This factory might be efficient, but Smart Factory technology can take it to the next level.

Smart Factory: What is it? What is the Value?

Smart Factory: What is it? What is the Value?

What is a Smart Factory? In simple terms, a Smart Factory is about 1) eliminating paper and collecting data digitally in the factory, and 2) providing that data and information to everyone at all levels of the factory so they can operate the business on that data and information. There is a lot more that goes into it. However, those are the key differences between a factory and a smart factory. It is the digitization of manufacturing. The idea is for people to consume information from software instead of paper.

Many manufacturers have been around for decades. Having someone go through the plant floor, and collecting data with a pen and paper would be a common practice. This has worked for years in the past, but what is the value in changing now? Why do you need to change the way you run your company now if pen and paper have been working for you for the last 30 years?

What is the Value in a Smart Factory?

Established manufacturers that have been around for decades, are progressive, open to new ideas, and want to survive and thrive in the future are working towards becoming a digital factory. This is because in all honesty, if you don’t you will not progress in today’s world, and you will probably find yourself on a downward slope. Manufacturing is becoming more and more competitive with costs rising, operators more difficult to find, customers are more demanding, and more. Factories are trying to grow and at the same time, they’re working to do more with what they have.

Also, keep in mind that data from an ERP won’t be able to help here. Sure, ERP systems are very valuable and a key to a factory’s success. However, ERP systems are transactional systems that operate primarily in the office tracking customers, orders, inventory, inventory, and the like. However, the real “business” of a factory, the activities that give the factory its purpose are happening on the factory floor. So many companies are simply not tracking this data, or if they are, they’re doing it on paper and spreadsheets.

So what is the value in moving towards becoming a Smart Factory? Why does the way you gather data matter so much?

The value is real-time instant information that you get and how it can be shared with everyone in a smart factory. You will know right that second:

  • What are we running?

  • When did we run?

  • How were we running?

  • What did we produce?

This is the information that you really want to know. This information gives the operators and leaders the data they need to know to, again, do more with what they have. They will gain real-time, accurate visibility into what’s going on across the plant floor. They will be able to more easily find and eliminate inefficiencies. They can find bottlenecks, improve schedules and on-time deliveries, find the cause of scrap, and more. It can be available to you instantly and accurately. You will not get this data the next day, or the next week after the production cycle has already been finished long ago.

That is the huge value in having a Smart Factory. Old processes just do not work anymore. They might have been best-practice at some point in the past. With the on-going advancement of technology today, having a Smart Factory that can gather Smart Analytics is quickly becoming the industry standard.

The Teslas and Amazons of the world are companies that are way ahead of the game because they have been using this technology from the start. These are huge companies with huge budgets and revenue, so it is unrealistic to expect everyone to be able to afford the same type of systems that these companies have in place.

 

But let’s look briefly at what they did and are still doing. They have created cultures around getting data digitally and sharing that data and information across the organization. Mandates established by their leaders have forced, or better yet, enabled the people in those companies to look deeper into the organization, better understand how it’s running, and do all of that in real-time….and that data doesn’t only come from the ERP system.

 

As new Smart Factory technology is being developed (i.e., IIoT or the Industrial Internet of Things), it is becoming more readily available to small-medium-sized businesses over time. This is now why today Smart Factory Analytics is far from being a technology only available to the top 1%, it is essential for any sized manufacturer to remain competitive.

A factory that could benefit from implementing Smart Factory technology.

More Smart Factory Benefits

Moving your factory from paper to gathering data digitally is beneficial for so many reasons. It is a lot more than just a different way to record data, as we have established so far. I have touched on the topic briefly already, but the power of real-time analytics is a game-changer.

If you have access to real-time metrics, you can make adjustments on the fly. You will have a dashboard of different metrics that you can see, and use that information right then and there to improve your efficiency.

Think about a situation where you didn’t have real-time data to work with, and you had data recorded manually at the end of the day. The next day, you see this data, and you make an adjustment.

This process could be so much faster, and so much more efficient! If you had real-time data and smart factory infrastructure, you would have been able to see the data and make an adjustment. You would make that adjustment right away when you see the information. Then what? Then you see the new data and you might have to make another adjustment. This process keeps going on and on.

The number of adjustments you can make with a smart factory system in just one day could take months if you are working with pen and paper recording data every other day or every week.

These will also be very confident decisions being made, that can yield instant results backed by the data that the machine is continually giving you. Don’t base decisions off of data from last week, base it off of data from right now.

We have also established that humans make mistakes. Using a person to gather data means you get data later, and no matter who it is, there are going to be some errors from time to time. Stop spending so much time gathering data, let the machines do that, and spend your time analyzing data and learning your machines to make efficiency improvements.

Big Data Analytics

Big Data Analytics is another topic that ties in with the smart factory idea. Big Data is exactly what it sounds like: large data sets that can number into millions or even billions of rows. Is someone going to write all of this down on paper? Absolutely not.

These large amounts of data are only available with a smart factory. You are never going to gather this much data manually, but computer systems can do this on a regular basis. You can have access to extremely deep sets of data, that can tell you everything you need to know about your machines.

Analyzing all of this data together can prove to be very valuable, and help especially in these areas:

  • Predicitve Machine MaintenanceThis factory might be efficient, but Smart Factory technology can take it to the next level.
  • Machine Learning
  • Product Quality
  • Demand Forecasting 
  • Customer Experience

 

This is just the start, big data analytics provides us with insights into certain metrics that were not very easily measurable before. It all starts with turning your factory into a smart factory.

Conclusion

At the end of the day, old strategies are getting outdated. The world is getting digitized, and the manufacturing world is no exception. Smart Factory systems are the future. Technology is getting smarter, and also more attainable for everyone. Jump onto the new wave now and kick your company up a notch. Implement a smart factory system now so that you can get the most out of your equipment, and make confident data-driven decisions.

Improve your factory by implementing Lean Principles.

Should you consider Lean Manufacturing?

Should you consider Lean Manufacturing?

Lean manufacturing focuses on creating an efficient, effective organization. Properly using Lean principles and techniques as a manufacturer allow that manufacturer to reduce waste, create more customer value, increase revenue and profits of the company, and more.

In this blog post, we will discuss what it is, where it came from, why it’s valuable, and tie in how IIoT can significantly increase the impact of Lean efforts.

What is Lean Manufacturing?

Lean Manufacturing was just a list of good manufacturing practices at the start. However, over time, it has had a large impact on the way manufacturers run their plants. It is like a set of guidelines that affect the way we work, what we learn, and how we manage everything. There are 5 main principles within Lean Manufacturing:

  • Define Value

Value is whatever someone is willing to pay for something. This is not one specific number, it will vary from person to person. Take time to discover the actual needs of your customer. A lot of times, a customer may not know exactly what they need, or may not be able to express it the right way. It is then your job to figure out your customer’s needs. Some ways you can reach this goal is conducting interviews or looking at case studies and analytics. Then, once you have defined what your customer needs, you can then determine what exactly they want, how much, when, and what fits their price range.

