system sand production line car factory simulation

factory simulation | flexsim

factory simulation | flexsim

Factory simulation is the process of using a computer model to understand and improve a real production system. Simulation technology allows organizations in the industry to analyze and experiment with their processes in a virtual setting, reducing the time and costs of physical testing. Materials, equipment, and personnel can all be considered within a simulation model, resulting in information that can maintain or improve production at the lowest possible cost.

FlexSim is a powerful yet easy-to-use software package for simulation modeling. A fast and accurate simulation engine is hidden behind drag and drop controls, drop-down lists, and many other intuitive features that make it accessible for anyone to build a model. All simulation models are created to scale and are presented using 3D visuals, so it becomes easy to view and recognize bottlenecks in the production line or other deficiencies within the system. FlexSim also gives decision makers the data to confirm their observations, with impressive statistical reporting and analysis built right into the software.

The need for efficiency in todays factories has never been greater, with equipment and labor costs continuing to rise each year. Successful companies need to ensure that every facet of their operations is being carefully considered and optimized. Factory simulation is an inexpensive, risk-free way to put your facility to the test, ensuring that you are meeting production goals and quality standards at the lowest possible cost. Simulation also provides a means to test and implement principles of Lean manufacturing and Six Sigma. And unlike spreadsheet-based analysis and forecasting, factory simulation software offers a quick and efficient way to adjust parameters and simulate againhelping to get the information you need to make better, safer decisions.

Full 3D simulation Take a leap out of the outdated and into our custom-built, true-to-life 3D environment! FlexSim brings a better visual experience to simulation modeling, providing rich 3D objects to make your model look more realistic. 3D simulation modeling brings the system to life and helps foster communication between personnel at every level.

Easy to use, yet powerful FlexSim contains user-friendly tools like drop-down menus and drag and drop functionality that allow beginners to build and test models in just minutes, with no background in computer coding. And our newest logic-building feature makes it easier when things start to get complex. Simply map out the process using activities in this powerful flowcharting toolno need for coding.

Consider every viewpoint FlexSim has the flexibility to answer any question about your facility. Did you encounter an unforeseen variable during production? Has a customer concern created the need for an immediate solution? Simulation models can easily be tweaked and adjusted, providing rapid responses to even the most abstract situations.

No project is too big FlexSim is capable of handling any sized project, no matter the scope or complexity. Do bottlenecks in an existing system need to be identified and eliminated? Does the plant layout need to be optimized? What about future growth? Project components such as factory flow, inventory control, and optimal staffing can all be considered within a FlexSim model.

Competitively priced and cost effective With a variety of license types and a comprehensive support network, FlexSim provides industry-leading value to its customers. Our 3D simulation software is competitively priced and doesnt come with any hidden costs or add-ons, so even the most basic license comes packaged withpowerful features such as the experimentation engine and the ExpertFit distribution-fitting software.

simulation in manufacturing: review and challenges - sciencedirect

simulation in manufacturing: review and challenges - sciencedirect

Simulation comprises an indispensable set of technological tools and methods for the successful implementation of digital manufacturing, since it allows for the experimentation and validation of product, process and system design and configuration. Especially in todays turbulent manufacturing environment, which is affected by megatrends such as globalisation and ever-increasing requirements for higher degree of product customisation and personalisation, the value of simulation is evident. This keynote paper investigates the major milestones in the evolution of simulation technologies and examines recent industrial and research applications and findings. Based on this review, the identification of gaps in current practices is presented, and future trends and challenges to be met on the field are outlined. The considered simulation methods and tools include CAx, Factory layout design, Material and Information flow design, Manufacturing Networks Design, Manufacturing Systems Planning and Control, Manufacturing Networks Planning and Control, Augmented and Virtual Reality in product and process design, planning and verification (ergonomics, robotics, etc.). The evolution, advances, current practices and future trends of these technologies, industrial applications and research results are discussed in the context of the contemporary manufacturing industry.

