Anje Marjorie Anderson, Andre Van der Merwe


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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DOI: https://doi.org/10.7166/32-1-2271


Copyright (c) 2021 Anje Marjorie Anderson, Andre Van der Merwe

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