How to get the True Causes of Downtime in Manufacturing Plants

Summary
At machine shops, precision metal shops, metal fabricators and other manufacturing companies downtime is not good for business. Downtime can be very expensive. However, it is possible to understand the downtime and address it.

There is no shortage of tips and hints on the web and in the knowledge of your employees on how to address the downtime. However, the real struggle is identifying what the actual downtime is.

When preparing to address downtime it’s best to start with objectively quantifying the downtime before trying to address and reduce the downtime. The chances of success and eventual increases in production throughput go up substantially.

What is Downtime?
Downtime is any period when a production machine or piece of equipment is not producing product. There are planned downtimes for tool changeovers, planned maintenance, etc., and there are unplanned downtimes. Unplanned downtime is detrimental to the production throughput and success of the manufacturing company.

Unplanned downtime could be due to equipment failure or breakdown, starvation of parts, the operator not at the machine to work with the machine, unplanned machine maintenance, lack of an available operator, and so on.

How Expensive Is It?
Downtime is one of the largest sources of lost production time. Some of the numbers of downtime can sound extreme, but are real. For example, the average manufacturer has 800 hours of downtime per year (source). The cost of downtime per minute for various size manufacturers in the automotive industry is can average around $22,000 ( https://news.thomasnet.com/companystory/downtime-costs-auto-industry-22k-minute-survey-481017)…again, that’s per minute!

Getting a little more down to earth for machine shops, metal fabricators, and other discrete manufacturers, the numbers may not be as big. However, for the size of the companies, the numbers can still be significant. Let’s say for one machine the number of units produced per hour is 20. The average revenue per unit is $50. If you have 10 hours of unplanned downtime for that one machine in a month the estimated loss of revenue is $10,000. Multiply that by a few machines or more, and you’re looking at maybe $100,000 or more in lost revenue per month. That could end up being $1,000,000+ per year. Of course, the numbers at your shop will vary.

You may also be thinking, “We don’t have that much downtime. There’s no way it’s that high.” Are you really sure about that? OK, maybe it’s not. I’ll simply ask you to be open to being off a little with the numbers if you’re not measuring it in an accurate and objective manner.

Ways to Reduce Downtime
There are many ways to reduce the downtime…lot’s of tips and tricks. They include investing in preventative maintenance (or using proactive maintenance ideas), smart investments in new technology, better training for employees, empowering employees to take action on issues, perform risk audits, and collect and study data.

The lists of ways to reduce downtime are great methods. However, on which of those areas should you spend time, energy, and money? How do you know what the causes of downtime are? If one is to invest resources into solving a problem, it’s best to understand that problem first.

Understand Downtime First
The first step is to quantify and understand your company’s downtime could be to manually record data. However, this approach often doesn’t provide accurate, usable, and timely data. Operators and shop supervisors can record the data on a log sheet. This process, though, may be fraught with issues such as adoption issues, no time to record the data, only some of the data is recorded, or the data could be accidentally inaccurate. The log sheets are often sent to a person in the office to enter. We have often seen situations where manufacturers will get the data for machine downtime a week or more after the fact after the data is entered in Excel and analyzed. When the manager or owner then looks at the data they’ll call a shop supervisor into their office or go to the plant floor to talk with an operator. Often that person working with the downtime in question will have forgotten what happened.

How to Understand Downtime
Therefore, it’s best to have a system that collects and analyzes downtime data without any effort by humans. The technology to collect downtime data is commonplace and affordable. Therefore, the price of these solutions can be paid for often in a year or less multiple times over relative to the savings and additional revenue that comes from significantly reducing downtime. Additionally, solutions can be put in place in a matter of weeks, not months or years.

These solutions are using IIoT (Industrial Internet of Things) technologies which include sensors to collect data or connecting to a machines PLC or controller, a gateway computer device to preprocess and send the data to be stored and analyzed, an IoT software platform with data storage and other capabilities, and display devices like inexpensive tablets and/or TV monitors in the plant floor.

Recommendations
These solutions can be set up to track data for a single machine to start, to work through technical challenges, if any, and to help with adoption by the shop floor. Those solutions can then be extended to additional machines, lines, and even plants.

It is best to use an IoT software platform that is an industry-standard, backed by a solid company, and which uses open standards for integrations with other products, multiple storage options, etc.

We also prefer to not use a rigid, single-use, off-the-shelf solution. Instead, we prefer to use off-the-shelf industrial innovation platforms like ThingWorx from PTC (source). This enables us to not only address the current challenges but also to lay a technical foundation for building solutions for additional challenges in the future by following best practices and models of maturity like ISA-95.

