This image illustrates a factory with smart technology. The image represents the benefits of smart manufacturing.

3 Vital Benefits of Smart Manufacturing

/in , , /by

3 Vital Benefits of Smart Manufacturing

There’s an extensive list of smart manufacturing benefits and many ways that smart manufacturing can add value to a company. Today, there is a “smart” everything and manufacturing companies are not getting left behind on this trend. Technology continues to advance at a quick pace in the 21st century. We have smartphones, smart TVs, but that’s old news at this point. Now we have smart lightbulbs, smart toasters, smart dental floss dispensers. Yeah, I’m not kidding, go ahead and look it up yourself.

10-15 years ago, you didn’t need to have a smartphone. It was cool to have and useful to a lot of people, but you weren’t weird or behind the times if you didn’t have one. Flash forward to today, you are completely lost without a smartphone. Can’t do a quick google search from your pocket-sized computer? Can’t check your email? Get directions anywhere in the world? We can’t live without these things today. Smartphones started as a cool option for people that wanted to spend a little extra money, now nobody can function without them.

Old phone showing how the world has changed into a "smart" everything.My point being, the world is getting digitized, the world is getting “smart.” The manufacturing sector is no exception. Many manufacturers still choose to ignore the benefits of smart manufacturing, IIoT, and Industry 4.0, and those are ones that will get left behind. Just like that big, bulky Nokia 3310 you used to carry around.

In this article, I will go over a few key benefits of smart manufacturing that you should be implementing in your factory today so that you can “future proof” your company.

1. Increased Plant Floor Efficiency with Real-time Data

Every manufacturer needs to focus on increasing efficiency. Nobody has a completely perfect process, or anywhere near one. There will always be places to squeeze out more throughput, increase capacity, cut back on downtime, etc. Manufacturers need to have a continuous improvement mindset. Be proactive about making improvements on the plant floor, and establish that type of culture among the company.

  • Make Quick Decisions and Take Action

It’s really hard to make improvements that actually help when you don’t have much substance or information to base your decisions on. Furthermore, it’s tough to measure success without seeing the numbers. How do you really know if the adjustment you made helped or not? If it did help, how much did it help?

This is where smart manufacturing benefits you and can step in to lend a hand. IIoT and manufacturing analytics systems are smart manufacturing solutions that are equipped with the ability to deliver real-time data. Real-time data gives you the substance to make quick decisions and then act on those decisions to make quick adjustments on the plant floor. Then, measure the success instantly with, again, real-time data.

  • Don’t Get Stuck in the Past

Don't get stuck in the past, focus on the future.

Think about how long this process would take as well as the accuracy if you had to have someone manually gather basic data on the plant floor, and deliver that basic, hopefully accurate data to a decision-maker in a somewhat timely manner. Sort of? hopefully? That data is getting there many hours, if not a couple of days later.

This is one of the major benefits of smart manufacturing, this process can be streamlined. These types of solutions are what the “trendy” manufacturers are turning to. As I said previously, some other manufacturers are willingly choosing to ignore these advancements in technology. Soon, it won’t be “trendy”, it will be the standard. Companies will either have to adapt and change to the new ways of manufacturing, or they will watch competing companies continue to grow past them. There is always someone out there willing to do your job better, cheaper, and faster, and today, the tools are readily available for people to take advantage.

Just like the smartphone example. Yes if you have a “dumb” phone, you could drive to a gas station, buy a map, and then go find where you are trying to go. Or, with a smartphone, you could type your destination address in and be on your way in 10 seconds. It’s the same concept with smart manufacturing.

data delivered instantly in real-time, nobody walking around the shop floor handing out information, gathering information, no operators stopping work to write down their data during a shift. Not only is this a slow and inefficient process, but it’s also poor communication, how often does that data get to anybody else on the plant floor? especially in a timely manner for that data to actually be useful.

2. Seamless Communication

Our second key smart manufacturing benefit is communication. This is one that not many people really talk about or make a big deal about. However, smart manufacturing revolutionizes the way people communicate with workmates, customers, machines, and data.

This image illustrates communication, seamless communication is one of the key smart manufacturing benefits.

Here’s why this is so important. Wasted time and wasted motion = lost production and an efficient process. This goes against lean principles, and every manufacturer should strive to become a lean manufacturer. Strive to eliminate waste and create a highly efficient environment. Smart manufacturing can have a large impact on a variety of different communication scenarios such as:

Machines to Employees:

  • Machines deliver data in real-time, constantly delivering information to operators and other employees. This information is instantly made available to each person within the company. 

Employees to Machines:

  • Employees and operators can consume real-time data to make a data-driven decision and make adjustments on the plant floor. 

Sales team to Customers: 

  • When customers call for an update on their job or a progress update, the sales representative can look at the data in real-time and give the customer an extremely accurate data-based answer with detail on how much is done, how much is left, and when they can expect delivery. With every metric being updated in real-time. 

The communication benefits of smart manufacturing are tremendous and will undoubtedly save you a headache here and there, create a more knowledgeable staff, and improve customer relationships.

Before you start tracking these types of processes, you likely don’t realize how much time and money you could be saving. We have come across clients that have told us that they have people in the company that are consistently spending half of their day running around the plant floor trying to find a certain order for a customer. What a waste of valuable time!

With a smart manufacturing IIoT / Manufacturing analytics solution, you can give customers accurate real-time adjusted numbers instead of giving a rough estimate based on your best judgment, or going to run around the shop floor to deliver an answer a few hours later.

Efficient processes = more production, more production = more time for more clients, good communication = happy customers, happy customers = return customers, and all of this = more revenue in less time.

3. Smart Manufacturing Creates more JobsThis picture illustrates the workforce problems in manufacturing.

Some people will say that automation is going to take over human jobs in manufacturing and that IIoT is going to turn everything into an automated process putting everyone out of work. This is not the case at all. While it is true that there are some jobs that will likely be largely handled by machines, IIoT and smart manufacturing creates more jobs.

It’s no secret that there is a shortage of skilled employees in the manufacturing sector. The industry is filled with a lot of people closing in on retirement. Furthermore, there isn’t a large number of young people that want to go into the manufacturing industry. What do young people want? Tech jobs (source). There is a lot of young people that want to do something related to tech. That is where everything is headed, technology drives our world today. Correspondingly, what will all of these smart manufacturers need? They will need people to fill these new jobs created by smart manufacturing and smart factories. See where I’m going here?

