Logistik-Simulation
Also: Simulationsstudie Logistik, Materialflusssimulation, Ablaufsimulation, Lager-Simulation, Logistiksimulation, Simulation Intralogistik, diskrete Simulation
AI-generated
Logistics simulation models the material flows, processes and resources of a warehouse or transport system computationally, before real investments or modifications take place. It makes throughput, bottlenecks and utilization visible without affecting ongoing operations.
Key facts
- Logistics simulation enables bottlenecks, throughput and resource utilization to be tested computationally before real investments are made.
- The quality of results depends directly on the completeness and accuracy of input data such as volume frameworks, process times and layout data.
- Simulations are used both for greenfield projects and for the modification or expansion of existing facilities (brownfield).
- Unlike a Digital Twin, a classic simulation study is not a system permanently coupled to real-time data.
- Simulation results also serve as a basis for comparing bids and validating performance commitments in tender processes.
What is logistics simulation?
Logistics simulation is a method in which a digital representation of a logistics system – such as a distribution warehouse, a picking zone or a conveyor network – is built and operated using realistic input parameters. The goal is to understand the behavior and performance of the system under various conditions before structural or technical decisions are made. The model captures process times, volume frameworks, layout data and resources, and runs them across simulated time periods. The result is a set of key performance indicators such as throughput, waiting times, buffer utilization and bottleneck frequency.
How does logistics simulation work in practice?
The process begins with data collection: item structures, order profiles, shift models and system data feed into the model. Discrete event-based simulators are commonly used, in which every event – such as a container arriving at a station – is modeled individually. The model is first calibrated against known actual data to ensure it represents the real system with sufficient accuracy. Scenarios can then be varied: How does throughput change if a conveyor fails? What buffer depth prevents a jam? What happens during a seasonal peak?
Typical areas of application include:
- Planning and design of new facilities (greenfield)
- Expansion or modification of existing systems (brownfield)
- Sizing of resources such as personnel, conveyor technology or storage areas
- Comparison of competing system concepts as part of a tender process
- Validation of performance commitments to operators or investors
What matters when selecting and implementing a simulation?
The quality of a simulation depends directly on the underlying data. Erroneous or overly rough input data leads to models that may be internally consistent but misrepresent the real system. It should therefore be clarified early on which data is available at what level of granularity and where assumptions must be made – these assumptions must be disclosed transparently in the results report.
Logistics simulation should be distinguished from the Digital Twin: a Digital Twin connects the simulation model with real-time operational data and can be continuously updated. The classic simulation study, by contrast, is a one-time analysis or is used on a project-accompanying basis. It also differs from pure reporting, which describes the current state without testing scenarios. Whether an external service provider or an internal team carries out the simulation depends on data availability, model complexity and existing expertise – both are common in practice.
Related terms
- Automatisierung
Automation refers to the use of technologies and systems to execute processes fully or partially without human intervention. In intralogistics, the spectrum ranges from simple conveyor systems to fully automated high-bay warehouses with autonomous vehicles.
- Bestandsmanagement
Inventory management refers to the systematic planning, control, and monitoring of stock levels across the entire supply chain. The goal is to ensure delivery capability while keeping capital commitment and warehousing costs within economically viable limits.
- Bottleneck Analyse
Bottleneck analysis identifies the process step with the lowest capacity in a logistics system, making the limiting factor for overall throughput visible.
- Brownfield Logistik
Brownfield logistics refers to the modernization, expansion, or restructuring of existing warehouse and logistics sites during ongoing operations. In contrast to the greenfield approach, it involves working with existing building structures, infrastructure, and technology.
- Business Case Automatisierung
The Business Case Automation evaluates, from an economic and operational perspective, whether an investment in warehouse or intralogistics automation is justified. It summarizes costs, benefits, risks, and assumptions in a structured way, forming the basis for management approval decisions.
- Digital Warehouse
Digital Warehouse refers to a warehouse in which processes, systems and decisions are consistently data-driven and system-guided. Typical building blocks include a warehouse management system, real-time visibility, IoT sensors, mobile data capture and automated optimization logic.
- Dimensionierung
Dimensioning refers to the systematic design of warehouse capacities, material handling equipment, and logistics systems based on volume frameworks, throughput requirements, and peak loads. It forms the planning foundation for investment decisions in intralogistics.
- Distributionslogistik
Distribution logistics encompasses all processes that move goods from the manufacturer or warehouse to the end customer – from network planning through storage and transportation to order fulfillment.