We suggest not creating a custom software application to collect and analyze machine data. That simply isn’t a strategic move for a company. Custom software can be very expensive, much more expensive than off-the-shelf opens discussed above.

What is needed to implement the solution is multiple skillsets in your manufacturing company or from a trusted vendor to understand your challenges, correctly interpret those challenges to create the appropriate solution, and have the technical capability to create, implement, and support the solution long term.

If this blog post was valuable and if you are in this situation and want to discuss challenges your company is having around downtime and how to get valuable, objective data, please call us any time. We’d be happy to talk about your situation.

Machine Downtime and OEE: How to Get the Data

In order to get the data needed for properly measuring machine downtime and improving OEE you will need to develop a few things. You will need a well-defined business case, you will need to work through a complete planning and scoping phase, and finally you will need to develop a proof of concept.

Planning and Scoping

In planning and scoping, a company will need to define a problem to solve. Some key things to keep in mind here are to keep it simple, as overcomplicating the process can create unnecessary difficulties and delays. If you’re not sure about the productivity of the machine and operator, you will want to get more data. More data provides more information to accurately answer this question. And you will want to get downtime data, as this is the ideal place to start in improving overall equipment efficiency.

Getting more data sounds great, but what data do you really need? To define this, you will need to answer these questions:

  • What KPIs use to drive operations excellence?
  • What metrics use to measure performance, production, quality, and availability?
  • What does the data from user say about when the machine is in use and not, and why?
  • What does the data from machine say about when it is running and not running?
  • What are the failures in the machine with failure codes?

Next, you will want to review the machine. This includes all relevant machine information such as:

  • Machine model number, vendor name
  • Is there an alarm screen, alarm lists or status screens about state and condition? What types of faults and alarms exist?
  • Do we have technical manuals that list functions, operations, and data points available?
  • Does it have a PLC? Model, vendor? Other type of controller?
  • What is the communications protocol?
  • Is there an ethernet port?
  • Are there extra modules or licenses to buy to pull data from the machine?
  • Can data be read real-time? Or must be downloaded via CSV or other method?

You will want to review your network. In order to gather data you will need to know if the machines are connected, and if so how they are connected. You will need to know if a network exists and if it is hardwired (Ethernet, RS-232, RS-485, other), if it is on Wi-Fi, and what type.  And finally, what kind of security this network has.

Selecting a software platform is the next step in your planning process and a crucial decision. If there is any chance you will connect to more machines, pull more data from machines, and expand the solution in other ways, a platform should be considered that has flexible visualizations, data models, many options for connectivity, data historian, etc. You will need to ascertain which IT servers and databases are already in place on-premises, if a cloud solution is acceptable and if you really need data to be viewed in real time.

You will of course need to design the solution. In doing so, you will design the LAN setup and other connectivity, required connectivity modules for the machine,  protocols, tools to translate the data, tools to push the data to data storage, data storage itself, data processing for calculations, and the software platform for visualizing the data.

Finally, you will create a plan. This project plan will include a projected schedule, WBS, team members, responsibility assignments, and pricing for all services and products to purchase for the project.

Proof of Concept

The proof of concept will be an experiment or pilot project. This of course will be done with all the previously mentioned data and preparation, but it is not a final product. Some may worry that creating an IoT solution may be a one time implementation – but it is instead an evolving process, and so a proof of concept is a valuable first step.

During this phase you will want to set up a machine with modules, and you need a vendor to do this. You will setup a network and gateway devices, and software including Kepware and the chosen IoT platform. Next, you will connect the machine. Then, you will map the data to the OPC UA product, push data to the IoT software platform and display the data in that IoT software platform.

Machine Downtime and OEE: How to Use the Data

How to Use the Data

How can you use data from your machines like downtime data to improve your production capacity? How can data be used to improve scheduling, reduce waste, use or materials efficiently?

  • Start with collecting the data and converting it to useful information such as downtime and OEE
  • Then provide it to your team on a tablet, a computer next to a machine, or even a big screen tv in the plan.

You’re now looking at real time data about your machine equipment states and events. You’re getting a real view of your capacity performance constraints measured against base lines with down time, starving, idle, and short stops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding around actual capacity, product materials, throughput, and promised delivery metrics.

You’ll understand 1) where your actual capacity and production is at, and 2) how machines are actually performing to enable that capacity, including the types of downtime. You can then use the actual machine data to troubleshoot and solve particular issues. For example, it’s possible in some cases where getting data on OEE, and then solving problems to improve OEE by 10% can result in an increased capacity of 19% with better efficiency and throughput. That can then result in increased operational net revenue which results in improved product margins.

