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The Fail-Proof Implementation Process

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The Fail-Proof Implementation Process

Every manufacturer has a problem on their hands and a goal in mind when they seek out a solution like Mingo. Better visibility into job tracking. Understanding how to improve inefficiencies. Increasing performance with fewer people on the floor. Whatever the problem, manufacturers just want to fix it… and fix it fast.  

What do we tell manufacturers when they start listing off their problems? Focus on one problem. Too often, we see manufacturers try, and fail, to solve all of their problems all at once. People, and in comparison, manufacturers, are not made for multitasking. When we focus on too many problems at once, it leads to decreased productivity, more confusion, more stress, and in the end, worse results. This is only exacerbated when manufacturers throw a new technology in the mix with the hope of it fixing all of the problems instantly. It’s a recipe for disaster. 

Instead, simply pick the biggest, most pressing problem, and drive the implementation and adoption of software like Mingo off of that one problem. What is the one thing in the plant, that if solved, would make the biggest difference? 

Strategic Discovery Leads to Fail-Proof Implementation

That’s where we come in. Manufacturing productivity software provides the right tools and the right expertise to collect, contextualize, and visualize the data that will point to a solution to that problem identified. 

From our first initial conversation to implementation, we’re helping you solve your problem by asking questions like, “What data needs to be collected to solve the problem? Can it be collected automatically? If not, can it be collected manually from an operator? How does that process become as painless as possible to encourage the operator to participate but not take away from their job? What do the dashboards look like?”  

These are all questions the discovery process will answer – and the implementation process will put into practice. 

Step 1: Only Collect Essential Data 

Don’t just collect data to collect data. If it’s not needed to fix the one problem identified, it isn’t needed. It’s as simple as that. 

Operators don’t want to enter data. They just want to do their jobs. Asking for the most important data is necessary to solve problems and improve the company. When the company does better, everyone does better overall, But too many data points make implementation and going live more difficult than necessary, from every point of view. 

Take it from us, too much data at the beginning of the implementation process gets messy and quick. Manufacturers get distracted from the goal. They try to boil the ocean. The unnecessary data will go unused, and inevitably, create more confusion than necessary. Think of the age-old adage, “Keep your eye on the prize.”

Only track the most important data that is key to solving the problem

Step 2: Don’t Forget About Your Operators

Once a process is decided, the next step is motivating the operators to actively participate in data collection. It’s as simple as explaining what’s in it for them, which typically means fixing the problems they face. 

On any given day, operators fight the machines to work correctly. They face tooling issues. They combat raw material delays. They wait for other jobs to be completed before they can finish theirs. They rely on maintenance to promptly fix machines. Day in and day out, operators face issues that put meeting their quota at risk. Operators are frustrated.

The issues are often communicated up the ladder, with hopes the supervisors are able to solve the issues, but the issues remain because historically there is no timely data to diagnose and correct. However, when a manufacturing productivity system is in place, the data exists. Supervisors see the recurring issues and find solutions to fix them, easing operators’ frustrations and allowing them to complete their jobs without constant barriers. Management monitors the solutions for efficiency. 

Word to the wise, don’t drop the ball here  – reviewing the data, applying solutions, and monitoring success are imperative. If that isn’t happening, the operator loses faith in the system, reverting back to old habits because they know no one is looking at the data.

In short, the promise of an easier, more seamless shift is what’s in it for the operators to engage with the system and adopt data entry habits. When operators are on board, the problem identified at the beginning of the manufacturing productivity process is one step closer to being solved.

Step 3: Learn and Adopt Positive Habits 

Habits are important to success, and what we’ve found is that it takes 3 full weeks of building positive habits to successfully implement Mingo. 

To do that, we work with the operators and supervisors, every single day, for 3 weeks – providing guidance on how to use the system, instructing what data points need to be collected and why, monitoring for any problems that arise, and checking the data. The point isn’t to be a micromanager but to help employees form the positive, efficient habits needed to make this software stick. 

If this is done every day over a 3 week period, the habits begin to stick. 

Step 4: Build a Dashboard to Find the Solution

At this point in the implementation process, everything is set up and ready to go live. Data is being collected. Operators are on board. Now is the time to buckle down and build the dashboards that will visually contextualize the data, bringing insights to the forefront. 

Mingo works with the team to build out the dashboards that will solve the problem identified at the start of this process. The dashboards are the final step in achieving the goal – reducing downtime, increasing throughput, reducing scrap, understanding where jobs stand, whatever the goal may be. 

The Mingo implementation team takes your problem, presents the data collected, and walks through that dashboard building process so building dashboards independently in the future is possible. The dashboards are built with the goal in mind, and most importantly, designed to be looked at every day so that the problem is solved efficiently and effectively. 

Then, there are the follow-up steps. Emails are set up to go out to key decision makers with an overview of key performance metrics. Daily production meetings are set up to review the previous day’s performance. Scoreboards are hung around the plant to provide real-time visibility into what’s happening. The data is entrenched in every process.

Step 5: Keep on Improving, Keep on Improving, Keep on Improving

What’s next? Solving the first problem is only the beginning. Throughout this process, other problems will begin to surface. Something new will pop up. The data might show there is more downtime, slower production, or different bottlenecks that weren’t obvious before.

Having contextualized data, well-built dashboards, and insight into the plant’s performance not only solves one problem but shows the overall health of the business, identifying areas for improvement. This provides the ability to react, pivot, and improve existing processes based on the things that are brought to the surface through manufacturing productivity software. 

Prove ROI Quickly

It’s easy to measure the ROI of manufacturing productivity software when the problems being solved are measurable. Job status, downtime reduction, or throughput improvements all become measurable, attainable goals.

What we’ve seen is that most customers find improvements within the first month that paid for the cost of the software. More than likely, something significant will be achieved within the first few weeks of going live, making the investment worth it.

The success of implementation is virtually fail-proof when problems are continuously solved and the benefits seen are significant.

Don’t Try to Boil the Ocean 

If we could impart one last piece of advice during this process, it’s to start small. 

No, this doesn’t mean starting with a handful of lines. When manufacturing technology projects are approached with that mindset, it creates duplicity. It causes multiple people to work with the existing and new systems to establish one source of truth, one big picture, and frankly, it can get very complicated quickly. 

Starting small should, instead, focus on the one KPI earmarked for improvement. What is the goal? What is the biggest problem? Take that one problem, collect the one data point across all lines in the plant, gather insights, and take what is learned to solve the problem. If that’s done across the board, one problem at a time, the odds of success increase while also providing the opportunity to develop healthy habits.

Then, when the time comes, expand the goal. Solve one problem, then another, and another, until there are no other problems to be solved. That is starting small. 

A fail-proof implementation follows these steps. It focuses on collecting only essential data, easing operators’ day-to-day frustrations, adopting positive habits, working together to build a dashboard and provide the contextualized information needed, and making constant improvements. 

And, most importantly, that is what you can expect from a team of experts like Mingo.

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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.