Could be using this laptop to analyze common problems in the manufacturing industry.

3 Common Problems in the Manufacturing Industry that are Holding You Back

3 Common Problems in the Manufacturing Industry Holding You Back

There are many common problems in the manufacturing industry. We can all agree that there is no shortage of problems that need to be dealt with. Especially in the industry, there are a lot of people involved and a lot of big machines with many moving parts. Inevitably, with that amount of moving parts and people, there are bound to be issues that come up frequently. These problems go far beyond just the plant floor.

All of this means that there is a lot of money moving around and changing hands often. Even small errors can equate to a large amount of lost revenue and profits. In this article, we will discuss 3 common problems in the manufacturing industry that could be holding you back. The goal is to help you make your factory as efficient as possible and maximize revenue and profits.

1. Lack of Skilled Workers

Let’s not waste any time, and get right to the facts. Manufacturing is a huge industry, employing a lot of people. It’s the fourth largest industry in the united states based on the total number of employed persons (source). This is good, a lot of people working in the industry. Here’s where it starts going downhill- almost one-fourth of the manufacturing workforce is age 55 or older (source). There’s not a lot of young people pursuing a career in manufacturing. This is a growing problem for manufacturers. It’s not a good sign when roughly a quarter of the manufacturing workforce is on the cusp of retiring, along with low numbers of new and young employees entering the field.

This is forcing manufacturers to come up with a stronger recruiting process, and looking for ways to attract more young people to the sector. This may not be the main problem that you think about on a daily basis, but it is not to be overlooked. The statistics are scary for the future of manufacturing.

2. Lack of Awareness

The second common problem in the manufacturing industry to go over is general awareness. There are many different angles you could take here.

Overall plant floor awareness, downtime awareness, accurate data awareness, digital transformation awareness. At the beginning of this article, I mentioned that there is a lot of moving parts and people in the manufacturing industry. It can be a challenge to keep track of and remain aware of everything happening. On the other hand, it is near impossible to make improvements if you aren’t even aware of what’s really going on. So the first step to improving operational efficiency is to become aware and accurately define where you need to focus or improve.

Downtime Awareness

More often than not, manufacturers cannot accurately estimate how much, or where their downtime is coming from. Downtime is a leading cause of lost revenue in manufacturing. That means it should definitely be made a point of emphasis, and that manufacturers should at the very least be aware of where of the amounts and causes of downtime. Sadly, downtime awareness is still a very common problem in the manufacturing industry.

Accurate Data

The way in which you gather data changes everything. It doesn’t matter how much data you are attempting to gather if it’s all data that you can’t validate, and you can’t trust. Many times, manufacturers will gather data, see the numbers, and not believe them or disregard them. This makes the initial act and effort in gathering the data completely useless and reinforces the value of using a reliable system for gathering data that you can trust. Manufacturers need to become aware of the validated, trustworthy, and accurate data that they could be extracting from their equipment and benefitting from.

With IIoT and manufacturing analytics technology becoming more available in recent years, it’s just that much more important to have accurate data if you want to continue to grow, stay competitive, and become a data-driven company.

3. IIoT and Industry 4.0

This leads us to our last common problem in the manufacturing industry. You might wonder, how are IIoT and Industry 4.0 common manufacturing problems? The problem is not the technology or the solutions themselves, it’s actually more of a mindset problem. A large number of manufacturers are choosing to ignore the value that IIoT and Industry 4.0 bring.

While others are open to the ideas, but struggle to become data-driven and use the technology to its fullest capacity. These companies need to focus on solving their business challenge, rather than focusing on the technology challenges. Instead of saying- “How can we equip our factory with the latest and best technology?” Ask yourself- “How will this actually help me?” Or, “What will get improved by gathering, analyzing, and acting on this set of data?”

