Browsing by Author "Van der Merwe, Hendrik Lodewyk"

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    Additive manufacturing costing parameter sensitivity
    (Stellenbosch : Stellenbosch University, 2019-12) Van der Merwe, Hendrik Lodewyk; De Beer, D. J.; Van der Merwe, A. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Additive manufacturing (AM) offers a perfect solution for the development and manufacturing of many products, but the burning issue is to determine which products should be manufactured in such a way? Also, of extreme importance, is to understand the economy of scale for the use of AM competitively. The latter requires knowledge-based decision-making systems based on product geometry, complexity, size, tolerance, material requirements, and mechanical properties, parallel with AM machine or process capabilities. Although directly involved in the material research and platform development from the onset, the Massachusetts Institute of Technology (MIT) classified AM as only one of ten breakthrough technologies in 2013. Forbes depicts AM as the technology that will equip manufacturers with the ability to turn product development into their competitive advantage. With the advancement in computer and software capabilities, it will rapidly dominate 40% of the market share (Gartner 2015). Capability alone will not suffice, however. To increase market share, focus should be placed on the analysis of AM costing. The thesis aims to determine if a more simplistic but accurate cost determination method can be developed to augment online costing opportunities that are fully integrated with the Enterprise Resource Planning (ERP) system. Costing is one of the critical business functions of any advanced manufacturing operation. This critical business function is also known as enterprise resource planning application components. Examples of these are aspects that allow an AM unit to use a system of integrated applications to manage the business and automate various back-office functions related to technology. It also allows for services and human resources to develop the data capturing, manipulations, calculation, and validation for a unique enterprise resource-planning model that is founded in a fail-safe quality management system (QMS).