Inventory optimization with (s, S) policy with lost sales under constant and stochastic discrete demand and lead time

This research conducts a simulation to find the optimal point of inventory and total annual cost under shortage of lost sales by applying the programing languagename’s Python to help export result of the simulation. There are 10 sample products and the raw data of the product contains the demand and information of each product. Each product was run in 4 scenario cases which are stochastic lead time with capacity, constant lead time with capacity, stochastic lead time with no capacity, and constant lead time with no capacity. Each case and each product were run with 25 replications of demand. Python was applied to help find the demand replication. I use Python to find the daily demand and daily demand probability to use it as an input to create the demandreplications. The demand replications are created by a random number generator in Microsoft Excel. After I got all the resources that I need for my simulation. I will startPython to search for the minimum total annual cost. The result of simulation will show the minimum total cost and the optimal point of inventory that give the minimum totalcost. In the end this research was able to find the best scenario cases to apply.