Department of Industrial Engineering

Permanent URI for this community

https://scholar.sun.ac.za/handle/10019.1/104

Browse

Browsing Department of Industrial Engineering by Subject "Acquisition of data sets"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Development and demonstration of a customer super-profiling tool to enable efficient targeting in marketing campaigns
    (South African Institute for Industrial Engineering, 2018) Walters, Marisa; Bekker, James
    ENGLISH ABSTRACT: Being part of a competitive generation demands having good marketing policies to attract new customers as well as to retain existing customers. This research outlines a general methodology for segmentation of customers by using the model of Recency, Frequency and Monetary (RFM) to identify types of customers, and then predict their customer profiles, based on demographic and behavioural features. A few previous studies dealt with the question using non-aggregate customer data. We, however, also address the problem by using decision trees, something which has rarely been done before. We applied and demonstrated this tool on a large customer dataset and found useful results.
  • Thumbnail Image
    Item
    A Framework for selecting data acquisition technologies in support of railway infrastructure predictive maintenance
    (Stellenbosch : Stellenbosch University, 2021-03) Van Schalkwyk, Johannes Wallace; Jooste, Johannes Lodewyk; Lucke, Dominik; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Digital technological advancements is a product of the continuous changes experienced by the technology sector, whereas the advancements are represented as new and emerging technologies. However, to utilise these emerging technologies, new management strategies must be adopted. This is the case for the emergence of a predictive maintenance strategy due to the improved capabilities of the maintenance equipment. Against the background of railway infrastructure maintenance, the problem is that maintenance managers have not leveraged the full potential of emerging data acquisition technologies to support a predictive maintenance approach. This research investigates railway infrastructure maintenance and the technologies capable of acquiring the condition monitoring data to determine why the full potential of the technologies have not been leveraged. It is found that the slow adoption is largely attributed to uncertainty based on technological capabilities, potential barriers and challenges experienced throughout the process. To address the problem in this research, it is decided to develop a framework that supports railway operators with the process of data acquisition technology identi cation, evaluation, and acquisition. The framework ultimately aids railway operators with the shift towards a predictive maintenance approach. This research is based on a mixed method exploratory sequential design methodology, as described by Creswell. The rst phase of this research is responsible for contextualising the problem, and in the second phase, the framework is developed. The completion of the two phases relied on systematic literature reviews, railway industry expert feedback obtained from survey questionnaires, and face validation interviews conducted with railway industry practitioners. Utilising the research approach, as described previously, the Railway Infrastructure Technology Selection Support (RITSS) framework is developed. The RITSS framework consists of three stages namely; Stage 1 mapping assets and technology, Stage 2 technology evaluation, and Stage 3 acquisition mode guide. These stages try and support railway operators to leverage the full potential of emerging data acquisition technologies. Face validations are used to test the real-world applicability of the RITSS framework; in other words, the frameworks feasibility, usability, and utility are assessed by railway industry practitioners. The thesis concludes with a summary of the research conducted, the research objectives achieved, the expected research contributions, the research limitations identi ed, and the recommendations for future research. The overall opinion is that the RITSS framework has enormous potential in the railway infrastructure maintenance sector; however, certain aspects are identi ed that require re nement, such as the acquisition mode guide (Stage 3).