---
title: Turn Scrap into Savings using Monte Carlo Simulation
description: Explore how an electronics manufacturer leveraged Monte Carlo Simulation to optimize supplier specifications, reduce scrap rates, and uncover cost savings without relying on physical testing.
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# Turn Scrap into Savings using Monte Carlo Simulation
[Alyssa Sarro](https://blog.minitab.com/en/blog/author/alyssa-sarro) | 7/10/2025
Topics: [Monte Carlo Simulation](https://blog.minitab.com/en/blog/tag/monte-carlo-simulation), [Minitab Statistical Software](https://blog.minitab.com/en/blog/tag/minitab-statistical-software), [Minitab Workspace](https://blog.minitab.com/en/blog/tag/minitab-workspace), [Minitab Solution Center](https://blog.minitab.com/en/blog/tag/minitab-solution-center)
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## How an Electronics Manufacturer Cut Costs with Smarter Supplier Specs Using Monte Carlo Simulation
---
Exploring process improvements across multiple suppliers can quickly become complex. But by using statistical experiments to identify key input variables and simulating outcomes with a Monte Carlo approach, teams can gain clear insights into potential results without the time and cost of physical testing.
Consider the case of Spaceman Electronics (as with all our use cases, this is based on real-life situations we have seen in the field, but Spaceman is a fictional company). To produce a part for one of their products, one supplier makes the core and then another coats it. Spaceman is then responsible for assembling the finished product.
In the past, Neil, a product engineer at Spaceman, has instructed the production line managers to implement a scrap factor of 7% to account for some units not fitting well enough into the product. A scrap factor is a percentage of the batch that you can anticipate will be destroyed or ruined during manufacturing or processing operations. For example, if you are making 100 units and have 7% scrap historically, that means 93% of the batch is good. You would divide 100 by 0.93 and round up, determining that you need to make 108 units to account for the scrap.
If Neil's team can reduce that scrap rate, which costs them $0.70 per unit, they can see cost savings upwards of $165,000 every year. It would be cumbersome and time-consuming to try to make improvements if they don't instruct both of their suppliers precisely. So, he sets out to simulate some potential results in order to make a well-informed decision before telling the suppliers what to change in their processes.
## Try Monte Carlo Simulation Now in The Minitab Solution Center
[](https://blog.minitab.com/hs/cta/wi/redirect?encryptedPayload=AVxigLK4TtpBapMxx4hFm8fqUiwfTTf8MXbnMCttOXw2k%2BGeG%2Bw1Ii1kx7OrqvRsLDkME%2Fbl5O%2Fm2I65YB7gsyeDW6Sgux3Kxnzo3ni%2BRVnO5CvUhMpXLOzzWljmeHkk%2FuIvLOEaQQEAoqqWd5DRPl4PSWX0lAj%2B%2F83NYNDpAMgZfv4aEjHjVX5p3gAijg2tM0d1XfnWT9WoBBTJWouP1x3oj%2BKfjYG2QzHIbB2oVa%2Bp&webInteractiveContentId=192450713662&portalId=3447555&hsLang=en)
**How Monte Carlo Simulation Works**
Monte Carlo Simulation uses a mathematical model of the system and the simulation provides expected values based on an equation defining the relationship between the inputs and outputs. Neil and his team planned and conducted a series of experiments in Minitab to determine significant factors in the process. They generated a Pareto Chart showing that transfer position and injection speed are the factors they want to focus on, and the equation they are going to use in [Minitab Workspace:](https://www.minitab.com/en-us/products/workspace/)
**Importing Your Model and Running the Simulation**
With the equation ready, Neil launches a new project in Minitab Workspace and opens the [Monte Carlo Simulation tool](https://support.minitab.com/en-us/workspace/help-and-how-to/monte-carlo-simulation/monte-carlo-simulation/). Instead of manually entering variables, he streamlines the process by importing the model directly from his statistical analysis. This allows him to quickly move from data to simulation, saving time and reducing the risk of input errors.
The equation automatically pulls in transfer position and injection speed as X inputs. He knows these factors have a normal distribution, so he selects Normal from the dropdown menu and then enters the means and standard deviations as well as the upper and lower specification limits. Then he hits the green Simulate button near the top and Minitab Workspace completes 50,000 simulations for the process within just a couple seconds:
The simulation tool automatically pulls in the key inputs and Neil defines their ranges based on known process data. With a click, the system runs 50,000 simulations in seconds, giving him a detailed view of how these variables affect performance.
Understanding the Results and Optimizing the Process
The initial simulation shows a process capability (Cpk) of just 0.48, well below the industry benchmark of 1.33. To improve it, Neil uses the built-in optimization tool to define a target output and explore input ranges. With those parameters set, the simulation identifies the optimal settings to meet the goal. In just moments, Neil has a data-backed recommendation to significantly enhance process performance.
With the optimal input settings in place, the simulation projects a dramatic improvement: a Cpk of 2.34. This far exceeds the industry standard of 1.33, indicating that the process will consistently produce in-spec parts and virtually eliminate defects, translating to major cost savings and improved reliability.
**Achieving Meaningful Results**
With clear data and optimized inputs, Neil and Spaceman Electronics is able to confidently guide his suppliers toward more precise specifications. This not only reduces scrap and material waste but also streamlines production thus shortening cycle times and improving overall efficiency. The result is a more reliable process, stronger supplier collaboration, and significant cost savings that scale with production.
*Thanks to Minitab Solutions Architect Antonio Vargas for the research and technical support on this use case!*
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