  • Map the Value Stream

The second principle in Lean Manufacturing is identifying and mapping the value stream. Once you have found the value, here you are using that value as a reference point. The first step is to review and diagram the flow of product through your plant and document the processes (manual and automated) that are executed on that production. Data should be recorded for each process including cycle and takt times, wait times, and others. Then, we need to evaluate all of those activities in and determine if each activity is adding to this value. If an activity does not add to the value, it is considered waste. Waste can be categorized in two different ways: Non-value added but necessary, this should be reduced as much as possible; Non-value added and not necessary, this should be completely eliminated. By following this principle, you can make sure the customer is getting exactly what they need. You are also making sure that your plant is running as efficiently as possible.

  • Create Flow

After establishing the value, and mapping the value stream, next you need to create flow with the remaining steps, avoid delays and interruptions. You can do this by making sure your employees are skilled and adaptive, break down each step, and make sure everyone is on the same page. These are just a few of the many things that you can do to keep production consistent and running smoothly.

  • Establish Pull

One of the biggest wastes in any production system is inventory. A pull-based system is meant to limit inventory as much as possible while ensuring that the materials needed are available right at the time you need them. This in effect, limits waste while also keeping a smooth workflow.

  • Pursue Perfection

Throughout the first four steps, you are eliminating as much waste as you possibly can. This step is making Lean thinking and continuous improvement a part of your culture. Everyone in the company should strive for perfection and deliver products to the customer based on their needs, and the first four principles. Developing a mindset that focuses on developing a better organization and finding ways to make more efficient processes is the goal.

Where did Lean Manufacturing come from?

To really understand this, we need to go back to the start of modern manufacturing. Henry Ford was the first person to start implementing mass production. In Ford’s production, there was a continuous movement of parts, it was not just one one-off production. This was a massive breakthrough at the time, and it allowed for the production time for a part to be just a few minutes. That is a huge difference from the few hours or days that it previously took.

Soon after, Sakichi Toyoda started to produce vehicles. This was the company that later became known as Toyota. Toyota spent a lot of time studying Ford’s production process. The problem was that the market in Japan was not the same as it was in America. The Japanese market was a lot smaller, and a lot more diverse. They knew that Ford’s production system was very efficient for mass-producing products, but Toyota needed to have a bigger emphasis on the customer’s voice, and what they really needed.

What was Toyota’s solution? They made their own new production process, still taking a lot of ideas from Ford. Toyota needed to make different sized machines for manufacturing different products. This introduced the idea of self-monitoring machines and processes. This was a refined version of Ford’s process. It allowed for more variety which was cheaper, faster, and provided higher quality. This became a well-known manufacturing process, commonly referred to as the “Toyota Production System” (TPS). This was the beginning of Lean Manufacturing.

Why does Lean Manufacturing matter, what’s the Value?

There are numerous benefits to Lean Manufacturing. If you use the principles of Lean Manufacturing you will have a more efficient plant, with less waste, and increased productivity. The overall idea is continually making your production process focus on what the customer needs, how to get there, and nothing else.

These are a few areas that will be improved if you are properly implementing Lean Manufacturing Principles.

Product Quality

Product Quality will be improved, the improved efficiency will lead to more innovation, and employees will have more time and resources to focus on product quality.

Sustainability

Creating more profit, in less time, with less waste will only lead you in the right direction to a long-lasting and growing business.

Profits

Using Lean principles, you get more productivity, smarter employees, better quality products, and better processes with continuous improvement. This allows you to generate more revenue and more profits.

Lead Times

You will develop a better understanding of your processes, and better understand how your machines operate. This gives you a better perspective so you can respond to market fluctuations and other unforeseen circumstances. The result is better lead times and reduced delays.

Employee Satisfaction

Employees will be more involved, and they will have a better understanding and expectation of what they should accomplish and focus on each day. They will be able to produce more and have more insight. This means happier, smarter employees.

How Does IIoT Tie in?Implement Lean Manufacturing Principles in your plant.

We know that there is a long list of benefits to Lean Manufacturing, but how can IIoT help you get these results?

Lean uses data to determine how well a system is running before and after it is improved. Much of that data collected and analyzed is done manually.

If you are going to make all of these changes to your production process, you need a lot of data to base your decisions on.  An IIoT system automates the gathering and analysis of that data. Going through and gathering all of this data manually might have been a common process some years ago. Today, we have systems that can automate this process for us entirely.

The data available from IIoT solutions enables us to drive the principles of Lean manufacturing. Remember, your goal is to make the most efficient and effective processes possible. An IIoT system can gather more data, in a much faster way compared to having a person collect manually. You can actually get data in real-time, and start making improvements right away, as opposed to looking at a datasheet from yesterday or last week. You can have real-time metrics, and metrics from weeks, months, even years prior right in front of you at any time.

This expedites your manufacturing process, while also giving your employees a deeper understanding of the machines they are working with, and more time to focus on improving their workflows, machines, and production inefficiencies.

Want more information on how IIoT and Lean Manufacturing? Check out our other blog post here, that solely focuses on that topic.

Industrial factory that can implement Industry 4.0 infrastructure.

What is Industry 4.0, and how does it help you?

What is Industry 4.0, and how does it help you?

Industry 4.0 is the fourth Industrial Revolution. Basically, we are taking everything from Industry 3.0, and optimizing it with modern, smart technology. Industry 3.0 allowed us to replace processes formerly done by humans, with machines and automation with some robots. This was a massive breakthrough, but today it is not as simple as just having the machine. Manufacturing products are becoming more expensive and competitive. Companies need to find new ways to compete and thrive. Industry 4.0 brings a whole new dynamic to the manufacturing industry. Having these nice, big machines that take the load off of humans in a much more efficient way is great. However, companies need to eliminate inefficiencies, increase production capacity and throughput, increase the flexibility of their production capacity, and in general do more with less. That is exactly where Industry 4.0 steps in. 

How did we get to Industry 4.0?

Industry 1.0

I will briefly go over what has happened in the past, that has lead up to Industry 4.0. Of course, it all started in the first industrial revolution when industrial manufacturing was first starting up. This began in the 18th century through the use of steam power. This was the first time that engines were used to manufacture products. Before this, it was spinning wheels, and using muscle as the common manufacturing process. The use of steam engines massively expedited the whole process.

Industry 2.0

Then comes along the second industrial revolution. This took place in the 19th century and brought along the discovery of electricity. This also leads to the beginning of assembly-line production. Mass producing separate parts of a product on an assembly line was a huge change in the way everything was made. Quickly, it proved to be a much faster process, at a significantly lower cost.

Old factory industrial manufacturing assembly line.Industry 3.0

The third industrial revolution was fairly recent, opposed to the first two revolutions that took place before any of us here today were even born yet. Industry 3.0 is almost like a slow beginning to the evolution of Industry 4.0. This was the introduction to mixing modern technology with manufacturing. Implementing robots, and partially automated systems to take the work off of humans. Manufacturers were starting to use the software, and computer programs to take control of parts of the process.

This is what begins to start Industry 4.0. Humans are continually having less responsibility within the actual manufacturing process. Machines are getting smarter to the point where humans are doing less hands-on work, and more monitoring.

