Push-based production planning makes greater benefit of advanced analytics compared to pull-based production planning and control. The reasons for this are manifold, but include the fact that push-based production planning involves forecasting of demand and subsequent production and capacity planning based on this demand. Advanced analytics techniques are used to improve the accuracy of demand […]
Following my recent article on job shop simulation in Python with SimPy I was asked to also publish an example covering salabim in Python. SimPy and salabim have similar syntax, with some differences. Both are used for discrete-event simulation (DES) model implementation. SimPy has certainly a larger user base and more third party packages. On […]
I am following up on my recent simplified job shop simulation example in SimPy. Today I want to show you how you can collect simulation data in a SimPy simulation model. I will use the data for data visualization. As you will see visualizing SimPy simulation data is easy and flexible. A simple job shop […]
Discrete-event simulation (DES) can be applied to a wide range of industries and domains where events occur in a discrete, sequential manner. It is applied when a system is so complex that it cannot be understood or solved with analytical methods. Complex systems are systems with many interdependencies and dynamic behaviour. DES is not the […]
At Production Support 56, we specialise in process improvement and manufacturing simulation. We have modelled many production lines and generally find that when a line is up to speed that it is relatively efficient. The more fruitful opportunities to improve are found outside of normal operations, when things go wrong or when line needs changing. […]