The development of a decision support system for decisions in property development in South Africa
- Authors: Haupt, Hermann Rocher
- Date: 2014-02-10
- Subjects: Management information systems , Decision support systems
- Type: Thesis
- Identifier: uj:3684 , http://hdl.handle.net/10210/9068
- Description: M.Com. (Business Management) , The ultimate objective of this research report was to develop a Decision Support System, (DSS), that can be used by property professionals to enable them to make better decisions regarding property development in South Africa. The DSS addresses the problem of numerous uncertain variables in property development investment decisions. The capability of the computer to do repetitive calculations with different combinations of variables, with probabilities linked to each variable, was used in a Monte Carlo analysis. The DSS was developed on a "Lotus 1-2-3™ Release 4 for Windows" spreadsheet which makes the program adaptable to suit specific applications if the need arises. The DSSwill, however, be able to address the majority of property developments without any adaptation. The DSS was appraised by property professionals and the comments received from the respondents indicate that the primary objective stated was achieved. The DSS is best suited for property investors who are also involved in the early development phases.
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Developing a decision support system to identify strategically located land for land reform in South Africa
- Authors: Musakwa, Walter , Makoni, E. N. , Kangethe, M. , Segooa, L.
- Date: 2014
- Subjects: Land reform - South Africa. , Development planning - South Africa , Geographic information systems - South Africa , Decision support systems
- Type: Article
- Identifier: uj:4871 , http://hdl.handle.net/10210/12547
- Description: Land reform is identified as a key tool in fostering development in South Africa. With two decades after the advent of democracy in South Africa, the land question remains a critical issue for policy makers. A number of frameworks have been put in place by the government to identify land which is strategically located for land reform. However, many of these frameworks are not well aligned and have hampered the government’s land reform initiative in promoting inclusive development. Strategically located land is herein defined as land parcels that are well positioned for the promotion of agriculture, human settlements, rural and tourism development. Accordingly, there is a need to develop a decision tool which facilitates the identification of strategically located land for development. This study proposes the use of geographic information systems (GIS), earth observation (EO) data and multi-criteria decision making (MCDM) to develop a spatial decision support system (SDSS) to identify strategically located land for land reform. The SDDS was therefore designed using GIS, EO data and MCDM to create an index for identification of strategically located land. Expert-led workshops were carried out to ascertain criteria for identifying strategically located land and the analytical hierarchy process (AHP) was utilised used to weight the criteria. The study demonstrates that GIS and EO are invaluable tools in facilitating evidence-based decisions for land reform. However, there is need for capacity building on GIS and EO in government departments responsible for land reform and development planning. The study suggests that there is an urgent need to develop sector specific criteria for the identification of strategically located land for inclusive development.
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The development of creative and innovative thinking and problem-solving skills in a financial services organisation
- Authors: De Jager, Cherylene
- Date: 2012-06-07
- Subjects: Creative thinking , Problem solving skills , Financial services organisations , Decision support systems
- Type: Thesis
- Identifier: uj:8646 , http://hdl.handle.net/10210/5002
- Description: M.Comm. , Globalization initiates rapid change and innovation that is: “… no longer an option, but it has become a business imperative” (Grulke, 2002, p. 18). Innovative organizations have developed the ability to satisfy both the shareholders’ demand for wealth (Hamel, 2000) and the customers’ demand for more creative and innovative products that facilitate ease of use (Kelley, 2001) while at the same time ensuring business sustainability (Skarzynski & Gibson, 2008). The development of creative and innovative thinking and problem-solving skills are crucial for the survival of organisations in the 21st century. Creative problem-solving training was generally found to be the most effective when organizations wanted to equip their employees with creative and innovative thinking and problem-solving skills. A specific financial services organisation in South Africa realised that they had to join the innovation revolution in order to remain commercially competitive in the twentyfirst century. With retailers and other competitors such as the telecommunication role players entering the traditional financial services domain, the organisation recognised that they required a novel approach to conduct their business. The highly regulated and to some extent conformist environment of the financial services organization constitute the sphere within which the research problem is situated. The organisation commissioned the researcher to design a Creativity and Innovation Workshop with the intent to improve the creative and innovative thinking and problem-solving skills of their employees. The evaluation question that the study purports to address therefore is whether employees in a corporate context such as a financial services organisation can develop appropriate creative and innovative thinking and problemsolving skills through an intervention such as a workshop and can a benefit for the business unit and organisation be identified. The unit of analysis is a niche business unit in a South African financial services organization. The sample used in this study comprises of managers (employees) and senior or executive management of those employees who attended the Creativity and Innovation Workshop.
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Business intelligence and the telecommunications industry : can business intelligence lead to higher profits?
- Authors: O'Brien, J. , Kok, J.A.
