Simulation is the digital replication of a dynamic system using a model that is used for experiments to gain real insights. In process simulation (DES), processes such as material flow or logistics are simulated to create transparency, minimise risks and implement optimisations throughout the entire life cycle.

Definition (according to VDI 3633 Sheet 1):

Simulation is the representation of a system with its dynamic processes in an experimental model in order to gain insights that can be transferred to reality.

In simple terms, this means:

• A digital model is created on the computer,
• experiments are carried out with the model,
• and valuable conclusions are drawn for real systems.

Form of application and method

In practice – especially at SimPlan – we use process simulation, often referred to as discrete event simulation (DES). This involves modelling processes in which piece goods or other objects flow through defined sequences with fixed time specifications, for example through production lines or warehouse systems.

Areas of application

Traditionally used in areas such as:

• Production & logistics (material flow, layout, resources)
• Virtual commissioning of plants
• Marketing & sales (e.g. visualisation of technical processes)

However, this method can also be successfully extended to simulations of people flows, business processes, transport networks or supply systems.

Benefits & added value

• Transparency & visualisation: Complex systems are presented in a clear and understandable way (e.g. through 3D animations).
• Risk reduction: Scenarios can be tested without risk before they are implemented in real life.
• Optimisation and planning reliability: Bottlenecks, throughput times and layout configurations can be realistically evaluated and improved.
• Life cycle use: Simulation reliably supports the planning, virtual commissioning and ongoing operation of a system.

In sales and marketing, simulation is primarily used to clearly visualise planned systems and processes. It creates transparency, facilitates decision-making and strengthens the confidence of potential customers – in special cases, also through detailed simulations with key figures.

Manufacturers of plants, machines or warehouse technology use simulation specifically in the quotation phase

• Visualisation instead of detailed simulation:
  In this early phase, the complete data foundation is usually still missing. Therefore, a visualisation simulation is used, which primarily shows in an animated form what the process could look like. This creates a common understanding of the process among potential customers. This is important for transparency and trust.
• Improved quotation communication:
  Such 3D visualisations help to explain complex processes clearly and facilitate decision-making processes. At this stage, they do not yet replace hard key figures such as throughput or lead time.
• Detailed simulation on customer request:
  If a concrete concept including all relevant data is already available – for example, through a tender – a precise simulation can be requested. This then provides valid key figures for evaluating and comparing different providers.
• Independent analysis by external service providers:
  If a neutral evaluation is desired, external simulation service providers often come into play. They analyse the system based on the data submitted and deliver objectively sound results.

Current benefits

In addition, modern research shows that simulation in marketing and sales is also used for sales forecast modelling, campaign planning and price scenario analysis. Simulations help to run through different strategies virtually – for example, to optimise prices or advertising channels. This enables data-driven decisions to be made and resources to be used efficiently.

Simulation supports the evaluation of new plants and the optimisation of existing systems. It saves time and money by allowing scenarios to be tested without risk. Modern approaches such as Plant Planning 4.0 or the combination with scheduling and optimisation make it a flexible planning tool.

Areas of application

• Testing planned plants
• Simulations examine new plants in terms of throughput, dimensioning, throughput times, performance limits, susceptibility to faults, personnel requirements and other planning parameters. Different variants can be simulated and compared with each other in order to make well-founded decisions.
• Optimisation of existing plants
• The current status of a plant is mapped and optimised through targeted adjustments such as modified control strategies. Even major changes such as layout variants or buffer design can be reliably tested.

Efficiency and safety

• Cost and time savings
• Changes to the simulation model can be implemented quickly and cost-effectively without disrupting ongoing operations.
• Early decision support
• Simulation provides important insights for fundamental decisions early on in the planning phase. As the project progresses, the model grows in detail and supports iterative solution finding.

Current developments

• Plant Planning 4.0
• Modern planning approaches combine simulation with technologies such as augmented reality. This improves the visualisation of complex systems and accelerates project cycles.
• Combination with scheduling and optimisation
• Simulation is increasingly being combined with methods for detailed planning. This allows ‘what-if’ scenarios to be played out and robust alternative plans to be created. Particularly dynamic models are gaining in importance as they realistically map interactions and process dynamics, thus enabling greater planning accuracy.

