Use this simple formula for lead time improvement

Simple Formula for Lead Time Improvement

Simple Formula for Lead Time Improvement

Lead time improvement for manufacturers will always be an issue. Simply because any manufacturing process will never be completely optimal, there is always going to be room for improvement. That is the mindset manufacturers must have in order to grow and remain successful.

On-time delivery and lead time improvement are complex issues because there are so many factors, everything is involved. The back office is involved, suppliers are involved, everyone and everything on the plant floor is involved. When you try to focus on improving lead times and OTD, you can get overwhelmed very quickly. Where do you start? Why? What will actually help? How do you know if the adjustment you made even made a positive impact or not?

Small, Incremental Changes

Small, incremental changes are the key to improving lead times. With a process so complex, you need to tackle it one step at a time. You aren’t going to be able to change everything at once, it won’t happen overnight. Furthermore, if you make too many changes at one time, how do you gauge what worked and what didn’t? It’s almost impossible to make that differentiation.

What do you need to help you get there? We’ve established that making small incremental changes is a viable approach, but there are still so many areas to improve. How do you decide where to start?

It’s like walking up a staircase. Each step is small, but after a little bit, once you get to the top and look at how far you’ve gone, you realize how each small step added up to make a big impact. Furthermore, you made it easy by taking it one step at a time, small incremental progress. Think about how much tougher it would have been if all you saw was where you stand, and where you need to end up, 50 feet higher. Don’t make it harder than it has to be!

Graphic showing how small incremental changes are the best approach for lead time improvement.

Equip Yourself with the Proper Tools

Managing the entire manufacturing process from beginning to end is no small task. That’s where modern manufacturing intelligence solutions, such as an MES, can step in. An MES can manage everything that happens on the plant floor- every person, every machine, every product, and every process. In order to start making changes, you need information. You need to know what is happening on the plant floor- when materials arrive, who is running what machine, how much downtime you encountered, how much scrap, how long do your changeovers take, and the list goes on.

If you don’t know any of these things, how will they get improved? How would you cut back on downtime if you don’t know what’s causing it? Or, how do you improve changeover times if you don’t know how long your current changeovers are?

On the flip side, if you have access to this information, you can start making decisions based on it. Decide on a few metrics to track in areas that you can improve, gather the data, analyze it, and make a data-driven decision.

Data drives our world, and it should be driving the changes you make on the plant floor to improve lead times and your overall manufacturing process. However, this is only the first step. Nothing happens until you take action.

Enable Yourself to Take Action

Once you are equipped- you have the tools you need, you’re tracking metrics and processes, and you’re making data-driven decisions to improve your process. Now, it’s time to take action. Nothing happens until you take action. Time to execute.

It almost sounds too simple. Not easy, but simple. The whole process is to gather information, make a decision, and take action. Make the data work for you, leverage it, use it to your advantage.

Example

The process could look something like this for example: Let’s say that you are a discrete manufacturer, and you determined that with the numerous product changeovers that take place every day, that would be a good process to start tracking and drive improvement.

You start gathering data and notice that operator 1 is much more efficient than operator 2. So, you start to analyze the data and find out why. Once you come to a conclusion based on the data, drive action. Maybe it was operator 1’s loading tendencies that made his product changeovers so much more efficient. Take note of that, and make it part of the standard process among the entire company.

Boom! One thing off the list- product changeovers are improved, you can use real-time data to instantly gauge results, and move on to the next area.

That’s one more small, incremental step towards an extremely efficient manufacturing process and improved lead times.

Concluding Thoughts on Lead Time Improvement

Now, will this cut your lead times in half overnight? No, but the sequences of small incremental changes make a big difference over time. Make sure that you are 1. Equipped, and 2. Enabled. This way, you will know:

  • Where you stand,
  • How to identify your own inefficiencies,
  • Where you can improve, and
  • How much of an impact your adjustments really made.

We all want results instantly, but oftentimes that is not reality. Position yourself to make quick wins on a consistent basis, and continually improve. Establishing these things will put you in a great position for company growth and improvement in many areas, not just in improving lead times.

The little things add up, small but consistent steps will make a big change over time. Remember to make your data work for you, take it one step at a time, and be proactive.

What digital transformation questions do you have after looking at this picture? The picture illustrates what types of technologies are associated with digital transformation.

Answering Common Digital Transformation Questions

Answering Common Digital Transformation Questions

Digital transformation has been a hot topic in the manufacturing industry for some time now. In this article, we put together some of the most-asked digital transformation questions and answer them. If you come up with any more questions about digital transformation, industry 4.0, or IoT, etc. Contact us here and let us know, we would be happy to answer.

Without further ado, let’s get started.

What does digital transformation really mean for today’s business leaders?