Peer-review under responsibility of The International Scientific Committee of the 8th International Conference on Digital Enterprise Technology - DET 2014 Disruptive Innovation in Manufacturing Engineering towards the 4th Industrial Revolution.

manufacturing simulation | flexsim

manufacturing simulation | flexsim

Its the computer-based modeling of a real production system. Inventory, assembly, transportation and production can all be considered within a simulation model, resulting in decisions that can maintain or improve efficiency at the lowest possible cost.

FlexSim is a powerful yet easy-to-use software package for simulation modeling. A fast and accurate simulation engine is hidden behind drag and drop controls, drop-down lists, and many other intuitive features that make it accessible for anyone to build a model. All simulation models are created to scale and are presented using 3D visuals, so it becomes easy to view and recognize bottlenecks in the production line or other deficiencies within the system. FlexSim also gives decision makers the data to confirm their observations, with impressive data reporting and analysis built right into the software.

The need for efficiency in the manufacturing industry has never been greater, with material, transportation and labor costs continuing to rise each year. Successful companies need to ensure that the costs associated with time, equipment and other investments are being considered and optimized. At its core, manufacturing simulation is an inexpensive, risk-free way to test anything from simple revisions to complete redesigns, always with the purpose of meeting production goals at the lowest possible cost. Simulation also provides a way to test and implement principles of Lean manufacturing and Six Sigma. And unlike spreadsheet-based analysis and forecasting, manufacturing simulation offers a quick and efficient method to adjust parameters and get faster results.

Full 3D simulation What do 3D visuals really add to a simulation model? How about an immersive experience that helps you and your colleagues truly understand whats going on! FlexSim brings a visual experience to simulation modeling, providing rich 3D objects and enhanced realism. 3D simulation modeling brings the model to life, and aids in communication and discourse for staff members at all levels.

Easy to use, yet powerful FlexSim contains user-friendly tools like drop-down menus and drag and drop functionality that allow beginners to build and test models in just minutes, with no background in computer coding. And our newest logic-building feature makes it easier when things start to get complex. Simply map out the process using activities in this powerful flowcharting toolno need for coding.

Consider every viewpoint FlexSim has the flexibility to look at every angle of your manufacturing process. Did you encounter an unforeseen variable during product development? Has a customer concern created the need for an immediate solution? Simulation models can easily be tweaked and adjusted, providing rapid responses to even the most abstract situations.

No project is too big FlexSim is capable of handling any sized project, no matter the scope or complexity. Do bottlenecks in an existing system need to be identified and eliminated? Does the plant layout need to be optimized? What about future growth? Project components such as factory flow, inventory control, and optimal staffing can all be considered within a FlexSim model.

Competitively priced and cost effective With a variety of license types and a comprehensive support network, FlexSim provides industry-leading value to its customers. Our 3D simulation software is competitively priced and doesnt come with any hidden costs or add-ons, so even the most basic license comes packaged with powerful features such as the experimentation engine and the ExpertFit distribution-fitting software.

case studies manufacturing anylogic simulation software

case studies manufacturing anylogic simulation software

CNH Industrial is a global leader in capital goods. It is financially controlled by the Italian investment company Exor and is comprised of 12 brands, including Case, New Holland, and Iveco. Through its brands, CNHi designs, produces, and sells a wide range of agricultural, industrial, and commercial vehicles and powertrains. CNH Industrial identified maintenance processes as a promising area to start applying new Industry 4.0 technologies. In the automotive and related industries, downtime costs can be large. As such, improving maintenance in order to reduce downtime can deliver significant success.

CNH Industrial is a global leader in capital goods. It is financially controlled by the Italian investment company Exor and is comprised of 12 brands, including Case, New Holland, and Iveco. Through its brands, CNHi designs, produces, and sells a wide range of agricultural, industrial, and commercial vehicles and powertrains. CNH Industrial identified maintenance processes as a promising area to start applying new Industry 4.0 technologies. In the automotive and related industries, downtime costs can be large. As such, improving maintenance in order to reduce downtime can deliver significant success.

