without looking at the asset size of the firm. A DEA model based on the Altman’s Z’’ score financial ratios was created as well as a revised DEA model. The overall accuracy of the models showed the revised DEA model to be more accurate than the original DEA model as well as the Altman Z’’ score. This indicated that bankruptcy could be predicted without the use of total assets or liabilities as variables. This also showed the ability of an SBM DEA model to predict bankruptcy.

The purpose of this work was to study the ability of the Slacks-Based Model of Data Envelopment Analysis in the prediction of corporate failure of non-manufacturing companies as compared to Altman’s Z’’ score model. This research looks at non-manufacturing firms specifically and attempts to classify companies without looking at the asset size of the firm. A DEA model based on the Altman’s Z’’ score financial ratios was created as well as a revised DEA model. The overall accuracy of the models showed the revised DEA model to be more accurate than the original DEA model as well as the Altman Z’’ score. This indicated that bankruptcy could be predicted without the use of total assets or liabilities as variables. This also showed the ability of an SBM DEA model to predict bankruptcy.

Yes / Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.

Yes / Although many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress.

The implementation of lean manufacturing concepts has shown significant impacts on various industries. Numerous tools and techniques have been developed to tackle specific problems in order to eliminate wastes and carry out lean concepts. With the focus on "how to make a system leaner," little effort has been made on determining "how lean the system is." Lean assessment surveys evaluate the current status of a system qualitatively against predefined lean indicators. Lean metrics are developed to quantify performance of improvement initiatives, but each metric only focuses on one specific area. Value Stream Maps demonstrate the current and future states graphically with the emphasis on time-based performance only. A truly quantitative and synthesized measure for overall leanness has not been established. In some circumstances, being lean may not be the only goal for manufacturers. In order to compete in the rapidly changing marketplace, manufacturing systems should also be agile to respond quickly to uncertain demands. Nevertheless, being extremely agile may increase the cost of regular operations and reduce the leanness of the system. Similarly, being extremely lean may reduce flexibility and lower the agility level. Therefore, a manufacturing system should be agile enough to handle the uncertainty of demands and meanwhile be lean enough to deliver goods with competitive prices and lead time. In order to achieve the appropriate leanness level, a leanness measure is needed to address not only "how lean the system is" but also "how lean it should be." In this research, a methodology is proposed to quantitatively measure leanness level of manufacturing systems using the Data Envelopment Analysis (DEA) technique. The production process of each work piece is defined as a Decision Making Unit (DMU) that transforms inputs of Cost and Time into output Value. Using a Slacks-Based Measure (SBM) model, the DEA-Leanness Measure is developed to quantify the leanness level of each DMU by comparing the DMU against the frontier of leanness. A Cost-Time-Value analysis is developed to create virtual DMUs to push the frontier towards ideal leanness so that an effective benchmark can be established. The DEA-Leanness Measure provides a unit-invariant leanness score valued between 0 and 1, which is an indication of "how lean the system is" and also "how much leaner the system can be." With the help of Cost-Time Profiling technique, directions of potential improvement can be identified by comparing the profiles of DMUs with different leanness scores. The leanness measure can also be weighted between Cost, Time and Value variables. The weighted DEA-Leanness Measure provides a way to evaluate the impacts of improvement initiatives with an emphasis on the company's strategic focus. Performing the DEA-Leanness measurement requires detailed cost and time data. A Web-Based Kanban is developed to facilitate automated data collection and real-time performance analysis. In some circumstances where detailed data is not readily available but a Value Stream Maps (VSM) has been constructed, the applications of DEA-Leanness Measure based on existing VSM are explored. Besides pursuing leanness, satisfying a customer's demand pattern requires certain level of agility. Based on the DEA-Leanness Measure, appropriate leanness targets can be identified for manufacturing systems considering sufficient agility level. The Online-Delay and Offline-Delay Targets are determined to represent the minimum acceptable delays considering inevitable waste within and beyond a manufacturing system. Combining the two targets, a Lean-Agile Performance Index can then be derived to evaluate if the system has achieved an appropriate level of leanness with sufficient agility for meeting the customers' demand. Hypothetical cases mimicking real manufacturing systems are developed to verify the proposed methodologies. An Excel-based DEA-Leanness Solver and a Web-Kanban System have been developed to solve the mathematical models and to substantiate potential applications of the leanness measure in real world. Finally, future research directions are suggested to further enhance the results of this research. / Ph. D.