During the implementation phase, simulation enables the virtual commissioning of control systems. Programmes can be tested independently of the actual plant, errors can be detected at an early stage, and commissioning times can be shortened, right through to real-time testing with hardware-in-the-loop.

Simulation as the basis for control programming

During the implementation phase, simulation models provide valuable results for programming the control system. In special cases, the control code can even be generated automatically from the model to a large extent.

Virtual commissioning (emulation)

Virtual commissioning allows control software to be tested independently of the real plant. Sensors, actuators or entire PLCs are emulated by the model or linked to it. Appropriately structured models allow easy switching between simulation and emulation mode.

Advantages and benefits

• Early error detection and quality assurance: Program errors can be identified and corrected in the model at an early stage.
• Time and cost savings: Virtual tests reduce commissioning effort and lower risks.
• Flexibility: In addition to validating the control system, training courses, fault scenarios or alternative processes can also be run through without placing any load on the real plant.
• Hardware-in-the-loop: In conjunction with a real PLC, the control system can be tested in real time before the plant is physically available.

Yes: Simulation can be used as a forecasting tool (digital twin) for day-to-day operations or as an operator model for future adjustments. This allows processes to be secured, scenarios to be compared and changes to be implemented quickly and without risk.

Simulation as a forecasting tool (digital twin)

Simulation can be used as a predictive tool during ongoing operations. Testing the daily schedule shows at an early stage how orders, batch sizes or machine utilisation affect throughput times, personnel requirements and plant utilisation. This allows scenarios to be played through and the best processes to be selected before production starts.

In order for a model to be used as a digital twin, it must be linked to real data. This includes, for example, current order statuses, cycle times, setup times and availability. The more complete these parameters are, the more accurate the forecasts will be. In some cases, the simulation results are additionally supported by optimisation methods such as heuristics.

Simulation as an operator model

Even after the project has been completed, a simulation model can be used in the long term. Operators use it to check future adjustments – for example, the integration of new products or logistics handling for new customers.

The advantage lies in the speed of implementation: since the actual model already exists, it only needs to be adapted to the planned changes. This saves time, facilitates decision-making and reduces risks.

Introduction to Simulation

Before the decision for or against a simulation study is made, it should be clarified whether all conditions for a successful project have been fulfilled. If there is a lack of experience with the simulation tool, it is recommended to call in a consultant already during the initial decisions. He will be able to judge whether simulation is suitable for the specific problem.

During the initial phase you should also decide whether:

1. to set up an internal simulation service provider or
2. to commission an external service provider.

This decision should be made based on the following conditions:

• Availability of skills: a minimum of two employees should be trained.
  Cost comparison for internal and external service provider (including support expenditure of the respective specialist department): Comparison of the costs for software procurement, training and getting acquainted with the tool with the costs for an external service Provider.
• Estimated scope of the simulation tasks over the next 2-3 years: are there projects beyond the current one that are going to require simulation? Will these projects utilise the capacity of 1-2 employees?

Example for a plant visualisation with Demo3D – source: Kuka Systems GmbH

Furthermore it must be noted that a lack of experience with handling simulation significantly

• increases the probability of modelling errors and
• leads to longer project durations.

In order to avoid this, an experienced consultant should ideally support the first project, even if internal resources are being set up. This guarantees an effective transfer of know-how to the newcomer.

However, other alternatives, such as the ‘external workbench’ are also possible. This means that an internal employee is trained in the execution of simulation projects and in the operation of the models, while the models themselves are created by an external service provider.

After the decision to carry out a simulation study has been made, the question of the right simulation system or the appropriate external service provider arises.

When purchasing a simulation system, several factors must be taken into account, for instance:

• Which qualifications does the future user of the software have?
• Is data from databases or CAD systems to be incorporated into the simulation model?
• Does the software offer specific solutions for the target application?

How to find the right software and service provider

Most simulation system vendors offer a trial installation or let customers rent their system for a limited period of time. These offers are particularly useful as it is only by handling the software that you get to know its’ advantages and disadvantages and will be able to effectively determine the appropriate system for your individual requirements.

