Dear Arvind,

I suggest you to see links, attached files and publications in topics.

Verifying and validating a simulation model - ACM Digital Library

dl.acm.org/citation.cfm?id=564208

A study for production simulation model generation system based on ...

www.sciencedirect.com/science/article/pii/S2092678216304733

Best regards

Article Approach for the Conceptual Design Validation of Production ...

Chapter Consideration of Legacy Structures Enabling a Double Helix D...

Dear Arvind,

I think you can either apply a case study with a real data and compare the model results with the actual performance of the system or you can use a data set from the literature and compare the model results with too. Both of them will make you able to judge the validity of the model. You can find to what extend the model results are justifiable, explainable and close to the real case measurements.

Hope my answer add a value..

Hello, please check the link below

https://link.springer.com/chapter/10.1007/978-3-642-84524-6_24

Design your simulation so the Key Performance Parameters (KPPs) and Measures of Effectiveness (MOEs) are easily measurable.  See my paper on RG "Simulation-Based Engineering of Complex Systems" that discusses your problem.  

Monte Carlo Simulation under different randomly chosen (feasible) scanarios, and statistical Analysis of the main KPIs variarion

I second Epioco's suggestion.

Further, I recommend a first, quick plausibility check of the outcome measurements straight after the simulation runs and before the more systematic methods like Monte Carlo Simulation.

Does the throughput, WIP, waiting time sound reasonable?

It might require some experience to check the plausibility of these measurements in accordance to the input variables, but in most case common sense gives already a good answer.