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8 Valuable Lessons Learned from Years of IIoT Projects

This is a guest post written by our partners at Ectobox, an Industrial Intelligence solutions company. Their mission is to help manufacturing companies operate more efficiently and grow. If you have a business problem that needs to be solved, contact Ectobox to get solutions.

At Ectobox, we specialize in manufacturing intelligence solutions. Over some years we have learned a few lessons from trial and error. Many of these lessons we have learned from and made the proper adjustments. All of these lessons are extremely valuable to any manufacturer that wants to keep its foot on the gas, improve efficiency, and become an industry leader.

We are still always constantly learning. So far, we have come up with a solid list of 8 lessons that have proven to be valuable for us, as well as our clients.

Without further ado, let’s get right into it starting with the first lesson.

Lesson 1: ERP Doesn’t do IIoT / Manufacturing Analytics

Ideally, most people want to use one tool for everything. Many ERP companies want to sell you on this idea and make you believe that they can do it all. In concept, this is a great idea, unfortunately, this isn’t the case. ERP systems cannot do everything, and they cannot do IIoT.

ERP systems are definitely valuable, they are good at what they do, they just do not do everything. ERP systems are great for managing high-level transactional data for the business of manufacturing, to plan and optimize the supply chain. These systems are not built to manage any of the actual plant floor details. Especially details of what is happening inside the machines or what the operators are doing. IoT on the other hand can provide remote monitoring and operations, predictive maintenance and smart asset management, and autonomous manufacturing. 

This can be a hot button issue, and there are some gray areas.

Simply put, machine data, and any related data should be managed in a machine-oriented platform such as an IIoT platform. While that means that your ERP system and IIoT system should definitely be separate, the two platforms should ultimately be integrated together. The integration of the two platforms is where the real value comes for a manufacturer, adding in parts, jobs, and operator data.

Lesson 2: Have a Strong Project Manager

“Failing to plan is planning to fail.”

This one might seem a little obvious, there has to be someone in charge of managing the manufacturing analytics project. If you are working with a partner, you might find it easier to rely on your partner’s project manager who is managing their end of the project and ensuring that everything is organized and getting the manufacturing analytics implementation project done.

However, it is important to have your own project manager to oversee the project, even if you are working with great partners like Mingo and Ectobox. Furthermore, it is especially important to have a strong project manager if you plan on self-implementing an IIoT solution.

If you don’t, the project could get off track, wonder, whither, and die.

A good project manager should know the focus of the project and have a project charter. Even a simple one with just a few steps. He should have a level of authority, know the factory floor, have good relations with IT, hold people accountable, and really have the time to focus on the project.

Lesson 3: Solve the Business Challenge, Not the Technology Challenge

This is a fairly common issue we run into. From small-and-medium-sized companies all the way up to multi-billion dollar companies. Many people get wrapped up in solving the technology problem. They want to gather as much data as possible, focusing on the PLCs and sensors, and not really focusing on what these things are actually there to do.

This is doing the whole process backward.

You need to start with the business challenge, and then the technology can assist you in solving that business challenge. After that is established, you can begin to define what data is needed to create the information you need. Then finally, what technology is required to get that information.

Also, when trying to figure out where to start with using manufacturing analytics / IIoT to get visibility into the plant floor should list and score the challenges you have based on metrics such as complexity or cost, and value to the company compared to other challenges. This way you have clarity and you will be absolutely positive that you are solving the right problem, at the right time, in the right order.

Lesson 4: Be Data-Driven

With a manufacturing analytics / IIoT solution, you are not adding any new data that didn’t already exist in your plant. The data is already there. What IIoT does is give you access to this data that is already there and just needs to be extracted. It unlocks the data trapped inside your machines and other parts of the business. Knowing this, the goal is to use this data to:

1. Drive better visibility
2. Drive awareness
3. Drive data-driven decisions

The idea of using data in your business to drive better decisions and similar to how advanced data analytics is used in major sporting leagues like the NBA, NFL, and MLB. If any team decided not to use any analytics today, they would have little, if any success. All of their decisions are based on data, and there is data recorded for just about everything.

In basketball, there is data on where the player shot from, a shot chart, arm angles, arc of the shot, and speed of the ball. These are just a few, but teams base their entire strategy, game plan, and decisions on this data. It should be no different for manufacturers- gathering data and then basing their strategy and decisions based on what the data tells you.

Keep your focus on becoming a data-driven company.

Lesson 5: Strategy, not a Project

In lesson 4, we mentioned that you need to adopt the data-driven strategy from the major sporting leagues. The approach for using data to drive better decisions isn’t a one-time event, nor a single project and you’re done. It is not over once the initial project is completed. There is no end date to this. It is a long-term strategy. There may be an initial project deadline for connecting to your machines and operators and using that data to get the first real-time visibility into the factory floor. But you will never be finished with the IIoT strategy and becoming a data-driven business.

It will be a new way of manufacturing for you, just as it has stuck around for major league sports. They didn’t just gather a set of data, make a couple of adjustments and move on. They made advanced analytics and data-driven decisions the new normal, and a requirement for success.

Lesson 6: Trust and Enable your Operators

The first step here is to set up a good culture with your operators. This is what a good leader does. It’s hard work, but in the long term, it’s very rewarding.

Don’t hide the data and information from your operators. They want to be a part of the company and work in a good place. It’s human nature to want to be included and trusted.

Many of your operators know how to improve production, and cut back on downtime. They are often untapped resources because they aren’t trusted and aren’t given valuable information they could use for awareness and better decisions.

So, unlock your plant floor to its fullest potential by providing data to the operators. Give them the tools and information they need to improve your organization. Once you have created a good culture and your operators begin to buy in, you will be on your way to creating the most efficient plant floor possible.

Lesson 7: Think Big, Start Small

We left these two lessons for the end because they are shorter and a bit more well-known and obvious. However, they are no less important than any of the first 6 lessons.

Start with a pilot project, just 1 or 2 machines, no more. Then, you can go from there and scale the solution. Keep it simple…keep the project simple, and the process of early adoption simple. Prove the concept of connecting to and pulling data from machines, as well as prove the idea one can improve operational efficiencies and productivity with real-time factory floor data.

Lesson 8: Be Agile, be Nimble

You should be ready to adjust, change your strategy, and head down the path of becoming a data-driven manufacturer. Keep in mind what you will know tomorrow is going to be different than what you know today. And what you know tomorrow might steer you in a different direction. there is nothing wrong with that.

What you need to remember is to keep a clear and strong vision defined and keep that as a priority. If you iterate along the way you will get to where you need to be.

These 8 lessons can ensure you gave a good focus. They’ve been expensive lessons for many to learn, including us. And we’re all the better for it now. We hope that you have found value in each of these lessons and can learn from our mistakes instead of making them yourself.

This is a guest post written by our partners at Ectobox, an Industrial Intelligence solutions company. Their mission is to help manufacturing companies operate more efficiently and grow. If you have a business problem that needs to be solved, contact Ectobox to get solutions.

Bryan Sapot
Bryan Sapot
Bryan Sapot is a lifelong entrepreneur, speaker, CEO, and founder of Mingo. With more than 24 years of experience in manufacturing technology, Bryan is known for his deep manufacturing industry insights. Throughout his career, he’s built products and started companies that leveraged technology to solve problems to make the lives of manufacturers easier. Follow Bryan on LinkedIn here.