Two Ways to Improve Production Schedules

We talk with a lot of manufacturers, especially high mix/low volume shops, which have challenges with their production schedules.
Typically, ERP systems don’t have great scheduling modules. Too often those production schedules are pipe dreams because they expect the world to be perfect…like an automotive manufacturer that has near-complete control over the supply chain, machine uptime, workforce, etc. You likely do not have that kind of control.  The predictions are often too optimistic and end up being inaccurate more times than not.

There is hope, don’t lose faith!

In this article, we will go over two ways to get better, more accurate production schedules.

Accurate Cycle Times- having accurate cycle times and very good data on the job setup and changeover times for jobs helps immensely in improving estimates for jobs so you lose money less often on jobs. This data also helps more accurately predict the actual time it’ll take for the job to move from station to station through the shop. This helps you but will also give the customer more clarity, and will make for a smoother transaction if the estimate that you gave the customer actually ends up being accurate at the end of the deal. A happy customer will help build your reputation as a knowledgeable and trustworthy manufacturer which will encourage a lot of customers to continue doing business with you.

This is why using an Industrial Internet of Things system to pull machine data is so important for you. The more data you have the more you will learn and understand about the machines. Over some amount of time, you will start to really see what is happening which will make it much easier to predict an accurate time. Also, this way it will be a more confident prediction, and completely data-based. Making a data-driven decision makes the process a lot clearer and will give you some real in-depth substance for more accuracy in your decision making.

You can have better communication and valuable updates with the customer this way. Everyone involved in the project will be better educated. This makes the entire plant more efficient and leaves you with a happier customer with a project finished on time that was planned and executed the way they were told.

Actual vs. Planned- data should be available from your systems for how well the production schedule performed. This data should also be available in real-time, no waiting for someone to manually calculate and analyze actual vs. planned to provide a report. When this real-time data is available performing a post-action review on your production schedule your team can get a lot of valuable lessons for how to improve the production schedules. A prime example of how important real-time data is for multiple reasons. Use the data to avoid problems before they happen, but it also helps to continually educate people on the plant floor that have complete visibility into the machines. Everyone will develop a deep understanding of machines across the plant, which will assist them in making the right decisions to keep projects on schedule.

Do you have this kind of data available to you? I hope so. If not, look to your Industrial Internet of Things/Manufacturing Analytics product. These should be out-of-the-box capabilities. Additionally, the Industrial Internet of Things/Manufacturing Analytics solution should also integrate well with your ERP system and automatically share the data. We have a lot of experience and help with these kinds of situations all the time. If you have any questions or want to get in touch call or email anytime, we are more than happy to see if we can help. https://ectobox.com/sensrtrx/

5 Steps to Solve Manufacturing Challenges with Manufacturing Iot

How do we ensure we’re focusing on the true essence of the challenge at hand when looking at challenges on the factory floor? Manufacturing IoT can help.

The answer is to “get back to the basics”.

Whether it’s little league baseball or the major leagues, or any other sport for that matter, you constantly hear the coaches talking about the basics…get back to the basics on hitting stance, throwing, etc. The same should be said for business and, specifically, manufacturing.

In many meetings I’m involved in where we’re helping a manufacturer solve operational efficiency challenges to help them succeed and grow, I’m often revisiting this simple 5-step process.

  1. Business Challenge
  2. Business Hypothesis
  3. Information
  4. Data
  5. Technology

(More details on each step can be found here.)

This is essentially the entire process simplified.

Let’s apply this to Manufacturing IoT / Data-Driven Manufacturing, something we do a lot of…

Even though manufacturing IoT is “high-tech” and can sound complicated, at its essence it isn’t. Manufacturing IoT is simply a set of technologies combined together to solve valuable business challenges. It may look like information-overload at times, but a structured plan around the right tools makes it easy.

