What Is OEM Analytics?
I’ll start this with a bit of an origin story. It is the origin story of Mingo and how we came up with the idea to build an OEM Analytics solution. Something that is still unique in the market today.
In 2015, I went to Microsoft Convergence in Atlanta. During this show, Satya Nadella talked a lot about the Internet of Things (IoT) with a heavy focus on industrial applications. There was one company called WASH that runs laundromats across the US and Canada. They were using IoT technology to understand when their washers would break, which models worked best in Wisconsin vs. California. They were doing this with Power BI and Azure.
At this time, I owned a software services company that implemented ERP systems for mid-market manufacturing companies and we worked with a lot of machine builders.
Watching this talk made me realize that every company that makes equipment needs this technology and needs to understand how their equipment is performing in the field. And, mid-sized manufacturing companies are not going to have the resources to build it themselves no matter how good the tools from Microsoft and AWS are.
The Idea of OEM Analytics
I wanted to build an application that would help machine manufacturers monitor machines at customer sites, provide better customer service, proactive maintenance, and build better products by analyzing this data.
Out of this story was born Mingo OEM Analytics.
So what is OEM Analytics and why build a product specifically for this?
First, what is it? We have a lot of content on the site explaining what OEM Analytics can do. To summarize, it helps manufacturing equipment companies do the following:
- Improve customer service with access to real-time data.
- Create new service plans using machine health and usage to drive revenue.
- Forecast tooling, consumables and spare part replacement and warranty needs
- Use data to build better machines.
- Prove the ROI on equipment.
- Offer your customers a solution to monitor all equipment in their plant.
Let’s look at how each one of these benefits helps you provide more value to your customers.
Improve Customer Service
This is one of the most powerful benefits of OEM Analytics.
Let’s walk through a scenario. Your customer calls in and tells you her machine is down, they can’t figure out why and their maintenance and engineering teams are unable to fix it.
Some OEMs will have the ability to dial in remotely to look at the system and see what happened. This is a reactive approach and all the data to diagnose the problem might not be available. Most companies will ask a few questions, guess at what the problem is, and send a tech out to fix it.
There are a couple of problems with this:
- While remote access is great, it only lets you look at the machine in its current state. You can’t see any history or usage patterns to help you understand what led to this problem.
- Sending a tech is costly to either the OEM if it is a warranty problem or the customer if they are paying for it. The tech will guess at what the problem is and bring the appropriate parts to fix that issue. If the problem is something different than he expected, he may have to come back for another visit, causing further lost production for your customer.
A better way is to have a continuous stream of data from the machine telling you how it is running, in near real-time.
As an OEM, you can have data on 100s of the same machine types which can help you see patterns.
When the OEM Analytics system predicts a problem on a machine, it can alert your customer service team to proactively reach out to the customer and help them solve the problem.
If the customer calls in first, the customer service team has all the data and history from that machine to diagnose the problem and suggest the solution.
By having the OEM Analytics system integrated with your CRM, ERP or field service software, OEM analytics can automatically create tickets for site visits and let you correlate those visits with increased or decreased reliability.
Service Plans, Preventative Maintenance
Many companies today provide service plans on equipment. But, what if you could take it further?
What if you could charge monthly or yearly for service plans on your machines? In those plans, you can include spare parts, tooling consumables, and regular maintenance.
Using OEM Analytics, you can easily create the types of service plans, increase revenue, and deliver value to your customers.
With preventive maintenance, you can look at machine health in real-time, automatically create service tickets and orders to send a tech on-site, or for customer service to call and check on a machine.
There is also a lot of talk about using Artificial Intelligence and Machine Learning to predict the failure of machines.
The companies that are best positioned to actually do this are the OEMs. They have enough data from enough machines to understand and predict failures.
Mingo has laid the foundation to create these models and use them to scan and flag data as it is received from the machines in the cloud or the models can be deployed locally on a server at the plant.
Using the data collected from the machines, they can also understand their warranty needs. How often do the components fail and how many do they need to keep in stock based on a customer’s actual usage rather than guessing?
Forecasting and Auto-replenishment of Tooling and Consumables
Most OEMs provide tooling or consumables for their customers. One of the big challenges is making sure that they have enough inventory. Another problem is that customers don’t always order in advance so there is the potential that a replacement part or tool is not in stock and the customer will be down while waiting for you to order it.
A process can also be set up to auto replenish tooling and consumables based on actual usage.
With Mingo OEM analytics, you can know how often they are changing tools, how many cycles have tool has been through to know when they have to be replaced. You can use this data to feed into your purchasing, production planning, and sales order process in your ERP system.
Building Better Machines
Using data from Mingo OEM Analytics, the product and application teams can understand how the machines they designed are actually being used. They can use that data to build equipment that lasts longer and better fits the needs of customers.
Customers buy machines based on application fit, return on investment, and total cost of ownership.
This data is usually calculated by visiting customers and running tests in the lab to understand when components are going to fail and determine the required maintenance on the machines.
This is a very error-prone and time-consuming process. It also relies on anecdotal information rather than hard facts.
By collecting data directly from the machines, you know and can prove the ROI and TCO (total cost of ownership of your machines). This will help you compete and increase sales.
Complete Plant Monitoring
I’m betting your customers don’t just buy machines from you. Most plants have machines from 5 or more vendors, and within the same vendor, they will have new – and also incredibly old equipment.
With Mingo, you can offer your customers a manufacturing analytics solution that can not only monitor the efficiency of your equipment but everything in the entire plant.
Summarizing “What is OEM Analytics?”
OEM Analytics is not something Mingo dreamed up yesterday; it is the culmination of 21 years’ worth of work in manufacturing technology.
Trends in the OEM market are changing fast and the companies that adopt tools like OEM Analytics are going to create a strong competitive advantage that will be hard for the laggards to overcome.
Check out the OEM analytics demo to see how Mingo can help drive more business for you.