Predicting the effects of common levels of variability on flow processing systems

摘要

The implementation of flow processing is essential to the successful application of lean manufacturing practices since it provides the infrastructure for pull production to take place and the focused elimination of waste. With the adoption of lean practices into a broader range of production environments there is an increasing need for flow processing to operate under a wider range of conditions particularly with respect to the sources and levels of variability that exist. In order to ensure efficient flow processing under such conditions a range of methods has been developed for both reducing levels of variability and for managing the effects of variability. However, ensuring the effective use of each of these methods requires detailed knowledge of the effects this variability has on the resource requirements of individual workstations. The current paper presents a methodology for developing predictive models that can be used to quantitatively estimate the levels of blocking and waiting that occur on individual workstations along a flow processing line. The methodology presented makes use of discrete event simulation to generate data from which estimating models are derived that relate %blocking and %waiting arising at individual workstations with the coefficient of variation of their job cycle time distributions.