Department of Industrial Engineering

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Browsing Department of Industrial Engineering by Author "Anderson, Anje Marjorie"

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    Time-driven activity-based costing related to digital twinning in additive manufacturing
    (Southern African Institute for Industrial Engineering, 2021-05) Anderson, Anje Marjorie; Van der Merwe, Andre
    ENGLISH ABSTRACT: Many businesses in the additive manufacturing industry have limited equipment capacity. This method of using time-driven activity-based costing in collaboration with digital twinning will be advantageous to optimise their use of time and their capacity. Optimising the use of time is essential to ensure efficient process flow and to waste less time and money. To optimise, we need to analyse system dynamics and model system responses, to enable us to consider various scenarios iteratively. This paper first considers activity-based costing, driven by its most precious resource, time. Standard time is defined as the base parameter by which cost is calculated. Charge-out rates of elements are based on the actual cost of equipment apportioned to activities, based on the time spent using such equipment. The process chain is broken into elements, each of which incurs full cost when started. The value chain develops accordingly, enabling us to predict the actual cost of production. Second, the use of digital twinning to model standard time is considered. Stochastic variation is evident, but standard time can be allocated to each element in the process chain, given a certain confidence level. Together, a cause—effect prediction model can be developed. The model would predict the time that a process chain, consisting of known elements, would take. However, in the event of an occurrence out of the norm, the updated expected time can be predicted. Using the same rates, the new cost can be determined immediately. We propose that the digital twin can predict production cost, based on a statistically measurable stochastic variation of element duration and the time-varying charge-out rate.
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    Towards digital twinning for additive manufacturing of medical implants
    (2021-12) Anderson, Anje Marjorie; Van der Merwe, André Francois ; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: The level of complexity of a business increases as additive manufacturing (AM) is introduced into their process chains. As AM of medical implants is becoming more popular, more complexity within process chains arises. As more intricate technologies are used, more steps are added to the process chain, more workers and supervision are needed, and the flawless flow of the process chain becomes essential. As more activities occur within the business, time becomes an essential resource that needs to be allocated optimally. Finding a tool to help with the optimisation of resources can put a business on the right path. In this study, an intensive literature review was conducted to inform the researcher on the day-to- day operations at the Centre for Rapid Prototyping and Manufacturing (CRPM) and how it works. An in-depth investigation of the process chain at CRPM was furthermore done to identify problem areas. By studying the process chain thoroughly, it was possible to gain in-depth insight into what the as-is state and the desired to-be state were. The optimisation of time was identified as one desired goal for the to-be state. As time is a limiting factor that cannot be replaced or stored, finding a bridge to fill the gap between current and desired states becomes even more important. Hence this investigation into digital twinning. Digital twins are virtual replicas of businesses, processes, or systems. The steps needed to develop a digital twin were researched. Two basic digital twin examples were developed and illustrated how the user can adjust data and the process to solve problems. The digital twin examples include a basic costing system that can predict what the total cost of a certain product will be. Whether or not to use digital twin technology within a business can be a daunting decision; therefore, a decision- making tool was suggested to help the business decide when to use a digital twin and when other quality tools would be more appropriate. The decision-making tool was applied to CRPM and showed that a digital twin is a better fit than other quality tools in some instances. Digital twins are adaptable and can therefore be adjusted and changed to suit the needs of the business. They are also found to be valuable planning tools that can aid in the optimisation of time usage by ensuring that a business knows what will happen in each type of situation. Changes to the process chain can be made and the effects were seen without having to implement the changes physically, thereby optimising time usage at CRPM as no time is wasted with unnecessary processes or planning. This study concludes that a digital twin can act as a possible tool to aid in the optimisation of time usage in AM businesses.