Most of today’s automotive plants rely heavily on building vehicles using sheet metal assemblies. Often these sub-assemblies are built in a separate facility referred to as the stamping plant. Finished sub-assemblies are then shipped to various plant locations such as the body shop.

The body shop is where all the sheet metal components are assembled to form the metal body which is then sent to the paint shop. Lastly, the painted body is then sent to final operations, where the drivetrain, dashboard, and all interior & external components are installed to complete the vehicle. The stamping plant’s capability is extremely important as it may be supporting several plants vehicle programs. For example, several vehicle types might be using the same floor-pan as a reusable part. This is a cost effective way to get the most out of a particular product design. Therefore, it is critical to keep the stamping plant’s production ahead of the automotive plant’s consumption.

In other words, an automotive plant would never want to be waiting on stamped sub-components. This is where simulation can make the difference on running your stamping plants in the most efficient and effective fashion to meet market demand. Let’s take a detailed look at an example stamping facility that supports automotive operations.  You can download the accompanying simulation at the bottom of the page.

Understanding the Process

In this example large sheet metal coils arrive at the plant via trucks or rail. An overhead crane then moves the coils into a storage area. Specially equipped forklifts that people purchase then deliver the coils to the coil roller machines. The large coils are then rolled out, cut, stacked, then banded. A fleet of forklifts then deliver the stock material to the presses. In this simulation we are depicting two press machines. The presses are set up at a stroke rate to rapidly stamp parts. Finished stamped parts are then set into racks; racks may hold several alike parts. The racked stamped components are then set to welding operations. Here several parts will get robotically-welded together to form the completed sub-assembly. Finally, the finished goods are then racked, and digitally labeled to be shipped to the body shop according to order.

All of these objectives can make or break the entire automotive assembly process. Stamping facilities often change due to fluctuations in market demand: this can be seasonal, such as ramped-up stamped components for summer convertibles. Stamping plants also have to react to new product designs, which may require new costly stamping dies, and also balance the correct amount of steel coils on hand. The cost of steel fluctuates and can drive costs up so it might sound logical to stock pile coils when the cost is low. Although, having too much steel can lead to waste, as the risk of damage increases.

Steel coils must be environmentally controlled to avoid rust and contaminates. Therefore, simulating the differing market demands and logistics of inbound raw material and outbound finished goods is critical to the success of a stamping plant. As we can see there are many reasons to include stamping operations within your automotive simulation analysis.

Scenarios & Results

Let’s take a look at the trucks carrying steel rolls into the plant and how often they should arrive. The truck arrival pattern is a poison process with an exponential arrival rate. The model is set up to run 6 scenarios with different truck arrival rates. We can see that the maximum capability of the facility is 297.5 JPH. An arrival rate of 30 hours between the trucks delivering steel to the plant comes close to hitting maximum capability, but it also runs the risk of leaving nothing in storage.

The plant would want to avoid the risk of running out of inventory; they might prefer to always have at least 6 rolls on hand. By having trucks arriving 20 hours apart this system reaches maximum capability and allows for an average of almost 10 rolls in storage. Either way, we can see that running this type of sensitivity analysis can help determine what is the most robust schedule for delivering steel to the stamping facility.We can also use this model to define the optimal number of forklifts supporting deliveries between various line segments. The above results for truck arrivals were all based on the following optimized forklifts between lines. These are the values entered into the related dialog box:

[2, 4, 2, 2, 6, 2]

From this example we can see that there are multiple configurations that can be tested. There are many more steps in this analysis, like analyzing the rack sizes or evaluating if it would be more cost effective for a tug system to replace some of the high traffic areas where more than 4 forklifts are in operation.

These are just some of the future scenarios we can run as we learn more and more about optimizing our stamping facility. To find out more about how we achieved this in our simulation you can download the simulation and see the results for yourself!