Manufacturers will have new needs for people who can work with new technology, software, and data.

So, no, smart manufacturing and IIoT are not going to take away manufacturing jobs. It is creating new, different jobs, jobs that might be more appealing to the younger generation. These new jobs will help boost up the manufacturing workforce, which desperately needs the help.

These three benefits of smart manufacturing are huge, and they are shaping the future of the industry. Start implementing them today. Put yourself ahead of the curve and don’t get stuck in the old ways of manufacturing.

Picture of a worker performing maintenance in a factory. Decision-makers in this factory could possibly be deciding on preventive vs predictive maintenance strategies.

Preventive Vs Predictive Maintenance for Manufacturers

/in /by

Preventive Vs Predictive Maintenance for Manufacturers

Preventive vs predictive maintenance, each of these maintenance strategies will help improve the longevity of equipment. Regular maintenance will help to keep your assets healthy, reliable, and running at peak performance. Machine downtime gets expensive, even just a few minutes of downtime can be extremely costly. Putting a strong emphasis on machine maintenance will help you to avoid machine failure and reduce operational costs on the plant floor.

Whether it’s preventive or predictive maintenance, consistency is key. Maintenance is something that you have to keep up with constantly. Inconsistent maintenance can nullify all of your previous efforts, and lead to asset failure.

So what are the differences between preventive maintenance vs predictive maintenance? What are the similarities? Which one is more effective? Which one is best for you?

In the following parts of this article, we will answer each of these questions.

Preventive Maintenance

Preventive maintenace is consistent and routine scheduled maintenance of machines and assets in order to keep them running optimally and prevent expensive unplanned downtime and unexpected asset failure. A successful preventive maintance strategy requires strong stragtegic planning before a stoppage or failure occurs.

 

Predictive Maintenance 

Predictive maintenance falls in line with proactive, data-driven mainteance strategies that gather data and analyze the health and condition of an asset to assist in predicting when the asset should receive maintenance. 

The main difference between preventive and predictive maintenance is that preventive is performed on a linear schedule. With a preventive maintenance strategy, you might schedule out a work order for maintenance every 4 weeks. Then, maintenance should be performed precisely on that date, exactly 4 weeks from the last maintenance work order. Whereas predictive maintenance can be a little bit more sporadic. Predictive maintenance uses data to determine when maintenance should be performed. Consequently, maintenance will not be performed on a linear schedule. The schedule might be 3 weeks, then 6 weeks, then 2 weeks, all over the place and only performed when needed.

This makes predictive maintenance a more complex strategy. However, it also has some valuable advantages over preventive maintenance that make it worth the initial setup. We will dive deeper into the advantages later in this article.

Preventive Vs Predictive Maintenance: SimilaritiesIllustration of maintenance.

Preventive and predictive maintenance are both strategies that are planned out ahead of time, making each of them a form of scheduled maintenance.

The biggest similarity between the two is the overall goal. Both strategies are there to increase health and reliability, as well as prevent stoppages, costly downtime, and asset failure on the plant floor.

Sometimes the term preventive maintenance gets confused with predictive maintenance simply because all forms of maintenance are in place to prevent something. Technically this makes all maintenance “preventive” maintenance.

However, here’s the issue- you can use the word preventive when referring to predictive maintenance, but, when referring to preventive maintenance, you cannot accurately interchange with the word predictive. It’s better to keep the terms separate for clarity.

Preventive Vs Predictive Maintenance: Which is More Effective

Predictive maintenance is the most effective form of maintenance. There are a number of reasons for this. I mentioned earlier that predictive maintenance is more complex, but also comes with its advantages. It’s more complex because there’s more setup and technology involved. Preventive maintenance can be as simple as reading the manual for the equipment and setting up a maintenance schedule incrementally as recommended.

This type of system is similar to the way the majority of us maintain our cars. You take it into the shop, they perform an oil change, replace the oil filter, maybe some other maintenance you need. Then they tell you to come back in 3 months or 3,000 miles. There’s no data or new information behind that decision, you are scheduled to come back in 3,000 miles this time, and next time, and next time, etc.

This sounds like a proven system, right? Well, yes it is. However, just because it works, doesn’t mean that it is the most efficient system. Back to manufacturing, each machine will have a different optimal maintenance schedule.

Predictive Maintenance runs on Data

The only way that you can find the optimal schedule is by gathering and analyzing data. If you have no substance or information, it’s near impossible to make an informed decision based on what is happening on the inside of the machines. One machine might not need some type of maintenance as regularly. A different machine might be deteriorating because it needs more attention, more maintenance than the standard recommendation. However, without the data, you won’t know that the machine was unhealthy until it fails prematurely. On the other hand, if you are over-maintaining you will never know, and you will just keep unnecessarily throwing money away.

You could argue that a little bit of over-maintaining is fine. That the point of maintenance is to make sure everything is in tip-top shape all the time. While this is true to an extent, the overall goal is actually to reduce operating costs. Whether those costs are over-maintaining or unplanned downtime. So, over-spending on maintenance would be counterintuitive.

Machines are generally inconsistent and tough to accurately predict, we need a continuous flow of real-time data to help make informed data-driven decisions on the plant floor. Predictive maintenance taking advantage of real-time data is what makes it the most effective maintenance strategy.

Preventive Vs Predictive Maintenance: Which is Best for You?

Predictive maintenance is the most advanced and effective form of maintenance. However, that does not automatically mean that it’s the best option for you today. It should, at the very least, be a goal to become a completely data-driven company and include predictive maintenance in your strategy. However, not all companies are at that stage, a great number of manufacturers still choose not to implement real-time data systems such as IIoT or manufacturing analytics. There are also some companies that are trying to adapt to data-driven manufacturing but aren’t far enough along in their journey yet.

For these companies, it might be a good idea to stick to preventive maintenance in the short term. But still, start gathering small amounts of data to work towards becoming a data-driven manufacturer.

The bottom line is that you should be continually improving your process, and keep working towards creating the most efficient factory possible.

Graphic illustrating proper vs improper production downtime tracking.

Improper Production Downtime Tracking is Flattening Your Profits

/in , , /by

Improper Production Downtime Tracking is Flattening Your Profits

How accurate is your production downtime tracking? Are you even tracking production downtime? Do you have a good idea of how much production downtime is costing you each week, month, year?