Today, many steel manufacturers are in need of lean manufacturing tools that will improve their return on investment and service levels. The minimum 80% reliability level most steel companies are struggling to achieve is nowhere near what todays customers and investors want to deal with. One of the largest and oldest European steel manufacturers came across these problems and was desperately trying to solve them. The company called upon the assistance of Goldratt Research Labs.

In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. GEs exciting opportunity brought on many new challenges, such as increasing production throughput and yield under evolving processes and uncertainties, and reducing manufacturing costs in order to gain market share. The GE Global Research Center sought out a powerful and flexible tool to analyze, not just the specific process, but the manufacturing system as a whole.

In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. GEs exciting opportunity brought on many new challenges, such as increasing production throughput and yield under evolving processes and uncertainties, and reducing manufacturing costs in order to gain market share. The GE Global Research Center sought out a powerful and flexible tool to analyze, not just the specific process, but the manufacturing system as a whole.

Conaprole, the biggest dairy production company in Uruguay, produces more than 150 SKUs in their ice cream plant, using five production lines, and up to five different packaging configurations for each line. The managements challenge was to be able to reformulate their plans in order to balance supply and demand and make sure they would avoid stock-outs in key products. They also sought ways to optimize the use of their production capacities.

With AnyLogic simulation software as the centerpiece, NASSCO utilizes a custom-built analysis system called the Large Scale Computer Simulation Modeling System for Shipbuilding (LSMSe) to provide highly detailed and accurate capacity analyses for both current production and potential new work.

Intel factories used a particular type of equipment that often broke down, which caused capacity constraints. These expensive parts were used in critical factory operations, and the repairs took significant time, so it was necessary to have extra spare parts on hand to avoid downtimes. Broken parts caused constraints at some of the factories while other factories over purchased spares.

The managers of one of the most important Italian yacht manufacturers needed a new, intelligent approach that would make the planning process simpler. The objective was to give the real production planner exceptionally rich planning information, which would allow the person to test and refine a plan before its implementation.

Engineering Ingegneria Informatica is an international specialist in the field of digital system integration. The company has more than 11,000 employees in more than 50 offices around the world. One of its flagship projects is an ecosystem of platforms that enable other technologies to interact with each other exchanging value, digitizing processes, developing digital services, and creating value for users, especially through the leverage of 8D.

The Airbus Group joined the European Union ARUM (Adaptive Production Management) project, which is focused on creating an IT solution for risk reduction, decision-making, and planning during new product ramp-ups. The project is aimed mainly at aircraft and shipbuilding industries. Simulation was chosen as a part of the ARUM solution, because it would allow the participants to reproduce the real production facility experience.

One of the largest turbine manufacturers in the world had a very promising five-year portfolio of gas turbines to produce and was planning an optimistic 30% net margin. The companys portfolio consisted of over 100 programs, made of 1000s of projects, with each project composed of a number of phases. Relying on its good past performance, the company built their strategic competitive edge around their reliability, enabling them to offer penalties for late delivery and bonuses for early performance.

One of the largest turbine manufacturers in the world had a very promising five-year portfolio of gas turbines to produce and was planning an optimistic 30% net margin. The companys portfolio consisted of over 100 programs, made of 1000s of projects, with each project composed of a number of phases. Relying on its good past performance, the company built their strategic competitive edge around their reliability, enabling them to offer penalties for late delivery and bonuses for early performance.

10 best simulation software 2021 (free and paid) - woofresh

10 best simulation software 2021 (free and paid) - woofresh

Autodesk is American software by Multinational Corporation which develops software to be used by engineers, architects, designers in construction, media, manufacturing and entertainment industry. It was released in 1980 in the United States.

It was best known for AutoCAD, but now it is used for multiple purposes. Similar to AutoCAD, Autodesk provides a free trial version to qualified students and teachers through the Autodesk Education Community. This software is very well known for color grading, visual effects, game development, and editing. You can also use it for film creation.

SIMUL8 simulation software is preferred for its cost reduction feature. This software aims at reducing cost and maximizing efficiency. It is used for planning, re-engineering, design, manufacturing, production, logistics and in-service systems.

