I was tasked with with recommending the production configuration for a low volume, variable product, assembly line (500 to 700 units per month). The request seemed simple enough. I have configured many systems in the past, but all with little product variability. This assembly line was different. The product was a data storage unit used to house information captured from the internet. The product could be ordered with a wide range of configurations.
Problems with Variable Assembly Line
The wide range of configurations available for the product introduced significant complexity and variability in the assembly process, making it challenging to accurately calculate throughput and resource assignments. The assembly times varied greatly depending on the specific configuration of the product, and the quality acceptance rates also fluctuated accordingly.
In the past, standard tools were sufficient for calculating throughput and resource assignments, but the complexity of the product configurations rendered them inadequate. These tools could not effectively capture the intricacies of the assembly process for the product, leading to inaccurate predictions and suboptimal resource allocation.
Attempting to estimate the necessary resources and assembly times based on guesswork would be risky and could lead to significant delays, quality issues, and cost overruns. As a result, it was imperative to find new and more effective methods for accurately assessing the assembly process’s requirements and optimizing resource allocation.
Using Process Simulation for Variability
A business associate showed me how I could use process simulation to configure and run the assembly line in the computer before running the actual system. I process mapped the flow of the assembly line. The assembly line had 14 stations, each of which could have zero to eight sub-assemblies added to the enclosure. For example a chassis goes to station one and could have one to four power supplies attached. In station two, one of two mother boards could be inserted. In station three, one to eight disk drives could be added, etc. A chassis that had a zero in the assembly requirement would simply slide through that assembly station. The configuration of the product dramatically altered the assembly time and the reject rates.
The assembly line ended by going into two massive test chambers. One test chamber heated and the other cooled the product and both provided vibration. Failure rates were predicted to change based on complexity of configuration. For example, a computer with an extremely complex configuration would fail at a higher rate than a base model. Failed systems would be taken to a separate repair line and later re-enter the testing chambers. Failure rate calculations were entered using Excel like formulas.
The simulation software I was using allowed definition of the product configuration on the chassis. This means a distribution could be used to define how many of each part type a customer would request. Other distributions were used to select configurations based on market research. This made defining the model incredibly simple. A few well planned entries in to the process simulation allowed representation of the wide range of customer orders.