Simulation using FlexSimSimulation Fig. 1 Table 1 Table 2 Average Demand Standard Deviation ...

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Simulation using FlexSimSimulation Fig. 1  Table 1 Table 2   Average Demand Standard Deviation  Demand  DC1 70 35  DC2 126 132.3  DC3 57 51.3  DC4 46 45 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  First Echelon        M1      S1        S2      S5           S4       S3        M2       DC1         DC4       PKG2        PKG1        DC3     DC2  Second Echelon  Fourth Echelon  Third Echelon  https://www.sciencedirect.com/science/article/pii/S0925527312000059?via%3Dihub#f0005 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

Banasree · Accepted Answer

Simulation Model:
a step-by-step methodology to input the data and create a project in FlexSim:
1. Open FlexSim and create a new project by clicking on "File" and then "New".
2. In the "New Project" dialog box, select "Blank Project" and give it a name. Click "Create" to create the new project.
3. Once you have created the project, you can start building the model by first creating the nodes and paths for the product flow. To do this, you can use the "Create" menu to create nodes and paths in the 3D view.
4. After creating the nodes and paths, you can set up the properties for each node and path. For example, you can set the capacity, processing time, and other parameters for each node.
5. Next, you can input the data from Table 1 and Table 2 into the model. To do this, you can create a spreadsheet and use the "Spreadsheet" object in FlexSim to import the data. You can also use the "Create" menu to create objects such as "Pipes" and "Queues" and set their properties based on the data.
6. Once you have input the data, you can start building the logic for the model. For example, you can use the "Process Flow" object to define the sequence of tasks for each node, or you can use the "Event" object to trigger actions based on certain conditions.
7. You can also set up the simulation parameters, such as the simulation length, replication settings, and random seed. You can access the simulation settings by clicking on "Simulation" and then "Settings".
8. After setting up the model, you can run the simulation to test its performance and analyze the results. You can use the "Run" button in the toolbar to start the simulation.
9. You can also use the "Statistics" menu to analyze the data generated by the simulation. This can help you identify trends, bottlenecks, and other factors affecting the performance of the system.
10. Finally, you can refine the model and test different scenarios to optimize the performance of the system. You can save the project and use it as a basis for future simulations.
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.
1. Define the objectives of the simulation model: Before starting to build a simulation model in FlexSim, it is important to have a clear understanding of the objectives of the model. What are the questions you want to answer with the model? What are the key performance indicators (KPIs) that you want to measure? Defining the objectives of the model upfront will help you design a simulation model that is aligned with your goals.
2. Define the scope of the simulation model: Once you have defined the objectives of the simulation model, you need to define the scope of the model. What are the parts of the system that you want to include in the model? What are the boundaries of the system? 
3. Collect data: Once you have defined the scope of the simulation model, you need to collect data on the system. This includes data on the process flow, resource utilization, and inventory levels. 
4. Build the model: Once you have collected the data, you can start building the simulation model in FlexSim. This involves defining the entities (such as products, orders, and shipments),

SimulationFig. 1 Table 1Table 2 Average Demand Standard Deviation Demand DC1 70 35 DC XXXXXXXXXX DC XXXXXXXXXX DC4 46 45A discrete-event simulation model is to be constructed to...

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