Consultants believe 4 out of 5 manufacturers (80%!!) are unable to accurately estimate their downtime (source). This is a crazy number because downtime is a leading cause of lost revenue in manufacturing. What’s even scarier, is when you look at the statistics for how much money production downtime is costing manufacturers on average.

Over 7  years ago in 2014, the average cost of downtime per hour across all businesses was $164,000 (source). If I was losing $164,000 per hour for any reason, I know for a fact that I would want to know exactly how and why right away. In addition, I would do everything in my power to minimize such a costly event in the future. Now, don’t let that first number sink in. Just two years later in 2016, that number jumped to $260,000 per hour on average across all businesses. That was 5 years ago, I’ll leave it up to you to determine where you think that number might be today in 2021.

That is a lot of money, there’s no questioning that. But, how many hours of downtime will a company encounter each year on average? 800 hours. Want to know what 800 x $260,000 is? Neither do I, I’ll just say that there are 9 digits in that number, and the first number is not a 1. 800 hours might sound like a lot, but when you break it down, you see how quickly it all adds up.

Why Aren’t Manufacturers Paying Attention to Production Downtime Tracking?

If production downtime is so expensive, why don’t most manufacturers have accurate and detailed data regarding their own downtime within their own facility, if any data at all? It’s tough to formulate a clear answer to this question.

A large number of manufacturers get stuck in their old ways. They have been doing business a certain way for decades successfully and see no reason to make a change now. The problem here is that decades ago, data was not a big part of manufacturing. There really were no tools or technology available to pull a large amount of data from machines and equipment. Consequently, there was not a huge amount of machine optimization or emphasis on increasing the efficiency of current equipment on the plant floor.

Today, it’s an entirely different story. The entire world has been going through a digital transformation. New technology everywhere, and this produces a lot of data. You need to find a way to access this data that is trapped inside of these devices that are loaded with technology, and just need to be unlocked. Companies like Facebook have thrived off of data. They have grown and scaled their business by gathering data and making informed data-driven decisions based on that information.

What does this mean for the manufacturing industry, and production downtime tracking? How can you leverage today’s tools and technology to improve the way you do business?

Leverage Today’s tools for Better Manufacturing and Better Business

The manufacturing industry is vastly different than what it was some years ago. Today, there are tools and technology that help with production downtime tracking, plant floor efficiency, increased throughput, and many other things. Do you need tools that help in these areas, or are they just icing on the cake?

The short answer is yes, you do need them if you want to keep growing your company in 2021 and into the future. The manufacturing industry is growing more competitive. It’s a challenge today to find good employees, draw in new clients, and improve your manufacturing process.

Industry 4.0 has brought new principles to manufacturing. While industry 3.0 focused on machine automation and computerization, industry 4.0 emphasized data and using data to improve business processes. Yes, we know that we can automate machines to take over a lot of the manufacturing work and activities that humans would otherwise have to handle manually. But now, it’s time to start refining and improving these processes. Gather data, and make data-driven decisions to improve operations across the plant floor.

Along with industry 4.0, came an abundance of industry 4.0 capable tools. IIoT systems are one of those industry 4.0 tools that can assist you in improving your process.

IIoT Can Give You the Edge You Need

These IIoT systems can be implemented into any factory, even on machines 30, 40, 50 years old. These systems pull data directly out of the machines and equipment on the plant floor and deliver that information to operators and decision-makers in real-time. This changes the way we do manufacturing. Instead of having a person walk around the plant floor and recording data on a spreadsheet and then hopefully getting that “accurate” information to the right people at some point, you can receive data instantly and make the adjustments right then and there.

Imagine the difference it would make to have access to real-time data giving you insight on production downtime tracking, how much scrap you have, how much throughput, predictive maintenance insights, and asset utilization just to name a few areas IIoT can help.

IIoT delivers information to all decision-makers in a timely manner. You gain the ability to see exactly where you need to improve instantly so that you can make an adjustment, and then see if that adjustment helped or not right away.

Before systems like this were available, it was much harder to gain real insight into the plant floor. Furthermore, it was near impossible to see how adjustments are affecting production in real-time. Production downtime gets extremely expensive. You need to be doing everything you can to continually, and proactively make improvements to give yourself the competitive edge.

Final Thoughts on Production Downtime Tracking

Production downtime is a leading cause of lost revenue for manufacturing companies. Yet, the vast majority of manufacturers struggle to accurately estimate and minimize downtime. Companies are losing millions of dollars from production downtime each year. For an industry with so many moving parts and people, it’s essential to track processes, make improvements, and do everything you can to get the most out of your equipment.

Set yourself apart, be that 1 out of every 5 manufacturers that are aware, and can accurately estimate downtime. Leverage the tools and technology available today to gather accurate real-time data. Make data-driven decisions to improve business processes, and climb past your competitors.

Graphic illustrating the development of a digital strategy vs digital transformation. Failing to plan is planning to fail. Develop a solid strategy to complete a successful digital transformation.

Digital Strategy vs Digital Transformation: Are you Forgetting one?

/in , , /by

Digital Strategy vs Digital Transformation: Are you Forgetting one?

Digital Strategy vs Digital Transformation. The benefits of a successful digital transformation in manufacturing are near endless. It opens a lot of doors once you have a truly connected factory. If you look around, the entire world has been going through a digital transformation during the last couple of decades. For the most part throughout the world, the change is welcomed, everyone appreciates the benefits they receive. Digital transformation streamlines a lot of processes, saves time, creates easier communication, and makes many day-to-day activities much more convenient. We can order groceries to our doorstep with a few clicks on our phone, we can communicate with anyone in the world in real-time whenever we want, we can receive alerts on our phones when somebody walks up to our front door.

Why should anything be different within the manufacturing sector? Why do some companies completely reject digital transformation in manufacturing?

The vast majority of people would likely agree that digital transformation has made life easier, especially in the workplace. So why would some companies in the manufacturing sector, workplaces with lots of people and moving parts, choose not to move from Industry 3.0 with automation of machines but still many manual business processes and data trapped in machines, to Industry 4.0 and automating the business and driving decisions based on data and information? Well, it has a lot to do with strategy, digital strategy vs digital transformation. In this article we will cover why some companies reject digital transformation, and what you need to do to complete a successful digital transformation.

