Make Capex Decisions for New Equipment


Have you ever won a big sale but were afraid you didn't have the capacity to run the product? Or have you had the suspicion that you needed more machines for your current workload, but didn't have a way to prove it? One common way customers use Amper utilization data is to justify to purchase, or not to purchase, a new machine(s).


Below is an example of how you can use Utilization data for Capex decision making. In this example we've just signed a new deal for a large recurring order. These general steps also apply to situations where you are not increasing your workload, but suspect you don't have enough machine time for existing orders.

  1. Determine how many parts you're expecting to run.
  2. -Example: 100,000 pieces per year

  3. Multiply that by the expected cycle time to see how much time per week/month/year will be needed.
  4. -Example: 2 min/piece. Will need 200,000 minutes, or 3,333 hours.

  5. See how much time you have available to run on the machines by looking at utilization data. if you don't have enough available time on the machines that need to run the job, you can continue to justify new equipment.
  6. -Example: This part can only run on Mazak #1 which is available 8 hours a day, 7 days a week (2,912 hours/year). According to Amper data, it is utilized with existing work 70.5% of the time, leaving just 859 hours of available time to run the new product.


  7. Identify how much a new machine would cost.
  8. -Example: $100,000 for a used Mazak.

  9. Determine how much revenue the new business is worth, and calculate and ROI on the new machine.
  10. -Example: 100,000 pieces sold at $5/piece is $500,000 in revenue. ROI = 400% with a payback period of 2.4 months.

Real-Life Example: An Amper customer suspected that they needed more machines to run existing work orders. They followed similar steps to the ones listed above and actually determined that they had capacity, but weren't running their machines efficiently enough. Instead of purchasing new machines, they invested in more labor, machine upgrades, and setup reduction to handle the workload - bringing their overall utilization up from 26% to 70%!