Calculate your return on investment from implementing smart factory software.
Calculating the return on investment (ROI) of using smart factory software can be complicated. The reason for this is that each manufacturer is unique and has different goals. This can make it challenging for organizations to determine the best course of action. To overcome this challenge, it’s crucial to identify the issues afflicting the factory and determine the goals necessary to solve them.
While some customers have reported improving an issue by a specific percentage, it’s essential to note that each manufacturing environment is vastly different. As such, it may be possible to achieve even better results than what has been reported. Ultimately, the success of using smart factory software depends on the specific issues and goals of each manufacturer.
Every manufacturer needs one thing: visibility.
The main idea for all manufacturers is to have access to data and visibility into their plant. By monitoring the machines on the plant floor for real-time data analysis, they can see what’s really happening. The goal is to use that information to solve issues on their own to the best of their abilities, as each manufacturer is unique and has different needs.
Mingo Smart Factory’s software is easy to implement, and it connects the entire organization with the right data for each person’s role. The value of the software is determined by our clients, as they know what they need and how they intend to use it.
To get to know potential clients, we ask questions about how they plan to use the software to tackle the issues that need to be addressed. This helps set the tone and start things off in the right direction. Every business wants a return on their investments, and investing in smart factory software and manufacturing analytics is no exception.
Every business wants a return on investment, and an investment in manufacturing analytics is no different.
But, success with manufacturing analytics doesn’t look the same for everyone. If you were to ask ten of our clients what they were originally looking to accomplish with Mingo, you might receive ten different answers.
In some companies, the focus is cost reduction while for others, it’s increased revenue. It’s easy to assume that machine availability will contribute to increased revenue, maybe it’s scrap, or it’s throughput, and it’s true that machine data analytics can help solve those specific issues.
What we’re finding, though, is that just because we have a customer who was able to increase their throughput by 30%, doesn’t mean that everyone else can do the same. It’s all relative.
Which makes you ask yourself, “What is the ROI for my company, specifically?”
If you know manufacturing analytics is the solution for the problems you’re facing on the floor, and you know it will prove to be successful for continuously improving, how do you prove ROI?
Below, we will look at each component in detail and determine how to calculate ROI in your factory. With formulas and examples, you’ll have that answer in no time.
ROI calculations can get very complex when trying to model exactly what happens in the business. As you go through the ROI calculator, remember that the formulas and examples serve as guidelines and are not conclusive.
Soft costs are things like labor required to collect and process the data from the plant.
For example, if the operators write down production totals and downtime on a whiteboard each hour, then a supervisor collects that data on a clipboard and gives the clipboard data to someone in the office who then compiles that data into an Excel spreadsheet, and emails the spreadsheet to the team.
How much time does that take for each one of these people each day or week? What is the effective hourly rate for each person including benefits?
It’s likely time consuming and costly. There’s ROI just in automating data collection because employees are now given the opportunity to focus on their specific role, not transport data throughout the plant. Any minor improvement you make on top of that is just an added bonus. Even for people that aren’t collecting data, hiring a specific person to do what Mingo does is costly. Either way, data is needed. It’s how you collect data that will deliver a positive ROI.
We’re leading the ROI calculator with soft costs because frankly, they are a constant for every manufacturer. These costs are also a surprisingly important pain point for many companies; they feel very frustrated by their inability to focus on solving issues because of how long they feel they have to spend getting the data in the first place.
Assumptions:
Soft Costs ROI Calculation:
Operator: $15 * (30/60) = $7.50
Supervisor: $25 * 1 = $25
Office Staff: $25 * 3 = $75
Total: $107.50
($107.50 – $57.69)/$57.69 = 85.34% ROI
85% return on investment might seem low in this example, but that is only for one shift. If you had two or three shifts, your savings increases by two to three times while costs remain the same.
When you combine cost-savings with all the other gains outlined below, it makes for a very compelling ROI. Consider soft costs a baseline you can add additional savings on top of.
Reducing downtime and increasing availability and utilization are relatively the same, but companies often think about them differently.
For example, machine shops are interested in utilization. If they can increase utilization, they increase direct labor absorption, paying employees to add value by making parts, which lowers their costs. It also gives them more flexibility about what they can run, when, and where.
A quick note before we move on to the calculation:
Many software companies talk about reducing downtime by 10%, but there’s 1 major problem with looking at it that way. Most companies without systems like Mingo have no idea how much downtime they really have. If you don’t have a baseline value of downtime, how do you know what reducing machine downtime by 10% would really look like?
This is why, in this case, manufacturing analytics is important to gauge increases in performance. (And, why proving ROI is simple.)
Our take is to look at it in two ways:
1. You can start day 1 with manufacturing analytics by setting up alerts or getting visibility into when a machine goes down. Both allow you to reduce your response time, effectively reducing downtime.
2. The other is looking at how much downtime would need to be eliminated per machine per day. For example, after looking at the contextualized data, you realize you spend 30 minutes per day waiting for the glue to get to temperature, and making that process faster has saved a ton of money.
Both reduce downtime and are much more real and concrete, than trying to reduce downtime without any tracking system in place.
Here is how to use the ROI calculator to determine downtime ROI:
Gains calculations:
Putting it all together: (Daily Savings – Daily Software Cost)/Daily Software Cost = ROI
Assumptions:
Downtime ROI Calculation:
Example 1: Let’s calculate the ROI when we eliminate 7 minutes of downtime per machine.
(7 * 10)/60 = 1.6667 Hours saved per day
$75*1.6667 = $125.00 saved per day($125 – $57.69)/$57.69 = 116.67% ROI
Example 2: Let’s calculate ROI when we eliminate 12 minutes of downtime per machine.
(12 * 10)/60 = 2 Hours saved per day
$75*2 = $150.00 saved per day($150 – $57.69)/$57.69 = 160.00% ROI
Reducing changeover time is very similar to reducing downtime because changeover time is a type of downtime. The formulas are the same, but the one difference is the input in the number of minutes on average you can reduce your changeover times.
For example, if you can reduce changeovers from 45 minutes to 27, the formula would look like this.
Reduce Changeover Time ROI Calculation:
((45 – 27) * 10)/60 = 3 Hours saved per day
$75*3 = $225.00 saved per day($225 – $57.69)/$57.69 = 290% ROI
($96 – $5.76)/$5.76 = 1,567% ROI
Quality improvements are typically easy to calculate. If you can reduce scrap by one or two parts a shift, what would that cost?
In the example below, we use the cost of the part at the time it finished on a certain operation. If you’re making a part that takes 3 steps to complete, the cost of scrap at step 1 is less than the cost of scrap at step 3. If you can catch scrap earlier in the process, it will reduce your costs.
Assumptions:
Quality Improvements ROI Calculation:
Savings: 11 * $21 = $231
($231 – $57.69)/$57.69 = 300% ROI
Find out why Manufacturing Analytics is so imporatant to improving your machine’s performance and OEE.
Read this guide to learn how to implement IIoT on the plant floor to solve business challenges.
OEE is the groundbreaking formula that forms the basis of manufacturing KPIs today. Learn how in this blog.
Learn how to assess problems with downtime and availability of machines, and what to do about it.
Built by people who know manufacturing, Mingo Smart Factory provides the 21st century “Smart Factory” experience that manufacturers need to grow in a modern environment. See how it can help you drive revenue.
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