Industry 4.0

The first three industrial revolutions all had to do with physical advancement. That is the major change with Industry 4.0, there really is not any specific new physical advancement that drives it. Industry 4.0 revolves around connecting the physical with the digital. There is a large emphasis on data collection and access to real-time data. It allows plant managers and business owners to better understand what is happening, and make adjustments based on data that was not available before. This gives you more control over machines. It lets you make a more efficient manufacturing process across the whole company. Not limited to just one machine, one plant floor. It creates a network of data across multiple plants, in multiple locations.

 

How does Industry 4.0 Affect Manufacturers?

Industry 4.0 brings higher levels of automation, and a lot more data to gather and analyze. Connecting all of these systems together is greatly beneficial. It gives the manufacturer a very accurate and detailed look into the plant floor to examine what is happening. This is important only if you are gathering the right data, analyzing it correctly, and making the proper adjustments to allow this data to really help you.

With machines being smarter and requiring less human interaction, what does this mean for the workforce? This might lead you to think that a lot of people might lose their job to a machine that does not need them anymore. The real answer is just the opposite. Although people do not need to be as involved in running the machine, people gathering and analyzing the data are needed more now than ever before. Machines can produce for the most part on their own. Industry 4.0 and IIoT systems (Industrial Internet of Things) can gather data telling you every little thing that is going on in the inside of your machine. People need to look at this data, and as they learn more about the machine, make the necessary adjustments to improve the efficiency of the plant.

How does Data make a Difference?

We have established that Industry 4.0 brings a digital aspect and detailed data to the manufacturing industry. What exactly does this mean for you? The goal of your business is to grow to increase profits, and generate more revenue. How does the implementation of Industry 4.0, and collection of data bring you more money at the end of the day?

There are endless ways to analyze data and find a way to make an improvement with that information. When you buy a machine, they will tell you that this machine can make x pieces in x amount of time. Usually, this number looks really great, how often does that really end up being the consistent production numbers of that machine? Never, because that is a perfect world scenario. There is never going to be a perfect situation, with no problems or setbacks. Also, a machine is never really running at 100% all the time.

How are you ever going to know, or be able to give a really accurate estimate to a customer when they say that they need x products in x amount of time? How consistent can that number be with no analytics to back it up? You need to gather data and become a data-driven company. An IIoT system will collect data on all of the machines, and you can use that data to calculate the OEE of a machine so that you know exactly how productive each piece of equipment is.

Now you will develop an understanding of how much product your machines are actually producing. First of all, now you have the substance to give customers accurate estimates that are backed by data. You can also use the data to improve your OEE now that you can see that information.

A typical OEE calculation for a manufacturer is around 30%. If one machine is supposed to be making 1000 pieces per hour, it is now only making 300. Let’s say that this machine is running for 8 hours per day. That machine should be making 8000 pieces, but it is only making 2400. Over the course of a month that is 112,000 pieces that were not produced. Of course, that number is in a perfect world, 100% OEE is next to impossible, but you can get very close to it, and a whole lot closer than 30%

After a company just sees the information showing them their OEE in these low numbers, within2-3 months that number jumps up to 80%+ a lot of the time. Just by being able to see the data, and start to understand what areas can be improved.

Assembly line that can use Industry 4.0 technology to improve efficiency.

How can Industry 4.0 cut Downtime?

We covered how data can help make machines more efficient, but downtime is undoubtedly one of the biggest challenges to overcome. So how can data improve your downtime?

Again, the more data you can gather, the more you will understand about your machines. Learn about how they work instead of just observing from the outside.

As we gain this understanding of how the machines work we will learn tendencies, start to see patterns, and learn what is actually causing the machines to stop working. Now we know what is breaking, and how the machine got to that point. We can use this information to better schedule preventative maintenance. So now at this point, we know how, and why a machine is breaking, and also learn when to address these problems before they happen.

You will have the ability to predict the optimal time to schedule maintenance when your machine needs it. All of the data you need will be right in front of you to make that decision.

This will help with planned downtime because there will be no unnecessary and excessive maintenance. However, you will know exactly when your machine needs work. You will know what part needs work, and you will see the data that tells you how it got to that point. So not only will you have a quick solution to your initial problem, but you now have a record of what happened and you can use that to improve the process so that it doesn’t happen again. Eliminating issues one by one with contextualized data so that a problem is fixed, and you know why.

This also applies to changeover times. A lot of companies don’t realize how much of an improvement they could be making in this area. You might think you have a really efficient process, but once you see the actual results you will see what areas can be improved to have the most efficient plant floor possible.

Now Industry 4.0 technology will also reduce unplanned downtime. Since your planned downtime will now be more efficient and effective since you now have the data to fix the right parts, at the time they need it. Of course, the goal of preventative maintenance is so that machines run properly for as long as they can without breaking down.

Conclusion

The big concept within Industry 4.0 is ultimately mixing physical machines with digital technology, extracting the data from deeper within the operations, and using it to run the plant more efficiently…to get more out of what you have. Industry 3.0 produced a lot of these machines. Those machines have had data “trapped” inside them for many years, and that data has rarely been used. Industry 4.0 will allow a manufacturer to use them to their fullest capacity by using the data to use them more efficiently and effectively. It is becoming absolutely essential to be a data-driven company. 

To be competitive in the next five years, transitioning into an Industry 4.0 infrastructure will be more important than ever for any sized manufacturing business. As technology advances it is becoming more affordable than ever. This means it is available to more people, but also means more of your competitors will be moving in this direction.

At Ectobox we are experts in the industry and would be more than happy to assist you in finding the right IIoT solution that fits your specialized needs. Contact us here to get in touch, we would love to answer any questions you have.

Digital 360 infographic
Insiders View on Reducing CNC Machine Downtime

Insiders View on Reducing CNC Machine Downtime

Exec Summary 

A leading manufacturer of specialized metal products for the aerospace industry had significant challenges with visibility into their finishing shop. The issues were primarily around downtime and bottlenecks. They were having a negative impact on the plant’s production throughput, revenue, and on-time delivery. After a few conversations and a Digital 360 review, it was clear a Manufacturing analytics / IIoT solution was required to monitor the machines to get accurate, real-time data from the plant. 

An IIoT solution was put in place to monitor 23 presses and CNC machines. The results of the solution were very valuable. They were able to get accurate, real-time data which enabled them to find the causes of downtime, do some root cause analysis, apply corrective actions, get a nearly 40% reduction in downtime, and set themselves up for addressing more valuable challenges. 

Lessons Learned

Introduction 

We’re doing something different here. We are going to talk transparently about a customer and how they were able to solve some challenges in their plant. In this situation, we will talk a little about the IIoT solution in place. However, most of this will focus on details typically not discussed regarding IIoT / Data-Driven Manufacturing solutions.  

Specifically, we’ll discuss topics including specific steps the manufacturer took to solve their challenges and their learnings, how we and the manufacturer worked together on certain aspects of the pilot project, the value the company got from the project, and even some hiccups along the way on both sides. Our hope is that you’ll find this valuable and can learn a thing or two from the discussion. We will highlight the lessons learned for the manufacturer which you might find valuable. (We have removed the names for sake of confidentiality.) 

Background 

The company is a specialized foundry/manufacturer that produces specialized ferrous and non-ferrous products from foundry mold to the final finished product. They have a finishing area comprised mostly of CNC machines. One of the final stages of processing the metal is to finish the products in 3- and 5-axis CNC machines.  