First off, it’s a real opportunity for change. It’s helping organizations go from point A to point B, from the current state to a future state. How do you do this? By recognizing the value of data, and then actually implementing practices, strategies, and principles to get value from data.

When you start the process of changing company culture and how the company works, then you can really start to see a positive difference. A large part of digital transformation is becoming a data-driven organization with all data coming from a single version of the truth, one source.

Leaders can think about how the company can attract new people and new generations. Digital transformation is also a way to increase company evaluations. Believe it or not, nowadays many private equity groups are investing in digital transformation initiatives when they purchase companies to drive the evaluations higher.

So, to sum it up in one sentence- Digital transformation brings opportunities for positive change and higher company evaluations for business leaders.

Make the transition from paper to digital on the plant floor.

Where should businesses start with Digital Transformation?

We believe that there are 3 main stages here.

The first stage is to figure out where you’re at. The second stage is to figure out where you want to go. And the third is to figure out what that roadmap looks like. So, to go through a digital transformation you need to go through those 3 steps.

Stage 1

Where are you today?

Start looking at the makeup of your organization- Who would be accepting of this change? Who will be opposed?

Figure out where you stand in the market today, start to do a S.W.A.T. analysis on your company. Take an inventory of the business, as well as the intelligence. The use of data, networks, and software, don’t forget to note the lack of data, networks, and software. What data do you have access to right now? What’s available within the company that you don’t have access to? Typically, the majority of data is actually available, but companies aren’t aware and don’t have access. Data stored in excel, on paper, whiteboard, in people’s heads, but no access.

Stage 2

Where do you want to go? 

Look at what the companies vision is, and if there is none, then now is a good time to set a clear vision. Then, you can start to think about how the company wants to work with data. Do they truly want to change and become a data-driven organization? Becoming a data-driven company has proven to be immensely valuable today. If you look at the companies that are taking over today, they are all data companies. Facebook, Google, Uber, Air Bnb, and even manufacturers such as Tesla.

Stage 3

Create a roadmap for how to get there. 

How are you going to get where you want to go? What do you need? How fast? What help will you need? Which people? What tools?

Start to define a digital strategy that is defined by company leadership and pushed down throughout the rest of the organization.

This digital strategy then helps companies realize:

  1. The data that they currently have,
  2. The value of that data, and
  3. How to use that data.

Along with this digital strategy, a number of rules, principles, and best practices should be put in place. This will help you to define the activities, technologies, and architectures that are used to implement a solution.

What are some Common Problems companies come across when attempting a Digital Transformation?

I try not to focus on IT departments so much here, but they seem to be a common roadblock. This sounds backward right? they are the IT guys, they work with data, software, and the latest technologies all the time. Sometimes, they do embrace this change, but many times this is not the case. A common reason IT departments are opposed to these technologies is that they might require a different approach than they are used to.

Example 1

A specific example of this would be when the IT department says that everything is in their ERP system, and they have no need for anything else. The truth is, an ERP system leaves a lot on the table and doesn’t pull much data from the plant floor if any at all.

Example 2

Another example is that the IT department might want to take data from the plant floor and throw it up to a data lake. The problem here is that you lose context, data gets lost, and there is no organization to the data. Furthermore, many people working on the plant floor lose access to this valuable data once it is thrown up with the other IT projects in that data lake.

Example 3

A third example- some IT departments (not all of them) act as a firewall. The difficulty here is that their mindset is to keep everybody out, which is understandable from their perspective, their job is to keep everything secure. However, there are of course ways around this. Every department within a company should be a service organization for the other departments. IT is no exception, they really should be a service organization as well as an enabler for this type of technology.

What Culture is Needed for a Digital Transformation?

Part of the culture for a company truly trying to grow and become a digitally transformed organization should entail identifying the people who are opposed. Then, find out how to work with or work around these ones.

The culture should also be a place where people are open to change. People should want to drive innovation, want to participate, want to see the company headed in the right direction for growth. Consequently, this also helps the people within the company grow, something that leaders should keep in mind.

People should be on the same page with leadership. If there is any disconnect or disrespect happening, it should be addressed. The goal should be with this digital transformation, and with these good leadership conversations, that can start to turn around so the entire company can run as one solid unit.

Illustration of a company leader leading his employees and trying to establish a new, better company culture.

Why do I need to transform digitally?

The whole purpose of a digital transformation is really to make a company data-driven. And the whole purpose of a company becoming a data-driven organization is to understand what’s going on. To get deep visibility directly into the plant floor in real-time so that you can solve challenges, drive innovation, and make improvements to your process.

How do you know whether the digital transformation is working at your organization?

A lot of this deals with the aspects of becoming a data-driven organization. Remember that single version of the truth that I mentioned earlier. For this to work, people need to have the tools to access the data, they need to be educated on how to use the data, they need to be enabled to work with that data. Most importantly, they need to proactively make decisions and take action.