Why Some Manufacturing Companies Reject Digital Transformation

There have been a lot of failed digital transformation projects, some by very well-known companies such as GE and Ford. The reasons for failure all have one common theme, they lack some part of the overall strategy. When you are digitizing your company, the overall goal is not to include as much new technology as you can. The technology can help you get there, but it’s a tool to help you, not the complete solution. With that being said, here are some reasons that some companies fail, or choose to reject digital transformation-

  • Unclear Goals
  • Single Project
  • Projects Aren’t Agile
  • Wrong Technology
  • Resistance Within the Company
  • No Culture Change
  • Focusing on the Technology Challenge, not the Business Challenge

Unclear Goals

It’s not beneficial to digitize your company just for the sake of digitizing. Many companies fail the digitization process because they fail to set clear goals and expectations. They might have been going through a digital transformation because they thought that was the next step and the future of manufacturing. While this is true, it is important to innovate with new technology to keep a competitive edge, companies need to define clear goals and make sure that everything they are doing is helping them achieve those goals.

Single Project

Digital Transformations are not a single, one-time event. As said above, there are a lot of moving parts in a manufacturing company and many people to work with when making the transition. Change takes time. Rome wasn’t built in a day, and neither was the Digital Transformation at GE or Ford or even for Bob’s Precision Metals shop, for that matter.

Projects Aren’t Agile

When implementing Digital Transformation projects, as already stated, it will take time to change a whole company and to change the hearts and minds of the people at that manufacturing company. Therefore, change in the company must be incremental, one project at a time.

Wrong Technology

All too often companies will see the flashy sales brochures of companies that provide the silver bullet products…the product that will take care of the whole digital transformation. “Just buy our whole stack of products that integrate together seamlessly and your issues will be taken care of.” That is a proprietary approach to a solution. Rest assured there’ll be a very hefty price tag for that full stack of products as well.

This is one example of the wrong choices that are made in digital transformations around technology. We use 4 rules or tenets to figure out what technology to use (Report by Exception, Edge Driven, Light Weight, and Open Technology). The resulting solution ends up being highly scalable and flexible, at less than half the cost of proprietary solutions.

Resistance within the Company

Everyone needs to be on the same page. The whole company needs to be on board with the process, it’s important that employees buy into the ideas presented. For a successful digital transformation, employees need to have the right mindset and adapt to the new way of manufacturing within the company. If there is a division within the company, it complicates the project. This becomes particularly important when we are working with the leadership team of a company to plan and execute the digital transformation. Even at the top levels of an organization, we’ll find resistance. We’ll often have a conversation with the people in the C-suite, the board, or others to recommend the resistors be swapped out for other people to ensure the digital transformation will succeed.

No Culture Change

Falling in line with the last reason for failure, culture change is also very important. Digital Strategy is just as important as digital transformation, if not more important. Make sure that everyone knows why the company is making a change, and how it will be beneficial. This is where everyone from the CEO down should know the Digital Strategy and have a part in executing it. Then, as the digital transformation process is well on its way, if employees are doing the same things they were doing before the digital transformation, and not utilizing the tools they have, or understand reasoning, the project will ultimately fail.

Focusing on the Technology Challenge, not the Business Challenge

A lot of companies get wrapped up in solving the technology challenge. Once they get started on their digital transformation they become more focused on the technology they can add, rather than the problem that technology is actually solving. They keep adding more technology, more tracking, and more sensors but forget the reasoning for any of the technology being there in the first place. Define your goals, and let that drive your actions to make changes that will actually yield good results.

These are all leading causes for a failed digital transformation, and it may scare some companies away. The entire world is becoming digitized, it’s getting to the point where it is a requirement for any modern-day organization. So what can you do to ensure that you have a successful digital transformation?

How you can Avoid Failure, and Complete a Successful Digital Transformation

The risk of not digitizing your company outweighs the risk of failure. It’s crucial for companies that want to remain competitive to adapt to the new ways of manufacturing. So, here is what you need to do to complete a successful digital transformation-

  1. Have a Clear Digital Strategy

  2. Start Small, Think Big, Get Wins

  3. Establish a Proactive, Data-Driven Company Culture

  4. Always Focus on the Business Challenge

  5. Be Agile

  6. Open Technology

  • Have a Clear Digital Strategy

Establish clear goals, clear expectations, and stick to them. When you compare your initial digital strategy vs digital transformation, the project should reflect that initial plan. Spend the time to make a good plan, and make sure you keep that plan in the front of your mind. Determine what you really need, what problems digitizing can help you solve, and make sure all of your actions fall in line with your digital strategy.

  • Start Small, Think Big, Get Wins

You don’t need to do everything at once, Start small. When connecting to machines on the plant floor, start with a pilot project. Connect to just one or two machines, gather data, find what works, what didn’t help, and go from there. Once you have established the value and found out what you need to be tracking, then you can scale the project. This way you are staying on task and making it much easier to follow your initial digital strategy. Make adjustments periodically instead of making tons of changes all at once, and not knowing what really made a difference, what did nothing, and even what made production worse.

  • Establish a Proactive, Data-Driven Company Culture

Everybody within the company needs to be on board with the transformation, furthermore, you need to establish a proactive mindset among the company. The digital transformation will bring lots of new data and new opportunities to improve operational efficiency. It’s very important to enable your employees and machine operators to make data-driven decisions and act on those decisions. A unified and proactive company culture can be what makes or breaks the digital transformation, as well as the overall future success of the company.

  • Always Focus on the Business Challenge

Earlier I mentioned how companies can get wrapped up in the technology challenge, this can be fatal for a digital transformation project. The technology itself is not going to improve operational efficiency, customer relationships, lead times, or anything. However, you can leverage technology as a tool to help you improve your company in all of these areas. Always remember, solve the business challenge first, and use technology as the tool that helps you get there. Keep this in mind to help you stay on track, and not add meaningless systems to your project that do not actually help solve the business challenge.

  • Be Agile

Projects need to be agile. The hope is that all projects can be back-to-back to keep the momentum and excitement going. Drive valuable results and keep a good pace. Small, incremental, but still consistent projects will ensure that people do not get overwhelmed but also stay on task continuously.

