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.

 

 

Machine 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.

Optimizing of Manufacturing

Companies can optimize manufacturing from 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 Efficiences

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better production capacity and lower overall costs of production, that enables 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, 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.

Downtime

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

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

Downtime is one of the biggest “killers” in production efficiency. It leads to material loss, resource loss, uptime loss, and capacity loss. Additionally, it reduces a company’s ability to deliver product 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 increases, costs of product are reduced, revenue increases and margins increase.

Downtime Study

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

      • 46% 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 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.

Downtime Fallacy

Companies who don’t track 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.

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 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 more fancy 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 have users mark what happened with that computer regarding it’s 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 colors pens/markers and/or 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 over all 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.