CNC Machines that can be equipped with a reliable manufacturing analytics / IIoT Solution

 

Challenge 

They knew they were having issues with production. They determined this by looking at the number of products going out the door, the number of operators and machines, and the estimated cycle times. The team felt they should be getting more production than they were, but didn’t have any strong data to back up that argument.  

The operators were reporting that they were working full tilt. So, some team members were recommending to the president that they buy another couple of machines and hire some new operators. However, there were a couple of instances where the president walked around the plant with one of the manufacturing engineers and asked a critical question…why were so many of the machines idle when he walked by. It was then that the manufacturing engineer realized they needed better data. 

Data could be obtained manually by putting together a spreadsheet with dropdowns for downtime reasons, and record data manually. But who will record the data reliably? As a manufacturing or process engineer, you can’t be there yourself. Our COO here at Ectobox, Dave Grafton, in a prior position, worked at a plant that didn’t have any manufacturing analytics / IIoT solution at the time. To solve a certain challenge in the plant he had to spend a full 3 days with the operators on the plant floor to understand what’s going on. Obviously, a manufacturing or process engineer can’t spend 3 days gathering data for each of the problems in the plant. There needs to be an automated way to get the data. 

If the manufacturer is truly working to become a data-driven company, they should be able to provide the data to the smart people in the plant who are solving the problems…the manufacturing engineers, process engineers, Lean and continuous improvement teams, and also the operators. 

With an IIoT solution providing up to the minute data directly from the plant floor and operators, the plant engineers can quickly iterate through issues, solving problems, and moving to the next problem. 

The manufacturing engineer at this plant realized getting operators to record more data manually wouldn’t work. She also realized the idea of spending anywhere between 1 and 3 days full-time on the plant floor also wouldn’t work. 

Her situation was also compounded by multiple other issues in the plant (no plant has only one issue, as we all know). The other issues included scheduling and on-time delivery challenges, bottlenecks, certain machines experiencing some excessive downtime for unknown reasons, a need to rearrange the flow in the foundry, and there were several Lean Kaizen events to finish out. So what does one do? What do you do in this kind of situation? 

Where to Start 

In this kind of situation, the first thing to do is step back and clarify your priorities. To do that one should use a process that answers these questions: 

  • Where should time be spent to get the biggest value to the company?  
  • What challenges in the company have the lowest complexity and highest value?  

These questions can be answered by spending a bit of time (not a lot) with a small team going through a straightforward process that we call a Digital 360 (aka Digital Operations 360 Review). The end of the process produces a valuable, validated roadmap of tasks and projects to tackle. This process gets all information about the challenges on the table and produces a validated and valuable roadmap of what challenges to tackle next and how.   

This clarity on the challenges for this manufacturing company and the resulting roadmap was important. There were a lot of issues, a lot of competing interests for the manufacturing engineer’s time and for the company’s other resources. Going through this process was our first recommendation to this company when we were introduced to them. 

We’ll admit, it can be a challenge to pull a small team of leadership and some team members together, and focus on this process. However, once it’s done the value of the process and the roadmap are huge. 

Here is a quick summary infographic on how it works. Digital 360 infographic, blueprint IIoT Solution

The result of the Digital 360 was a roadmap with the priority being a pilot project to monitor machine utilization with downtime reason codes. Gathering production data turned out to be a nice-to-have solution in the short term and, therefore, wasn’t required. The downtime data with reason codes was selected because it was a lower-cost option for monitoring the machines and provided the highest value and return, relative to other projects on the roadmap. The team also decided to run the project as a pilot project on 2 machines. This turned out to be a nice way to introduce the new technology and the new data to operators, manufacturing engineers, and leadership. 

 

Lesson Learned 

Use the Digital 360 process to identify low complexity, high value challenges to solve and create a roadmap. That helped this company clarify that a pilot project to address machine downtime was the most valuable and simplest project to execute. 

 

Project Planning 

Once the decision on the pilot project for monitoring 2 machines for downtime with reason codes, we recommended the project be treated like a “real” project. Some companies will immediately dive into setting up the technology. Then when things go wrong in the project and scope creeps there is no limit defined, no line drawn in the sand to know when to adjust, approve changes, or pull the plug. 

For this, we always use a Project Charter document. Here is a sample we use for some larger projects. We are big believers in keeping things simple. Having said that there is also a Goldilocks zone for planning projects, even the small ones…not too much, not too little, spend just the right amount of time planning. And that time planning is proportional to the size, complexity, and value of the project. 

Ectobox Project Charter

If nothing else, define the following basic criteria and check in with the people in the project around these parameters on a weekly basis: 

  • Goal/Objective of the project: make the goal a clear, SMART goal, maybe include ROI (Return on Investment) 
  • Due Date: when will it be done 
  • Budget: money and time/resources to spend on the project 
  • First several tasks, to get the project started 
  • Primary Stakeholders: Who is the PM on each side of a project (vendor and client), and who are making the decisions on the project 

 

Lesson Learned 

Run every project like a project, with a goal, budget, due date, several next tasks defined, primary stakeholders defined, and check status weekly. For more formal projects use a Project Charter document. 

 

This concept helped the manufacturer gain clarity on the project, success criteria, and the potential ROI. It seemed this wasn’t practiced often at the company. The manufacturing engineer liked the process and has since used it on other projects with success. 

Business Case and ROI 

Part of defining the project plan is defining the goal. That goal should include the project achieving some value for the company. This can also be the ROI (Return on Investment). The people planning the project should define a metric for determining whether the project and solution were a success. Simply saying the project is done, the machines are connected, and pulling data isn’t sufficient. You need to dig another level deeper. 

What is a hypothesis you can create about the project? Here’s an example: “We believe we can reduce downtime on a single machine by 40%, which produces an additional $30K in product each month.” Creating a hypothesis provides extreme clarity for measuring success. The IIoT system implemented, if that’s the solution being put in place, should also be able to provide those actual numbers…you should be able to see 40% downtime improvement on a chart, and see the $30K additional manufacturing value on a screen. 

Project Launch 

For this project, as we do for all projects, we went through a short Project Launch call/meeting with the manufacturer. On the customer’s side, the call included the CEO as the Sponsor in the Project Charter, the Manufacturing Engineer as the Champion, an IT person, and the plant manager. In the meeting, we reviewed the outlines of the project plan, confirmed some details, scheduled the first couple of activities including the On-site Visit and when to connect the machines, and ended the meeting. 

Site Survey- Choosing the correct IIoT Solution 

We conducted a Site Survey as the next step in the project. The idea of a Site Survey is to review the machines in question to confirm how we will connect to and get data from them, review the layout of the machines and network availability, etc. For getting data from the machines often it’s a question of whether they have the capability to provide data via a common data communications protocol like MTConnect, FOCAS/FOCAS2, or similar. If that’s not possible our alternative is to use external/noninvasive sensors with small, reliable PLCs to get data from the machine (e.g., Banner Engineering sensors with a DXM gateway or similar products). We then create our BOMs (Bills of Materials), pull or order the products needed, and schedule the installation on-site. 

