How can you simulate supply chain networks?

2 Types of simulation models

Different simulation models can be used to capture the characteristics and dynamics of supply chain networks. Discrete-event simulation (DES) models the system as a sequence of events that occur at discrete points in time and change the state of the system, such as orders, materials, inventory, and capacity levels. Agent-based simulation (ABS) models the system as a collection of autonomous and interacting agents that have their own behaviors, rules, and objectives. System dynamics simulation (SDS) models the system as a set of feedback loops and stocks and flows that capture the causal relationships and delays in the system. These models can be used to simulate the long-term effects of supply and demand fluctuations, inventory policies, market conditions, decision-making, coordination of suppliers, manufacturers, distributors, customers, and more.

3 How to build a simulation model

Building a simulation model of your supply chain network requires following a few general steps. First, you need to define the problem and objectives, and then collect and analyze data relevant to your supply chain network. Afterward, you should select and design the simulation model, taking into account the type and level of detail, logic, assumptions, variables, and equations that govern your system. You must also implement and verify the simulation model by coding or using a software tool. Additionally, you need to experiment and validate the simulation model by running it under different scenarios and conditions. Finally, you should analyze and report the results of your simulation experiments to stakeholders and decision-makers.

5 Benefits and challenges of simulation

Simulation can be a powerful tool for your supply chain network, as it can help you understand the behavior and performance of your system, test and compare alternative solutions, and optimize and improve your decisions and operations. However, it can also be time-consuming and costly to collect and analyze data, build and validate models, and run and report experiments. Additionally, it can be complex to capture and model all the relevant aspects of your system, as well as to ensure the accuracy of your models and results. Moreover, human factors such as biases, assumptions, and preferences can influence simulation results, requiring careful interpretation and communication.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?