A Happy Medium

There are two extremes here, the best spot is a happy medium in a way. No doubt that there is enormous value in gathering accurate real-time data. IIoT along with Industry 4.0 principles bring this to the table. Becoming a data-driven company is also very important. However, becoming a data-driven company means that you are acting on the data, driving decisions based on this data. Becoming a data-driven company does not mean just extract as much information, data, and generate as many reports as you possibly can. Gather what you need to improve efficiency, and solve your business challenge, any data you aren’t acting on is useless.

How you can Take Initiative to Solve These Problems

These are common manufacturing problems, but that doesn’t mean that you have to just live with them. There are things you can do to minimize these issues in your factory.

The problem with a lack of skilled workers might be a job for the industry as a whole. However, becoming aware of everything happening on your plant floor, with your workers, and inside your machines is something that you can improve. Taking advantage of IIoT and Industry 4.0 can prove to be greatly beneficial. Equip yourself with the right system that meets your needs and solves your business challenge. Adjust your company culture to become a data-driven company. Your operators want to improve their process, they’re smart, allow them to benefit from accurate data. IIoT can deliver real-time data to operators and decision-makers instantly.

We established that awareness is also a very common problem in the manufacturing industry. Be aware of your specific problems, and use the tools available today to solve those problems. If you actively search for ways to get better and generate a solid plan of action, the results will be rewarding.

Machine Downtime and OEE: Example, Tech Choices, and Recommendations

Machine Downtime and OEE: Example, Tech Choices, and Recommendations

In previous articles, we have discussed machine downtime and OEE including why machine data is valuable, how to use the data, and how to get the data. Here we will provide an example and some other helpful information on monitoring machine downtime and how to improve overall equipment effectiveness. To calculate your own OEE, you can use this OEE calculator.

 

Example

In this example, we will use Kepware as a recommendation. Kepware is a very powerful and flexible tool. It is well accepted across industries and very well known. They have great support, can connect to many types of protocols and devices, and is well maintained.

Using Kepware to connect, you will:

  • Set up a channel over which to communicate with the device driver (MTConnect, Bacnet, Modbus, etc), and the network card to connect
  • Add a device or machine to connect to including the IP address and set up various data settings
  • Then add the Tags or fields with names and addresses in the machine
  • Open the Quick Connect tool
  • Connect to the machine
  • Test to see the data coming through

When pulling data you can either have Kepware push the data directly to a destination such as a database or a setup another system to pull the data from Kepware. You will view this data in an IoT software platform. What you view is depending on the IoT software platform you’ve selected including its capabilities, what is required to set it up and connect to data sources. Viewing data will also depend on how you’ve set up the logic in the system to process data and how you’ve set up screens to display it.

Choices of Tech

There are several options available out there to choose from and deciding which is best for you can be tricky. While you will need to decide which best suits your needs, here are a few options for machine downtime data gathering and processing we recommend:

Kepware

      • Industry-standard product, widely recognized
      • Is a PTC company
      • Translates data from multiple protocols including for CNC machines: MTConnect, FOCAS for the GE FANUC controllers, and others
      • Great support
      • Can fit into multiple solution architectures

ThingWorx

      • PTC is a well known and trusted brand in engineering and IoT solutions
      • Very flexible development environment for solutions
      • Multiple products to greatly extend the solutions
      • Wide support by many partners, of which we are one

Microsoft

      • Household name brand
      • Database, software development, and cloud tools are industry standards, including in manufacturing
      • Flexible tools provide multiple options for solution designs

Best Practices and What’s Next

Use best practices for LAN design, security, database structure, naming conventions in the data layer, etc. Use open standards: OPC, OPC UA, MTConnect, ISA 95, for example: OPC can be useful because it creates intelligent rules for how to collect data and how to verify have right data.

The next steps are to monitor the machine, look for issues that are valuable low-hanging fruit, address them, then watch the numbers over time to see if there are improvements.