SensrTrx Manufacturing analytics / IIoT Solution complete setup

 

The Site Survey was a valuable activity. We’re often told by the company what machines we’ll be connecting to and sometimes provided a lot more detail than needed. However, we always find some surprises which, if not uncovered, would’ve been very costly during the project. This alone makes the On-Site Visits worth it. 

 

For this project, we were given some information on the machines regarding the manufacturers of the CNC machines and the controllers on the CNC machines. This list included the make and model numbers of the controllers and other data. It was a good thing we required the Site Survey, as we always do. It turned out some of the information on the machines was incorrect. Had we showed up with equipment based on the list of machines without seeing them we would have had to make some extra trips. The ethernet ports on one of the machines were buried inside the machine after some customization by the vendor. The other machine required an external sensor and IoT gateway. 

 

Lesson Learned 

Always do a Site Survey and review the machines in person to confirm how to connect and get data out, understand the network availability, etc. 

 

IIoT Solution 

We then installed a single IoT gateway device (i.e., an industrial PC with machine connectivity software) and connected it to their Haas machines. We worked with the client to make small adjustments to the configuration of some machines (e.g., modify some settings in the Haas machines to make MTConnect data available). Then the machine connectivity software was configured, the machine connected, and tests performed for pulling data from the machine.  

While the machines were being connected our team worked with the manufacturer’s team including the manufacturing engineer and some leadership to set up SensrTrx. One of the best solutions we can put in place is where the customer knows how to set up and use the product so they don’t need to rely on our team for every little change in the future. 

Once the IoT gateways were connected to the SensrTrx platform in the cloud over a secure firewall the client was able to immediately see data for their machines. 

 Simplified IIoT Solution Design

 

Training of a few team members on the SensrTrx platform was brief, about 1.5 hours in total. Once the manufacturing engineer was training on the system she then trained the operators on the 2 machines for how to use the Operator Screens. That training also took only 1-1.5 hours. She emphasized to the operators, as everyone should including the company’s leadership, that the system wasn’t being used to allow big brother to watch them. It was about giving them and her data they all needed to do their job better, which is to do a better job for themselves, for their teammates on the plant floor, to operate more efficiently, and to grow the company because it’ll all benefit the staff even more. 

It turned out that some operators that were initially resistant to the project ended up being champions of it, especially with the profit share program already in place for the staff. They realized the level at which they were producing (i.e., not up to snuff), and they were accountable to each other and themselves once they saw actual downtime and production numbers.  

We have many times over that the numbers always go up after an IIoT solution is put in place. Often companies measure at an OEE of around 30% and in a matter of 6-9 months are able to get OEE up to 60% to 90% merely by implementing IIoT systems and sharing the data. 

Downtime Pareto chart

Lesson Learned 

Emphasize to the operations staff that IIoT solutions are there to help them. The numbers always go up after an IIoT solution is put in place and the data is shared with everyone. The people on the shop floor do care and do want to do better for themselves and for the company.  

 

How to Clearly Identify Issue 

The manufacturing engineer was ecstatic to finally see accurate, real-time from the machines that made sense. She was immediately able to see some of the downtime issues with reason codes. She let the system collect data for about a week to ensure she had good data for a normal operating week. This is when the manufacturing engineer’s real work started…finding out where the issues are and solving them. 

She needed to figure out where the biggest bucket of wasted time was, nonvalue add time. This analysis of finding nonvalue add time can be done by creating an accurate Value Stream map during the initial analysis process (Digital 360), which we often like to do. The issue with the Value Stream map is that is a snapshot of a point in time and can quickly become out of date. That data is also available on an ongoing basis from an IIoT solution to get accurate, real-time data. 

In this situation, the Pareto charts on downtime by occurrence and downtime by time from the SensrTrx IIoT platform were the best places to get the data. 

 The idea for addressing the downtime issue is to clearly identify the biggest bucket, and then to get as specific as possible with that biggest bucket to enable valuable analysis. In other words, for downtime, get reason codes that are actionable. 

For this client, the biggest bucket or issue was a lot of calls to maintenance. It was clear “Call Maintenance” as a downtime reason code wasn’t actionable, it wasn’t the information that helped determine the core cause. Therefore, the manufacturing engineer quickly realized she needed to adjust the setup of SensrTrx to ask the operator for the 2nd level of detail if they selected Call Maintenance. She was able to do that within minutes and without having to take any new training classes or write any new code. 

Once the data is specific and actionable, then the next step is to determine the cause using Root Cause Analysis.  One should ask themselves if the problem is uptime, utilization, runtime, or cycle time. Is the machine running in the most efficient way?  

Typically, most issues don’t occur during the production of the part on the machine, i.e., the value adds time on the machine. The cycle times or value-added time on the machine are often the smallest part of the time spent creating the project. The issues are often in the nonvalue add time, which is before and after the cycle time (i.e., changeovers, setups, programming, etc.). A typical number for the nonvalue-add time we see in discrete manufacturing time is 80-95%. It’s a startling number, 80%-95% of the time spent by a company to produce a product is not considered value-add time, time that is directly valuable to creating the project. 

Maybe the issue is the changeovers are too long. Digging deeper in a Root Cause Analysis could determine that the person doing the changeovers isn’t very good at them and therefore takes too long. Or maybe the run-time could be the issueit’s too long. Why would that be? Maybe the tools are dulling and need to be changed out. Getting accurate, real-time data on these issues into an IIoT platform for problem-solving can help identify these issues in a Root Cause Analysis process. 

Once the cause is determined, then a corrective action needed to be created. 

After the corrective action is in place the IIoT solution should be used to validate the corrective action was appropriate and the core issue is being solved. 

Lesson Learned 

To solve a challenge around machines, find the biggest buckets of time or resources spent. Then get clear, actionable reasons for the cause. Then do a Root Cause Analysis to drill into the true cause. Finally, establish a Corrective Action to put a solution in place.

Often the true causes of challenges will be in that 80%-95% nonvalue-add time spent before and after the part is produced.

For this manufacturer they found the cause of the issues were twofold:

  1. Misclassification of some downtime as maintenance where it should’ve been classified as changeovers; and
  1. Changeovers being done incorrectly then caused problems with the machine running efficiently, which then resulted in calls to maintenance.

The corrective actions were:

  1. better training on changeovers; and
  1. identifying a couple of operators as the most efficient and accurate change-over people, and setting them up for success as the go-to people for doing certain complex changeovers.

The manufacturing engineer monitored the downtime data for the two pilot project machines for an additional 1 ½ months and confirmed the corrective actions helped reduce the downtime by an initial 25%. She has since worked to reduce downtime by an additional 20% and beat the original metrics that defined success for the project.

Hiccups in the IIoT Solution 

There were some hiccups in the project, there always are. Any company is lying if they said a project went perfectly. In this story, we’ll open up the kimono to show that no project is ever perfect. The challenge is then to still reach successful conclusions on valuable projects in spite of the challenges that inevitably come up. We’ll briefly review some of those challenges and how they were handled by everyone. 

