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.

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 is a key tool in plant and system planning, both for new and existing plants.

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.

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.

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 supports the planning of new complex processes and the optimisation of existing ones. It reveals interrelationships and enables objective comparison of alternative solutions.

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.

In order for a process simulation to be used successfully, organisational, business, technical and methodological conditions must be taken into account.

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.

A simulation project follows a clearly structured process that ensures that the results are reliable and practical.

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

Every simulation study starts out with the definition of targets. The fundamental purpose of the construction of a new or the change of an existing plant is to increase the profitability of a company.

Concrete aims of a simulation study could be:

• Increase of machine utilisation
• Reduction of staffing requirements
• Reduction of storage requirements
• Higher performance
• Shorter lead times
• Evaluation of layout alternatives
• Determination of the number of vehicles required within a transportation system
• Determination of the required buffer sizes
• Optimisation of control strategies

Increase of profitability according to VDI 3633 (2010)

Several concepts form the basis of present-day simulation systems. A building block concept is very wide-spread. Thereby a simulation model is put together from individual building blocks. Each building block can be described as follows:

Description of the building blocks of a simulation system

The individual building blocks and the operations within the building blocks are linked in an overall process. Thus a network is set up. With the aid of the building blocks and network, various logistic systems can be modelled.

All processes within the network can be visualised in 2D and 3D animations.

The constantly increasing range of applications for process simulation offers a multitude of opportunities for your company.

Recent examples are the application of simulation for calculating the carbon footprint of a company across the entire supply chain, for optimising the energy consumption in production processes or for planning the assembly of off-shore wind farms.

The development of assistance systems for simulation aims at simplifying the collection and preparation of data, as well as the evaluation and documentation of experiments.

Software solutions to increase the efficiency within simulation projects and to expand the range of functions and their integration into existing IT environments are available and rolled out for example in the automotive industry.

The assistence system SimAssist

In order to achieve credible simulation results, the model must have the highest possible correlation with the real processes.

This level of realism essentially depends on two factors:

• Quality of the model (level of detail, model structure)
• Data Quality

The decision for the right level of detail and a suitable model structure require experience with the implementation of simulation models. Simulation software can support the modelling in this respect, for instance by providing suitable building block libraries.

Of course, the quality of the input data determines the accuracy of the results. Therefore, the simulation data should be prepared with the greatest care.

Particular attention should be paid to the definition of disturbances (e.g. machine failure) and strongly volatile parameters (e.g. rework times).

Factoring in breakdowns and random influencing variables

In simulation, so-called random generators are used to model random variables.

In contrast to static planning processes, where the disturbances are often calculated via a fixed adjustment of the system performance and volatile process parameters via mean values, the simulation model delivers a result interval, as well as a more accurate picture of the impact of stochastic influences.

As simulation is a cross-divisional function and may concern several areas, such as the logistics and production planning or, for instance, the order management, it would be well-placed as a staff position at plant or management level.

It is also possible to position it within the department with the most comprehensive simulation requirements. In many companies this is the department that deals with the layout and/or material flow design and engineering.In particular, the simulation expert should be closely integrated in the relevant projects at an early stage.

Fast access to the required data and a direct exchange of information with the members of the project team ensure the efficient execution of simulation studies.

What differentiates a digital twin from a simulation – and what advantages does it offer?

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 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.