Circling back to our initial question, how do you know if the digital transformation is working? Answer these questions- do people in all departments and layers of the organization have access to the data? Do they have the tools they need? Are they enabled? Finally, are they making decisions and taking action? That is how you can measure success.

Remember, digital transformation is not a project, it’s an ongoing strategy.

What other Questions should we Answer?

These are a few common questions that we hear all the time when talking about digital transformation. What other digital transformation questions do you have that we could answer? Leave us a message and we will be sure to answer.

Digitally transforming your company is one essential step to preparing yourself for Industry 4.0 and the future of manufacturing. Historically, the vast majority of companies fail to transition from one industrial revolution to the next. Much of this can be attributed to mindset, company culture, and not being willing to adapt and change with the times.

Think big, but start small. Start planning, start preparing, and start working towards digitally transforming your manufacturing company to stay competitive and ensure a successful future.

Illustration showing two cars, one with many "micro stops" holding it back, and the other with only a couple stops- allowing the second car to reach the destination much quicker.

Are Micro Stops Quietly Gashing Your Production Efficiency?

Are Micro Stops Quietly Gashing Your Production Efficiency?

Micro stop: When a machine or piece of equipment quickly stops and resumes production, typically as a result of a temporary issue that is resolved in just a few seconds or minutes.

These small, quick micro stops may seem harmless and just a part of the process. While it’s near impossible to completely eliminate all micro stops on the plant floor, they can actually make a huge impact on your overall production efficiency and your bottom line.

Although downtime is a popular topic, capturing downtime occurrences, reasons, and developing a deeper understanding of what is really happening on the plant floor has proven to be a struggle for the majority of manufacturers.

If manufacturers are unwilling or not properly equipped to capture downtime reason codes from complete machine failures, you can only imagine how many micro stoppages are being completely ignored on a daily basis.

These micro stops are eating into your production efficiency, yet it seems to be the downtime that no one pays any attention to.

The Downtime that Nobody Talks About

As I mentioned, everyone talks about downtime, but not this kind, not micro stops.

It’s fair to say that you want to tackle the biggest problem first. You want to spend your time, energy, and resources solving the most important, most valuable problems. With that being said, how do you really know what your biggest problem or biggest inefficiency is? You might be surprised if you saw some numbers rather than relying on a gut feeling or reasoning that x is just obviously more important compared to other issues.

For many manufacturers, especially discrete manufacturers- micro stoppages end up accounting for more downtime than “big” downtime. The high volume of product changeovers and movement around the plant floor makes the numbers add up quickly.

The problem is that manufacturers A) are not tracking processes, and B) constantly overlook and completely ignore micro stops. If a machine fails and is down for a couple of days, you would of course realize it, and ensure that the issue is resolved and the machine is up and running again as soon as possible. On the other hand, when a machine stops production for 30 seconds here and a couple of minutes there due to a small temporary issue, you very likely just fix it and keep going. The odds are, you never think about the issue again and just keep moving.

These micro stops occur many, many times throughout the day. I’m not saying that you should spend 20 minutes analyzing a 30-second problem all day long. However, I am saying that gathering information and making the proper adjustments to increase throughput and machine utilization in the long term is unquestionably worth it.

What Counts as a Micro Stop?

Micro stops can account for a large portion of overall downtime and have a major impact on your OEE. Micro Stops can really be anything that causes downtime for a short period of time. Here are some common reasons for Micro Stops:

  • Operator error
  • Small machine configuration errors
  • Machine process parameters
  • Inefficient process loading/unloading parts

Why Manufacturers Struggle to Reduce Micro Stops

It’s actually a really simple answer. Nobody notices micro stops, nobody pays them any attention, so nobody tracks them. How do you expect to improve a process that you know nothing about and that you ignore? Simple answer again – you don’t. However, that is the bigger problem.

The fact that the majority of manufacturers don’t pay attention to micro stops and don’t see a real reason to, shows how much of a non-issue they view it to be. In reality, micro stops have the potential to be your leading cause of downtime or the biggest area for improvement. Manufacturers should strive to have a continuous improvement mindset, which does not mean to only give attention when something is broken. You should be proactively looking for inefficiencies, quickly making adjustments, and keep moving.

What You Can Do

One simple way to cut back on micro stops is by analyzing your top-performing employees. Gather basic data detailing when their machines were producing and not producing, gather time-stamped basic information, track their habits, how they approach situations, their whole process from start to finish.

Then, you analyze that data and make it part of the training. This process works particularly well for machine setups and dealing with product changeovers- times when micro stops occur very often.