In this project the issues included: 

  • IT/OT connectivity issues – Communication over the firewall between the IT network and the plant network). 
  • Addressing Wi-Fi coverage – The pilot machine was located around the corner and behind a metal latticework from the Wi-Fi router. This prevented good connectivity and required the use of a repeater. 
  • Supplies of products – The pilot project was implemented during the COVID pandemic which interrupted the supply chain and slowed delivery of a couple of devices). It has a small effect on the project schedule. 
  • Extra trip – Ectobox missed a couple of details on the connectivity of the machines and had to make an extra trip to fix the issue. It had a small effect on the project schedule. 
  • Leadership – The project was slow to start due to a lack of support by some leadership at the manufacturer, but then the ship was righted and quickly got on track. 

All these issues ended up being small and were readily handled. The star of the project was the manufacturing engineer. She worked tirelessly to ensure everyone was on the same page, machines and operators were prepped, she learned the tool in 1 ½ hoursgot a lot of value out of the completed solution, was a champion of implementing the IIoT solution on the remaining machines, and was wonderful to work with. 

 

Lesson Learned 

Project planning, the Project Charter, the Project Launch meeting, and continuous communication throughout the project between the manufacturer and Ectobox enabled the project to go smoothly and handle the small hiccups that are bound to happen in any project. Without that work ahead of time, the project could have gone much differently. 

Summary 

The manufacturer in this situation had issues with excessive downtime on some CNC machines. The recommended approach in this project was to use a logical process to identify the priorities in the plant and a roadmap for tackling the most valuable and least complex challenges. This lead to implementing a pilot project on two machines to test the SensrTrx system, drive adoption in the plant incrementally, and solve some real, live issues with machine downtime.   

The project was successful, reducing downtime on the pilot project machines by about 45% to start. The manufacturing engineer is working on the process and using the SensrTrx IIoT platform to further the downtime. The next challenge to tackle on the roadmap defined from the Digital 360 is production scheduling and bottlenecks. SensrTrx and the real-time, accurate data from the machines will be critical to identifying the bottlenecks, why they exist, and how to eliminate them. 

The IIoT solution is now being scale up to all the other machines in the plant. In the end, the solution will likely prevent the company from spending about $500K in capital costs to buy a new machine which is no longer necessary at current order levels (do more with what you have). Additionally, the company is increasing production and revenue by about $340K/machine/yr (2 shifts/day, 7 hrs/shift, 20 days/mo, 6 parts/hr, $110/part = $185K/mo production revenue for 1 machine). 

 

Impact of Accurate Data for Lean Projects

Impact of Accurate Data for Lean Projects

Exec Summary

Many small manufacturers struggle with successful Lean efforts. Often the cause is bad data. Industrial IoT solutions can drive improvements in Lean efforts by providing accurate, real-time data. The data comes from remotely monitoring production, scrap, equipment downtime, processes, and waste in the production system with Industrial IoT solutions. Manufacturing staff can then closely monitor the systems over the short term to determine if the Lean efforts were effective and the long term to ensure the issues don’t return.

Value Stream Maps and Kata’s are specific tools often used in Lean efforts to identify and eliminate waste. These Lean tools use metrics to measure and reduce waste. Data from the Industrial IoT solutions can feed the data for these metrics. Using Industrial IoT as the source of the data provides accurate, real-time data which enables successful Lean efforts and compounds the positive results. Additionally, Industrial IoT saves significant time for recording and analyzing data compared to gathering data manually.

Introduction

Are your Lean efforts as effective as they could be? Where do you get data to identify and monitor the impact of your Lean efforts in your factory? The answers to these questions can have a significant effect on the ROI of your Lean efforts. The best results of Lean work come from having accurate, real-time data. Without it, your results will suffer.

Accurate Data is Important

I’m sure you remember the essence of Lean is to reduce waste in your plant. People are often surprised to hear the statistic that nearly 80% – 95% of all activities in producing your products for your customers are non-value-add, especially in high-mix/low-volume plants. That means there is a significant amount of wasted motion, transportation, waiting, and more going on in your plant.

This waste can come in multiple forms: waiting and non-utilized talent of the operators during unplanned machine downtime due to machine breakdowns, overproduction of parts as a machine processes the part more than is needed, transportation as people and tools are moved around more than required, machine downtime due to longer change-overs which could be reduced, excess inventory piling up in front of a workstation, or excess inventory of spare parts for maintaining machines due to reactive maintenance practices, and more.

What is your process to identify that waste? One way to identify waste in terms of Lean is to create a Value Stream Map of your plant’s current operations and a Value Stream Map of your plant’s preferred future state. Creating the Value Stream map includes gathering data about the manufacturing processes, like cycle time, takt time, uptime, planned downtime, unplanned downtime, production lead time, processing time, transportation time, production counts, and other values. Users of the Value Stream Map then review the details to identify where waste is and how it can be reduced or eliminated.

The key to finding non-value-add activities in your plant is the data. Those values are captured on the Value Stream Map like cycle time, wait time, transportation time, and so forth. Everyone knows the expression “garbage in, garbage out.” It applies well in this situation. Suppose the data gathered and placed into the Value Stream Map isn’t accurate. In that case, the Value Stream Map user may incorrectly identify a source of waste or misapply resources to reduce a given non-value-add activity.

Case Study

To truly understand the impact of no data or bad data when reducing waste with Lean efforts, think about a situation in your plant. Let’s say we have a foundry manufacturer with a finishing area. They are a high-mix, low-volume manufacturer. The finishing area has presses and CNC machines. There are parts in a job that pass through several machines in sequence in the finishing area. Each machine performs a different task and has different cycle times. Currently, operators are recording production for each job and overall machine downtime on paper at the end of a shift. The company is so busy handling issues that it’s rare the shift supervisory or production manager has time to hand-enter data into an Excel file for analysis. It is also likely that maybe a couple of operators might be writing down data that may not be entirely accurate. The only reliable data that exists is the number of products packed at the end of the line.

In this situation, we don’t have much data to work with, only some basic spreadsheets with incomplete and possibly inaccurate data. Therefore, it’s difficult to identify what operators are working efficiently, what kind of wait times exist, is the machine downtime due to change-overs optimal, and whether there any issues with machines causing problems and resulting in unplanned downtime, etc.

What data should we get to measure? It’s hard to say until we use a Value Stream map to tell us, which we’ll do below. When creating the Value Stream Map, we’ll find there are two stages where we need to gather data: 1) at the point when we’re creating the Value Stream Map, and 2) on an on-going basis to confirm the Lean work has produced good results and those results continue.

Industrial IoT Turbocharge

For the example above, how will we gather and compile that data? You’ve already seen the challenge: operators have a job to produce parts, not be data entry clerks. So, entering data is not a priority for them. Additionally, entering the data by operators and compiling the data by others is a form of waste. It could be considered excess processing and non-utilized talent, if not more. It also creates an issue because the data is often incomplete and inaccurate. This means garbage in, garbage out in terms of results and value from the data.

If instead, we were able to automatically gather data from machines, operators, and other systems without manual intervention by anyone, we would get data directly from the machine and the data would be available immediately; the data would be accurate and real-time. This data will then provide clear visibility into what is going on the factory floor. This process of gathering data from the plant floor is where Industrial IoT comes into play.

What is Industrial IoT

What is the Industrial IoT (Internet of Things)? IoT is the process of pulling data from sensors on devices like machines in a manufacturing plant, pulling that data over a network, converting that data to valuable information, and then presenting that data to people to make decisions and, most importantly, take valuable action based on that information.