We often use these technologies to solve challenges around excessive unplanned machine downtime, machines run to failure due to lack of getting those early P-F Curve signals, or they’re trying to figure out why a machine or cell isn’t producing as much as it should. This is one of the biggest problems for manufacturers and one that definitely needs to be addressed. 30 years ago running your machines into the ground was a common practice. Today there are so many constant technological advancements being made all the time. Using them effectively in the manufacturing industry will help you to stay on top of the competition.

To keep yourself grounded use a simple and systematic, step-by-step process to apply Manufacturing IoT to solving business challenges. You can get a PDF of the infograph here.

We have seen situations where companies don’t do this. To solve challenges like those listed above they first select some certain technology and try to pull as much data as possible. They’re quickly overwhelmed with data but have absolutely no clarity on what to do with the data, what challenges they’re attempting to solve, and why. They end up getting wrapped around the axle, waste time and money and don’t accomplish anything valuable.

This is why we need to use a simplified process. Yes, manufacturing IoT systems are immensely valuable. They are completely changing the industrial manufacturing landscape. As these technologies become more readily available to small-medium manufacturers, you need to learn more about how to use them. The data is only valuable if you know how to use it. There is no one way to look at and analyze the data. Every business will operate differently, and need to set specific goals tailored to them.

Determining the causes of manufacturing problems is a headache. The solution is, of course, implementing an IIoT system to eliminate the guessing and headache. So make sure that you are using the tool properly to start eliminating problems, make confident data-driven decisions with the data now available, and stick to the structured 5-step plan to keep it simple and goal-oriented.

A simple 5-step process applied to IIoT can save so much time, money, and also frustration.

If you have any questions or want to get in touch, contact us at any time. We are more than happy to answer your questions.

For more information, check out our other blog articles. We are an IIoT based company with an abundance of helpful information on topics related to the manufacturing industry.

Equipment alarms and IIoT systems are really valuable, when used well

Did anyone ever tell you it’s better to “work smart” than it is to “work hard”? That idea applies in many contexts including running manufacturing equipment and the alarms on that equipment.

My good friend Jay Kriner, CMRP, wrote this article on when to act based on alarms from the equipment, and the value of it.

He discusses the three types of alarms (information, passive, and active), what they’re for, and how to use them to your advantage. He also tells some interesting stories on what happens when operators continually push the “ignore” button or reset thresholds, so they don’t get the alarms anymore.

The alarms are good examples of the equipment essentially raising its hand to say, “Hey, I need some help here…a little attention please.” Those warnings should be heeded, within the context of appropriate procedures that focus on safety, quality, and production.

Ignoring the alarms is the completely wrong mindset not only for the health of the equipment but also for production. In every story, Jay tells the complete failure of the machine cost significantly more in time, money, and production downtime than if the equipment had been taking down for the maintenance the machine was asking for.

Another important point is IIoT systems can add a lot more value to alarms. IIoT systems can:

  • Record the alarms from the equipment for visibility and analysis;
  • Be configured to throw its own alarms where the machine can’t be configured to throw an alarm;
  • Analyze the alarms across all the connected equipment;
  • Provide better visibility across the plant to alarms and related equipment health; and
  • Drive more proactive action on the part of the Maintenance and Reliability team by integrating the IIoT solution with a CMMS (i.e., maintenance software system) to create and assign work orders for machines that need attention.

Providing more visibility, better information for decisions, and most importantly driving valuable action is where the alarms become most valuable.

Connect IIoT platform to CMMS?

Should we connect our CMMS to our IIoT platform? Would that effort provide us with valuable results? Yes…only if you keep a few points in mind. These points are honestly a reminder to stick to the basics.

Note: Computerized Maintenance Management System is software systems used by Maintenance & Reliability teams to, among other things, manage machine health using work orders and other data.)

Back to the question, should we connect the CMMS with the IIoT system?

Connecting these kinds of systems can be really valuable. It enables Maintenance and Reliability teams to monitor more equipment, attain better uptime, with fewer resources (“do more with the same”). However, there’s a balance to strike….Goldilocks strikes again (I reference “Goldilocks and the Three Bears” often).

