Consider another example of a process creating a simulation model for a distribution center consisting of four product-sorting machines.
Simulation models can be built using computer software. Box said, “Essentially, all models are wrong, but some are useful,” which reminds the practitioner that neither is a model the real-world process nor can that process be fully represented.
A physical model is not common to Lean applications but is frequently used for experimental purposes in engineering, architectural and science applications. The question of how good a model can be is answered using verification validation.
Based on the assumption of the processing time at these three machines, and the arrival profile of products B and C, the team realizes that there could be an error in the model code or parameters.
The team ensures that all parameters have been entered correctly, including breaks and lunch times, processing time and distribution types, staffing and time available in a day.
Figure 2 shows the schematic of the distribution center.
A LSS team collects data on cycle time and processing step at each machine.
The first pitfall that many LSS practitioners fall into is using the model that they created without both verifying and validating it.
The second pitfall is that they go through one and assume that’s all that’s necessary.
Products B and C arrive with equal distribution at Machine A every 5 minutes.
After the model was created, the team ran the model until reaching a steady state and found that there is an excessive queue in front of Machine B, but none in front of Machine C.
A model is created in order to understand relationships among independent variables or inputs (s).