This project showcases the Autostore type warehouse. By analyzing key components and their interactions, the initiative aims to improve overall performance, boost productivity, and enhance operational efficiency.
We have developed a state-of-the-art simulation model using Simio to accurately replicate warehouse operations. This dynamic model provides detailed visualizations of trolley movements and all associated processes, including picking, digging, moving and dropping.
This project provides an immersive and dynamic visualization of the autostore, bridging the gap between design and performance analysis. It empowers stakeholders with actionable insights, minimizes inefficiencies, and optimizes built-in processes, fostering smarter, data-driven decision-making.
This project showcases an innovative automated warehouse system featuring storage positions, lifts, and conveyors. By analyzing key components and their interactions, the initiative aims to improve overall performance, boost productivity, and enhance operational efficiency.
A state-of-the-art simulation model developed with Visual Components to accurately replicate warehouse operations. This dynamic model provides detailed visualizations of pallet movements and all associated processes, including the actions of lifts and shuttles.
This project provides an immersive and dynamic visualization of an automated warehouse, bridging the gap between design and performance analysis. It empowers stakeholders with actionable insights, minimizes inefficiencies, and optimizes assembly processes, fostering smarter, data-driven decision-making.
This project focuses on revolutionizing wall assembly production lines. It addresses the limitations of traditional static CAD models. These models fail to give meaningful insights into the dynamic behavior of complex systems. By examining key elements and their interactions, the project aims to enhance performance, productivity, and operational efficiency.
A cutting-edge simulation model is developed using Visual Components to replicate the operations within the work cell. This dynamic model offers precise visualizations of part movements and robotic processes, including those performed by Cartesian and six-axis robots.
This project offers an immersive, dynamic visualization of automated workcell operations, bridging the gap between design and performance analysis. It empowers stakeholders with actionable insights, reduces inefficiencies, and optimizes assembly processes, ensuring smarter, data-driven decision-making.
This project addresses the challenges of understanding and optimizing assembly efficiency within an automated work cell. Traditional static CAD models couldn’t offer enough insights into the system’s dynamic behavior. By analyzing key elements and their interactions, this project aims to improve overall performance and productivity.
A sophisticated simulation model is developed using Visual Components to emulate the operations within the work cell. The model provides a precise and dynamic visualization of part movements. It also shows robotic operations, including Cartesian and six-axis robots. The model illustrates the handling of rejected parts, which are dropped onto a conveyor for removal.
This project delivers an advanced visualization of automated work cell operations. It bridges the gap between static design and dynamic performance analysis. By providing stakeholders with an immersive understanding of the platform, it facilitates informed decision-making, minimizes inefficiencies, and optimizes assembly processes.
This project focuses on analyzing the logistics efficiency of a production site. The aim is to enhance efficiency by evaluating critical factors. These factors include trolley speed, the number of trolleys, and daily working hours. These elements are studied to determine their impact on overall performance and productivity.
A robust simulation model is developed using Simio to replicate the track-based operations within the production site. The model accurately shows the movement of trolleys transporting products between the warehouse and production facilities. By simulating different scenarios, it identifies optimal configurations of trolley resources to improve logistics and productivity.
This project delivers actionable insights into optimizing overhead transport within a production site. The scalable simulation approach enables stakeholders to make data-informed decisions, ultimately improving resource allocation, reducing downtime, and enhancing overall efficiency.
We aim to find the optimal number of forklifts. This will help efficiently manage operations in a large warehouse. We use simulation modeling. It helps us analyze the impact of various factors. These include forklift speed, number of forklifts, and daily working hours on warehouse performance.
A simulation model is developed using Simio to represent forklift operations in the warehouse. Forklifts are tasked with receiving picking orders, transporting pallets to the picking area, and bringing boxes to storage. The simulation model tests and evaluates different scenarios. It provides valuable insights into how changes in forklift resources affect overall efficiency. It also impacts productivity.
This project provides a detailed solution to improve forklift fleet management in large warehouses. It ensures greatest efficiency with minimal resource waste.
This project explores the optimal number of Automated Guided Vehicles (AGVs). The goal is to maximize throughput and operational efficiency in a production site. Through simulation modeling, we analyze the impact of key factors. These factors include AGV fleet size, operational speed, and daily working hours. They all affect productivity.
A detailed simulation model is developed using Simio to emulate AGV operations within the production site. Products progress through various facilities before being transported by AGVs to later destinations. The simulation evaluates multiple scenarios, offering actionable insights into how adjustments to AGV resources influence overall efficiency and throughput.