Final Recommendations

Be thoughtful for now and the future with Industry Best Practices. Solve the problem now and at the same time set up a foundation for growing the solution in the future. Setup a framework with a data model, and a network that will standardize how to interface with any machines in your plant. This will greatly simplify setting up new machines and simplify how to access the data.

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 used to measure performance, production, quality, and availability?
  • What does the data from the user say about when the machine is in use and not, and why?
  • What does the data from the 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 types of controllers?
  • 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 in real-time? Or must be downloaded via CSV or another 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 set up 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.

Reduce Manufacturing downtime to keep optimal production flow.

Manufacturing Downtime and OEE: How to Use the Data

Reduce Manufacturing downtime to keep optimal production flow.How to Use the Data

How can you use data from your machines like manufacturing 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 baselines with manufacturing downtime, starving, idle, and shortstops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding of 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 manufacturing 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 a 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.

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

This article is just one part of a four-part series, check out our article on how to get the data.

statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.

Production Downtime and OEE: Why Machine Data is Valuable

Production 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. An efficient manufacturing operation means, among other things:

  • Product flows through the plant seamlessly;
  • Machines have the highest asset utilization possible to drive as much production and revenue as possible; and
  • The plant’s machines, operators, and systems are providing data that is shared across the organization to enable visibility into the factory floor for making valuable decisions throughout the organization.

Optimizing of Manufacturing

Companies can optimize manufacturing in 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 Efficiencies

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better asset utilization, production capacity, and lower overall costs of production. The higher production capacity and asset utilization, and flexible operations enable 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, asset utilization ratios, the 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.

Production Downtime

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

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

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

Downtime Study

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

      • 46% of 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 production 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.

statistics on OEE and machine data, use this information to keep productivity optimal, and reduce production downtime.
Downtime Fallacy

Companies who don’t track production 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.

This article is part of a series, if you found this information insightful, check out our next article on downtime and how to use the data.

 

Why Should I Calculate OEE Manually?

 

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 a 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 fancier 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 has users mark what happened with that computer regarding its 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 color pens/markers and/or a 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 overall 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.

How is OEE Calculated?

OEE (Overall Equipment Effectiveness) provides an indication of how effectively machines are utilized on a manufacturing plant floor. It’s valuable for identifying and removing production constraints and for driving up revenue earned by every machine.

The calculation has some mysticism surrounding the complexity of its calculation. We’ll simplify the calculation to a level where the reader can immediately start calculating it by hand.

How to Calculate OEE

OEE for any machine is made up of 3 parts multiplied together: Availability (up-time) x Performance (production speed) x Quality (widgets produced correctly the first time). Below is a brief explanation of each part and how it’s calculated.

Availability Rate

Machine’s up-time, the percentage it is ready to produce products and is working properly, excluding changeover and setup time.

Formula: (Scheduled Operating Time – Downtime) / Scheduled Operating Time

Note that Scheduled Operating Time is not “total time”. Availability refers to when the machine could be running based on when it is needed or planned to run. There could be reasons it won’t run when it is needed (i.e., setup, breakdown) and this calculation must account for those downtime reasons.

Performance Rate

The rate a machine actually produces products relative to it’s best known or standard production rate.

Formula: Actual Output / Standard Output

Note it’s critical to come to terms at your plant with what the standard production rate is for your equipment. It’s best not to use the specified or design production rate by the vendor of the product. In this case, if the machine were running faster than the design production rate and you were using the designed production rate this could mask quality or availability issues in the OEE calculation.

It’s also worth noting that the production rate will highlight losses due to idling, slowdowns, and minor stoppages.

Quality Rate

The rate the machine outputs good parts.

Formula: Right First-Time Output / Actual Output

Note that products produced by the machine which require rework or any sort of adjustments, along with scrapped products, are not counted as a quality product.