Industrial IoT solutions are typically made of a set of sensors, a gateway or edge device, a network, a software platform, and a set of security protocols and tools to ensure all data at rest and motion is secure. The sensors will communicate with the gateway/edge device, and the data is then sent to the software platform (aka, Industrial IoT platform). The data is often processed, filtered, and analyzed in the edge device and more commonly in the Industrial IoT platform. That platform is also where manufacturing staff view and analyze the data on a variety of devices. For legacy equipment, the sensors are added to the existing machine. For newer equipment, there may be sensors and a software system inside the machine (e.g., a controller in a CNC), or a PLC with sensors. In these situations, the gateway/edge device is connected to the newer piece of equipment or PLC, and the data is pulled, translated from the existing data protocol, and then sent on to the Industrial IoT platform.

The structure of an Industrial IoT solution above sounds complex. It turns out it does take some experience setting up these solutions, but it is by no means rocket science; it is also a straightforward solution. The types of tools, sensors, IoT gateways, and other devices that exist today make each project a repeatable solution, not a new science project for every situation. Additionally, highly experienced Industrial IoT engineers and project managers, along with an approach to solve business challenges first rather than technical challenges, are key to successful implementations.

One of the issues we have seen with small and mid-sized manufacturers is a fear of projects going out of control and costing hundreds of thousands of dollars to get any kind of reasonable data and a return on investment. This is undoubtedly not the case. The approach of working on a problem incrementally and starting with a pilot project for a Lean effort can limit risk and increase the ROI of the Lean effort. Pilot projects for Lean efforts can typically be put in place within a couple of days or a couple of weeks and cost a lot less than you might think.

3 Examples of Lean with IIoT

Let’s get back to our case study above, the foundry manufacturer with a finishing area. We’ll review how accurate data can significantly impact Value Stream mapping for identifying and monitoring wastes in a Lean project.

Visual Stream Maps

“Value stream mapping is a lean manufacturing or lean enterprise technique used to document, analyze, and improve the flow of information or materials required to produce a product or service for a customer.” (ref.iSixSigma.com) Value Stream maps are a stepping stone to lead into various Lean techniques that fall under the Continuous Improvement umbrella. It enables the transition into the various Lean techniques because the resulting map clearly defines how the shop floor currently operates, no matter how well or how poorly. Value stream maps can also be used to define a preferred, future state shop floor operation. It can establish a clear goal of how the shop should be running after the issues identified in the current state map are addressed.

The definition of a Value Stream map includes references to the flow of information. This reference is a direct indication that data is a vital part of Value Stream Maps. Data in the Value Stream map provides context on the manufacturing process; where there are delays, where data is gathered manually, how many times a part is processed, where the parts are moved, etc. In other words, it is an important tool for identifying wastes in the manufacturing process.


When reviewing a high-level Value Stream map like the map above, you can see the data points to gather cycle times, change over times when there is planned downtime, wait times, production, number of people per shift, etc. These values are used in the example in this post. You can also display first-pass yields, quality, scrap, reliability, expediting costs, and more. This data is collected using Industrial IoT solutions. Once collected, the Lean team can monitor the data after changes are made to the processes. This way the team can ensure the changes were valuable and then continue to monitor and improve the systems over time.

Let’s return to the foundry with the finishing area example above. Documenting the manufacturing process in a Value Stream map with current data would be a valuable exercise. This will provide valuable insights such as finding that parts typically pile up in front of a certain machine which happens to have a faster cycle time. Before creating the Value Stream map the inventory piling up may not have been a concern, possibly because they figured the piles didn’t site long due to the faster cycle times. However, additional data was added to the map which indicated the machine has long changeover times creating a lot of machine downtime and lost opportunities for production. The Lean team might then realize that if they worked to dig deeper into the changeovers they may discover some waste they could eliminate and vastly decrease the changeover times and related machine downtime. If successful, there would be a cascading effect that reduces the inventory and thereby increases asset utilization, production throughput, and revenue for that machine.

Other Examples

Here are a couple of real-world examples of manufacturers using Lean to identify and reduce waste with Value Stream Maps. These case studies are good examples of how accurate and real-time data provide big impacts when combined with Lean efforts.

Case Study 1

Here’s a situation we heard about recently which underscores the impact of using Industrial IoT with Lean. Additionally, the manufacturer identified the issue from a Value Stream mapping effort. This large manufacturer produces huge machines. Those machines were, in fact, train engines (i.e., locomotives). The challenge was forms of waste resulting from the distance between the shop producing engines and the shop of repair technicians. The repair technicians had to walk a long distance to get to new locomotives about to come off the line that required servicing. When the technician arrived at the engine, there’d be a list of work to be done. If the technician didn’t have the tools and parts required, they’d have to go back to their area, get the right tools and parts, and come back. This caused issues with downtime of the crew assembling the engines and last-minute scrambling to reassign them during that production downtime. What wastes can be found in this situation? The three easiest to identify are transportation, waiting, and non-utilized talent. This particular issue was one of several around the plant.

Early on, the Lean/continuous improvement team couldn’t clearly identify this as an issue until they had some data from a Value Stream mapping effort. Once the data about transportation and waiting were captured “on paper,” it became clear this was a big issue. It quickly became clear that the best solution was to pull production and quality data for the engine from an Industrial IoT solution and provide each engine’s data to the technicians. They can then review the quality issues that exist from their shop, determine the parts and tools required, and go the long distance to the engine in question prepared for the work at hand. This access to accurate, real-time data resulted in eliminating nearly all the wastes identified, improving the production throughput, and reducing production costs.

Case Study 2

A manufacturer experienced issues with on-time delivery and was a mid-sized manufacturer with high-mix, relatively low-volume production (i.e., they produced highly customized parts in medium to low volumes). The engineering team’s customizations included work to modify drawings, confirm they are correct, and send them through a set of approvals. Part of the leadership team in engineering took on the challenge to solve the issue.

One of the first efforts was to create a high-level Value Stream map to document product and data flow. It started at the beginning, which included the sales team. Documenting the process was straightforward. Once the first version (without data) was completed, it was nearly impossible to see where the waste issues existed. Some effort was put into identifying part counts, cycle times, wait times, etc. As soon as the numbers were added to the Value Stream map one of the major issues quickly became self-evident. There was a ten-day wait time for orders from sales to get into the engineering department for processing. It turns out that sales wouldn’t request the right drawings and other detailed information from the customers. When engineering would get an incomplete order, they would push it back to sales or sit on it without notifying sales that the order was incomplete. Once accurate data was available in the Value Stream Map, the issue was quickly identified.

The company then created a custom ECN (Engineering Change Notification) software product created by Ectobox (LINK TO CASE STUDY). Once implemented, the engineering leader monitored the improvements in wait times and throughput for his engineering department. The data also helped identify where they could make many additional improvements in processes to reduce the wait times, further increase throughput, and ultimately significantly impact on-time delivery to the customer.

Takeaways

Lean manufacturing relies heavily on data. In manufacturing, as with any business, the data in the business processes is gold. Having access to that data to understand, analyze, and improve the processes can make a company operate much more efficiently. The positive results from Lean efforts are compounded when accurate, real-time data is used.