Here is how tracking this type of process could work + making a data-driven decision to improve the process:

  1. Pick out a top-performing employee
  2. Track their entire manufacturing process from machine setup to finished product
  3. Gather basic time-stamped data detailing when the machine was producing and not producing
  4. Analyze the data to determine why this employee has fewer downtime occurrences, and what makes their process superior to others
  5. Make this process part of the training for other employees

Simple, but extremely effective. Use your best employees’ experience and good habits to your advantage. We have another article dedicated to detailing this process further and points out how you can create value by taking one of your top employees and “cloning” your other employees to match their tendencies. Click here to check it out.

Track Processes

Manufacturers need to start tracking these processes. Track changeovers, how machines are loaded, which operators have more micro stops than others- and analyze the data to figure out why. Maybe scrap and machine jamming is causing a high volume of micro stops that go unnoticed. Maybe operator changeover times are inefficient. You won’t really know until you start gathering data.

Get Visibility

Machines are only becoming more complex, and harder to understand thoroughly. It’s important to get visibility into your machines, help operators better understand what is actually happening on the inside of them. This will help them to learn what they can do better, why certain problems occur, why the machines are jamming, why so many micro stoppages are continually pausing production, and what specifically is causing downtime.

This data is just sitting trapped inside machines on the plant floor. All you need to do is equip yourself with the right tools to get that data out.

Once you have the data, you can analyze it to find inefficiencies, make quick adjustments, and improve your manufacturing process.

 

Don’t Forget About the Little Things

We’ve all heard somebody say that the little things add up. Whether you’re talking about spending a few dollars here and there or something else, it adds up. We all know it’s true and have seen little things add up to a large sum in one way or another. So, don’t forget about the little things, they make a huge impact. Don’t ignore and dismiss micro stops as just a part of the process or a small hiccup to take care of and forget about.

Processes that are tracked get improved. On the other hand, if you have no information, you have no substance to base a decision on or make any real improvement. Start tracking, start simple, prove the value to yourself and go from there.

Worker confused on what decision to make because of poor data reliability.

Data Reliability: Do you Trust Your Data?

Data Reliability: Do you Trust Your Data?

First of all, if you are even gathering and analyzing data at all, that’s a great start. It encourages the right mindset and means that you are trying to improve your manufacturing process. On the other hand, if you don’t trust your data and have weak data reliability, that data becomes completely useless.

  • Are you hesitant to act on your data?
  • Do you frequently want to rerun the numbers to make sure everything was correct?
  • Do you question the credibility of your data, and seek information from other sources?

If you answered yes to any of those questions, you need to take a step back and reevaluate your options. Data that can’t be trusted is a huge issue, and it becomes a large waste of time among other things.

Data Does Not Help You

(On its own)

If you find that you are hesitant to use your data, hesitant to make any decisions based on your data, and hesitant to act on your data, then what’s the point of acquiring it? Spoiler alert- there really isn’t one. At this point, you are basically just gathering data and information for the sake of gathering it. Not only does this not help you, but it is also setting you back in other areas. You are wasting time, money, resources, and losing production by gathering and analyzing data that you won’t use.

This is why data reliability is so important. Without reliable data, your time and monetary investment get thrown out the window.

What Now?

If you see yourself in a situation similar to this, what do you do? Do you just have to learn to trust your data? Of course not. If you currently don’t trust your data, there’s likely a good reason for that. Maybe you have proven the data to be wrong in the past, maybe you know that it is not being gathered from a reliable source. It could simply be the fact that the data is reliant on humans recording it. We are all well aware that humans make mistakes, and in this case, it causes inaccuracy in the data. Even if a human recording data is “usually’ spot on, what happens when you see a surprising number? Would you trust it enough to act on it, or question it and blame the person who recorded it?

You need to have complete confidence in the data if you are going to actually take advantage of it. You need to get yourself into a system that you can trust. A solution that delivers high-quality and accurate data that is efficient, does not rely on humans recording information, and comes from a very reliable source. Don’t waste your time and energy with a solution that you don’t trust.

Good Data Reliability = Confident Decisions that Drive Action

Even if you “kinda” trust your data, do you trust it enough to make a confident decision and take action? To invest more time, effort, money, and resources into fixing the inefficiency you found? The answer is likely no, or a hesitant maybe. It’s tough to say yes, you can’t be all-in if you are unsure of the credibility.

Illustration of a confident employee because of strong data reliability, information that he can trust and act on.

That is why it’s so important to have a reliable system for gathering and analyzing data. Good data reliability translates to a confident decision. Furthermore, when it comes to acting on your decisions, it’s much easier to act on a decision that you are 100% confident in.

As I mentioned previously, data alone does not help you, simply looking at a set of numbers will not get you anywhere. Making decisions and turning those decisions into action is what will drive improvement. Equip yourself with the right tools to help you solve real challenges.

