Browsing by Author "Fourie, Christian"
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- ItemAsset information decision-making framework for the South African navy(Stellenbosch : Stellenbosch University, 2020-03) Fourie, Christian; Jooste, J. L.; Stellenbosch University. Faculty of Industrial Engineering. Dept. of Industrial Engineering.ENGLISH ABSTRACT: Asset Information (AI) is essential for effective Asset Management (AM). Decision-makers rely on it for AM decision-making, where productive decisionmaking underpins success in AM. It became apparent that the effect of AI on the output of the mission-performing systems in the SA Navy (SAN) is not defined. Without defining the value of individual AI elements to organisational outputs it is difficult to determine which critical AI elements to acquire and maintain, and which are not beneficial. The purpose of this research is therefore to develop a framework to support decision making regarding AI elements in the SAN. The intention with this framework is to optimise AI in terms of cost effectiveness and support of higher order decision making requiring AI. Operational Availability (AO) is a performance metric that is directly linked to the core outputs of the SAN and falls within the scope of AM. Therefore determining the effect of AI on the AO of the SAN’s systems is at the crux of this research. This framework is developed from two sources in the research, theoretical knowledge and fieldwork. The literature study provides the theoretical base for the thesis as a whole and the Multi-Criteria Decision Making algorithm forms the structure of the framework. Research in the field, making use of experts in the SAN environment provides the content of the framework. Due to the complexity in firstly identifying critical AI elements and secondly determining their value to AO, an exploratory mixed method design is used to collect data. After the first round of data collection a preliminary framework based on Analytical Hierarchy Process and Multi-Attribute Utility Theory (AHP-MAUT) principles are developed. The preliminary framework is used for the second round data collection. Data analysis is carried out using a combination of qualitative and quantitative methods. The final framework is presented in an Excel format (for ease of use) with automated processes that calculates the ranking of AI elements as well as statistical analysis which assists decision makers by offering some suggestions regarding the management of the AI elements. The framework is validated through face validation and user assessment, both via questionnaires posed to an expert panel. According to the expert panel the framework is perceived as 1) useful 2) easy to use 3) practical 4) understandable and 5) flexible. Construct validity is also established, mainly via feedback from the face validation panel. The framework is a baseline version in an unexplored field in the SAN. As part of the conclusion of the thesis is noted that further refinements and validation in the field is required to verify the findings from this thesis.