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.

 

 

What Is Smart Manufacturing and Why Should You Care?

Smart Manufacturing is a term with many definitions that all have one theme in common:

using new technologies and capabilities to gain competitive advantage.

The Ideal and The Reality

At Ectobox, we say that a company is a “Smart Manufacturing” company when they have fully integrated solutions which empower them to be highly flexible and responsive to changing conditions. A Smart Manufacturer produces a quality product, uses finely-tuned processes, monitors their supply chain and coordinates it with production, and overall, maintains systems that work well together. Theoretically, the best Smart Manufacturers can even fix their own problems automatically.

But what do we typically see the most of? Engineers huddled around a problem trying to solve it. Inventory pilling up in various work areas or cells. Inventory “starvation” in other areas. People and machines not working when they should be. These situations are so common that we see them at almost every manufacturing company we visit. The good news is that these issues are all fixable. The bad news is that manufacturers who don’t make the effort to eliminate these issues and increase their efficiencies are the ones who will struggle to stay competitive.

Why it Pays to Pursue Smart Manufacturing

Besides the appeal of new technology, why does Smart Manufacturing really matter to the average company? It matters because the specific technologies and capabilities under its umbrella can make companies more efficient, enable cost reductions, and potentially, drive new revenue. Some of the capabilities Smart Manufacturing offers are:

  • Better insight into what’s going on at your plant floor
  • New and better products and services to meet your customers’ needs
  • Faster development and launch of new products
  • Reduced costs of production
  • More revenue per employee and per machine
  • New business models (such selling your products as a service)

Smart Manufacturing is the Future

Manufacturers all over the country are under pressure to be more competitive and overcome new challenges. In reality, factories of the future probably won’t look much different than they do now. The biggest difference is that they will be working much more efficiently. The machines and people and supply chain of the future will be more flexible, better-coordinated, and self-healing. The technology is already here, so companies can start getting “Smart” right away.

In our next blog post we will talk about the specific technologies within Smart Manufacturing.

Ectobox celebrates 20 years!

The Past

2018 marks 20 years of Ectobox developing solutions outside the box.

Ectobox began as a software consulting firm driven by a simple ideal: we don’t like to see companies suffer needlessly doing work manually that can hold a business back, when there are opportunities to automate processes. Our President and founder wanted to help. With a servant leader mentality, Ectobox moved forward with helping other companies improve, get better at what they do, and grow with software and data.

The Present

IIoT, Software Expertise and the Manufacturing Sector

Ectobox has recognized the power and value that IoT can bring to manufacturers in becoming better, more efficient organizations. Therefore, Ectobox has transformed into an IoT and software consulting and solutions company. We are able to help manufacturing companies grow with strategic level consulting as well as the tactical details of technical implementations. We have made this transformation by, among other things, being thought leaders and experts in IoT, making strategic moves to expand our consulting and technical capabilities, and using the latest tools and technology including PTC ThingWorx, AWS IoT, and Azure IoT Hub.

The (near) Future

Continuing to Share Manufacturing IoT and Software Knowledge

As Ectobox sees significant company growth in size and market presence, we hope to continue to build strong partnerships with all of you that last into the next 20 years, and beyond.

We have many exciting trade shows, speaking events and webinars coming up the end of this year and into 2019 including:

How to Profit from IoT Webinar – October 3rd
Business Transformation with IoT – October 17th
Manufacturing Day – October 4, 2018
Industrial IoT World Atlanta – October 29-30
Digital Bridge Industry 4.0 Conference – November 1st
Westmoreland Chamber IoT Roundtable Panel talk – November 2nd (registration info TBA)
NWIRC IoT/Industry 4.0 Webinar – November 7th (registration info TBA)
NWIRC IoT/Industry 4.0 Webinar December 11th (registration info TBA)

Why should your manufacturing company look at the Internet of Things?

Your manufacturing company likely has business goals which include growing revenue, margins, and profits, as well as beating out the competition.

In today’s market, those goals are increasingly difficult to achieve.  Companies must continue to become more efficient, increase output, work for more demanding customers, work under increasing regulations, all the while continuing  to earn current or better margins and profits.

In the past, installing ERP systems or using Lean and Six Sigma frameworks were enough to improve the business. Many businesses can still benefit from those kinds of efforts. However, more and more those efforts aren’t  enough to get the required efficiencies, production and competitive differentiation.