This project delivers a scalable and data-driven approach to improving AGV fleet management in production environments. By leveraging simulation, it ensures enhanced decision-making, reduced operational bottlenecks, and increased throughput with optimized resource utilization.
The project focuses on simulating AMR operations in a production environment. AMRs have three main tasks. They get full boxes at two loading positions. They deliver these boxes to 12 machines. Finally, they transport empty boxes back to an outbound conveyor.
A detailed simulation model was developed using Simio. AMRs are modeled as dynamic entities that navigate within a defined area. Simulation experiments allow evaluation of performance based on the number of AMRs, providing insights for improvement.
This simulation approach enables comprehensive analysis, offering valuable data to improve AMR performance and enhance production efficiency.
The project focuses on optimizing logistics operations, including bufferdimensioning, conveyor adjustments, and portal operations for improvedefficiency.
A simulation model was developed using Simio, integrating dailyoperational data to show real-time conditions. Machines and processesare dynamically adjusted based on current orders, ensuring a realisticand flexible simulation environment.
This simulation provides actionable insights into logistics operations,enabling data-driven decisions to enhance throughput and efficiency.
This project tackles periodic order rescheduling in the face of productionvariability. It ensures optimal resource utilization. It also maintains workflow efficiency.
A simulation model was developed using Simio, designed to manageorders with complex and highly constrained routing requirements. The model incorporates constraint programming (CP) algorithms to maximize the efficiency of production facilities. These algorithms dynamically adjust scheduling based on real-time conditions.
This approach provides a robust framework for managing production schedules, enabling enhanced adaptability and improved operational efficiency.
This project focuses on optimizing machine work time to meet the demands of current production orders efficiently.
A simulation model was developed using Simio, incorporating daily operational data for realistic analysis. The model enables comparison of multiple scenarios through experiments, providing actionable insights with detailed and interactive dashboards.
This simulation empowers production teams to explore and implement efficient strategies, enhancing overall site performance and reducing downtime.
The project aims to find and resolve bottlenecks in a commissioning center. This ensures efficient daily operations. It also ensures optimal resource allocation.
A simulation model was developed using Simio to support the planning of daily operations. The model allows for proactive determination of the optimal number of employees and forklifts. It bases its calculations on the current picking load. This approach improves efficiency and minimizes delays.
This simulation provides valuable insights into operational efficiency,enabling dynamic planning and resource optimization tailored to daily demands.
This project focuses on verifying the design of a warehouse system. It also involves optimizing the dimensions to guarantee they meet operational requirements. These tasks are essential before construction begins.
A simulation model was developed using Simio, providing a highly customizable layout to show specific warehouse requirements. The model enables thorough testing of the warehouse’s performance undervarying conditions, ensuring a well-informed design process..
This simulation ensures a data-driven approach to warehouse design,delivering a robust, efficient system tailored to operational needs.
This project optimizes the design and operation of an ASRS. It determines the ideal number of rows and levels. Additionally, it evaluates warehouse loading and unloading strategies.
A simulation model was created using Simio. It incorporates daily stocklevels. It also uses planned orders for arrivals and deliveries as input data.This ensures the model reflects real-world operational conditions.
This simulation empowers decision-makers to refine ASRS dimensions and strategies, ensuring a high-performing, efficient warehouse system tailored to operational demands.
The project aims to find out the optimal number and usage of Automated Guided Vehicles (AGVs). These vehicles need to efficiently supply assembly lines. The findings will give critical insights for an investment decision.
A simulation model was developed using Simio to evaluate AGV performance under varying conditions. Simio experiments analyze the impact of AGV numbers and speeds on overall efficiency, ensuring data-driven investment planning.
This simulation provides actionable insights into AGV deployment,ensuring cost-effective and efficient manufacturing line operations.
This project focuses on optimizing the operation of Automated Guided Vehicles (AGVs). It works together with an Automated Storage and Retrieval System (ASRS). This occurs in a scalable warehouse environment. The aim is to decide the ideal number, speed, and movement patterns of AGVs to maximize efficiency and throughput.
A simulation model is developed using Simio, leveraging the SimulateFirst framework to replicate the warehouse’s ASRS layout and processes. The model includes a flexible AGV network. This network enables precise control and analysis of AGV behavior. It ensures seamless coordination with the ASRS..
This comprehensive simulation aims to guide the design of efficient AGV systems for modern, scalable warehouses.
