This manufacturing process could have improved efficiency if they would engage in data-driven manufacturing.

Why Data Driven Manufacturing gives You the Competitive Edge

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What is Data-Driven Manufacturing?

Data-Driven Manufacturing is leveraging data gathered from the plant floor so that everyone in the plant from operators to COO’s can adjust and make improvements to the overall manufacturing process. Data from your equipment allows everyone to make confident decisions based on solid information. 

There are many ways that you can acquire data, with some being much more efficient than others. You could hire someone to go to your plant floor, and manually record data from machines with pen and paper or have the operators do it themselves. Although this is a very straightforward concept, it likely is not the best solution for you. Having people manually record data sounds like a simple, efficient, and very straightforward task. However, the data typically recorded on paper ends up being old (once it’s in a state to be analyzed), moderately inaccurate, and limited. The truth is, there is a much more accurate and efficient solution available today. 

Implementing an IIoT manufacturing analytics system. These kinds of systems will allow you to pull data directly from the machines on your plant floor, to provide you with data that was never available before, in real-time. Some wonder if we can rely on this data to be accurate, without human interference. As humans, we will make errors, and improperly record data from time to time. IIoT systems pull data from PLCs, controllers, and sensors to pull data right from the equipment. There is nothing in between, it is the most straightforward path possible. This means that you get the most accurate, and detailed information in the quickest way possible. For the data that may not be available from the equipment, HMI or operator data entry screens can be provided for operators to enter the pertinent process, production, quality, and availability/downtime data. 

Why You Need to be a Data-Driven Manufacturer

Making decisions with solid substance behind your reasoning is invaluable. We have established that implementing an IIoT system is the best way to gather data. How does the data you are gathering really make such a big difference? There are so many different categories of data that IIoT systems can attain today. You can track downtime with reason codes, production throughput, temperatures, vibration, and scrap just to name a few. That data can then be converted into more valuable information like OEE, downtime Pareto chart with reason codes listed by occurrence or duration, product goals versus actuals during the middle of the day, and more. 

If you are tracking these metrics and seeing the real performance, inevitably you will find room for improvement, even in areas where you might have thought you were doing pretty good. Seeing the actual numbers is shocking to most people. 

Continuous Improvement

When you are driven by data, you will see all the areas that need improvement. Since you are looking at precise data you will also see exactly how much room for improvement there is in one area. Right from the start, you will clearly recognize your biggest inefficiencies. Now you will know exactly where to focus your efforts. Real-time data will assist you in solving the problem, and getting instant results. In our experience it can take from 1-2 days to 2 weeks to get data recorded manually, data entered into a spreadsheet or database and then converted into usable metrics. The real-time availability of data with IIoT solutions is immensely valuable and puts you right on track to running the most efficient factory possible. 

Confident Decisions 

Being a data-driven manufacturer can help you in other ways that you might not put much thought into initially. You’re able to make confident data-based decisions. Empirical data including numbers and large amounts of the right data backing up your decision are undoubtedly a massive benefit. You’re able to work in a clearer state of mind, not continuously thinking about a past decision, wondering and hoping that you made the right move. Realistically, you have more than one thing on your plate and the added stress holds you back. When you eliminate the guesswork and know that you made the right decision based on facts, it will help you tremendously. Allowing you to solve issues quickly and effectively, and shift your focus onto the next task. 

Why Being Data-Driven will Generate You more Revenue

IIoT systems, manufacturing analytics, and real-time data are great tools and features to help convert you into a data-driven manufacturer. This is, of course, an investment. Here is how data-driven manufacturing will take your company to the next level by increasing efficiencies, reducing costs, increasing production, and in the end increasing revenue. 

Once you see the data, you will have an accurate perspective on efficiencies in your operations. Once you gain that awareness you’re better able to improve efficiencies. You’ll be able to track data to decrease downtime, increase throughput, increase OEE, you will understand your machines better, and move aware from expensive maintenance practices like reactive and preventative to more valuable practices like condition-based monitoring and other efficient and proactive maintenance practices. 

Here are some additional areas of plant operations that can be improved with IIoT solutions: 

  1. Project Lead Times

    This area will be improved if you act upon the data that you are now receiving. More units manufactured faster, as your manufacturing process efficiency is improved, your project and lead time will inevitably go down. Now that you can get projects done faster, you have time for more projects. More projects is more money.  

  2. Downtime

    Downtime will decrease. Whether it is scheduled or unscheduled downtime, the numbers will improve. Once you have PLCs and sensors installed that allow you to see what is happening inside the machine, you will learn the reasons why that machine might be going down. You will have the historic data to find trends in machine behavior, this will help you avoid unscheduled downtime. You will also know when you will need to schedule maintenance on the machine based on the actual, current health of the machine. Before you were tracking this metric, you might have even been spending too much time on maintenance that the machine did not require. Unscheduled downtime is always bad, but having inefficient scheduled downtime can be just as detrimental.  We have another blog post focuesed solely on reducing downtime- If you think you could benefit from reduced downtime check it out here.

  3. Customer Relationships

    Improved customer relationships. Your customers will appreciate you engaging in data-driven manufacturing just as much as everyone on the plant floor. Each job has different requirements and a lot of variables. Those variables can cause issues including poor quality and/or late deliveries. If you have historical data and an awareness of current conditions in the plant you can then be more proactive to make adjustments on staffing, routing, shifts, parts and raw materials needed for jobs, etc. You can then give the customer updates with precise and detailed data for when their orders will be delivered. If you can provide more insight into your manufacturing process, you will establish trust and transparency. Happier customers lead to more return customers, better reviews, and a great brand reputation.  

Put Yourself in Position to be an Industry Leader

More insight, more throughput, less downtime, happier customer, and smarter employees that have machine insights and make decisions based on the facts. These are many of the traits companies have that are leaders in the manufacturing sector. 

Technology is advancing at a quick pace. These capabilities are becoming more and more available to everyone. Being a data-driven manufacturer is not just for the Teslas and the Amazon’s of the world. Today, it is available to even small-medium-sized businesses. The ones who are taking advantage of this are the ones that are growing, making a name for themselves, and will thrive in the coming decades. Don’t sit back relying on the old way manufacturing has been done for the last 30+ years, put yourself in a position to lead the industry.  Seemingly small details are the difference that can make or break your results in the future. 

Machine Downtime and OEE: How to Get the Data

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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

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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

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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?

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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?

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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)

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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

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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. To find 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.