In “Using an ERP for Product Development - Part I” we analyzed a variety of factors to determine the costs and benefits of launching a new product. In Part II, we will analyze a competitor’s product and find market gaps on which we can capitalize.
Now let’s try this for some competitive analysis. Let’s change our scenario a bit.
We have a competitor that makes a small, quiet, drone helicopter.
We can use an ERP to simulate how much it costs to make, and how many systems our competitor can make during a day.
We would use the same process described in “Using an ERP System for Product Development - Part I”. We would create a product out of their bill of materials:
We are making an assumption that there are two major subcomponents in their manufacturing. We think they assemble the frame (all the hardware bits) separately from producing the “brains” as a Power and Nav assembly. We have these broken out as assemblies that will be mated together for the final product.
Next, we can simulate their assembly systems.
We can get their address, then look them up on one of the popular web mapping sites. From here, we can make assumptions about how big their building is and how many people work in the office.
With a little bit of analysis, we can count approximately 20 cars in the parking lot, and one truck at a ground level door location. We can assume they have approximately 20 employees, and perhaps 10 work in assembly. (The balance may work in administration, sales, shipping/receiving or other functions).
Doing a bit of measurement, we can guess the building is approximately 4700 square feet. Let’s assume 1/2 of the building is available for manufacturing, so 2350 square feet for assembly.
We know that our competitor uses state of the art machine tools and robotic assembly tools that take up approximately 100 square feet. Assuming each machine has one person working with it, and some room between the machines, let’s round this up to 200 square feet each.
These machine and robotic assembly stations are set up to each do one task. When we try to build one with the parts in the BOM, we notice it takes 10 steps to assemble and test. We can assume that it may take 10 work stations, one for each step. This also corresponds well to our workforce estimates.
If we have 10 work stations, each at 200 square feet, this calculates to roughly 2000 square feet, and we have figured out that our competitor has only one production line. (There isn’t room for two in this space).
Let’s build the routing of work. Here is the first one for the Power and Nav Assembly:
Now, let’s run this example.
We will create a manufacturing order for 25 finished products. We are assuming that each have raw materials in stock. Based on the routings that are set up and the time budgeted for each step, we can get the following results:
We can sum up the total estimated costs for this manufacturing run and the capacity of the factory used:
We start analyzing the moves and quantities for each component needed to make up the order.
And look for sensitivity based on component lead times, and the min and max ordering quantity for these components.
We will repeat this process, changing one thing at a time, and running the different systems in parallel. We will determine probability distribution functions to model future system performance based on these variations:
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