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.

 

Ectobox Announces Partnership with SensrTrx

Ectobox Announces Partnership with SensrTrx 

Ectobox announces that they have become a certified partner of the SensrTrx manufacturing analytics platform.

In the manufacturing world, visibility into the factory floor is vital. Without it, there is no way of knowing if a quality product is being produced or if it will be delivered to  customers on time. Manufacturing analytics software gives manufacturers the power to gain both visibility and accountability, in real-time.

Partnering with SensrTrx allows Ectobox to continue providing outstanding service and solutions to our customers. While helping drive their businesses in a positive, data-driven direction.

“We’re excited about our partnership with Ectobox,” Bryan Sapot, CEO of SensrTrx, said, “Ectobox has a long, successful history in manufacturing and an abundance of manufacturing expertise,” Sapot added.

Kevin Jones, CEO of Ectobox says, “We’re excited about the possibilities we see in this partnership. Adding this product will enable us to solve many operational and production challenges for our small and mid-sized manufacturing clients. This tool will be a great compliment to the other software and consulting tools in our toolbelt.”

“With their knowledge and skill, I have no doubt our partnership will prove to be valuable and insightful for both existing and future customers,” Sapot added, “and I’m excited to see where this will lead, especially considering our partnership with Ectobox is the first in the United States.”

Our goal as partners is to help manufacturers produce a quality product and deliver to customers on time, with the guidance of data on the factory floor, from beginning to end. SensrTrx and Ectobox deliver the complete package as both a software solution and a software consulting service.

About Ectobox

Ectobox applies custom industrial intelligence solutions to manufacturing businesses to drive efficiency and growth. Ectobox is a team of software technologists who are driven to deliver outstanding business outcomes, passionate about using technology to solve complex business problems and have a proven track record for consistently exceeding customer’s expectations.

Ectobox specializes in Internet of Things (IoT) solutions and customized software consulting and development and for growing data-driven businesses. 

About SensrTrx

SensrTrx is a manufacturing analytics platform that provides visibility into the data from machines, devices, and people and puts it all into context. SensrTrx is revolutionizing manufacturing by offering a complete solution from sensors to data collection to analytics that provides real-time insights into the productivity of the factory. Those insights give a manufacturer the ability to reduce costs, ensure customer on-time delivery, and improve quality.

SensrTrx enables companies to start small, think big, move fast and easily grow from a single machine at one plant to thousands of machines across the globe with manufacturing analytics. https://www.sensrtrx.com/

 

The Ultimate Guide to Selling to Your Boss

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 use to measure performance, production, quality, and availability?
  • What does the data from user say about when the machine is in use and not, and why?
  • What does the data from 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 type of controller?
  • 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 real-time? Or must be downloaded via CSV or other 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 setup 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.

Machine Downtime and OEE: How to Use the Data

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 base lines with down time, starving, idle, and short stops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding around 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 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 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.

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 box and fluidized dryers in drying process.
  • Problem: Plant was steadily losing capacity over multiple months that seemed to point to excessive down time in their dryers and evaporators.  Data was collected manually showed a 14 day schedule with 6 minute down time cycles but upon investigation they discovered excessive down time with their dryers and CIP processes.
  • Solution: Invested in a real time data collection and process visualization system with OEE to track down time with performance measure metrics.
  • Result: Collecting real time data and OEE showed improvements could reduce down time by 25-50% with an OEE index of 60%. This converted to a 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 base lines with down time, starving, idle, and short stops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding around 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 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 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.

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 box and fluidized dryers in drying process.
  • Problem: Plant was steadily losing capacity over multiple months that seemed to point to excessive down time in their dryers and evaporators.  Data was collected manually showed a 14 day schedule with 6 minute down time cycles but upon investigation they discovered excessive down time with their dryers and CIP processes.
  • Solution: Invested in a real time data collection and process visualization system with OEE to track down time with performance measure metrics.
  • Result: Collecting real time data and OEE showed improvements could reduce down time by 25-50% with an OEE index of 60%. This converted to a 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.