  • Open Technology

Don’t get stuck in a proprietary solution. For a successful, scalable, and flexible solution the technology must be open. This keeps the door open to connect to new systems in the future. It will provide a solution that works today, and one that won’t hold you back, keeping your hands tied in the future. Keep in mind these four rules when choosing technology-

  • Open Architecture
  • Report by Exception
  • Edge Driven 
  • Lightweight

Digital Strategy vs Digital Transformation: Conclusion

Digital transformation is essential for manufacturing success now and in the future. Many companies have failed in the past because they get off track, lose focus, and do not structure the project correctly. Worse yet, companies that choose to ignore the benefits of a digital transformation won’t be able to compete in the future. It’s becoming more apparent each day that digitizing your manufacturing company is a must, but you have to do it right. Establish a strong digital strategy. Use accurate digital data and information to drive decision-making, quickly, and in real-time, and you can put yourself in prime position for a successful digital transformation.

Steps to properly using data, creating a proper data-driven culture.

The 4 Stages to Creating a Data-Driven Culture

/in , , /by

The 4 Stages to Creating a Data-Driven Culture

What is a data-driven culture, and data-driven manufacturing? It’s simply gathering, analyzing, making decisions, and acting on data. It’s for companies that have the expectation to be more productive than they were the day before. Going from point A to point B, continually improving, continually setting goals, and being proactive about reaching those goals.

Company success is typically defined in terms of revenue, profits, and evaluation of the company. To improve in these areas, companies need to do more with what they already have. They need to work towards increased efficiency, increased plant floor production, quality control, on-time delivery of products, and they need to meet and exceed customer expectations. To accomplish this you need to be able to find and eliminate inefficiencies, get visibility into the plant floor and understand what’s going on. You need data to understand, and better yet, real-time data.

Data-Driven Culture Simple Breakdown

To have success as a manufacturing company, you need to become a data-driven company and establish a strong data-driven culture among the entire company. There are 3 key aspects to a data-driven culture. These aspects are simple to define but can be tougher to truly implement.

Steps to properly using data, creating a proper data-driven culture.

  • Understand What’s going on

    • You need to develop a deep and detailed understanding of what is happening on the plant floor, inside your machines, and in each department of the company. This is your baseline, your starting point, the initial gathering of data and analysis. Everything starts with what you already have so it is essential to know these things very well.

  • Make Decisions

    • Once you have an understanding of what’s going on, you can start to make decisions based on the data you have. Make sure you are making your decisions based on complete and accurate data.

  • Take Action

    • You have a deep understanding of everything happening within the company, and you have made your data-based decision. Now you need to take action based on the decision. Remember the goal is to find and eliminate inefficiencies to drive revenue and profit. You already have the data to find the inefficiencies, now carry out the decision and make the proper adjustments.

In doing these 3 things, you are developing a competitive advantage. You are creating data asymmetry between yourself and the competition, giving yourself the edge.

5 Facets to Creating a Data-Driven Culture

A graphic showing the 5 facets of creating a data-driven culture.

1. Single Version of the Truth

    • All data needs to come together into one place that is available and acceptable for everyone to analyze and consume. This creates a centralized solution with one version of the truth. No separate databases for different departments inside the company.

2. Data and Tools

    • Proper data and tools need to be available for each person within the organization. They should be readily available to any person at any given time.

3. Broad Data Access

    • Falling right in line with having proper data and tools, the data itself also needs to be widely accessible for everyone. Everyone should be able to benefit from accurate real-time data. It’s important to ensure everyone will have access when creating a data-driven culture.

4. Data Dictionary

    • There needs to be some definition somewhere of what data is being tracked. It should show each data metric that is being gathered. Some type of literature should be made available so that each person will know and understand what data will be available even though not every person will use every single data metric.

5. Decision Making

    • People need to understand how to analyze and consume the data, how to review it, and come to a data-based conclusion. Then, be enabled to take action based on that data-driven conclusion.

These are all great characteristics of a data-driven company and principles that company leaders should consider when trying to establish a data-driven culture. But where should you start? There are 4 stages most companies go through on their journey to creating a data-driven culture.

4 Stages to Creating a Data-Driven Culture

The 4 stages most companies go through on their journey to creating a data-driven culture.

Stage 1. Conjecture

This is when the company has little to no data available to them. Making adjustments and decisions based on past personal experiences- it might be knowledge from a past job, or just a gut feeling. Generally making quick decisions on what to do without much confidence and no real substance backing the decision. Many times they are not very well thought out or consistent conclusions.

Stage 2. DIY

The company is starting to collect data using an excel sheet or similar program. Typically a small database is scattered across the company. You might be recording data manually on paper, or using a whiteboard reporting system. There is a possibility that you are manually sharing data with a handful of people in the company. Many times in this stage, it’s the tech-savvy people who are asked to record, manipulate, and analyze some basic data. Of course, this data will be very limited in detail, accuracy, amount of data, and will often times be old by the time it gets used by a decision-maker.

Stage 3. Towers of Babel

First of all, I love this term, it makes perfect sense. This is when companies are further along in their journey of creating a data-driven culture and truly becoming data-driven. What typically happens in this stage, is the company unintentionally ends up creating numerous silos of data. Each separate department has 1 or 2 primary databases for collecting data. The good news is they are collecting more data, better data, and more systematically.

It’s much easier for the company to get data from these databases as opposed to the DIY stage. Much better for gathering data and getting some basic visualizations. However, it’s a situation of BYOBI (bring your own business intelligence).

This can work for a while but for a company to continue down the path of maturing, growing, driving more revenue, profits, evaluations, etc. These companies need to keep improving and drive even further towards becoming data-driven companies.

Stage 4. Nirvana

In stage 3, companies got their databases to drive data-based decisions with accurate data. So what’s next? Creating a single version of the truth that everyone has access to. The problem with stage 3 is that each department has different data, different data sources. These are all separate solutions, not centralized for everyone. Everyone needs to be on the same page, with the same data. Sales won’t know what marketing is doing, marketing won’t know what operations is doing, operations won’t know what purchasing is doing.

There needs to be one single, full architecture for data with a single version of the truth. A data architecture where all data is consistent and shared between every person and every department of the company. A Unified namespace. This type of solution allows for flexibility and scalability. It is also the most accurate data when it is from one single location and extracted directly from the equipment itself. One centralized database for everyone to make decisions and drive action.

