A discrete-event simulation model Assignment

A discrete-event simulation model is to be constructed to capture the product flow depicted in Fig. 1 that will allow for disruptions at each of the nodes. All inputs were set independently and were fixed for each simulation scenario run. The risk profiles are one set of inputs, as well as the recovery data: for each site that could be disrupted, a back-up (or mitigation) was built into the model to provide additional capacity.

Risk profiles for the locations and connections in the supply chain of figure 1 are developed using Monte Carlo simulation, and the flow of material and network interactions are modeled using discrete event simulation. Capturing both the risk profiles and material flow with simulation allows for a clear view of the impact of disruptions on the system.

For the disruption simulation, the selected nodes are S3, S5,M2, PKG2. The disruption parameters are duration of disruption, supply disruption, demand disruption, and presence of mitigation.

Demand values at DC1, DC2, Dc3, and DC4 under a steady state is computed in terms of average demand and standard deviation demand, and these are shown in table 2. The demand values follow a uniform distribution. .

The response variable total service level was measured in a two-step process. First, the entire supply chain’s daily service level was measured by using the percentage of orders fulfilled on time for all demand nodes. Second, the total service level response variable was calculated by taking the average of the daily service level over the given measurement period. The total service level response variable was measured as the aggregate of all the demand nodes as the supply chain is assumed to be operated by a single entity. The base service level is 95%.

The service level response variable values were collected in three distinct measurement periods (i.e., pre-, during- and post-disruption) over the course of a single simulation replication. The pre-disruption period lasted for a total of 100 days, starting on day 100 and ending on day 200. Day 0 through day 100 was used to accommodate the warm-up period. The length of the disruption period varied from 60 to 180 days depending on the treatment combination being tested. Finally, the length of the post disruption period was fixed at 200 days.

Events

Each simulation run started on day 0 with the inventory levels of each node set to the calculated base stock level (see Table 2). At the beginning of each day, each node calculated its inventory level and placed an order to ensure that the base level value is maintained. Inventory was then received, queued for production, and delivered where applicable. Finally, demand was realized and inventory holding costs and stockouts were calculated. Stockout costs were only calculated for unmet external demand, which was realized daily at the four demand nodes (i.e., DC1-DC4) according to a normal distribution with parameter values equal to those shown in Table 2. The simulated supply chain operated in a steady state following the conclusion of the warm-up period through day 200. On day 200, the disruption parameters for the given treatment combination were activated. The specified disruption node had its production capacity reduced, and all demand nodes (i.e., D1- D4) began to receive increased demand according to the demand disruption factor level. In the presence of a capacity recovery mitigation strategy, the capacity of the disrupted node began to increase. The disruption persisted for the specified duration of 60, 120, or 180 days. At the conclusion of the disruption period, the demand nodes returned to their original parameters and the disrupted node returned to 100% production capacity.

Summary of What I want to Do

(1) I would like the simulation to be done in such a manner that there would be a period of pre-disruption when evrything about the supply chain was okay, a period of disruption at the mentioned specific nodes when the demand rose to 40% and 100% with these two factors: presence of mitigation and absence of mitigation. Also, the disruption is simulated to have 75% supply disruption, and 50% supply disruption. This is defined as S5-180-100-75-N for node S5 for instance.
At the end of the disruption, I would like to see when the supply chain would return back to the initial performance status before the disruption.
Here is an example of how I would like the graph to look like:

                                                                                               Fig. 2

The graph shows the combination of S3-180-100-75-N and S3-180-100-75-M factors. The meaning of this is S3-180-100-75-N is Supplier S3 experienced disruption for 180 days which led to 100% increase in demand and 75% disruption in supply without mitigation. The ‘N’ stands for No Mitigation, while ‘M’ stands for mitigation. So, basically, I will like to do for the following combinations with respect to service level:

(1)S3-180-100-75-N vs S3-180-100-75-M
(2) S3-60-40-50-N vs S3-60-40-50-M
(3) S5-180-100-75-N vs S3-180-100-75-M
(4) S5-180-100-75-N vs S5-180-100-75-M
(5) PKG2-180-100-75-N vs PKG2-180-100-75-M
(6) PKG2-60-40-50-N vs PKG2-60-40-50-M

The graph is expected to look like what is in figure 2

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