 

1) Not in the Pilot Project, Keep it Simple: We recommend not doing it as part of your IIoT pilot project where you’re connecting 1-5 machines for the very first time to get maintenance and production-related data.

Integrating an ERP or CMMS with an IIoT / Manufacturing Analytics platform isn’t rocket science, far from it. Though it does make that first pilot project a little more complicated. Pilot projects are supposed to be simple. Adding another big moving part like getting two software/data systems to talk with one another adds a bit more complexity and is best done after the pilot project. KISS…Keep it Simple Silly.

 

2) Are the Maintenance Culture and Practices Ready? Are your Maintenance & Reliability guys and gals ready for it? Many companies are still in the Reactive Maintenance mindset, no matter the cause. If you connect a CMMS with the IIoT system, set the IIoT system to start creating work orders in the CMMS when certain conditions are met, the guys and gals in maintenance might be overwhelmed with work orders and not see the value. “Last time I fixed this machine it was smoking and whining. Why should I fix it now if it’s not smoking and whining?”

Changing the culture and mindset around Maintenance & Reliability to move to Preventative and better yet, Proactive Maintenance or Condition-Based Monitoring would be optimal. Companies that are already on this path, as many are, are well suited for this type of integration.

For those that aren’t on the path forward, this doesn’t mean you can’t and never should set up this integration of IIoT and CMMS platforms. It simply means there’s a little more thought that goes into it.

 

3) Start Small, Less is Better…Incremental Approach: A thought…maybe hold off trying to track a lot of data and various conditions of many machines all at once at the outset of getting IIoT setup. If you try to track a whole bunch of data points on each machine you connect with, with no approach for solving a specific challenge with the machines, then you’ll be drinking from a fire hose and it won’t taste very good. You’ll get a whole bunch of data, and it potentially won’t be terribly useful.

Instead, as part of the Pilot Project, look to solve a specific, common, valuable problem with a limited set of data. Also, do that on one specific type of machine or a small set of machines that are very similar.

However, if you’ve already done that and the value of the solution is proven…then keep going…add more data to solve additional, narrow challenges. Work the problem step-by-step, incrementally.

These are simple reminders of key ideas that many of us often forget when we get into the fray of or workdays. A lot of life and work is about getting back to the basics.

4 Best Sources of Data for IIoT solutions

Does your IIoT / Data-Driven Manufacturing (DDM) solution have good sources of data? Or are you considering these sources if you’re looking at IIoT?

Too often we see very small, cheap IIoT solutions put in place that has nothing more than a CT sensor to pull data. They can usually get production counts and machine utilization but that’s it.

Try these 4 sources of data on for size:

  • Wireless Sensors- We Like to use non-invasive sensors from Banner Engineering (a great, solid, well-recognized national company) to get data from a variety of machines new and old. They’re great for a non-invasive approach for getting data when you’re not allowed by maintenance or the machine manufacturer to open it up and get inside. They’re also great because they make it straightforward to get data from legacy machines…even if it’s turn of the century…the previous century.
  • Direct Connection- When it’s possible and when you need more data from inside a machine, like a PLC or a controller of a CNC machine, then you can pull data there as well. We typically use an IoT gateway (i.e., an industrial PC) to run the software for connecting to the machine, pulling the data out, and sending it on it’s way to SensrTrx or other IIoT platforms.
  • ERP Systems- ERP systems can be a great source of data like part data, production schedules, and standard cycle times. This data adds incredibly valuable context to the data coming from the machines and the operator. A much richer set of real-time data is then available for viewing, analysis, and decision-making.
  • Operators- Let’s not forget some of the most important people in a manufacturing plant, the machine operators (aka manufacturing technicians, manufacturing engineers). They can be a great source of data if you have an operator interface like Sensrtrx.

Why this data is so Important

Having a great, solid lineup of machines is of course very important to the success of your business. However, that is not all you need to be efficient. This can be like buying a brand new luxury car and never taking it in for an oil change, or a routine preventative maintenance check. Just because the car is driving fine one day, does not mean everything is good and you won’t have any issues tomorrow. Of course, you are going to do your best to make sure it is in tip-top shape so that it lasts a long time, and is worth the investment.