This software is privately owned by American Corporation and was released in 1984. It specializes in providing mathematical computing solutions. Simulation software helps in predicting the action of a system. You can evaluate a new design, check for problems and test a model under various conditions to get output. The main products under MathWorks include MATLAB and Simulink.

It is comparatively less expensive to create and simulate models than building and testing prototypes. Hence, we can easily test different designs before building one in hardware. We can further connect and integrate the design fully in the system. It provides the user with time-based simulation, event-based simulation and physical-systems simulation.

This simulation software makes it possible to optimize and study any system in any industry. It is in a category of discrete event simulation tools developed by Flexisim Software Products. It was released in 1993 in the USA. It uses little or no computer code. Most of the work is done with arrays or drop-down lists and property windows to customize user-required models. Flexisim supports user-oriented design.

This software provides simulation and modeling to improve productivity across different areas. Simulations Plus provides solutions for biochemical, pharmaceutical, chemical, cosmetics and herbicide industries. It supports specific product software such as GastroPlus, MembranePlus, ADMET, DDDPlus, KIWI etc.

The user can choose from 1D, 2D or 3D as per requirements and obtain results as needed. The simulations require parameters and build libraries with integration. The software includes signal blocks, mechanics, fluid power and power transmission. It is used for designing, analyzing and modeling complex systems and transforms them into simpler solutions.

The software provides solutions by unlocking profitability, maximizing output, and minimizing costs related to design. It optimizes efficiency and increases productivity. It is comparatively less expensive to create and simulate models than to build and test prototypes. Users can easily test different designs before building the real thing in hardware. Afterwards they can connect and integrate the design fully in the system.

Exa software is a provider of Computer-aided engineering. The product it provides is called PowerFlow. It was released in 1991. The main aim of Exa Services is to provide you with solutions fast. Qualitative product people like engineers, designers and architects can rely on this software for accurate results.

It has top brands associated with it such as NASA, JAGUAR, TESLA, ONROAK AUTOMOTIVE etc. It is used in various fields such as aerospace design (in-cabin comfort), avionics cooling and system thermal management.

optimizing automotive manufacturing processes with discrete-event simulation - matlab & simulink

optimizing automotive manufacturing processes with discrete-event simulation - matlab & simulink

Before new vehicles leave the production line they undergo a series of end-of-line checks, including static and dynamic tests. In static tests, both technicians and automated test procedures run electronic diagnostics; in dynamic tests, technicians, testing software, a dynamometer, and other test stations work jointly to check the engine and adjust the suspension or other components.

Orchestrating and coordinating the workers, machines, and vehicles involved in end-of-line testing is a complex task. Many companies do not have a formal method for optimizing the process, instead relying on the subjective recommendations of senior engineers; on best practices from other manufacturing plants, which may have different requirements; or even on trial and error.

To maximize production throughput and capacity while minimizing manpower and waste, I developed a platform for running simulations with Simulink and SimEvents. The simulations are used to aid operational decision-making, forecast the outcomes of proposed manufacturing process changes, and improve the efficiency of Daimler production lines (Figure 1).

Several factors complicate the optimization of end-of-line testing.First, it is difficult to estimate processing times at any given test station.Variances in suspension, for example, mean that some vehicles require more time at the suspension adjustment station than others. Second, introducing new equipment that can complete tests faster can also disrupt established processes. Likewise, introducing new technologies into the vehicles results in new optional extras that require new test procedures.

Third, the sheer complexity of the process improvement options available makes it almost impossible even for an expert to predict how changes will affect overall process performance.Adding workers, completing tests in parallel, handling reworked cars, inserting buffers (queues) before each test station, permitting vehicles to cross between test stations, advancing the cycle timethe expert would need to understand the effect of every possible combination of these options to find the best configuration.

I knew that my simulations would need to take into account an immense amount of data. Often in simulation studies, data is exchanged between disparate software packages, risking loss of precision and completeness. With MATLAB and Simulink, I use the same environment for collection, analysis, and preparation of data as for the optimizations and simulations based on that data. Plus, I can accelerate processing by running analyses on multiple computing cores with Parallel Computing Toolbox.

