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 supply chain, machine uptime, workforce, etc. You probably don’t have that level of control.

There is hope, don’t lose faith!

There are 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 accurate predict the actual time it’ll take for the job to move from station to station through the shop.

Actual vs Planned – Data should be available from your systems for how well the production schedule performed. This data should also be available 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.

Do you have this kind of data above available to you? I hope so. If not, look to your IIoT/Manufacturing Analytics product. These should be out-of-the-box capabilities. Additionally, the IIoT/Manufacturing Analytics solution should also integrate well with your ERP system automatically share the data. We help with these kinds of situations all the time. Call or email anytime, happy to see if we can help. https://ectobox.com/sensrtrx/

5 Steps to Solve Manufacturing Challenges with IIoT

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

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.)

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

Even though IIoT is “high-tech” and can sound complicated, at it’s essence it isn’t. IIoT is simply a set of technologies combined together to solve valuable business challenges.

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.

To keep yourself grounded use a simple and systematic, step-by-step process to apply IIoT 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 overwhelm with data but have absolutely no clarity on what to do with the data, what challenge 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.

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

 

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

Did anyone every 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 are 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 complex 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 setup 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 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 have 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 (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 aloud by maintenance or the machine manufacturer to open it up and get inside. They’re also great because they make it straight forward 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 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.
Data Driven Manufacturing Equation

What is Data Driven Manufacturing / Manufacturing Analytics / IIoT

Let’s get on the same page about what Data Driven Manufacturing / Manufacturing 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 it’s 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 Driven Culture
  • IoT

Data Driven Manufacturing EquationIoT 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.

However, this shouldn’t be a huge stage gate in the process to implementing Data Driven Manufacturing. 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 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.