Browsing by Author "Dewa, Mncedisi Trinity"

Now showing 1 - 5 of 5
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    A decision support system framework for machine selection
    (South African Institute for Industrial Engineering, 2014) Nyanga, Lungile; Van der Merwe, Andre Francois; Matope, Stephen; Dewa, Mncedisi Trinity
    ENGLISH ABSTRACT: Selecting the right machinery to machine a part is a multi criteria decision making problem which is crucial in production planning. The process becomes more complex and tedious when one has to choose from a variety of machines available in an online registry. This paper investigates this decision making process with the objective to increase machineutilisation for small, medium and micro enterprises. A real time information based decision support system is necessary to assist the decision maker. A decision support system framework for machine selection for a manufacturing agent is proposed based on the Analytical Hierarchy Process (AHP). A human expect uploads parameters for a part that has to be machines. Based on these parameters, suitable machines are sought for from an online machine registry and ranked according to their capability to produce the desired part.
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    A decision-making framework for implementing digitalisation in the South African tooling industry
    (Southern African Institute for Industrial Engineering, 2018-12-10) Dewa, Mncedisi Trinity; Van Der Merwe, Andre Francois; Matope, Stephen
    ENGLISH ABSTRACT: In this paper, a decision-making framework for implementing the concept of digitalisation within the South African tool, die, and mould-making (TDM) industry context is developed and employed. The purpose of this framework is to answer the following question: “Which digital technologies currently available on the market can be employed to improve the efficiency of shop-floor operations in the South African TDM industry?” An exhaustive literature study of existing digital technologies is conducted. Thereafter, TDM processes requiring digitalisation are identified through knowledge engineering. Based on the system requirements analysis, digital technologies relevant to the South African TDM context are then proposed.
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    Digitalisation of shop-floor operations in the South African tool, die and mould making industry
    (Stellenbosch : Stellenbosch University, 2018-12) Dewa, Mncedisi Trinity; Matope, Stephen; Van der Merwe, A. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: This doctoral dissertation focuses on the digitalisation of shop-floor operations in the South African Tool, Die and Mould Making (TDM) industry through the development of a Mobile Data Collection (MDC) tool known as a Shop-floor Management System (SMS). Recent results of the benchmarking initiative in the South African TDM industry have shown that most firms struggle on the global market due to intense external competition and internal shortcomings. Digitalisation has been advocated as a possible solution that can improve the competitiveness of tooling companies in the 21st century. The recent rise of digital technologies makes digitalisation an achievable reality now. However, how does one adopt such a factor in a South African tool-room environment? This study aims to answer this question through a systematic method of analysis, design, development and testing of a solution. This research demonstrates the application of systems thinking by implementation of current technology in a tooling factory environment. The theoretical framework established by Professor Schuh for the “Fast Forward Tooling Approach” on digitalisation was employed in this study. A Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of recent South African TDM industry benchmarking study results was conducted to decide on areas of deficiency which can be improved through digitalisation. A deficiency in the areas of shop-floor data collection and manipulation within most TDM firms was identified and selected as one of the major problems to be addressed. Thereafter, company visits were conducted to finalise the industry specific characteristics in the South African tooling industry and desired system goals were established. Mobile and web-based technologies were selected as a sustainable solution for near real-time data collection in production environments. Furthermore, an analysis followed of the tool production value chain through company visits of firms within the Western Cape Province. The sub-processes of cost estimation, process planning and job-card data collection were identified as areas which can be improved by digitalisation. Key parameters for each of the identified processes were derived through knowledge engineering and the Analytical Hierarchical Process (AHP) methodology was used to rank the identified parameters. Based on a decision matrix for development software platform selection, the AppSheet platform was chosen and utilised in the development of mobile data collection modules for the abovementioned functions. Another decision matrix for selection of hardware tools was used to determine the appropriate input devices to be used for recording of shop-floor data by the tooling factory personnel. The ‘Google Sheets’ cloud computing platform was utilised for the development of the back-end database. Reports on cost analytics, resource performance and order progress status were generated by the system in real-time for process-planning, rescheduling and maintenance decisions. The system also facilitates alerts in cases of event changes within the tooling value stream process. The developed SMS was validated in a selected company for various scenarios and cases. The system outputs show that the use of mobile devices and web-based cloud computing platforms for data collection and manipulation effectively improves the shop-floor real-time data collection and visibility of a tooling factory environment.
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    Digitalisation of shop-floor operations in the south African tool, DIE, and mould-making industry
    (Southern African Institute for Industrial Engineering, 2018-08-31) Dewa, Mncedisi Trinity; Van Der Merwe, Andre Francois; Matope, Stephen
    ENGLISH ABSTRACT: Digitalisation has been advocated as a possible strategy to improve the competitiveness of tool, die, and mould-making (TDM) companies in the 21st century. The recent rise of digital technologies, such as Internet of Things devices, now makes digitalisation an achievable reality. This paper focuses on the digitalisation of shop-floor operations in the South African TDM industry through the development of a novel mobile data collection (MDC) tool known as a shop-floor management system (SMS). The developed SMS was deployed to, and validated in, a selected tooling company for various products. The developed system improved the shop-floor’s real-time data collection.
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    An empirical analysis of operational disturbances and their impact on business performance in manufacturing firms : case tooling industry South Africa
    (South African Institute for Industrial Engineering, 2014) Dewa, Mncedisi Trinity; Matope, Stephen; Van der Merwe, Andre Francois; Nyanga, L.
    ENGLISH ABSTRACT: Globalization has managed to break trade barriers and the manufacturing environment has become more competitive. Market share is now determined by quality of goods and services irrespective of location. Today’s business environment for manufacturers requires flexible, responsive and robust systems, which produce a variety of products at competitive prices. To gain a competitive edge, the paradigms of e-manufacturing and distributed manufacturing have been recently advocated by researchers as potential solutions. However, irrespective of these technological advancements, manufacturing firms in the tool and die sector are still struggling to perform efficiently in the face of recurring operational disturbances. The paper identifies the most prevalent operational disturbances which occur in South Africa’s manufacturing firms in the tooling industry and their impact on business performance. A field study was conducted on a number of organizations which form an industrial cluster in the Western Cape manufacturing sector and seven typical disturbances were evaluated together with their root causes. The results gathered portrayed the correlation between identified disturbances and their corresponding consequences. The findings of the study were recommended to be used to develop models and computerized systems to solve the pending pandemic.