The fact that every manufacturer is different and has different goals, makes calculating ROI complicated. As one might imagine, it’s a head-scratcher from an organizational standpoint. This is why determining the problems afflicting the plant and deciding on the goals to fix those problems is important.
We have had customers who improved X issue by Y percentage, but the reality is that each manufacturing environment is so vastly different that say, 30%, might be lower than what’s actually achievable.
This is the crux of calculating ROI– attaining value from manufacturing analytics will depend on the problems and goals of each manufacturer.
Every manufacturer needs one thing: visibility.
That is the clear, unifying message for all manufacturers. When a manufacturer admits that having data and visibility into the plant is useful, they’re already 1 step ahead of the game. They want to monitor the machines on the plant floor for real-time data analysis to get a peek into what’s really happening.
So what do they do with that information once we give them access to it? That’s where we say, “You tell us.”
We give them access to the data they need in order to solve the issue themselves to the best of their abilities. (Hence the ROI calculator.)
Mingo is easy to implement, it doesn’t require specialized skills or the hiring of new employees, and it connects the whole organization from top to bottom with the right data each person in each role needs. We don’t determine the value of the software. Our clients do.
When we’re getting to know a potential client, we ask a lot of questions about how they intend to use the software. Those questions are equally as important for them as they are for us. If we can get them thinking about how they’re going to tackle the issues that truly need to be dealt with, we’re starting things off the right way and setting the tone.
Every business wants a return on their investments an investment in manufacturing analytics is no different.
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.
Soft Costs ROI Calculation:
Operator: $15 * (30/60) = $7.50
Supervisor: $25 * 1 = $25
Office Staff: $25 * 3 = $75
($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:
Putting it all together: (Daily Savings – Daily Software Cost)/Daily Software Cost = ROI
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.
Quality Improvements ROI Calculation:
Savings: 11 * $21 = $231
($231 – $57.69)/$57.69 = 300% ROI