Example

Now let’s run a quick example calculation for one shift:

Availability: 

Scheduled Operating Time: shift 8 hrs or 480 mins, 20 mins planned downtime, 0 mins breaks; total 460 mins

Downtime: breakdowns 30 mins, setups and adjustments 15 mins, minor stoppages 15 minutes; total 60 mins

Available time = Scheduled time – Downtime = 460 – 60 = 400 mins

Availability rate = (Scheduled Operating Time – Downtime) / Scheduled Operating Time = (460 – 60) / 460 = 87%

Performance:

Actual Output: 400 parts or 1 part/min for 400 mins of Available time

Standard Output: 800 parts or 1/2 part/min for 400 mins of Available time

Performance rate = Actual Output / Standard Output = 400 / 800 = 50%

Quality

Right First-Time Output: 400 parts – 20 defective parts = 380 parts

Actual Output: 400 parts

Right First-Time Output / Actual Output = 380 / 400 = 95%

OEE = Availability x Performance x Quality = 87% x 50% x 95% = 41%

Additional Considerations

One must keep in mind that OEE is not a value that can be used to compare the performance of many different machines, different production lines, or even less so various plants. As you can see above, there is a lot of uniqueness built into the calculation for each machine. Each machine is unique for the schedule required of it, it’s inherent production rate (by its design), and other factors. Therefore, OEE is really only meant to be used as a metric to improve the performance of each individual machine. Monitor the OEE value for the machine, break it down into its component pieces, and hone in on the problem, fix, and then watch OEE to see if it improves after the fix.

Even if the above is true, OEE can be used to compare machines and lines, but only if they are used in a similar fashion and have similar demands.

Comparison of OEE across different lines and plants can be done but should only be done 1) when using an average OEE value, and 2) with the understanding the value will not be highly accurate.

Introduction to OEE (Overall Equipment Effectiveness)

Many companies have jumped on the bandwagon of using OEE (Overall Equipment Effectiveness)

as a KPI for their manufacturing equipment, lines, and plants. This article will introduce what OEE

is and briefly discuss how it should and should not be used.

What is OEE

OEE indicates how effectively machines are utilized on the manufacturing plant floor. Another way to put it is OEE is the percentage value the machine is performing up to its true potential.

OEE is made up of three parts, each of which is calculated as percentages, and the total value is calculated by multiplying all three parts: Availability rate x Performance rate x Quality rate. Ex: 70% Availability x 80% Performance x 90% Quality = 50% OEE

Each of these three parts can be further broken down as follows:

Availability (uptime of the machine)

  • Equipment failure or breakdowns
  • Setup and adjustment

Performance (speed producing widgets)

  • Idling and minor stoppages
  • Reduced speed of operation

Quality (products produced correctly the first time, without rework)

  • Process defects (scrap, repairs)
  • Reduced yield (from startup to stable production)

Why is OEE Valuable?OEE

This is very valuable because the utilization of an asset has a direct correlation to the revenue the machine provides the manufacturer. Of course, you want to make sure that your machine is operating as efficiently as possible. paying attention to OEE will help you to keep your machines running more, with less downtime. OEE can also assist in making sure that your machine is making a good quality product to be sent to the customer. If your machine is down or making poor quality products that cannot be sent to the customer, then the machine is not generating as much revenue as it otherwise would.

Having machines down or just not working at an effective pace can greatly affect the overall production of the plant, and cause you to miss deadlines. That means less happy customers and a higher chance of them taking their business elsewhere. Happy customers mean more business, more money, less stress, and a great strong reputation to retain a good customer base and push your growing business up to the next level.

The bottom line is that your machines across the plant floor will be more productive, making better products. Making sure that your machines are being used to their full potential will lead you to generate higher revenue.

