The BEC provides discrete event simulation modeling to assist project teams in gaining process insight and dramatically improving their probability of success. Business processes are inherently dynamic, with varying degrees of complexity, interdependence and variation. Simulation is the only form of modeling that can evaluate process alternatives and arrive at optimized solutions for dynamic environments.

Video Example

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System Description

3 assembly lines operating at different takt rates producing more than 50 different product models.
Multiple sub-assembly lines produce more than 20 different sub-assemblies to support main assembly lines.
Sub-assemblies must be painted 1 of 3 colors before the final assembly process.

Changing manufacturing or business strategies can sometimes require a leap of faith. Simulation models can accurately predict the outcome of a new strategy before it is implemented. The insight gained from the modeling process can then be used to refine a strategy and serve as a baseline for the process implementation phase of the project.

The simulation process includes:

Business or manufacturing system definition
Simulation model construction
Analysis and recommendations

Benefits of the modeling experience:

Ability to visualize the plan
Build team consensus
Prepare for change
Identify constraints and potential problem areas
Diagnose problems
Accurately predict the output of the proposed system
Explore process alternatives
Optimize the system for various product mixes
Maximize resource utilization
Minimize capital investment

Typical simulation applications:

Minimize work in process: Simulation allows you to visualize the benefits of changing from "push" to 'pull" manufacturing strategies. Once you have chosen a lean approach, you must deal with process time variation and unplanned random events. This system variability dictates the need for some WIP (work in progress) as "time buffers" in order to maintain maximum product flow through the system. Simulation models provide a means to identify the critical time buffer locations and quantify the amount of WIP for an optimized process system.
Increase product flow: System velocity is determined by identifying the system constraint and either changing its location or managing it at its current location. A simulation model identifies these critical process locations and predicts the quantity of resources required to maximize their efficiency.
Reduce capital expenditure: Systems that are designed with static models must strike a throughput compromise. They work with average demands, so lead time performance and finished goods inventory are difficult to predict when dealing with fluctuations in daily and/or seasonal demand. A simulation model allows you to visualize your system's dynamics based on your demand history and thereby purchase only the equipment and resources that will satisfy your lead-time and finished goods inventory targets.
Evaluate new equipment: Often we must choose between equipment that runs fast but has a longer set-up time or needs more attention than a machine that runs slower but needs less attention. Which is the best machine for your specific order mix? A simulation model can use a three-month, six-month or a full year of historical data to provide the insight required to insure the correct choice. Typical model outputs can be product lead-time, finished goods inventory or cost per unit of production.
Improve operator utilization: Automation in conjunction with lean manufacturing environments has changed the way you must look at operator performance. With a system throughput focus, it is more important for operators to be available when you need them rather than striving for maximum operator utilization. Numerous operator tasks in an automated production system such as part checks, tool changes, part loading/unloading and set-ups can create a task backlog that can result in lower system throughput. A simulation model can provides a means to walk in the operators' shoes so that you can help him prioritize his work to achieve maximum system throughput.
Process synchronization: Multiple sub-assemblies associated with mixed-model assembly lines can become a scheduling nightmare. Process time variation, random breakdowns and process fallout can render a static model useless. Again, simulation models can be used to establish a shop-floor scheduling method that accommodates system variation. For example the model can also provide the insight necessary to set Kanban size and replenishment quantities that are within the upstream process capabilities or sequence sub-assemblies through a process constraint.

http://bec.msoe.edu/simulation/