Alternatively, you may decide to use our tool laboratory. Within one or two days (depending on the scope of the task and the number of simulation systems to be tested) you can test established systems based on your individual project requirements.

This will provide you with a solid overview of the range of features and the user-friendliness of the different software systems. Today a constantly increasing number of consultancies offer simulation services.

Criteria for the selection of the right partner are:

• Has the service provider got experience in the specific area? (Ask for references.)
• Who will head the project on the part of the service provider? (Ask for profile.)
• Does the service provider use standard systems and industry-oriented building block libraries? (Avoid dependence on proprietary solutions.)
• What procedure model for the project does the service provider suggest? (E.g. according to VDI guideline 3633.)
• How will the know-how transfer to you be guaranteed?

A worthwhile investment for your company

The following table shows the basic classification of simulation projects and the expected costs.

Simulation pays off economically: studies show an average ratio of 1:6. In addition to direct cost savings, it creates transparency, reduces risks and speeds up commissioning.

Economic benefits

The specific monetary advantage cannot be determined precisely in advance. Studies and benchmarks (e.g. VDI) estimate the average cost-benefit ratio to be around 1:6 – meaning that every pound invested in simulation yields a multiple return. For large investments, such as in car body construction in the automotive industry, the ratio is often even more favourable.

However, there are also projects in which simulation primarily serves to validate plans without identifying additional optimisation potential. Even in such cases, simulation creates transparency and reduces risks.

Decision criteria for use

The following questions should be used to decide whether simulation is appropriate:

• Can the plan also be sufficiently validated using simpler methods?
• How high are the costs in relation to the investment sum? A guideline value: up to 1% of the investment for simulation.
• What optimisation potential can be expected? Example: In a driverless transport system, the elimination of just one vehicle can offset the costs of simulation.
• How high are the risks of the planned system? Particularly complex processes (e.g. order picking systems with sophisticated order control) benefit greatly, as simulation provides a detailed and tested concept.

Added value in practice

In addition to pure cost advantages, simulation also offers qualitative benefits – such as shorter commissioning times, faster start-up of systems and a significant reduction in investment risk.

The decision regarding the use of simulation should be made based on the following criteria:

• Can the design and engineering risks be covered by means of alternative, less time-consuming methods?
• How high are the costs for simulation in relation to the investment? As a reference value, the cost for the simulation should not exceed 1% of the relevant investment.
• What are the expected optimisation potentials? If, for example, the task is to design an automated guided vehicle system, then making just one vehicle redundant may cover the cost for the simulation.
• How high are the risks of the engineered system? For instance, do you have to develop complex order controls for a picking system in order to ensure that the operation is profitable? If so, simulation can be used to develop a detailed concept and test it virtually. The savings are primarily a result of the shorter time needed to implement the real-world control, as well as the commissioning and the start-up of the plant.

The success of a simulation project depends on clear objectives, good data quality, the right team composition and the early integration of simulation into the planning process.

Simulation only proves its worth if a number of key factors are taken into account:

• Clear definition of objectives: Which questions need to be answered?
• Data quality: Without valid input data, it is not possible to achieve reliable results.
• Interdisciplinary team: Simulation requires expertise from planning, IT and the relevant specialist departments.
• Early integration: The earlier simulation is incorporated into projects, the greater its benefits.
• Acceptance & communication: Results must be understood and accepted by decision-makers.
• Iterative approach: Successful projects develop the model step by step and adapt it to the current state of knowledge.

The decision regarding the use of simulation should be made based on the following criteria:

• Can the design and engineering risks be covered by means of alternative, less time-consuming methods?
• How high are the costs for simulation in relation to the investment? As a reference value, the cost for the simulation should not exceed 1% of the relevant investment.
• What are the expected optimisation potentials? If, for example, the task is to design an automated guided vehicle system, then making just one vehicle redundant may cover the cost for the simulation.
• How high are the risks of the engineered system? For instance, do you have to develop complex order controls for a picking system in order to ensure that the operation is profitable? If so, simulation can be used to develop a detailed concept and test it virtually. The savings are primarily a result of the shorter time needed to implement the real-world control, as well as the commissioning and the start-up of the plant.