Where to Start

It takes some time and effort to completely transition into becoming a data-driven company. It’s easy to get overwhelmed, and tough to determine where you should start today.

Studies say that analytics pay back $13.01 for each $1 spent (source). There’s a lot of value in being data-driven, getting started as quickly as possible can be crucial for company success.

Analytics typically returns $13.01 for every $1 spent visualization.

A good way to get started on your journey to creating a data-driven culture is to define your own business challenge. Determine where you need the most improvement, and start gathering data. Even if you are just starting with pen and paper. There are stages to becoming a data-driven manufacturer, and you need to start somewhere.

Example

Let’s say that your specific business challenge is not understanding your asset utilization. Not understanding why your machine line or plant overall is not producing as much as you think it should. This is a perfect opportunity to start gathering some basic data. Start now, and try to develop a good understanding of where you are today. How active are your machines? When are the machines off completely? When are they on, but not running? When are they on and running, actually adding value and producing parts?

Gather and analyze that data over time to understand what’s going on. Start capturing downtime reasons– not just that the machine broke, capture the cause of downtime and the machine’s symptoms. Gather data, analyze it, make a decision, and take action. You can look at an asset utilization + downtime reason code Pareto chart to understand what’s going on and what the inefficiencies are.

Conclusion

The above example is a great way to start. The important thing is that you get started, start working towards creating a completely data-driven culture. Use these first small collections of data to help you determine which long-term metrics would benefit you most to keep tracking, and go from there. Start small, think big, and take action.

 

Could be using this laptop to analyze common problems in the manufacturing industry.

3 Common Problems in the Manufacturing Industry that are Holding You Back

/in , , , /by

3 Common Problems in the Manufacturing Industry Holding You Back

There are many common problems in the manufacturing industry. We can all agree that there is no shortage of problems that need to be dealt with. Especially in the industry, there are a lot of people involved and a lot of big machines with many moving parts. Inevitably, with that amount of moving parts and people, there are bound to be issues that come up frequently. These problems go far beyond just the plant floor.

All of this means that there is a lot of money moving around and changing hands often. Even small errors can equate to a large amount of lost revenue and profits. In this article, we will discuss 3 common problems in the manufacturing industry that could be holding you back. The goal is to help you make your factory as efficient as possible and maximize revenue and profits.

1. Lack of Skilled Workers

Let’s not waste any time, and get right to the facts. Manufacturing is a huge industry, employing a lot of people. It’s the fourth largest industry in the united states based on the total number of employed persons (source). This is good, a lot of people working in the industry. Here’s where it starts going downhill- almost one-fourth of the manufacturing workforce is age 55 or older (source). There’s not a lot of young people pursuing a career in manufacturing. This is a growing problem for manufacturers. It’s not a good sign when roughly a quarter of the manufacturing workforce is on the cusp of retiring, along with low numbers of new and young employees entering the field.

This is forcing manufacturers to come up with a stronger recruiting process, and looking for ways to attract more young people to the sector. This may not be the main problem that you think about on a daily basis, but it is not to be overlooked. The statistics are scary for the future of manufacturing.

2. Lack of Awareness

The second common problem in the manufacturing industry to go over is general awareness. There are many different angles you could take here.

Overall plant floor awareness, downtime awareness, accurate data awareness, digital transformation awareness. At the beginning of this article, I mentioned that there is a lot of moving parts and people in the manufacturing industry. It can be a challenge to keep track of and remain aware of everything happening. On the other hand, it is near impossible to make improvements if you aren’t even aware of what’s really going on. So the first step to improving operational efficiency is to become aware and accurately define where you need to focus or improve.

Downtime Awareness

More often than not, manufacturers cannot accurately estimate how much, or where their downtime is coming from. Downtime is a leading cause of lost revenue in manufacturing. That means it should definitely be made a point of emphasis, and that manufacturers should at the very least be aware of where of the amounts and causes of downtime. Sadly, downtime awareness is still a very common problem in the manufacturing industry.

Accurate Data

The way in which you gather data changes everything. It doesn’t matter how much data you are attempting to gather if it’s all data that you can’t validate, and you can’t trust. Many times, manufacturers will gather data, see the numbers, and not believe them or disregard them. This makes the initial act and effort in gathering the data completely useless and reinforces the value of using a reliable system for gathering data that you can trust. Manufacturers need to become aware of the validated, trustworthy, and accurate data that they could be extracting from their equipment and benefitting from.

With IIoT and manufacturing analytics technology becoming more available in recent years, it’s just that much more important to have accurate data if you want to continue to grow, stay competitive, and become a data-driven company.

3. IIoT and Industry 4.0

This leads us to our last common problem in the manufacturing industry. You might wonder, how are IIoT and Industry 4.0 common manufacturing problems? The problem is not the technology or the solutions themselves, it’s actually more of a mindset problem. A large number of manufacturers are choosing to ignore the value that IIoT and Industry 4.0 bring.

While others are open to the ideas, but struggle to become data-driven and use the technology to its fullest capacity. These companies need to focus on solving their business challenge, rather than focusing on the technology challenges. Instead of saying- “How can we equip our factory with the latest and best technology?” Ask yourself- “How will this actually help me?” Or, “What will get improved by gathering, analyzing, and acting on this set of data?”

A Happy Medium

There are two extremes here, the best spot is a happy medium in a way. No doubt that there is enormous value in gathering accurate real-time data. IIoT along with Industry 4.0 principles bring this to the table. Becoming a data-driven company is also very important. However, becoming a data-driven company means that you are acting on the data, driving decisions based on this data. Becoming a data-driven company does not mean just extract as much information, data, and generate as many reports as you possibly can. Gather what you need to improve efficiency, and solve your business challenge, any data you aren’t acting on is useless.

How you can Take Initiative to Solve These Problems

These are common manufacturing problems, but that doesn’t mean that you have to just live with them. There are things you can do to minimize these issues in your factory.

The problem with a lack of skilled workers might be a job for the industry as a whole. However, becoming aware of everything happening on your plant floor, with your workers, and inside your machines is something that you can improve. Taking advantage of IIoT and Industry 4.0 can prove to be greatly beneficial. Equip yourself with the right system that meets your needs and solves your business challenge. Adjust your company culture to become a data-driven company. Your operators want to improve their process, they’re smart, allow them to benefit from accurate data. IIoT can deliver real-time data to operators and decision-makers instantly.