The same goes for the machines on your plant floor. Using one or multiple of these four sources of data will give you an abundance of information on the health of the machine and how well it is performing, not just the basic stats. You will see everything that is happening on the inside of your machine. Everyone that can see that data will also develop a deeper understanding of the way the machine works, being able to see the statistics on real-time OEE, scrap/quality by reason codes, real-time alerts, and more. You will also get informat0n on preventative maintenance like temperature, vibration, and velocity.

All of this information is crucial to the efficiency of your plant. Real-time alerts will allow you to solve a problem before it even happens. Avoid machines breaking and leading to large amounts of downtime, and also look at the data you are pulling to ensure that your machines are in good shape so that they can last a long time. Putting all of this together will help you save money and meet deadlines on time without running into so many problems, generate more revenue faster, and leave customers happy.

 

Data Driven Manufacturing Equation

What is Manufacturing Data Analytics / Data-Driven Manufacturing

Let’s get on the same page about what Data-Driven Manufacturing / Manufacturing Data Analytics / IIoT is. This will be brief. We dig into this topic more in other blog posts.

Please note: I use the phrase Data-Driven Manufacturing interchangeably with IIoT (Industrial Internet of Things) and Manufacturing Analytics. There is a whole semantics discussion in there. However, we’re going to skip that semantic discussion today.

Data-Driven Manufacturing

At its core Data-Driven Manufacturing is about making decisions on manufacturing based on facts, not guesses and opinions You get that capability of making decisions based on facts by combining 3 things:

  • Manufacturing Data Analytics
  • Data-Driven Culture
  • IoT

Data Driven Manufacturing Equation to assist in Manufacturing data analyticsIoT is pulling data from devices and sensors on machines, making that data valuable, and getting that new information in front of the right people to use for making decisions and taking valuable action.

Being Data Driven means that all of the people in that company that require it have access to the right data, can use that data to make decisions, and most importantly are empowered to take valuable action based on that data….typically corrective or Continuous Improvement actions.

Having a Data-Driven culture at your manufacturing company is really important. Some companies may not be prepared to take on a Data-Driven Manufacturing solution because they lack a Data-Driven culture. People need to be open to change, improving operations, and using data to do it. At some companies that isn’t the case. In those cases you’ll often hear, “We’ve been doing it this way for the past 30 years…” and on and on.

That might be the case, but what worked yesterday may not work today. A large number of manufactures have their own certain way of doing things and have gotten accustomed to using the same process. Not too long ago we did not have this access to all of the data that we do have access to today. So it was acceptable and common practice to make decisions in a number of different ways, without any of those ways really being based on the data, the facts. The world is changing, technology is advancing every day. One major way the world has changed and continues to change is not only the devices that are being developed and more readily available, but also the consumer capabilities with these platforms. We can gather more data than ever before, and you need to know how to use it to your advantage. Data-driven manufacturing is the way to stay competitive. You can completely eliminate the tough decisions, and guessing what to do next. Industrial IoT systems will pull the data from machines that you need to see so that you can analyze that data and make your decision based on completely factual information.

Implementing a data-driven manufacturing system does not have to be a tough process. A lot of companies are already using data in their Continuous Improvement projects, gathering Lean data, having daily production meetings. So, they’re already using data. Data-Driven Manufacturing simply takes them a couple of steps further…it’s not a leap light years into the future. Data-Driven Manufacturing simply provides more accurate, additional valuable, and real-time information to use in the existing normal processes of daily production meetings, Lean, Continuous Improvement, etc. Once the Data-Driven Manufacturing solution is in place and used in some of the existing processes and once it’s proven to be really valuable, then the manufacturer can move to expand the Data-Driven Manufacturing solution across more machines, lines, and plants.

With the definition of Data-Driven Manufacturing covered, and a little extra flavor for how it can fit into a business, let’s talk about the Journey of putting in place and experiencing Data-Driven Manufacturing in our next post.