How to Use it

Here are 3 ways we recommend using OEE if your company is new to this manufacturing KPI:

  1. Find machines that need attention: A good manufacturer will attempt to remove all constraints in its production process whether or not that additional capacity is currently needed. Users can find constraints or issues with machines by looking at the individual OEE values for each machine and determine which are performing effectively and which are not. Lower OEE values will indicate which may need some attention. Addressing issues with machines and thereby improving their operating effectiveness can then increase the revenue the machine produces.
  2. Find the cause: Once a machine with a lower OEE is identified the staff can look at each of the three components of OEE (Availability, Performance, and Quality) and find a leading cause of the OEE difficulties. Additional analysis is then required to find the cause and fix. Then setup improvement projects to address the issues.
  3. Confirm improvement: Once the issues are addressed with the machines continue to monitor the OEE values for those machines. If the OEE numbers go up, especially in the parts where the issues were addressed, then the efforts to fix may have been worthwhile. Otherwise, the machine and its issues may warrant a second look.

Breaking it Down: Building Blocks of Smart Manufacturing

A Smart Manufacturing company is a manufacturer with fully integrated solutions that empower it to be highly flexible and responsive
to changing conditions, with self-healing systems. Those capabilities enable a company to reduce costs, generate more revenue, and

increase their competitiveness in their market.

So how does it all work, and where do these capabilities come from?

The Nine Blocks that Stack Up to Smart

There are 9 technologies or building blocks that stack up to create Smart Manufacturing.

  1. IoT (Internet of Things): Taking data from sensors on machines, moving it over the internet, and converting it to valuable information to help people make decisions and drive action.
  2. Automation and robots: Using robots and software to enable machines and people to work together more efficiently.
  3. Big data: Storing and using massive amounts of machine data for data analysis to gain insights about operations and machines.
  4. AI (Artificial Intelligence): Using software tools to build models and algorithms which can learn about niche areas of your facility to distinguish between normal and critical conditions.
  5. AR (Augmented Reality): Viewing models or images overlaid onto the real world on a mobile device.
  6. Additive Manufacturing: Creating objects from 3D models by joining materials layer by layer.
  7. Modeling: Portraying or defining aspects of the physical world in digital form for gauging equipment status, simulating operations, or controlling live systems.
  8. Cyber Security: Applying old and new ideas of encryption, protecting attack vectors, and combining digital with physical security tactics to protect data in motion and data at rest.
  9. Cloud: Moving away from local, on-site servers towards 3rd parties like Amazon, Google to store, service, and process data.

Stacking the Blocks

Each of these technologies can deliver value on its own. However, companies can realize exponential value by combining technologies.

Here are a few examples:

     Predictive Maintenance

  • Technologies: IoT + AI
  • Method: AI experts create a machine learning model. IoT pulls data from machines and channels it through the model to assess the probability that a part or whole machine may fail within a certain period of time.
  • Benefits: Clear identification of critical maintenance windows, resulting in up to 50% cost reduction by addressing issues only when needed but before failure.

     Robot-Assisted production

  • Technologies: Robots + Automation + IoT
  • Method: A “human assistant” robot is programmed to work alongside people to handle repetitive and mundane tasks or sets of tasks. The robot is pre-programmed to handle multiple task sets. Input from IoT data (such as production data further up or down the line) directs the robot to change its tasking based on real-time production needs.
  • Benefits: Increased production with existing staff and reduced pressure to hire/replace workers.

     Data-driven quality control

  • Technologies: IoT + Automation + Big Data
  • Method: Set up machines to test products immediately after a certain stage of production. Automatically analyze that data relative to product quality standards. Based on results, either trigger alarms for humans to intervene or have the equipment modify its own behavior appropriately to meet the required standards.
  • Benefits: Earlier notification of equipment problems, earlier identification of product quality issues, reduced waste, and rework. Increased production of a product to standards.

Choosing the Right Blocks

Some of the most exciting benefits of Smart Manufacturing come from the myriad and creative ways that new technologies can be combined and implemented. Finding the right combination requires effort: thoughtful planning, employee input, and focus. But even small and mid-size companies can increase their competitive position, with access to the same building blocks as larger manufacturers.