The Internet of Things, which is a set of technologies put together into a system to convert data into useful information, is the next solution for enabling a business to accomplish their goals. Here are a few ways IoT becomes the next solution:

  1. Digging Deeper: ERP and MES (Manufacturing Execution System) systems are setup to provide data down to a certain level of detail for manufacturing processes. That used to be enough for companies to make major improvements in production efficiencies. However, IoT can provide data that is another level or two deeper. It can, among other tasks, provide data about conditions inside equipment. That data can be used to understand how close the machine is to its effective operational limits, in addition to providing production output and related data.
  2. More Uptime: The internal data can also be used to provide information on the condition of machines and even predict if and when they might break down. This allows maintenance and service technicians to fix the machines before major issues occur and keep them up and running longer, thereby increasing OEE (Optimal Equipment Effectiveness).
  3. Sharing Data: The information that comes from an IoT system about production and equipment conditions can be shared beyond the manufacturing plant floor. Equipment often has an HMI (Human Machine Interface), or a screen from which users can view status information. However, that data is often not available outside of the shop floor. Utilizing the “Internet” portion of the Internet of Things allows maintenance technicians, management, and business leaders to see important data as needed.

Once the IoT system is in place, people across the organization can use the deeper, more useful data from equipment, manufacturing processes, and other areas of the business to drive decisions and actions. This is where the data becomes valuable information, and thereby becomes the differentiator in moving the business to the next level of growth in revenue, margins and profit, and also in competitive advantage.

Questions? Contact us at info@ectobox.com to learn more.

Consider off-the-shelf IoT software platforms

 

We have seen some companies attempt to create a custom IoT software platform for their own use.

Generally, we recommend against this for several good reasons.

First, some background information. [Or, if you are already familiar with IoT, skip ahead to the Why Not to Build section below].

 

 

What is the Internet of Things?

The Internet of Things (IoT) is the process of pulling data from sensors on equipment or other devices, moving it over the internet, adding other data from external systems, and then transforming that data into valuable information.

IoT converts physical action and data into digital data. A sensor detects a certain type of activity on a piece of equipment. That “activity” is transformed into a digital signal, and then sent over the wire to a collection point, and then pushed on into the IoT software platform. The platform is where the data is transformed into valuable information through processing, adding other data from external systems, often run through machine learning algorithms, and then displayed to end users.

Some uses of IoT:

  • IoT solutions allows companies to monitor and manage activities in various areas of the business, such as the condition of machines
  • IoT allows physicians to monitor the heart beats and respiration of babies in home, and allows parents to maintain a warm home environment to allow babies to develop.
  • IoT can tell you what food is in your refrigerator and then automatically order missing items from your grocery store for delivery.

What is an IoT software platform?

An IoT software platform is the place where the data transforms into valuable information. There are a number of components in the platform, as follows.

  • Software: The software contains the data and cyber model and orchestrates various parts of the software platform. It also provides the visualization, and often the capability for end users (not only software developers) to configure the system or make changes to the system.
  • Data processing and storage: When data comes into the platform, data must be processed and then stored.
  • Analytics: A lot of the transformation of the data into valuable information happens in the analysis of the data. This analysis can include basic descriptive statistics all the way up to machine learning algorithms for predictive analytics.
  • Interface to External Systems: Any good software platform must be open such that it can interface with other systems. Part of the value of IoT comes from data other than the data from equipment and other devices.
  • Security: Security is increasingly important to protect the company’s intellectual property. The IoT software platform should provide strong protection for data at rest (in storage) as well as in motion (moving over the network and into the platform).

Why Not to Build an IoT platform

Now, imagine creating a tool from scratch that does everything described above? The project would be immense, long, expensive, and likely experience the unfortunate issues and delays too many software projects experience unless you have the right team and leadership running the software project.

It’s analogous to building your own ERP (Enterprise Resource Planning) system. We talk with a lot of manufacturing companies, and out of the hundreds I can think of only 3 companies that have a custom ERP system, all of which are now being replaced; two because they are old and inflexible, and one because the company truly is unique and no other system did what they needed.

There are a lot of ERP systems to select from and many of them provide virtually the same features and benefits because the needs of manufacturers are often very similar.

This same principle applies to IoT software platforms. IoT is about pulling data from sensors on equipment, moving that data over the internet, adding other valuable data to it, and converting it to valuable information. This means the needs for different companies are often very similar. Additionally, at a high level the components of systems are frequently the same.

When needs are the same, and there are a lot of similar systems which can provide the required features and benefits, it’s likely that there is an IoT software platform out there that will provide what a company needs.

Which System to Use?

We are believers in choosing the platform that best fits your needs. We provide IoT solutions using either or both the Microsoft Azure IoT platform and the PTC ThingWorx suite of IoT products.

If you have questions , or are looking at either Azure or ThingWorx, please call us at 412-923-3002. We’d be happy to provide a  free consultative meeting with recommendations.

Where to Start with IoT

In our post on Four Ways to Create Value with IoT we discussed at the end the fact that making products better and making better products has a bigger impact on profit. And most companies want the biggest possible impact on profit.

So, – is making better products the place to start? No.

Why? Because it’s a difficult and complicated process. It’s best to start simple and grow into the more complex and impactful benefits of IoT.