This project focuses on ensuring a continuous and efficient material supply to various work areas using Automated Guided Vehicles (AGVs). By simulating AGV movements, the project aims to improve material flow and reduce downtime in manufacturing or warehouse environments.
A detailed simulation model is developed using Visual Components,replicating the behavior and logic of Omron mobile robots. The model integrates CAD designs and advanced AGV logic to closely resemble real-world operations.
This simulation provides a robust framework for evaluating and optimizing AGV-based material supply systems in various industrial settings.
This project involves creating a data-driven simulation model to optimize the daily planning of batch production for small automotive parts. The focus is on analyzing and improving the utilization of machining stations,assembly lines, and facility occupancy. It also involves managing pallets and buffers to streamline production processes.
A 3D simulation model is developed using Simio, providing a detailed analysis of key production factors. The model evaluates buffer capacities, identifies congestion points, reduces waiting times, and improves resource utilization.
This simulation tool provides actionable insights for optimizing daily batch production, ensuring efficiency and adaptability in a competitive manufacturing environment.
This project focuses on optimizing the cycle times of an automated production line. The line handles multiple product variants. These variants have varying processing times. The aim is to enhance efficiency, reduce delays, and maximize resource utilization across the production system.
A robot simulation model is developed using Simio to analyze and enhance the production flow. The model identifies potential cycle delays caused by improper part sequencing and provides actionable insights to improve overall system performance.
This project provides a robust tool for fine-tuning automated production lines. It ensures they operate at peak efficiency. This project accommodates diverse product variants.
This project focuses on planning and simulating a production system designed for a lot size of 1. It enables mass customization with individual product configurations. A custom user application manages daily production data. This data is integrated with Simio for advanced simulation and result feedback. This integration ensures seamless dataflow and decision-making.
A .NET-based user application with Access is developed to manage production data and store it centrally in an SQL Server. Simio connects to this database to import production data. It carries out simulations. Then,it returns results to the application for actionable insights.
This system provides a comprehensive solution for achieving efficient,flexible production in a highly customized manufacturing environment.
This project aims to support decision-makers by demonstrating the quality and feasibility of a proposed automation system through simulation. The flexible simulation model allows for real-time customization during meetings, enabling stakeholders to explore various scenarios and make informed decisions.
A high-speed simulation model is developed to run experiments, visualize production flows, and analyze key performance indicators (KPIs) within seconds. This approach ensures rapid and effective communication of system performance and potential improvements.
This consulting tool bridges the gap between technical details and strategic decision-making, enabling efficient evaluation and adoption of automation solutions.
This project focuses on the early validation of a control system under realistic operational loads. A simulation model replicates production processes. It exchanges requests, replies, and status messages with the controller. This helps evaluate its performance and reliability before physical deployment.
A real-time simulation model is developed in Simio, enhanced with acustom .NET AddOn for seamless communication with the controller viaTCP/IP. This setup ensures continuous interaction between the model and the controller, enabling comprehensive testing and debugging.
This virtual commissioning solution reduces risks, shortens development cycles, and ensures robust control system performance in live environments.
This project involves the creation of a highly flexible simulation model foran entire plant. It encompasses orders, resources, and employees. It also includes warehouses and transportation systems like AGVs and Automated Material Handling Systems (AMHS). The aim is to develop a digital twin to optimize production and logistics processes.
The model is developed using Enterprise Dynamics (ED). It serves as a digital twin to simulate and analyze the production and logistics system.The design is data-driven and highly parametrized, allowing for detailed customization and scalability. Advanced animation enhances visualization, making complex systems more intuitive for stakeholders.
This simulation solution provides a robust foundation for decision-making and operational improvements in large-scale production and logistics environments.
This project aims to increase the pick rate and throughput of a production line. It does this by optimizing the coordination of delta robots tasked with picking biscuits. The goal is to improve efficiency while minimizing the number of robots required, reducing costs and enhancing system performance.
A simulation model was developed using Visual Components,incorporating dynamic algorithms to allow efficient task distribution among delta robots. The robots communicate in real-time, exchanging messages to coordinate their picking tasks, ensuring no overlap and maximizing throughput.
This simulation demonstrates the potential of coordinated robotics to enhance productivity in high-speed production environments.
This project involves simulating the interactions and coordination between various types of industrial robots, including SCARA, Delta, and 6-axis robots. The aim is to analyze their functions, compatibility, and performance in a shared workspace, ensuring optimal collaboration and efficiency.