Improve Efficiency with Good Data Reliability

Solutions with strong data reliability allow you to tackle a problem or inefficiency and move right on to the next. Modern plant floor solutions such as an MES or IIoT solution deliver real-time data to decision-makers constantly. This means you can find an inefficiency quickly, make an adjustment, and again quickly gauge whether that adjustment made a positive impact or not.

This gives employees and operators a lot of clarity. No lingering thoughts in your head wondering if you made the right decision or not based on weak or incomplete data.

Weak data and a lack of data continues to be a huge problem among manufacturing companies. A recent study concluded that 4 out of 5 manufacturers are completely unaware of their own downtime. They have no idea where it’s coming from. You can easily attribute this to simply not having the proper data or systems in place.

Further on that point, downtime get’s extremely expensive. In some industries within the manufacturing sector, the cost of downtime gets up to over $250,000 per hour. That’s a lot of money being thrown away. To each their own, but to me, I’d say that’s a problem worth looking into, and it all starts with gathering data.

This further stresses the importance of having good and reliable data that you can trust. Invest in a solution that provides you with credible information, and the results will quickly prove the value.

Graph illustrating manufacturing efficiency improving.

2 Strikingly Easy Steps to Improve Manufacturing Efficiency

2 Strikingly Easy Steps to Improve Manufacturing Efficiency

Improving manufacturing efficiency doesn’t have to be a super complex process that takes 6 months to get started. Before I give you the wrong idea, I have to say that at Ectobox, we put a massive emphasis on strategic planning. We believe that developing a strong company strategy and thorough planning is far more important than any physical solution or technology put in place. Now that that’s out of the way, we can get on with our main topic for today.

My point is, data comes from many different sources, different solutions, and can be gathered and analyzed in many different ways with each technique having its own pros and cons. Today, we are going to talk about a simple 2 step solution that depending on how you choose to approach it, could be planned out and started within a day.

There are 2 major steps to improving machine setups:
  • Gather and visualize data from the machine during the setups;
  • Review the data with the operator to get insights into what they’re doing.

Simple right? Almost too simple, let me explain more about how one might go about executing these 2 steps.

Every machine shop and precision metals manufacturer is always looking for ways to increase production, reduce scrap, and overall improve manufacturing efficiency (or at least they should be). As a discrete manufacturer with a large number of product changeovers- one area that can always use some attention is the setup of machines. Setups are among the most difficult work one can do in a machine shop.

The Impact Machine Setups have on Overall Manufacturing Efficiency

Improving machine setup procedures increases quality and production on each job. Many shops will have only a few operators that are exceptionally good at the setup work. These few operators prove how efficient machine setup can be. You need to capture and take advantage of their process and experience.

Many times, we see that these operators are asked to handle all of the setups.

Instead of just accepting that one person knows how to tackle a certain situation better than another, why not find out why and use it as training for the other willing and capable operators? If one person can accomplish a process effectively, so can your other operators. Start gathering and analyzing data from your top employees, determine why they are better, and make that part of the training.

2 Ways To Attain the Information You Need

How do you capture their knowledge and best practices? There are two options, both can get the job done. Although, one of which is obviously better than the other.

1. In-Person Observation

The first option is directly observing the operator in person while they perform their setup tasks and take written notes. This method is difficult and many details can be missed. It is also very time-consuming. Therefore, the value and potential for success with this process is limited.

2. Capture Data & Review with Operator

The second option starts with gathering and visualizing data directly from the machine while the operator is performing the setup. A good data chart is a timeline of events and activities from the machine. That chart, combined with a focused conversation with the operator, can provide some key insights.

An operations leader can pick out certain patterns in the data and discuss those with the operator. The operator can then explain exactly what they were doing at that time. During this conversation, the operations leader may find practices that make the process more efficient or yield better results than other operators. Those practices can then be codified and taught to other operators.

The process of using data and reviewing with the operator provides more accurate and valuable insights in less time than direct observation and note-taking.

What’s Next?

Now we know what we need to do and why. For the remaining parts of this article, we are going to put ourselves in this situation, and make an example of how this type of solution might work. Furthermore, we will see how much of a difference tracking this process can make, and the potential improvement on your overall manufacturing efficiency.

Setting the Scene 

In this theoretical situation, here are the details we’re working with:

  • We are a Precision Metals Company that Creates Machine Tools
  • Have High-Mix, Low-Volume Production
  • Vertical machining, CNC lathes, horizontal machines, mill-turns, and grinders
  • The focus is to keep the machine shop as productive as possible

Capture and Visualize Data from the Machine

In this situation, we are going to go with the second option that does not require constant manual data entry. Remember that this is a very simple solution. All you need is a program that can collect very basic data, a full-blown MES or IIoT solution is not necessarily required.

The first step is to connect to the machine. Potentially using a relatively universal CNC communication standard like MTConnect or the very common FOCUS2 protocol for FANUC controllers.