Each test station generates a log file for each vehicle.If 1000 vehicles are tested at three test stations, then 3000 data sets are logged.For a single vehicle on one station, the log files contain up to 200,000 lines of information. Each log file contains only a small fraction of the necessary information, which includes vehicle details, the results of each test, and how long each test took to complete. To extract this data rapidly I create one DOS-based batch-file, call it for each log file, and distribute these jobs on each available core.

Before I developed the simulation, I needed to understand the current testing process.I collected the log files from every test station and analyzed the data numerically and graphically in MATLAB. I plotted histograms and bar graphs of testing times and vehicle variations, and performed statistical analysis to correlate these variables (Figure 2). I accelerated the parsing and processing of the log files by a factor of almost four by using Parallel Computing Toolbox to execute these tasks on a four-core processor.

After interactively exploring and analyzing the data, I created an interface in MATLAB to simplify common analysis tasks (Figure 3).I packaged the interface and the analysis functions I developed in MATLAB as a standalone Microsoft Windows application, PARSE (Process Analysis Routine for Site-overlapping Exploration). Created with MATLAB Compiler, PARSE enables my colleagues at Daimler to explore end-of-line testing data without installing MATLAB. PARSE also provides the database for the following modeling and simulation.

Most engineers create models for discrete-event simulations by linking together queue, server, entity, and other blocks from predefined libraries. The predefined elements in most simulation environments make it difficult to understand their fundamental functionality and their impact on the simulated system. I decided to take a different approach: I developed a MATLAB script to construct the SimEvents model programmatically. Building a model with SimEvents base-line elements has the advantage that all functional, logical, and strategical behaviors of the modeled system are known from the beginning. A programmatic approach makes it possible to run optimization algorithms that can both adjust model parameters and generate new models. It also enables models to be defined via a second interface that I built in MATLAB.

This interface enables engineers to define testing processes by specifying the number and configuration of test stations, the number of workers, and so on.The engineers selections are captured in a data model that the MATLAB script uses to generate a SimEvents model with station and worker subsystems (Figure 4).

In the generated model, which contains about 1500 blocks, worker and vehicle entities are brought together at each station with an entity combiner. The stations are represented by multiple single servers that represent individual processes within the station. The time spent at each station is calculated by an event-based random number block that uses an arbitrary discrete distribution based on the processed log data for that station.

Logical behaviors at the stations, as well as strategical control of the entities, are modeled using MATLAB scripts incorporated into the model as S-Function blocks.The model saves statistics from each station, including how many vehicles were processed, how long each vehicle spent at the station, and how much time it spent waiting between stations, as well as from peripheral processes such as delivery of vehicles, worker flow, and pause time. I used MATLAB to postprocess and visualize this data (Figure 5).

One of the first models I created using the interface and model generator simply replicated the existing factory setup with a database built on real-world raw data. I ran simulations of this model and compared its results with real-world results from the factory floor to validate the model and the model generation script.

Once I had a way to process and analyze log data and programmatically generate models, I could begin running systematic simulations to optimize end-of-line testing performance. In the simulations, the optimization algorithms make structural changes to reflect different factory layouts as well as parameter changes on individual test stations. I provided boundaries and initial values and then applied a pattern search algorithm in Global Optimization Toolbox to optimize for factors such as throughput, required production equipment, manpower, and waste.It would take thousands of experiments to assess all possible model variants. I could achieve the same results with a fraction of this number using the pattern search algorithm.

The SimEvents models enabled me to adjust boundary values to run what-if scenarios. I ran simulations, for example, to see how vehicle variations affected the time required for specific tests, enabling me to identify the variations that most affect process performance.

Traditionally, automotive manufacturers have expended considerable effort on shortening test durations, with little awareness of how end-of-line layouts affect the overall process. At Daimler, my simulations studies changed this. The simulations and optimizations I conduct with SimEvents provide insights into the influence of changes in plant structure. Before designing a new manufacturing plant, Daimler can now evaluate how factors such as the size of the provision area and buffers, the number of stations, enabling junctions, and personnel will affect plant testing performance.

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