In this article I highlight some exemplary application areas of agent-based simulation for business. In previous articles I already introduced various major simulation methods, including agent-based simulation. I want to remind you of the main simulation methods available to supply chain analysts: 1) System dynamics, 2) discrete-event simulation, 3) monte-carlo simulation, 4) gaming, 5) spreadsheet calculations, and 6) agent-based simulation. From a business problem perspective these methods can be grouped by (i) planning horizon and (ii) level of detail. I illustrated this in below figure.
I introduced these methods in my previous article on multi-method simulation:
I have also developed a framework in Python for grid-based agent-based simulations which I previously shared on GitHub and now made available here:
Agent-based simulation has a wide range of commercial application comprising various industries. In this article I will introduce some of them.
What is agent-based simulation?
Agent-based simulation is a computational modeling technique. Agent-based simulations simulate the behavior of agents and their interactions with each other and the environment that they are in. Agents are modelled as autonomous entities that have attributes, behaviors, and also decision-making processes and logic. Agents interact with eachother in an environment. The environment influences the agents, is influenced by the agents and can also be influenced by external factors.
In previous articles I already explained how agent-based simulation models can be used in research for e.g. studying emergence of macroscopic system properties and phenomena, through interactions between agents on the microscopic system level. In this article I want to highlight commercial application examples of agent-based simulation instead.
Making use of agent-based simulation for business
Agent-based simulation has a wide range of applications in various industries. It can be deployed for improving significant aspects of both medium-sized and large-sized companies. The areas of potential application include manufacturing and logistics. For example, agent-based simulation can be used for optimizing production lines and for predicting inventory levels in a warehouse. Let me introduce some more specific use case scenarios from manufacturing industry:
Agent-based simulation for production line optimization
Agent-based simulation can be used to optimize a production line. This can be achieved by simulating the behavior of individual machines, workers, materials, control systems and therewith associated decision logics and processes. If necessary, agent-based simulation can be combined with discrete-event simulation. This might be especially useful in the case of process improvements and corporate decision processes.
By analyzing machine performance, worker behavior, inventory levels, material flows and other production-related KPIs the simulation can identify bottlenecks and inefficiencies in the production process. This can help manufacturers, allowing them to make better decisions on capacity planning, production schedules and shift calendars, as well as resource and capital allocation. Agent-based simulation can also optimize maintenance schedules for machines and equipment.
Agent-based simulation for supply chain management
Agent-based simulation models can simulate the behavior of suppliers, manufacturers, distributors, and customers in a supply chain. This approach can be deployed for e.g. analyzing data on order fulfillment, inventory levels, and demand patterns – throughout the various stages of the supply chain. In this way a simulation contributes to inventory cost and delivery time reduction.
Inventory management with agent-based models
Agent-based simulation can be used to predict inventory levels and optimize inventory management. Agents can incorporate inventory policies. To learn more about inventory policies and inventory simulation, check out the following articles:
- Link: Inventory simulation for optimized stock
- Link: Price and inventory optimization
- Link: Price optimization and inventory control
With agent-based simulation various inventory policies can be tested and their impact on stock levels and availability as well as supply chain backlog and disruptions can be analyzed.
Analyzing and predicting worker behaviour with agent-based models
Agent-based simulation can be used to simulate worker behavior, including how they interact with machines, materials, inventory levels, lead times, backlog, and also co-workers. The latter aspect is especially interesting. By developing models that reflect worker characteristics, behaviour and interactions, a simulation can help manufacturers in identifying ways of improving worker productivity, worker health, worker satisfaction. A simulation can also be used to reduce worker errors.
Testing control and dispatch logics in e.g. vehicle routing networks
Agents are characterized by attributes, behaviours, and inner decision-making processes. An agent or several agents can be used to model e.g. a production control department – or a dispatcher in a routing network (i.e. transport problems). In this way agent-based simulation provides a rather flexible approach for defining, testing and comparing e.g. competing dispatch strategies and rules.
Related content regarding agent-based simulation
If you are interested in agent-based simulation you can check our some grid-based agent-based simulation examples that I published on this blog. All of these examples are implemented in Python and can be realized with the agent-based simulation library developed by me for Python.
- Link: Agent-based SIR model Python example
- Link: Word-of-mouth agent-based sales model
- Link: Conway’s game of life in Python
- Link: Agent-based segregation model (Python)
- Link: Agent-based simulation in Python
Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python