We established that awareness is also a very common problem in the manufacturing industry. Be aware of your specific problems, and use the tools available today to solve those problems. If you actively search for ways to get better and generate a solid plan of action, the results will be rewarding.

This manufacturing process could have improved efficiency if they would engage in data-driven manufacturing.

Why Data Driven Manufacturing gives You the Competitive Edge

/in , /by

What is Data-Driven Manufacturing?

Data-Driven Manufacturing is leveraging data gathered from the plant floor so that everyone in the plant from operators to COO’s can adjust and make improvements to the overall manufacturing process. Data from your equipment allows everyone to make confident decisions based on solid information. 

There are many ways that you can acquire data, with some being much more efficient than others. You could hire someone to go to your plant floor, and manually record data from machines with pen and paper or have the operators do it themselves. Although this is a very straightforward concept, it likely is not the best solution for you. Having people manually record data sounds like a simple, efficient, and very straightforward task. However, the data typically recorded on paper ends up being old (once it’s in a state to be analyzed), moderately inaccurate, and limited. The truth is, there is a much more accurate and efficient solution available today. 

Implementing an IIoT manufacturing analytics system. These kinds of systems will allow you to pull data directly from the machines on your plant floor, to provide you with data that was never available before, in real-time. Some wonder if we can rely on this data to be accurate, without human interference. As humans, we will make errors, and improperly record data from time to time. IIoT systems pull data from PLCs, controllers, and sensors to pull data right from the equipment. There is nothing in between, it is the most straightforward path possible. This means that you get the most accurate, and detailed information in the quickest way possible. For the data that may not be available from the equipment, HMI or operator data entry screens can be provided for operators to enter the pertinent process, production, quality, and availability/downtime data. 

Why You Need to be a Data-Driven Manufacturer

Making decisions with solid substance behind your reasoning is invaluable. We have established that implementing an IIoT system is the best way to gather data. How does the data you are gathering really make such a big difference? There are so many different categories of data that IIoT systems can attain today. You can track downtime with reason codes, production throughput, temperatures, vibration, and scrap just to name a few. That data can then be converted into more valuable information like OEE, downtime Pareto chart with reason codes listed by occurrence or duration, product goals versus actuals during the middle of the day, and more. 

If you are tracking these metrics and seeing the real performance, inevitably you will find room for improvement, even in areas where you might have thought you were doing pretty good. Seeing the actual numbers is shocking to most people. 

Continuous Improvement

When you are driven by data, you will see all the areas that need improvement. Since you are looking at precise data you will also see exactly how much room for improvement there is in one area. Right from the start, you will clearly recognize your biggest inefficiencies. Now you will know exactly where to focus your efforts. Real-time data will assist you in solving the problem, and getting instant results. In our experience it can take from 1-2 days to 2 weeks to get data recorded manually, data entered into a spreadsheet or database and then converted into usable metrics. The real-time availability of data with IIoT solutions is immensely valuable and puts you right on track to running the most efficient factory possible. 

Confident Decisions 

Being a data-driven manufacturer can help you in other ways that you might not put much thought into initially. You’re able to make confident data-based decisions. Empirical data including numbers and large amounts of the right data backing up your decision are undoubtedly a massive benefit. You’re able to work in a clearer state of mind, not continuously thinking about a past decision, wondering and hoping that you made the right move. Realistically, you have more than one thing on your plate and the added stress holds you back. When you eliminate the guesswork and know that you made the right decision based on facts, it will help you tremendously. Allowing you to solve issues quickly and effectively, and shift your focus onto the next task. 

Why Being Data-Driven will Generate You more Revenue

IIoT systems, manufacturing analytics, and real-time data are great tools and features to help convert you into a data-driven manufacturer. This is, of course, an investment. Here is how data-driven manufacturing will take your company to the next level by increasing efficiencies, reducing costs, increasing production, and in the end increasing revenue. 

Once you see the data, you will have an accurate perspective on efficiencies in your operations. Once you gain that awareness you’re better able to improve efficiencies. You’ll be able to track data to decrease downtime, increase throughput, increase OEE, you will understand your machines better, and move aware from expensive maintenance practices like reactive and preventative to more valuable practices like condition-based monitoring and other efficient and proactive maintenance practices. 

Here are some additional areas of plant operations that can be improved with IIoT solutions: 

  1. Project Lead Times

    This area will be improved if you act upon the data that you are now receiving. More units manufactured faster, as your manufacturing process efficiency is improved, your project and lead time will inevitably go down. Now that you can get projects done faster, you have time for more projects. More projects is more money.  

  2. Downtime

    Downtime will decrease. Whether it is scheduled or unscheduled downtime, the numbers will improve. Once you have PLCs and sensors installed that allow you to see what is happening inside the machine, you will learn the reasons why that machine might be going down. You will have the historic data to find trends in machine behavior, this will help you avoid unscheduled downtime. You will also know when you will need to schedule maintenance on the machine based on the actual, current health of the machine. Before you were tracking this metric, you might have even been spending too much time on maintenance that the machine did not require. Unscheduled downtime is always bad, but having inefficient scheduled downtime can be just as detrimental.  We have another blog post focuesed solely on reducing downtime- If you think you could benefit from reduced downtime check it out here.

  3. Customer Relationships

    Improved customer relationships. Your customers will appreciate you engaging in data-driven manufacturing just as much as everyone on the plant floor. Each job has different requirements and a lot of variables. Those variables can cause issues including poor quality and/or late deliveries. If you have historical data and an awareness of current conditions in the plant you can then be more proactive to make adjustments on staffing, routing, shifts, parts and raw materials needed for jobs, etc. You can then give the customer updates with precise and detailed data for when their orders will be delivered. If you can provide more insight into your manufacturing process, you will establish trust and transparency. Happier customers lead to more return customers, better reviews, and a great brand reputation.  

Put Yourself in Position to be an Industry Leader

More insight, more throughput, less downtime, happier customer, and smarter employees that have machine insights and make decisions based on the facts. These are many of the traits companies have that are leaders in the manufacturing sector. 