Gather and store the following data:
  • Machine’s Mode (Manual or Automatic)
  • Status (Running or Stopped)
  • Program Run Time with the Program Number that is Running
  • Part Count

Once connected record the data for one job that will run, even if it’s for a few days. Be sure to start recording the data before the operator starts the setup process.

Create a timeline view. It’s possible to use some basic data visualization tools too.

We also have a product called SensrTrx that can connect to nearly any machine and visualize any of the machine’s data in timeline charts. In addition, the platform provides downtime reason codes, analysis, and much more valuable data that you can gather from the plant floor. Although the information that the SensrTrx platform provides is extremely valuable, for this example we will stick to the basics.

Downtime analysis chart on the SensrTrx platform that can be utilized to improve manufacturing efficiency.

An interesting point is that for this example, the data can be raw from the machine. There is no additional contextual data needed. We most often work with additional contextual data because it significantly extends the value of the machine data. However, in this situation, the only contextual data we need is the dates/times and the job being run.

Review with the Operator

An operations leader, such as a production supervisor or someone higher up in the chain, should review the results. This person should be closely familiar with the machines and tools.

The operations leader should then review the operator’s activities in the timeline chart with the operator present. The operator can explain exactly what he was doing for particular events in the timeline. The combination of data from the machine as well as input from the operator with the data in front of them is the combination that will provide the best results possible.

The best setup operators are very efficient with their work. Their tools are well organized, they are effective in how they use their instruments for measurement, execute their procedures correctly and thoroughly, and they perform their work with a sense of urgency.

Capture insights and Start Improving Manufacturing Efficiency

One example of where the review of information turns out to be valuable could be focusing on multiple starts and stops during the setup.

This basic chart can provide the insights needed to improve manufacturing efficiency.

After a review of the timeline and some discussions, the operations leader should be able to determine that the operator was performing stop checks to ensure the cutting tools would be within tolerance limits before the job was started. This process ensures the parts will be within spec while the job is running.

The visualization could reveal that the operator was loading new tools into the turret and sections of the machine required for the job that would run for a few days. While loading each tool he was using a micrometer or caliper to check feature dimensions. This would ensure the parts produced would be within required tolerances.

What You’re Left With, and How Manufacturing Efficiency Will Get Improved

The value of the timeline along with the operator’s explanations was:

  • Have an objective source of information on the operator’s activities.
  • Have accurate measurements of those activities (how long they took) and their sequence.
  • Zooming in and out of the timeline to change the perspective; and
  • Have data on the production run after the setup to confirm the quality of the setup, during normal shift hours and in “lights-out” production hours.

The alternative to using a timeline with accurate, objective data on the operator’s activities is to visually observe the operator in real-time and takedown written notes. The operator of choice’s work is likely so smooth and well done that the details are difficult to observe. Furthermore, even more difficult to accurately capture in writing while they’re performed.

This diligent setup work done at the beginning of the job will reduce the time the operator needs to monitor and adjust the machine during the job which enables him to run more machines and jobs at once. This efficiency then decreases the downtime while the job is running and increases production.

Conclusion

As a quick summary- Gather basic data, basically just time-stamps detailing when the machine was and wasn’t producing, analyze your top-performing employee’s tendencies, and make an example out of them.

This is a great way to get your foot in the door and get started along your data-driven manufacturing journey. Once you implement a solution and strategy similar to this, you can prove to yourself just how valuable data can be to improve manufacturing processes.

In time you can scale the solution. Once you find that your machine setup times and manufacturing efficiency have greatly improved, you can move on to a different process or metric to start tracking.

Scaling the solution is of course great. It means you have made great progress and found a lot of value in the data. Implementing a solution equipped with real-time data, contextual data, numerous reporting features, and the ability to communicate with other systems can bring a lot to the table. However, what is really important is that you get started with data-driven manufacturing, and develop and stick to a strong strategy with clear goals.

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

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 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.

Reduce Manufacturing downtime to keep optimal production flow.

Manufacturing Downtime and OEE: How to Use the Data

Reduce Manufacturing downtime to keep optimal production flow.How to Use the Data

How can you use data from your machines like manufacturing downtime data to improve your production capacity? How can data be used to improve scheduling, reduce waste, use, or materials efficiently?

  • Start with collecting the data and converting it to useful information such as downtime and OEE
  • Then provide it to your team on a tablet, a computer next to a machine, or even a big screen tv in the plan.

You’re now looking at real-time data about your machine equipment states and events. You’re getting a real view of your capacity performance constraints measured against baselines with manufacturing downtime, starving, idle, and shortstops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding of actual capacity, product materials, throughput, and promised delivery metrics.

