Matrix-based input-output models enhance production processes in manufacturing, suggesting a quicker alternative to simulation.
Matrix-based input-output methods are commonly used to model the economic linkages between industries within an economy. Effectively, they describe the flow of dollars or widgets between value-adding processes. This framework is also applicable to manufacturing processes. However, these methods have not been widely applied to model manufacturing or production processes within the industrial or systems engineering domain. Simulation techniques, specifically discrete-event simulation, are commonly employed to model complex manufacturing processes, but the creation of these models is time consuming and costly. In practice, there can be a gap between the need for quick model results and the effort required to build a DES model. As an analytical solution, matrix-based input-output models present themselves as a quick and easy to implement alternative to simulation models. This work develops input-output production process models whose relationship between each step in the process is described by matrices. The effectiveness of this approach is demonstrated through a generic deterministic process flow that is then adapted to a stochastic case to indicate how variability can be incorporated within the model. The input-output framework can facilitate understanding of many metrics-of-interest, such as production capacity, utilization rates, waste generation, number of cycles need to complete a production run, and limiting factors of production.
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Bloodwood et al. (2024) studied this question.
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