A simulation model was developed using Visual Components, allowing for realistic visualization of robot functions and interactions. The model supports over 1,000 predefined robot types from various manufacturers.These include Kuka, ABB, Stäubli, Fanuc, Motoman, and Kawasaki. It can be exported as a 3D PDF for enhanced visualization. This feature facilitates stakeholder communication.
This project delivers a powerful tool for evaluating and optimizing multi-robot systems in industrial settings.
This project combines mathematical process optimization with 3D simulation to create robust and efficient operation plans. The near-optimal process flows generated by optimization algorithms are dynamically tested against potential disturbances in a simulation model.This approach ensures the plans stay resilient and adaptable under real-world conditions.
A simulation model is developed to integrate and check optimization outputs. This system dynamically simulates disturbances and “what-if”scenarios. This approach allows fine-tuning of algorithms and production plans. These adjustments are made before implementation in real systems. This ensures both efficiency and adaptability in automated workflows.
This project provides a reliable framework for developing and validating operational plans. It minimizes risks and optimizes performance inautomated production systems.
This project aims to address traffic congestion at specific junctions in Freiburg. It does so by analyzing traffic light cycle times and exploring different redirection routes. The aim is to reduce waiting times and improve overall traffic flow efficiency.
A detailed simulation model of Freiburg’s traffic network was developed,incorporating relevant traffic volumes, vehicle movements, and signal timings. The model allows for the dynamic adjustment of cycle times and provides rapid feedback on the effects of changes.
This simulation tool offers a robust solution for planning traffic management in Freiburg. It improves traffic systems, ensuring smoother and more efficient road use.
This project aims to find optimal train cycle times to maximize networkutilization based on demand. The project evaluates scenarios withdifferent numbers of trains. It also considers shorter cycle times and safestop operations. This ensures efficient, high-capacity rail transport whilemaintaining safety.
A simulation model of the railway network is created based on the actual layout. Trains are modeled with detailed passenger capacities, including seating and standing areas. The model allows for flexibility in adjusting thenumber of trains and operational distances to evaluate different scenarios dynamically.
This simulation tool provides valuable insights into improving rail network efficiency, balancing capacity, and safety while meeting fluctuating demand.
This project focuses on simulating and optimizing the coordination of Automated Guided Vehicles (AGVs) within a manufacturing line. The aim is to guarantee prompt delivery and pickup of boxes. It also seeks to prevent double order reservations. Additionally, it aims to reduce wasted time and movements.
A simulation model is developed in Simio to analyze AGV operations. The analysis includes the number of AGVs required and their optimal speeds.Simio Experiments are used to evaluate scenarios, ensuring continuous box availability and efficient task execution without unnecessary delays or movements.
This simulation provides a robust framework for improving AGV coordination in manufacturing lines, enhancing productivity and operational efficiency.
This project focuses on simulating work cells with multiple robots. The aimis to recognize and resolve errors, deadlocks, collisions, and inefficiencies in automated systems. The goal is to improve process flowand test enhancements before implementation.
A simulation model is developed using Visual Components, enabling offline programming and collision testing for multiple robots. The model evaluates and optimizes robot sequences to reduce cycle times and guarantee safe, efficient operations..
This simulation tool provides a comprehensive solution for enhancing the performance and reliability of robotic work cells in automated systems.
This project involves creating a digital twin of the assembly area for a car manufacturer. The purpose is to continuously improve production flow. It also aims to increase throughput. The goal is to find and tackle inefficiencies early in the process, reducing costs and enhancing operational performance
A digital twin is developed using Simio, incorporating data imports for product requirements, routing, work-in-progress (WIP), and production plans. This tool enables in-depth analysis of key performance indicators(KPIs) and provides actionable insights to balance and streamline production.
This digital twin solution offers a robust platform to improve assembly line efficiency. It ensures the car manufacturer meets production targets while minimizing costs.
This project involves simulating a comprehensive assembly system. It includes machining stations, Automated Storage and Retrieval Systems(ASRS), employees, buffers, and robotics. The goal is to illustrate the process steps, evaluate the project environment, and guarantee safe,efficient operations.
A detailed 3D simulation model is developed using Visual Components. It enables the analysis of critical aspects like danger zones, congestion points, and potential collisions. The model integrates various robotic systems and assembly components for a thorough visualization of workflows.
This simulation solution delivers a powerful tool for planning, validating,and optimizing assembly systems with ASRS and robotics.
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