You’ll understand 1) where your actual capacity and production is at, and 2) how machines are actually performing to enable that capacity, including the types of manufacturing downtime. You can then use the actual machine data to troubleshoot and solve particular issues. For example, it’s possible in some cases where getting data on OEE and then solving problems to improve OEE by 10% can result in an increased capacity of 19% with better efficiency and throughput. That can then result in increased operational net revenue which results in improved product margins.

Two key issues to consider are education and culture:

  • Education: Companies will need to ensure teams are educated such that they accurately understand what the data means, and what actions they are empowered to take which will have a positive result on the machine data they see.
  • Culture: Awareness of the culture on the plant floor is important. There can be a perception of a big brother when operators and shop managers know that management can more accurately see what they’re doing and not doing. However, the culture can be developed such that staff can welcome opportunities to do a better job, to improve production capacity, quality, etc.

Manufacturing Downtime Use Cases

Sometimes data concepts can seem abstract, so here we will provide two real use cases.

Food Plant

  • Situation: Food production plant processing 980K lbs product per day to make broths and fat products. They used 6 different boxes and fluidized dryers in the drying process.
  • Problem: The plant was steadily losing capacity over multiple months that seemed to point to excessive downtime in their dryers and evaporators.  Data was collected manually showed a 14-day schedule with 6-minute downtime cycles but upon investigation, they discovered excessive downtime with their dryers and CIP processes.
  • Solution: Invested in a real-time data collection and process visualization system with OEE to track manufacturing downtime with performance measure metrics.
  • Result: Collecting real-time data and OEE showed improvements could reduce downtime by 25-50% with an OEE index of 60%. This converted to savings on the evaporators and dryers between $760K-$1.5M in cost avoidance over a 12 month period with a recovery of 25%-50% in plant equipment capacity and 10-20% product material loss recovery.

How to Use the Data

How can you use data from your machines like downtime data to improve your production capacity? How can data be used to improve scheduling, reduce waste, use, or materials efficiently?

  • Start with collecting the data and converting it to useful information such as downtime and OEE
  • Then provide it to your team on a tablet, a computer next to a machine, or even a big screen tv in the plan.

You’re now looking at real-time data about your machine equipment states and events. You’re getting a real view of your capacity performance constraints measured against baselines with downtime, starving, idle, and shortstops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding of actual capacity, product materials, throughput, and promised delivery metrics.

You’ll understand 1) where your actual capacity and production is at, and 2) how machines are actually performing to enable that capacity, including the types of manufacturing downtime. You can then use the actual machine data to troubleshoot and solve particular issues. For example, it’s possible in some cases where getting data on OEE and then solving problems to improve OEE by 10% can result in an increased capacity of 19% with better efficiency and throughput. That can then result in increased operational net revenue which results in improved product margins.

Two key issues to consider are education and culture:

  • Education: Companies will need to ensure teams are educated such that they accurately understand what the data means, and what actions they are empowered to take which will have a positive result on the machine data they see.
  • Culture: Awareness of the culture on the plant floor is important. There can be a perception of a big brother when operators and shop managers know that management can more accurately see what they’re doing and not doing. However, the culture can be developed such that staff can welcome opportunities to do a better job, to improve production capacity, quality, etc.

This article is just one part of a four-part series, check out our article on how to get the data.

statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.

Production Downtime and OEE: Why Machine Data is Valuable

Production Downtime and OEE: Why Machine Data is Valuable

Smart Manufacturing is the use of one or more technologies including IoT, automation and robots, big data, artificial intelligence, and modeling to optimize manufacturing. An efficient manufacturing operation means, among other things:

  • Product flows through the plant seamlessly;
  • Machines have the highest asset utilization possible to drive as much production and revenue as possible; and
  • The plant’s machines, operators, and systems are providing data that is shared across the organization to enable visibility into the factory floor for making valuable decisions throughout the organization.

Optimizing of Manufacturing

Companies can optimize manufacturing in product concept generation, flexible operations for changing customer needs, manufacturing efficiencies, machines becoming self-optimizing and self-diagnosing, and creating a dynamic manufacturing supply chain.

Manufacturing Efficiencies

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better asset utilization, production capacity, and lower overall costs of production. The higher production capacity and asset utilization, and flexible operations enable them to handle more dynamic customer needs and a dynamic supply chain. They can then dominate their market as a more flexible and consistent low-cost producer with solid quality and delivery to dominate their markets.

Need Data to Improve Company

To reach these levels of better production capacity and low-cost production the key is people must have access to data about machines and how they’re operating… uptime and downtime, asset utilization ratios, the productivity of the machine, quality of the products it creates, it’s running condition, how the operator is running the machine, etc. With this data, companies can then monitor production lines and pieces of equipment, find issues, and make improvements.