Technology is advancing at a quick pace. These capabilities are becoming more and more available to everyone. Being a data-driven manufacturer is not just for the Teslas and the Amazon’s of the world. Today, it is available to even small-medium-sized businesses. The ones who are taking advantage of this are the ones that are growing, making a name for themselves, and will thrive in the coming decades. Don’t sit back relying on the old way manufacturing has been done for the last 30+ years, put yourself in a position to lead the industry.  Seemingly small details are the difference that can make or break your results in the future. 

Machine Downtime and OEE: Example, Tech Choices, and Recommendations

/in , , , , , /by

Machine Downtime and OEE: Example, Tech Choices, and Recommendations

In previous articles, we have discussed machine downtime and OEE including why machine data is valuable, how to use the data, and how to get the data. Here we will provide an example and some other helpful information on monitoring machine downtime and how to improve overall equipment effectiveness. To calculate your own OEE, you can use this OEE calculator.

 

Example

In this example, we will use Kepware as a recommendation. Kepware is a very powerful and flexible tool. It is well accepted across industries and very well known. They have great support, can connect to many types of protocols and devices, and is well maintained.

Using Kepware to connect, you will:

  • Set up a channel over which to communicate with the device driver (MTConnect, Bacnet, Modbus, etc), and the network card to connect
  • Add a device or machine to connect to including the IP address and set up various data settings
  • Then add the Tags or fields with names and addresses in the machine
  • Open the Quick Connect tool
  • Connect to the machine
  • Test to see the data coming through

When pulling data you can either have Kepware push the data directly to a destination such as a database or a setup another system to pull the data from Kepware. You will view this data in an IoT software platform. What you view is depending on the IoT software platform you’ve selected including its capabilities, what is required to set it up and connect to data sources. Viewing data will also depend on how you’ve set up the logic in the system to process data and how you’ve set up screens to display it.

Choices of Tech

There are several options available out there to choose from and deciding which is best for you can be tricky. While you will need to decide which best suits your needs, here are a few options for machine downtime data gathering and processing we recommend:

Kepware

      • Industry-standard product, widely recognized
      • Is a PTC company
      • Translates data from multiple protocols including for CNC machines: MTConnect, FOCAS for the GE FANUC controllers, and others
      • Great support
      • Can fit into multiple solution architectures

ThingWorx

      • PTC is a well known and trusted brand in engineering and IoT solutions
      • Very flexible development environment for solutions
      • Multiple products to greatly extend the solutions
      • Wide support by many partners, of which we are one

Microsoft

      • Household name brand
      • Database, software development, and cloud tools are industry standards, including in manufacturing
      • Flexible tools provide multiple options for solution designs

Best Practices and What’s Next

Use best practices for LAN design, security, database structure, naming conventions in the data layer, etc. Use open standards: OPC, OPC UA, MTConnect, ISA 95, for example: OPC can be useful because it creates intelligent rules for how to collect data and how to verify have right data.

The next steps are to monitor the machine, look for issues that are valuable low-hanging fruit, address them, then watch the numbers over time to see if there are improvements.

Final Recommendations

Be thoughtful for now and the future with Industry Best Practices. Solve the problem now and at the same time set up a foundation for growing the solution in the future. Setup a framework with a data model, and a network that will standardize how to interface with any machines in your plant. This will greatly simplify setting up new machines and simplify how to access the data.

Machine Downtime and OEE: How to Get the Data

/in , , , , /by

In order to get the data needed for properly measuring machine downtime and improving OEE, you will need to develop a few things. You will need a well-defined business case, you will need to work through a complete planning and scoping phase, and finally, you will need to develop a proof of concept.

Planning and Scoping

In planning and scoping, a company will need to define a problem to solve. Some key things to keep in mind here are to keep it simple, as overcomplicating the process can create unnecessary difficulties and delays. If you’re not sure about the productivity of the machine and operator, you will want to get more data. More data provides more information to accurately answer this question. And you will want to get downtime data, as this is the ideal place to start in improving overall equipment efficiency.

Getting more data sounds great, but what data do you really need? To define this, you will need to answer these questions:

  • What KPIs use to drive operations excellence?
  • What metrics used to measure performance, production, quality, and availability?
  • What does the data from the user say about when the machine is in use and not, and why?
  • What does the data from the machine say about when it is running and not running?
  • What are the failures in the machine with failure codes?

Next, you will want to review the machine. This includes all relevant machine information such as:

  • Machine model number, vendor name
  • Is there an alarm screen, alarm lists, or status screens about state and condition? What types of faults and alarms exist?
  • Do we have technical manuals that list functions, operations, and data points available?
  • Does it have a PLC? Model, vendor? Other types of controllers?
  • What is the communications protocol?
  • Is there an ethernet port?
  • Are there extra modules or licenses to buy to pull data from the machine?
  • Can data be read in real-time? Or must be downloaded via CSV or another method?

You will want to review your network. In order to gather data, you will need to know if the machines are connected, and if so how they are connected. You will need to know if a network exists and if it is hardwired (Ethernet, RS-232, RS-485, other), if it is on Wi-Fi, and what type.  And finally, what kind of security this network has.

Selecting a software platform is the next step in your planning process and a crucial decision. If there is any chance you will connect to more machines, pull more data from machines, and expand the solution in other ways, a platform should be considered that has flexible visualizations, data models, many options for connectivity, data historian, etc. You will need to ascertain which IT servers and databases are already in place on-premises, if a cloud solution is acceptable and if you really need data to be viewed in real-time.

You will of course need to design the solution. In doing so, you will design the LAN setup and other connectivity required connectivity modules for the machine,  protocols, tools to translate the data, tools to push the data to data storage, data storage itself, data processing for calculations, and the software platform for visualizing the data.

Finally, you will create a plan. This project plan will include a projected schedule, WBS, team members, responsibility assignments, and pricing for all services and products to purchase for the project.

Proof of Concept

The proof of concept will be an experiment or pilot project. This of course will be done with all the previously mentioned data and preparation, but it is not a final product. Some may worry that creating an IoT solution may be a one-time implementation – but it is instead an evolving process, and so a proof of concept is a valuable first step.

During this phase, you will want to set up a machine with modules, and you need a vendor to do this. You will set up a network and gateway devices, and software including Kepware and the chosen IoT platform. Next, you will connect the machine. Then, you will map the data to the OPC UA product, push data to the IoT software platform, and display the data in that IoT software platform.