Successful process simulation requires clear objectives, appropriate data, an interdisciplinary team, a budget and suitable software. Equally important are openness to results and a willingness to draw conclusions from the simulations.

Organisational requirements

• Simulation should ideally take place before implementation.
• Clear definition of objectives and questions is necessary.
• An interdisciplinary team of planners and simulators must be formed.
• All relevant input data must be obtained.
• The time required should be planned realistically:
  • Effort required by the simulation team (approx. 60%)
  • Support from the specialist department (approx. 35%)
  • Contribution from other departments such as suppliers (approx. 5%).

Business requirements

• Costs must be determined in advance and taken into account in the project budget.
• The benefits must be estimated realistically in order to assess the economic viability.

Technical requirements

• Clarify the existing hardware and software basis.
• Define data sources and ensure data quality.
• Structured data preparation is necessary in order to build the model accurately and efficiently.

General conditions

• Ensure openness to alternative solutions.
• Question existing constraints.
• Gain acceptance of the simulation results within the project team.
• Be prepared to draw conclusions from the results.

Precise and complete data on processes, resources and structures are essential for a successful simulation. The better the data quality, the more realistic and meaningful the results.

The quality of a simulation stands and falls with the available input data.

• Structure and layout data: Plans of plants, production lines, storage areas.
• Process data: Cycle times, set-up times, transport times, material flows.
• Resource data: Availability of machines, personnel, vehicles.
• Order and master data: Quantities, order sequences, batch sizes.
• Disturbance variables: failure rates, rework, delivery delays.

Missing or uncertain data can sometimes be supplemented with assumptions or empirical values. It is important to make these uncertainties transparent and to take them into account in the model using sensitivity or scenario analyses.

A simulation project follows clear steps: from defining objectives and data acquisition to modelling and implementation to analysis, verification and validation. This produces reliable results for informed decisions.

1. Definition of objectives and task description

At the beginning, the project objectives are defined and the task is described precisely. A clear question is crucial for the later use of the simulation.

2. System analysis and data collection

The system to be examined is analysed and the relevant data is collected. This includes, for example, process times, capacities, layouts or control rules.

3. Data preparation and model formalisation

The raw data obtained is structured and prepared before being transferred to the simulation model. The model is then formalised in order to realistically map the processes.

4. Implementation and execution of experiments

The formal model is implemented in simulation software. Various scenarios and experiments are then carried out to test different approaches.

5. Analysis, verification and validation

The results are evaluated and compared with the project objectives. Verification and validation ensure that the model and data are correct and that the simulation realistically reflects reality.

Result

The end result is an executable model with reliable simulation results that serves as a basis for informed decisions.

The following graphic shows the typical steps from data acquisition to analysis and validation.

Source: Rabe, M.; Spieckermann, S.; Wenzel, S.: A New Procedure Model for Verification and Validation in Production and Logistics Simulation. In: Mason, S. J.; Hill, R. R.; Mönch, L.; Rose, O.; Jefferson, T.; Fowler, J. W. (eds.): Proceedings of the 2008 Winter Simulation Conference, 2008, p. 1720

Formulating clear objectives is the first step in any simulation study. The primary focus is always on increasing a company’s profitability. Process simulation increases profitability by increasing throughput and utilisation, shortening lead times, reducing inventories and evaluating layout or control alternatives.

Typical optimisation goals

Depending on the issue at hand, a simulation study can have different focal points, for example:

• Increasing machine utilisation
• Reducing personnel or storage space requirements
• Increasing throughput
• Shortening lead times
• Evaluating layout alternatives
• Determining the optimal number of vehicles in a driverless transport system
• Determining suitable buffer sizes
• Optimising control strategies

Economic impact

These objectives can be used to influence the key control variables:

• Increasing delivery readiness
• Shortening throughput times
• Reducing inventories
• Improving adherence to delivery dates
• Maximising plant and personnel utilisation

This increases the economic efficiency of the system while reducing operating and capital costs – the decisive benefit of systematic process simulation.