Production Downtime

Unplanned production downtime is the time a machine is down due to circumstances that weren’t planned ahead of time. This includes times when:

  • The machine is available but not running: the machine could be used and there is a product to produce but no operator is available, or there is an operator but no raw materials to produce a product.
  • The machine is not available: The machine is down because it had a failure and is under repair.

Production downtime is one of the biggest “killers” in production efficiency. It leads to material loss, resource loss, uptime or asset utilization loss, and capacity loss. Additionally, it reduces a company’s ability to deliver products to quality standards which leads to missing promised delivery dates and lost trust with a customer. In the end, it all results in higher costs and less revenue.

Benefits of Tracking Downtime

Tracking downtime gives you the data you need to both find root causes of downtime and make improvements to users, machines, and processes. When downtime improves then production capacity and asset utilization increase, costs of a product are reduced, revenue increases, and margins increase.

Downtime Study

In 2017, GE ServiceMax commissioned a study that surveyed 450 decision-makers across manufacturing and other industrial verticals. They found companies had issues as a result of unplanned downtime including:

      • 46% of companies couldn’t deliver services to customers
      • 37% lost production time on a critical asset
      • 29% were completely unable to service specific equipment

They also found that production downtime in a factory affects 62% of its productivity with scheduled resources and equipment. These numbers indicate unplanned downtime is common and has a big impact on companies.

statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.
Downtime Fallacy

Companies who don’t track production downtime of their machines may believe they know what the equipment downtime is. However, they are often off by as much as 50%. Without the actual data to measure and analyze, many companies are not seeing the whole picture, and taking all information into account when measuring downtime – and this can greatly impact their business.

This article is part of a series, if you found this information insightful, check out our next article on downtime and how to use the data.

 

Why Should I Calculate OEE Manually?

 

Why Should I Calculate OEE Manually?

OEE (Overall Equipment Effectiveness) is a valuable KPI for how well machines on the plant floor are being utilized. (More information on what OEE is can be found in the Intro to IoT blog post) It is often thought to be a complicated value to calculate, and sometimes it can be. However, it can be greatly simplified. Given the value and the potential simplicity, it is recommended that operations staff including their supervisors and managers should know how the calculation works, what OEE is, and how to use it effectively.

Why is OEE Valuable?

OEE has a direct correlation to the revenue the machine provides the manufacturer. If the machine isn’t utilized well, i.e., if it’s down or if it’s producing poor quality parts that cannot be sent to a customer, then the machine isn’t generating as much revenue as it otherwise would.

It’s also valuable because breaking the value down into is parts can help hone in on the cause of issues with a machine. It is made up of three separate percentage values: Availability, Performance, and Quality. One can look at the component values and determine where to start their analysis to find issues with the machine.

Why Calculate Manually

Why should operators and related staff calculate OEE manually? Two reasons:

  1. To understand what OEE really means and what it’s made up of, and
  2. To get a solution calculating OEE in place when an automation solution isn’t available.

Understanding OEE: One can only understand another person’s perspective when they’ve walked a mile in their shoes. In the same way, a person can only understand a concept once they’ve recreated it themselves and worked with it. The nice thing is that OEE, or at least some parts of it, can be calculated easily on paper.

Automation isn’t available: In many manufacturing companies, OEE will likely be calculated automatically by various systems such as IoT software platforms (these are the solutions we often implement). These solutions are becoming more predominant as companies look for more ways to pull valuable data from their machines to complete…all under the moniker of Smart Manufacturing. However, some companies may want to start utilizing OEE before budgets can be made available for these solutions. So, why not start when pen and paper!?

How to Calculate Manually

A deeper discussion of the calculation of OEE can be found in this How OEE is Calculated blog post. So we won’t cover the details here.

To calculate OEE all you need is a pen, paper, maybe a clipboard, and diligent effort to record and calculate the data regularly. It is also helpful to have Excel available. Don’t need anything fancier than that.

You can start with Availability, one of the components of OEE. Put the paper, pen, and clipboard close to the machine to measure. Then every house has users mark what happened with that computer regarding its availability: If it was up and running at normal speed mark a “G” for Green (can include planned downtime if appropriate). If the machine experienced some setup or adjustment time during that hour, mark a “Y” for Yellow. If the machine was down, mark “R” for Red. You could also use color pens/markers and/or a whiteboard.

Then count the total number of G’s, Y’s, R’s. Subtract the total number of Y’s and R’s from the total number of G’s to get the number of hours the machine was available to run. Count the total number of hours overall on the paper no matter the condition for the scheduled operating time. Then divide available time by the scheduled operating time and you get the Availability Rate % value.

Do this for each day, record the results in Excel, maybe even create a simple chart, and you’ll start to see a trend for where the OA (Overall Availability) for the machine is. If you do this for multiple machines you can then get some solid, objective data on the status of the various machines, and which might need some attention.

You can also repeat this process recording similar data for Performance and Quality.