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Improve Sales & Make Better Supply Chain Decisions

What?

Generally speaking, production data won't help you "sell" more products. But, it could be used to make more informed decisions around sales and what products you run in your plant.

Below are a few ways you can use Amper utilization data to help with sales and supply chain decisions.

  • Target Sales: Look at Amper data to see which machines are the least utilized. Then focus your resources on selling more of the products that run on those pieces of equipment. If you can only sell products that run on highly utilized equipment, it may be time to consider a capex purchase.
  • Insourcing: Another way you can use Amper data in the sales/supply chain world is to insource your your work back to certain machines. Over the years, your company may have outsourced work to different internal sites or outside companies. With utilization data, you can review which machines can handle a higher workload, and bring jobs back into your plant. This can save in overall costs of goods and transportation.
    • Look at utilization across all machines and plants.
    • Identify which machines are operating at lower capacity.
    • Review if anything that is being outsourced can run on those machines.
    • Bring work back into the plant to run on those machines with low capacity.
    • Example: An Amper customer has several plants around the globe. When COVID hit in early 2020, they had to shut down several sites, but work was spread evenly across them. Instead of cancelling customer orders, they brought jobs from the sites that were scheduled to close into one facility. They used Amper utilization data to identify which machines had capacity to take on more jobs and were able to offload the work to those machines.

How?

Here's a simple process for you to do the same:

  1. First you'll want to calculate your cost of production. What are all of your costs per hour, divided by the number of machines?
  2. To get the most accurate number, you'll want to consider costs for direct and indirect labor. That means beyond just an employee's salary, but additionally: employment taxes, health benefits, and PTO. You'll also want to take into account things like: consumable raw materials, building costs, or monthly payments on rented machinery.

    If you don't want to do the math, a conservative estimate is to assume your cost of production is $40/hour. So, every hour a machine is making parts, or not making parts, will cost the business $40 at each machine. If you're running, you're ideally selling that product for a profit, thereby offsetting those costs. If you're down, you're just losing $40/hour/machine.

    👉 You can also use downtime labeling to justify improvements before making them. For more information on this, please refer to

  3. Then you'll use utilization data to determine where to improve (typically your highest running machines or bottleneck areas).
  4. Next, apply your cost of production to the hours being lost in your target area, and determine the opportunity cost (Example: a machine that runs 24 hours a day for 5 days a week will be available about 6,240 hours in a year. If they're running at 50% utilization, they will lose about $124,800 at a $40/hour cost of production).
  5. Now identify improvements to be made and execute! Review the examples below.
  6. Lastly, quantify your efforts and determine how much you've saved - celebrate!
  7. With Amper, any project can be quantified. After you make an improvement, use your cost of production to show how much you actually saved. Each sustained minute of increased uptime as a result of an improvement project will equal savings.

    -Look at the average hours of uptime before the project & multiply that by your cost of production (discussed in step 3 above).

    -Then do the same, after the project. Ensure there is a large enough sample size before finalizing your savings. The rule of thumb is to measure and compare the 30 days before and after the time period of sustained growth.

    Example: See the screenshot below. Do not include implementation time in measurements. Only measure 30 days before implementation and 30 days after implementation is stable.

    image