Increase of profitability according to VDI 3633 (2010)

Simulation digitally maps complex systems by creating models from building blocks and connecting them to form a network. This model can be visualised using 2D or 3D animation, enabling a clear understanding of processes and procedures.

Today’s simulation software often works with the building block concept:

• Building blocks as modules: Individual elements (e.g. machines, conveyor belts, storage areas) are modelled, standardised and reused.
• Connection & network formation: The building blocks are linked to form an overall process so that workflows can be mapped – often as logistical networks.
• Visualisation: 2D or 3D animation makes processes visible – ideal for understanding workflows, identifying weak points and involving stakeholders.

Description of the building blocks of a simulation system

The individual building blocks and the processes within the building blocks are linked to form an overall process. This creates a network. With the help of the building blocks and the network, a wide variety of logistics systems can be mapped.

All processes within the network can be visualised using 2D or 3D animation.

Current trends

Several developments are currently underway to make simulation even more effective:

• AI-supported simulation & machine learning: AI is used to automatically optimise models, support the interpretation of results and make simulations adaptive.
• Real-time digital twins: Simulations are increasingly being linked to real-time data so that models can react to changes virtually live – for example, through IoT sensors or streaming data.
• Cloud-based simulation: Simulations, model management and experiment execution are being moved to the cloud. This enables collaboration across locations, lower hardware costs and better scalability.
• Multimethod simulation: Combining different forms of simulation (e.g. discrete events, agent-based, system dynamics) in a single model to represent complex systems more realistically.
• Advanced visualisation & immersive technologies: Higher-quality 3D representations, VR/AR and more realistic animations help to make results understandable and engage stakeholders.

Process simulation is increasingly being integrated with digital twins, AI and sustainability goals. In the future, simulations will not only help to increase efficiency and optimise layouts, but also to reduce carbon footprints, enable real-time analyses and provide enhanced visualisations.

With the constant expansion of the range of applications, new opportunities are emerging for companies to use simulation in the future to increase profitability and sustainability.

Current developments and trend areas

• Carbon footprint & sustainability in supply chains
• More and more studies are combining simulation and optimisation models to measure and reduce emissions along production and logistics processes. Example: Cold chain logistics is simulated to optimise energy consumption and emissions.
• Integration of AI and predictions
• AI-supported forecasts (predictive insights) are used to predict process failures, optimise maintenance and enable early decision-making.
• Cloud, real-time data & digital twins
• Simulations are increasingly linked to real-time data (e.g. through IoT) to create digital twins that monitor and optimise processes during operation. Cloud solutions enable flexibility, scalability and collaboration across locations.
• Advanced visualisation & immersive technologies
• VR/AR and advanced visual feedback help to present simulation results more clearly. Immersive environments allow stakeholders to dive directly into models and experience scenarios more vividly.
• Multimethod and hybrid models
• Combining different simulation paradigms (e.g. discrete events, agent-based, system dynamics) and linking them with other tools (e.g. co-simulation) is becoming increasingly important for mapping complex systems more realistically.

Implications for companies

• Companies must prepare for higher requirements in terms of data availability and quality, especially if simulations are to be run with real-time data.
• Investments in software, hardware and training remain important, but at the same time, the possibilities for integrating simulation more closely into existing IT landscapes and operational processes are also increasing.
• Sustainability and regulatory requirements will increasingly influence simulation studies: emissions measurement, CO₂ reporting and energy optimisation are no longer marginal issues, but a central part of planning.

Simulation helps to reduce emissions and energy consumption and make supply chains more resilient. It allows CO₂ footprints, material flows and alternative scenarios to be analysed before they are implemented in reality.

The requirements for supply chains are changing: companies must act sustainably while remaining crisis-proof. Simulation is an effective tool for this.

• Sustainability: Models show energy and resource consumption as well as CO₂ emissions along processes and supply chains. Optimisations can be specifically targeted at reducing emissions.
• Resilience: ‘What-if’ analyses can be used to simulate disruptions, such as supply bottlenecks or machine failures. This allows robust alternatives and contingency plans to be developed.
• Holistic planning: Simulation combines economic efficiency with ecological and social criteria, thereby creating a basis for sustainable, future-proof decisions.

