Traditional production and logistics planning and optimisation methods use static models and assumptions that do not account for changes and dynamic events occurring during the process. This can lead to suboptimal results and increased costs. Conversely, successful project implementation hinges on the ability to predict the impact of various influencing factors before the project is implemented in actual production. This is where dynamic simulation comes into play.
Dynamic simulation is a tool that enables real-time modelling of processes and systems, predicting their behaviour under different conditions and scenarios. It provides a better understanding of process dynamics and their interactions at different points in time, which is essential for effective planning. We therefore highlight the benefits of using dynamic simulation tools and share our many years of experience in planning production and logistics processes in a modern manufacturing environment.
The main benefits of dynamic process simulation
When planning, it is not enough to know the capacity of the equipment and its average performance, whether it is a production system, a logistics system, or a combination of both. The logic of how the process functions, its procedures, production variability, and how the individual elements cooperate must also be considered. Conventional planning methods often deviate by up to 20% from reality precisely because they do not take the system’s dynamics into account. From our point of view, the main advantages of dynamic simulation for process planning and capacity are:
- More accurate modelling of processes and systems: dynamic simulation takes into account the dynamics and interactions of processes and systems, enabling more accurate modelling. This improves understanding of process complexity and enables the identification of potential weaknesses and areas for improvement.
- Testing different scenarios: Dynamic simulation can be used to test different scenarios and planning strategies. This enables managers to identify the optimal solution for specific conditions and requirements.
- Predicting outcomes: Simulation enables you to predict future outcomes and process behaviour. These predictions are based on real data, enabling you to make informed decisions and optimise planning.
- Faster response to change: In a dynamic and constantly changing environment, the ability to respond quickly to change is crucial. Simulation enables you to adapt your plans based on real-time data to meet current needs.
Simulation enables better planning and optimisation of resources such as labour, time, materials and finances. This results in more efficient management of resources and lower operating costs.
What tool do we use for process planning?
Tecnomatix Plant Simulation, an application from Siemens Digital Industries Software. It helps users to design and optimise material flows, resource utilisation and logistics processes at all planning levels. It is mainly used for strategic planning, verifying proposed logic and supporting daily production planning.
The Plant Simulation environment enables the creation of advanced, customisable production objects and intangible representations that reflect real devices (e.g. machines and AGVs) and can adopt their properties based on defined parameters. Individual elements can be arranged logically into predefined templates to create a user library. These libraries significantly reduce the time and knowledge required to use the application when creating the simulation model, conducting simulation experiments and evaluating them. Asseco CEIT has created its own library called CLL (CEIT Logistics Library), which streamlines the planning processes of automated logistics systems using dynamic simulation. The expanding library contains the following modules:
- Object database: contains a complete portfolio of our company’s logistics technology models in 2D and 3D, as well as general logistics technology representations. Individual objects contain defined parameters based on technical specifications, such as speed, safety zones and loading/unloading algorithms that take the weight of the material being handled into account.
- The control interface is a user-customisable interface used to add and modify individual object elements in the simulation model. It contains a database of templates for creating individual logical commands, control rules for intersections, speed limits and defined methods for loading and unloading equipment.
- The statistical module is a standardised module containing detailed statistical data on the use of individual devices, device response times, transport delays, maximum transport capacity, battery power management, Sankey diagrams and overall transport efficiency (OTE).
How do we approach the implementation of dynamic simulation in process planning?
Regardless of the nature of the process, we always start by identifying the objectives and processes to be simulated. During this initial phase, we define the primary and secondary objectives and determine the sources of high-quality input data required to generate the most accurate virtual representation of the simulated system. This is followed by collecting the necessary process data and creating an accurate mathematical or computer model, which forms the basis for the simulation. The model must be comprehensive, taking into account all relevant factors and their interrelationships.
It is important to verify the model’s accuracy during its creation, for instance by simulating past scenarios and comparing the actual results with those obtained from the simulation. Once the logistics system and the boundary conditions of the production system have been configured, simulation experiments can be carried out.
These experiments are conducted on a specific scenario or series of scenarios, which are limited by the boundary values of the transport and production systems.
You can follow the simulation process using 2D or 3D animations, or focus directly on evaluating the experiments. The library’s statistical module automatically collects and evaluates individual KPIs (e.g. equipment performance, transport system throughput, charging process, overall battery capacity, response times) and generates a standardised HTML report, which can be exported to a PDF file or as an offline web page.
In addition to the standard report, collision analysis, Sankey diagrams, intersection loads and communication based on intralogistics principles can be used to analyse the simulation scenario. The content of the displayed statistical report can be adjusted flexibly using the control menu according to the user’s needs.
Once the proposed solutions have been successfully implemented, the project is complete. However, in some cases, our clients use the simulation model on a daily or weekly basis as a planning tool. To this end, we provide a customisable control menu that allows customers to independently adjust system parameters without requiring knowledge of simulation software.
Simulation plays an important role in the planning process, ensuring comprehensive assessment requirements are met effectively. Using additional libraries containing standardised objects and templates for creating the model itself is important for minimising the time-consuming nature of the planning process using simulation.
Using the extended CLL library enabled our company to reduce the planning and optimisation process for the AGV automated logistics system by an average of 35%, compared to the original planning method using dynamic simulation without the CLL logistics library.
