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https://github.com/loccx78vn/simulation_in_r

This is a tutorial for R-user learning about simulation and how to apply it in supply chain management
https://github.com/loccx78vn/simulation_in_r

r simulation supply-chain-management

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This is a tutorial for R-user learning about simulation and how to apply it in supply chain management

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README 

        
## What is simulation:

Simulation in supply chain management is the process of creating a digital model of a supply chain or its components to analyze, test, and optimize the behavior of the system under different conditions. This approach enables decision-makers to experiment with strategies and policies in a risk-free environment. Here are some common types of simulation models used in supply chains:



Simulation involves creating a virtual model of supply chain operations to analyze and optimize performance. By simulating processes such as production, logistics, inventory management, and customer demand, organizations can test strategies under various scenarios without risking real-world disruptions.



Simulation model contains:

### Discrete Event Simulation (DES):

DES models systems as a sequence of distinct events, each occurring at a particular point in time. It’s particularly useful for systems where processes happen in a step-by-step manner, like processing orders, transporting goods, or handling customer requests.

-> **Application**: DES is commonly used for modeling warehouses, production lines, transportation logistics, and inventory management, where timing and event-driven changes are crucial.



### Agent-Based Modeling (ABM):

Overview: ABM simulates interactions of individual agents, each with unique behaviors, to analyze complex systems. Agents can represent entities like suppliers, manufacturers, distributors, and customers, each following rules or strategies that influence the overall system.

->**Application**: ABM is useful for studying decentralized systems with independent decision-makers. It’s applied in analyzing supply chain resilience, demand fluctuations, and effects of individual agent decisions on the entire system.



### System Dynamics (SD):

SD is a top-down simulation method that uses feedback loops and stocks and flows to model and analyze the behavior of complex systems over time.

->**Application**: SD is often used for high-level policy analysis and to study the long-term impact of strategic decisions, such as capacity planning, demand forecasting, and inventory policies.



## Simulation in R:

### Definition:

In R, the `simmer` package is commonly used for Discrete Event Simulation (DES) and Agent-Based Modeling (ABM), which helps build detailed models of supply chains. Using `simmer`, you can create agents that represent entities (e.g., suppliers, customers) and simulate their interactions and activities over time. This allows for in-depth testing and analysis, helping to improve decision-making in supply chain management.



`simmer` is an R package designed specifically for Discrete Event Simulation (DES). It is inspired by the SimPy framework in Python and is particularly suitable for simulating complex systems like supply chains, healthcare processes, and manufacturing operations.



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