How to Reduce Machine Downtime in 2021

How to Reduce Machine Downtime in 2021

Manufacturers all across the world are focused on trying to reduce machine downtime. Why? Because it is one of the biggest obstacles to overcome in the manufacturing industry, and expensive to deal with. From the machine itself to the product that is not being produced, this valuable time can get expensive very fast. This makes it clear that you need to do anything you can, and have a strong emphasis on your efforts to reduce machine downtime.

Gathering data to help reduce machine downtime.

What is the best solution?

As we have addressed, this is a huge problem. There are many ways to approach the issue, with one being the best, most efficient option that just makes sense. IIoT systems are the answer. Technology is advancing, automation is the future. Not just within the manufacturing industry, but in the entire world for every single thing we do in our daily lives. There is no way around it, that is the direction the world is going. If you want to stay in the game, you have to adapt. 

“You can either ride the wave of change, or you will find yourself beneath it.” This is a quote I like to keep in mind. Picture yourself on a surfboard in the ocean, you are just sitting there relaxing, enjoying the view. A huge wave comes into view and is approaching. You can either keep sitting there and be consumed by the wave, completely submerged. Or, you can start paddling and put in the effort to catch the wave, ride it, and stay on top. The same principle applies to nearly every situation one might encounter today. 

Companies that are thriving today are taking advantage of the technology that is available. What worked 20 years ago, is vastly different from what can lead to a successful business today. The “I’ve been doing this for 35 years and I’m still here today” mentality will not work anymore. 

This is not just a new piece of equipment, people are calling this the fourth industrial revolution (Industry 4.0). IIoT systems are not just for huge companies like General Electric. Remember when a 50” TV would cost you $3k 15 years ago? And now you can get yourself a brand new one for $200. Technology, in general, has become substantially cheaper, and more available. 

How does IIoT Reduce machine downtime?

I told you all about why you need an IIoT system to reduce machine downtime. Now I will tell you how it works. 

IIoT systems allow you to see everything that is going on inside of your machines. A set of sensors get placed onto the machines or we can connect directly into the machine and pull data out. We then connect the sensors or machine to a gateway edge device to filter and transform the data and then send it on to an IIoT platform. Then, tablets or TVs (which are cheap now as I mentioned in an illustration earlier, smooth right?) will display numerous dashboards showing an abundance of information from the IIoT platform. You can track the availability of products, increase productivity and performance, determine the real cause of problems, and of course, reduce machine downtime. The best part? You get all of this information in real-time!

No more spending time with machines off, inspecting them, doing routine, preventative maintenance to ensure nothing is wrong. This is all wasted time, very expensive wasted time! To do this manually, you have to stop production (wasted time), pay for someone to inspect the equipment (wasted time), and that is if there is absolutely nothing wrong with the equipment. If there does happen to be something that requires attention, that adds another level of problems that need to be diagnosed and fixed (wasted time). Our common goal here is to reduce machine downtime, not unnecessarily add to it. 

It is expensive, and it is a headache to fix all of these problems. Even just diagnosing the issue, finding out why a machine stopped working is a long process that can be avoided. With all of the data gathered and alerts from an IIoT system, you will know exactly what is going on and have a chance to avoid the problem completely before it even happens. Now you’re moving into doing Condition-Based Monitoring and also Predictive Maintenance. You will also gather insights from the data, and know exactly why this issue occurred. That way you can know your machines better, make better-educated decisions, and have the time to shift your focus onto other aspects of your company. Using these tools correctly will reduce machine downtime substantially, and result in more time, and money in other areas, without the headache.

Conclusion

IIoT systems are the key to a lasting business in today’s manufacturing world. Automation is changing the way the world does business across the globe. Again, this is not just a new idea or piece of equipment, this is the fourth industrial revolution. Changes are taking place across the board. Learn more about your machines, gather data, insights, reduce downtime. Use this information to make easier, more educated, clear, and confident decisions. Ride the wave, don’t get caught beneath. 

Feel free to contact us at Ectobox anytime. We are more than happy to answer your questions and help you along the way to reduce machine downtime and take your business to the next level.

Breaking it Down: Building Blocks of Smart Manufacturing

A Smart Manufacturing company is a manufacturer with fully integrated solutions that empower it to be highly flexible and responsive
to changing conditions, with self-healing systems. Those capabilities enable a company to reduce costs, generate more revenue, and

increase their competitiveness in their market.

So how does it all work, and where do these capabilities come from?

The Nine Blocks that Stack Up to Smart

There are 9 technologies or building blocks that stack up to create Smart Manufacturing.

  1. IoT (Internet of Things): Taking data from sensors on machines, moving it over the internet, and converting it to valuable information to help people make decisions and drive action.
  2. Automation and robots: Using robots and software to enable machines and people to work together more efficiently.
  3. Big data: Storing and using massive amounts of machine data for data analysis to gain insights about operations and machines.
  4. AI (Artificial Intelligence): Using software tools to build models and algorithms which can learn about niche areas of your facility to distinguish between normal and critical conditions.
  5. AR (Augmented Reality): Viewing models or images overlaid onto the real world on a mobile device.
  6. Additive Manufacturing: Creating objects from 3D models by joining materials layer by layer.
  7. Modeling: Portraying or defining aspects of the physical world in digital form for gauging equipment status, simulating operations, or controlling live systems.
  8. Cyber Security: Applying old and new ideas of encryption, protecting attack vectors, and combining digital with physical security tactics to protect data in motion and data at rest.
  9. Cloud: Moving away from local, on-site servers towards 3rd parties like Amazon, Google to store, service, and process data.

Stacking the Blocks

Each of these technologies can deliver value on its own. However, companies can realize exponential value by combining technologies.

Here are a few examples:

     Predictive Maintenance

  • Technologies: IoT + AI
  • Method: AI experts create a machine learning model. IoT pulls data from machines and channels it through the model to assess the probability that a part or whole machine may fail within a certain period of time.
  • Benefits: Clear identification of critical maintenance windows, resulting in up to 50% cost reduction by addressing issues only when needed but before failure.

     Robot-Assisted production

  • Technologies: Robots + Automation + IoT
  • Method: A “human assistant” robot is programmed to work alongside people to handle repetitive and mundane tasks or sets of tasks. The robot is pre-programmed to handle multiple task sets. Input from IoT data (such as production data further up or down the line) directs the robot to change its tasking based on real-time production needs.
  • Benefits: Increased production with existing staff and reduced pressure to hire/replace workers.

     Data-driven quality control

  • Technologies: IoT + Automation + Big Data
  • Method: Set up machines to test products immediately after a certain stage of production. Automatically analyze that data relative to product quality standards. Based on results, either trigger alarms for humans to intervene or have the equipment modify its own behavior appropriately to meet the required standards.
  • Benefits: Earlier notification of equipment problems, earlier identification of product quality issues, reduced waste, and rework. Increased production of a product to standards.

Choosing the Right Blocks

Some of the most exciting benefits of Smart Manufacturing come from the myriad and creative ways that new technologies can be combined and implemented. To find the right combination requires effort: thoughtful planning, employee input, and focus. But even small and mid-size companies can increase their competitive position, with access to the same building blocks as larger manufacturers.