- Date: 2006-12
- Subjects: Telecommunications industry , Business intelligence , Competitive intelligence , Decision support systems
- Type: Article
- Identifier: uj:5711 , http://hdl.handle.net/10210/3405
- Description: Organizations are finding it increasingly difficult to increase profits as competition in the marketplace continually pressurizes margins. Organizations will have to do more to enjoy sustainable profits in the future and information technology could arguably be the key to assisting management with the task of increasing profits on a sustainable basis. Business intelligence (BI) could be the competitive advantage for organizations to increase profitability. South Africa is faced with an unemployment rate of over 40% and it is not desirable that costs are contained by reducing staff. It is clear that innovative ideas should be looked at to ensure that organizations continue to make profits. Information management programmes offer the necessary tools to ensure that efficient and strategic decisions are made.
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Development of a decision making model for the CorexR iron making facility
- Authors: Penney, A.T.
- Date: 2015-03-18
- Subjects: Management information systems , Decision making - Data processing , Decision support systems , Iron industry and trade - South Africa - Management , CorexR (Firm)
- Type: Thesis
- Identifier: uj:13459 , http://hdl.handle.net/10210/13495
- Description: M.Com. (Business Management) , Please refer to full text to view abstract
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Empirical evaluation of optimization techniques for classification and prediction tasks
- Authors: Leke, Collins Achepsah
- Date: 2014-03-27
- Subjects: Genetic algorithms , Statistical decision - Data processing , Decision support systems , Missing observations (Statistics) , Computational intelligence
- Type: Thesis
- Identifier: uj:4520 , http://hdl.handle.net/10210/9858
- Description: M.Ing. (Electrical and Electronic Engineering) , Missing data is an issue which leads to a variety of problems in the analysis and processing of data in datasets in almost every aspect of day−to−day life. Due to this reason missing data and ways of handling this problem have been an area of research in a variety of disciplines in recent times. This thesis presents a method which is aimed at finding approximations to missing values in a dataset by making use of Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Random Forest (RF), Negative Selection (NS) in combination with auto-associative neural networks, and also provides a comparative analysis of these algorithms. The methods suggested use the optimization algorithms to minimize an error function derived from training an auto-associative neural network during which the interrelationships between the inputs and the outputs are obtained and stored in the weights connecting the different layers of the network. The error function is expressed as the square of the difference between the actual observations and predicted values from an auto-associative neural network. In the event of missing data, all the values of the actual observations are not known hence, the error function is decomposed to depend on the known and unknown variable values. Multi Layer Perceptron (MLP) neural network is employed to train the neural networks using the Scaled Conjugate Gradient (SCG) method. The research primarily focusses on predicting missing data entries from two datasets being the Manufacturing dataset and the Forest Fire dataset. Prediction is a representation of how things will occur in the future based on past occurrences and experiences. The research also focuses on investigating the use of this proposed technique in approximating and classifying missing data with great accuracy from five classification datasets being the Australian Credit, German Credit, Japanese Credit, Heart Disease and Car Evaluation datasets. It also investigates the impact of using different neural network architectures in training the neural network and finding approximations for the missing values, and using the best possible architecture for evaluation purposes. It is revealed in this research that the approximated values for the missing data obtained by applying the proposed models are accurate with a high percentage of correlation between the actual missing values and corresponding approximated values using the proposed models on the Manufacturing dataset ranging between 94.7% and 95.2% with the exception of the Negative Selection algorithm which resulted in a 49.6% correlation coefficient value. On the Forest Fire dataset, it was observed that there was a low percentage correlation between the actual missing values and the corresponding approximated values in the range 0.95% to 4.49% due to the nature of the values of the variables in the dataset. The Negative Selection algorithm on this dataset revealed a negative percentage correlation between the actual values and the approximated values with a value of 100%. Approximations found for missing data are also observed to depend on the particular neural network architecture employed in training the dataset. Further analysis revealed that the Random Forest algorithm on average performed better than the GA, SA, PSO, and NS algorithms yielding the lowest Mean Square Error, Root Mean Square Error, and Mean Absolute Error values. On the other end of the scale was the NS algorithm which produced the highest values for the three error metrics bearing in mind that for these, the lower the values, the better the performance, and vice versa. The evaluation of the algorithms on the classification datasets revealed that the most accurate in classifying and identifying to which of a set of categories a new observation belonged on the basis of the training set of data is the Random Forest algorithm, which yielded the highest AUC percentage values on all of the five classification datasets. The differences between its AUC values and those of the GA, SA, PSO, and NS algorithms were statistically significant, with the most statistically significant differences observed when the AUC values for the Random Forest algorithm were compared to those of the Negative Selection algorithm on all five classification datasets. The GA, SA, and PSO algorithms produced AUC values which when compared against each other on all five classification datasets were not very different. Overall analysis on the datasets considered revealed that the algorithm which performed best in solving both the prediction and classification problems was the Random Forest algorithm as seen by the results obtained. The algorithm on the other end of the scale after comparisons of results was the Negative Selection algorithm which produced the highest error metric values for the prediction problems and the lowest AUC values for the classification problems.
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