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Historically, educator post provisioning in South African public schools has been a / Thesis (Ph.D.) - University of Kwazulu-Natal, 2005.

Service delivery in post-apartheid South Africa has become a topical issue both in the academia and the political arena . The rise of social movements, the xenophobic tensions of May 2008 and protest actions could be noted as the major traits of post-apartheid South Africa. Though there are divergent views on the underlying causes of these protests, lack of service delivery has most significantly been at the centre stage. In this thesis we investigate the relationship between household/population changes and the demand for piped-water connection in South Africa. There is an ample, albeit at times of questionable accuracy, supply of statistics from official and other sources. These statistics are both the source of inspiration of particular societal measures to be investigated and a gauge of the accuracy of the mathematical/statistical modelling which is the central feature of this project. We construct mathematical/statistical models which take into account demographic constituents of the problem using differential equations for modelling household dynamics and we also investigate the interaction of demographic parameters and the demand for piped-water connection using multivariate statistical techniques. The results show that with a boost in delivery the rich provinces seem to be in better standing of meeting targets and that the increasing demand in household-based services could be most significantly attributed to the fragmentation of households against other demographic processes like natural increase in population and net migration. The results imply that in as much as service delivery policies and programmes should focus on formerly disadvantaged poor communities, adequate provisions for increasing service demands in urban centres should also be a priority in view of the increasing in-migration from rural areas as households fragment. Most of the findings/results are in tabular and graphical forms for easy understanding of the reader. / Thesis (Ph.D.)-University of KwaZulu-Natal, Westville, 2010.

To date, the collection of comprehensive household travel data has been a challenge for most metropolitan planning organizations (MPOs) and state departments of transportation (DOTs) due mainly to high costs. Urban population growth, the expansion of metropolitan regions, and the general unwillingness of the public to complete surveys conflict with limited public funds. The purpose of this research is to leverage targeted marketing data, sometimes referred to as consumer data or just simply marketing data, for travel demand modeling applications. This research reveals a first step in exploring the use of targeted marketing data for representing population characteristics of a region. Four studies were completed: an aggregate validation, a household-level validation for hard-to-reach population groups, an airport passenger model, and a residential location choice model. The two validation studies of this work suggest that targeted marketing data are similar to U.S. Census data at small geographic levels for basic demographic and socioeconomic information. The studies also suggest that the existing coverage errors are at least similar, if not lower than, the levels of those in household travel surveys used today to build travel demand models. The two application studies of this work highlight the benefits of the targeted marketing data over traditional household travel surveys and U.S. Census data particularly well, including the additional behavioral information available at the household-level and the very large sample sizes. These results suggest that the combination of targeted marketing data with other third-party and non-traditional data could be particularly powerful. It offers tremendous opportunities to enhance, or even transform, existing travel demand modeling systems and data collection practices. Inexpensive, up-to-date, and detailed data would allow researchers and decision-makers alike to better understand travel behavior and to be more equipped to make important transportation-related decisions that affect our lives each day.

Over the past of decade, the bullwhip effect has increasingly become a popular topic for researchers and practitioners in the area of supply chain management since it negatively influences cost, inventory, reliability and other important business processes in supply chain agents. Although there are many remedies for the bullwhip effect summarised in existing literature, it still occurs in several industries. This is partly because it is difficult to apply the results from existing research which analyse the bullwhip effect mainly in a simple supply chain. In addition, several tools and methodologies developed are used for analysing the bullwhip effect in a simple supply chain with several constraints. Therefore, this research aims to develop a unique simulation approach based on system dynamics modelling and Adaptive Network Based Fuzzy Inference System (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. System dynamics modelling which is a powerful simulation approach for studying and managing complex feedback system was selected as a main tool in this research. In addition, ANFIS was implemented in system dynamics modelling in order to increase the reliability of a system dynamics model for modelling soft variables. The proposed model covers variables influencing the bullwhip effect which are the structure of supply chain network, supply chain contributions and supply chain performances. As a result, a two layer simulation with three generic models was developed. The flexibility of this proposed model is the ability to model various types of ordering policies which are basic inventory policies, Material requirement planning (MRP) system and Just in time (JIT) approach. Three actual manufacturing supply chains were used as case studies to validate and demonstrate the flexibility of the model developed in this research. This model satisfactorily quantifies the bullwhip effect and the bullwhip effect levels identified in these case studies are significantly decreased by using the proposed simulation model. The successful results indicate that the model can be a useful alternative tool for supply chain managers to quantify and reduce the bullwhip effect in multi-product, multi-stage supply chains.

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