Simulation is a powerful tool, but it reaches its limits when the model and data are not sufficiently accurate. Missing data, incorrect levels of detail or insufficient consideration of disturbance variables limit its informative value. New trends such as AI, real-time data and uncertainty analyses are helping to increasingly overcome these limitations.

Simulation only delivers reliable results if the model and data quality are correct.

Typical limitations

• Model quality: The right level of detail is crucial – too rough leads to inaccurate models, too detailed to complex models.
• Data quality: Carefully prepared input data is essential, otherwise incorrect results will be produced.
• Disruptions & randomness: Machine failures or fluctuating process parameters must be taken into account. Simulations map such influences using random generators, but always deliver result intervals instead of exact values.
• Practical limitations: Building and maintaining models requires time, expertise and acceptance by decision-makers.

Current trends

• AI & machine learning: help with data preparation and model optimisation.
• Real-time data & digital twins: connect simulation more closely with reality.
• Uncertainty analyses: make the effect of data gaps transparent.
• Hybrid approaches: combination of different simulation methods for more realistic results.

Simulation is a cross-functional task and should be integrated into the organisation in such a way that it is involved in projects at an early stage, has quick access to data and works closely with specialist departments. It is most effective when positioned as a staff unit at plant or management level or in the departments with the greatest need.

Simulation affects many areas of a company – from supply chain and logistics to production and order control. That is why the question of the right organisational anchoring is crucial.

Possible organisational locations

• Staff unit: Located at the plant or management level in order to be effective across departments.
• Demand-oriented: Integrated into departments with high demand, e.g. layout, process or material flow planning.

Success factors for positioning

• Early involvement: Simulation experts should be integrated into projects from the outset.
• Data access: Smooth access to current planning and operating data is a basic requirement.
• Information exchange: Direct contact with project participants enables efficient studies and rapid iterations.

Good organisational embedding ensures that simulation is used systematically rather than selectively, allowing it to fully unfold its effect on profitability, planning reliability and innovation capability.

A digital twin is a virtual replica of a real product, system or process that is continuously supplied with real-time data and reflects its physical state throughout its entire life cycle – from planning and operation to maintenance. In contrast, a traditional simulation is usually static or scenario-based – it uses historical or assumed data to test hypothetical ‘what-if’ scenarios without automatic updating.

Advantages of a digital twin over conventional simulation:

• Real-time monitoring & behaviour view: The twin reflects the current state in real time – ideal for monitoring, fault detection or performance analysis.
• Lifecycle consistency: Can be used throughout the entire lifecycle – design, operation, maintenance – not just at specific points in time as with simulations.
• Predictive and prescriptive capabilities: Thanks to real-time data, a digital twin can provide forecasts and recommend actions.
• Continuous optimisation: Through continuous data feed, the digital twin adapts continuously, supporting dynamic decisions.

A digital twin requires smart sensors and IoT devices, a reliable data infrastructure with open interfaces, edge and cloud computing, and clean, standardised data. Secure integration into existing IT systems and processes is also crucial.

A functional digital twin requires several technical components that work together seamlessly:

• Sensors & IoT devices: Smart sensors such as temperature sensors, cameras, RFID tags or motion sensors continuously collect relevant data about the physical condition and the environment. The selection depends on the required measured values such as position, movement or environmental conditions.
• Data infrastructure & interfaces: The collected data must be transmitted reliably and in real time. This requires robust IoT networks and open interfaces (APIs) for bidirectional data exchange between the real and digital systems.
• Edge and cloud computing: Edge systems process data directly at the source to minimise delays. Cloud platforms handle storage, scaling and complex analyses.
• Data quality & standardisation: To ensure that the digital twin delivers accurate results, data must be cleaned, structured and standardised. Uniform data models and metadata ensure that no isolated solutions arise.
• IT/OT integration & security: The digital twin must be securely integrated into existing systems such as ERP, WMS or TMS. Encrypted communication, access rights and compliance guidelines are essential here.