It’s easier to wire up equipment with only a few sensors (or use existing sensors) and pull a limited amount of data, and to focus on simpler tasks…improving asset utilization and optimization.

You can start with either improving maintenance or operating your equipment better. This requires less data, less analysis, and smaller changes in the product and company processes and culture, when compared to the amounts of data and company changes required for innovation and invention.

Let’s take an electric motor as an example. Let’s say you manufacture these motors and sell them to customers. It’d be wonderful to start inventing new types of motors. However, you would need a lot of data about all aspects of how the motor is run, the environment in which it’s used, and the customer’s business. You would also need the capability and team to analyze that data to come up with new ideas. That sounds complicated.

Let’s compare that to adding a vibration sensor to the barring housing and a temperature sensor to the body of the motor. We should start by doing this for a single motor in our test or service shop. By adding “slap and stick” sensors retrofitted on to an existing motor, we’re already making the process simpler because we’re not having to reengineer any aspect of the motor.

Now let’s setup a gateway, pull it into an off-the-shelf IoT software platform to get the data from the motor, collect the data, and monitor the data. Are we able to see what normal conditions look like? Can we run the machine enough such that it starts to exhibit the behaviors we often see in the field such as vibrations from a barring that isn’t lubricated? What do those signals look like? Depending on the IoT software platform and related analytics tools we’re using, we might be able to relatively easily define some algorithms that can identify normal and specific abnormal behaviors. [KP1]  Then, the IoT platform allows us to set alerts to trigger  the need for a service event. If we have this working well enough, then we can start to retrofit some motors in the field at a few customers’ sites. We can then continually improve the analytics, business activities and decisions that come from the analytics, and business value from the whole setup.

Once the value is proven, then it’s time to move forward to execute other projects in this IoT journey which can drive down more costs and generate more revenue. Next might be adding the sensors to the motor during manufacture, providing data to our company and to customers to enable more efficient operations of the equipment. Eventually the company can move to innovation and invention which require a lot more work and internal capabilities to realize value.

Keep in mind starting with maintenance should be accompanied by a review of your current equipment maintenance records, performance of your equipment, and maintenance culture and practices. We can put you in contact with people and companies that perform these assessments and provide very valuable recommendations and follow-on services.

If you have any questions on how to start the IoT journey, or on maintenance and reliability best practices please call us, we’ll be happy to chat. We can help evaluate if an IoT journey makes sense. Additionally, we have many contacts in the maintenance and reliability community through our trusted partners and from our heavy participation in SMRP (Society for Maintenance and Reliability, smrp.org).

Ectobox Launches Internet of Things (IoT) Service

IoT Business Transformation Plan Launched Targets Growth-Minded Manufacturers

Ectobox, Inc., a custom software development firm specializing in the manufacturing industry, announced the launch of its Internet of Things (IoT) Business Transformation Plan for manufacturers today.

The Internet of Things, or IoT, refers to data that is pulled from devices or machines (“things”) and then securely transmitted in real time over the Internet. The data is then transformed into valuable insights to drive business decisions.

By offering specialized IoT services to manufacturers, Ectobox provides a simple way for growth-minded companies to evaluate whether IoT makes sense for their operations and easily plan and implement these new solutions.

Ectobox’s IoT services provide a framework specifically designed to streamline the assessment and decision-making process around IoT initiatives, which are frequently delayed by confusion and internal red tape given the lack of on-staff IoT expertise that is typical in many companies. Starting with a simple checklist completed by the manufacturer, Ectobox leads each company through a process that results in IoT projects that are completed within specified time and budget parameters. Examples of IoT projects include improving business operations, using equipment more efficiently, inventing new and more useful products, and transforming companies and competition.

“Service-minded companies who want to grow and improve, to build a great business providing great products to great customers… those companies may be struggling to get there and IoT can help,” says Ectobox Founder Kevin Jones. “I want them to connect with the idea that there is a solution, there is a way to get it done. They simply need to work with a company that has experience in manufacturing, software, and IOT to lead the way and help them get there.”

Ectobox’s IoT services are designed for manufacturers with annual revenues ranging from $20 million to $200 million annual gross revenues. With deep roots in the industry and an extensive custom software portfolio, Ectobox will be using IoT software platforms ThingWorx and Azure to build IoT apps for manufacturing and service operations. Companies thinking of implementing IoT for the first time, or companies who have tried IoT initiatives and are struggling, are good candidates for the IoT Business Transformation Program, says Jones.

About Ectobox

Ectobox is a custom software and IoT developer helping mid-sized companies grow their businesses with software and data for more than 20 years. Known for their experienced team of software rescue experts and system integrators, they specialize in helping businesses in need of custom software support to achieve next-level growth. Their IoT Business Transformation Service was launched to give manufacturers a simple plan to implement IoT initiatives to help their businesses. For more information, visit www.ectobox.com.