No description available.

Prediction of corporate bankruptcy (or distress) is one of the major activities in auditing firms’ risks and uncertainties. The design of reliable models to predict distress is crucial for many decision-making processes. Although a variety of models have been designed to predict distress, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature. To be more specific, although some studies use several performance criteria and their measures to assess the relative performance of distress prediction models, the assessment exercise of competing prediction models is restricted to their ranking by a single measure of a single criterion at a time, which leads to reporting conflicting results. The first essay of this research overcomes this methodological issue by proposing an orientation-free super-efficiency Data Envelopment Analysis (DEA) model as a multi-criteria assessment framework. Furthermore, the study performs an exhaustive comparative analysis of the most popular bankruptcy modelling frameworks for UK data. Also, it addresses two important research questions; namely, do some modelling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modelling frameworks? Further, using different static and dynamic statistical frameworks, this chapter proposes new Failure Prediction Models (FPMs). However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another one, which in some contexts might be viewed as “unfair” benchmarking. The second essay overcomes this issue by proposing a Slacks-Based Measure Context-Dependent DEA (SBM-CDEA) framework to evaluate the competing Distress Prediction Models (DPMs). Moreover, it performs an exhaustive comparative analysis of the most popular corporate distress prediction frameworks under both a single criterion and multiple criteria using data of UK firms listed on London Stock Exchange (LSE). Further, this chapter proposes new DPMs using different static and dynamic statistical frameworks. Another shortcoming of the existing studies on performance evaluation lies in the use of static frameworks to compare the performance of DPMs. The third essay overcomes this methodological issue by suggesting a dynamic multi-criteria performance assessment framework, namely, Malmquist SBM-DEA, which by design, can monitor the performance of competing prediction models over time. Further, this study proposes new static and dynamic distress prediction models. Also, the study addresses several research questions as follows; what is the effect of information on the performance of DPMs? How the out-of-sample performance of dynamic DPMs compares to the out-of-sample performance of static ones? What is the effect of the length of training sample on the performance of static and dynamic models? Which models perform better in forecasting distress during the years with Higher Distress Rate (HDR)? On feature selection, studies have used different types of information including accounting, market, macroeconomic variables and the management efficiency scores as predictors. The recently applied techniques to take into account the management efficiency of firms are two-stage models. The two-stage DPMs incorporate multiple inputs and outputs to estimate the efficiency measure of a corporation relative to the most efficient ones, in the first stage, and use the efficiency score as a predictor in the second stage. The survey of the literature reveals that most of the existing studies failed to have a comprehensive comparison between two-stage DPMs. Moreover, the choice of inputs and outputs for DEA models that estimate the efficiency measures of a company has been restricted to accounting variables and features of the company. The fourth essay adds to the current literature of two-stage DPMs in several respects. First, the study proposes to consider the decomposition of Slack-Based Measure (SBM) of efficiency into Pure Technical Efficiency (PTE), Scale Efficiency (SE), and Mix Efficiency (ME), to analyse how each of these measures individually contributes to developing distress prediction models. Second, in addition to the conventional approach of using accounting variables as inputs and outputs of DEA models to estimate the measure of management efficiency, this study uses market information variables to calculate the measure of the market efficiency of companies. Third, this research provides a comprehensive analysis of two-stage DPMs through applying different DEA models at the first stage – e.g., input-oriented vs. output oriented, radial vs. non-radial, static vs. dynamic, to compute the measures of management efficiency and market efficiency of companies; and also using dynamic and static classifier frameworks at the second stage to design new distress prediction models.