Two key issues to consider are education and culture:

  • Education: Companies will need to ensure teams are educated such that they accurately understand what the data means, and what actions they are empowered to take which will have a positive result on the machine data they see.
  • Culture: Awareness of the culture on the plant floor is important. There can be a perception of big brother when operators and shop managers know that management can more accurately see what they’re doing and not doing. However, the culture can be developed such that staff can welcome opportunities to do a better job, to improve production capacity, quality, etc.

Downtime Use Cases

Sometimes data concepts can seem abstract, so here we will provide two real use cases.

Food Plant

  • Situation: Food production plant processing 980K lbs product per day to make broths and fat products. They used 6 different box and fluidized dryers in drying process.
  • Problem: Plant was steadily losing capacity over multiple months that seemed to point to excessive down time in their dryers and evaporators.  Data was collected manually showed a 14 day schedule with 6 minute down time cycles but upon investigation they discovered excessive down time with their dryers and CIP processes.
  • Solution: Invested in a real time data collection and process visualization system with OEE to track down time with performance measure metrics.
  • Result: Collecting real time data and OEE showed improvements could reduce down time by 25-50% with an OEE index of 60%. This converted to a savings on the evaporators and dryers between $760K-$1.5M in cost avoidance over a 12 month period with a recovery of 25%-50% in plant equipment capacity and 10-20% product material loss recovery.

 

How to Use the Data

How can you use data from your machines like downtime data to improve your production capacity? How can data be used to improve scheduling, reduce waste, use or materials efficiently?

  • Start with collecting the data and converting it to useful information such as downtime and OEE
  • Then provide it to your team on a tablet, a computer next to a machine, or even a big screen tv in the plan.

You’re now looking at real time data about your machine equipment states and events. You’re getting a real view of your capacity performance constraints measured against base lines with down time, starving, idle, and short stops.  When looking at the data, machine patterns will often reveal themselves and you can gain a new understanding around actual capacity, product materials, throughput, and promised delivery metrics.

You’ll understand 1) where your actual capacity and production is at, and 2) how machines are actually performing to enable that capacity, including the types of downtime. You can then use the actual machine data to troubleshoot and solve particular issues. For example, it’s possible in some cases where getting data on OEE, and then solving problems to improve OEE by 10% can result in an increased capacity of 19% with better efficiency and throughput. That can then result in increased operational net revenue which results in improved product margins.

Two key issues to consider are education and culture:

  • Education: Companies will need to ensure teams are educated such that they accurately understand what the data means, and what actions they are empowered to take which will have a positive result on the machine data they see.
  • Culture: Awareness of the culture on the plant floor is important. There can be a perception of big brother when operators and shop managers know that management can more accurately see what they’re doing and not doing. However, the culture can be developed such that staff can welcome opportunities to do a better job, to improve production capacity, quality, etc.

Downtime Use Cases

Sometimes data concepts can seem abstract, so here we will provide two real use cases.

Food Plant

  • Situation: Food production plant processing 980K lbs product per day to make broths and fat products. They used 6 different box and fluidized dryers in drying process.
  • Problem: Plant was steadily losing capacity over multiple months that seemed to point to excessive down time in their dryers and evaporators.  Data was collected manually showed a 14 day schedule with 6 minute down time cycles but upon investigation they discovered excessive down time with their dryers and CIP processes.
  • Solution: Invested in a real time data collection and process visualization system with OEE to track down time with performance measure metrics.
  • Result: Collecting real time data and OEE showed improvements could reduce down time by 25-50% with an OEE index of 60%. This converted to a savings on the evaporators and dryers between $760K-$1.5M in cost avoidance over a 12 month period with a recovery of 25%-50% in plant equipment capacity and 10-20% product material loss recovery.

 

 

Machine Downtime and OEE: Why Machine Data is Valuable

Smart Manufacturing is the use of one or more technologies including IoT, automation and robots, big data, artificial intelligence and modeling to optimize manufacturing.

Optimizing of Manufacturing

Companies can optimize manufacturing from product concept generation, flexible operations for changing customer needs, manufacturing efficiencies, machines becoming self-optimizing and self-diagnosing, and creating a dynamic manufacturing supply chain.

Manufacturing Efficiences

Manufacturers can work on developing more efficient and flexible operations. This greater efficiency and flexibility enables manufacturers to attain better production capacity and lower overall costs of production, that enables them to handle more dynamic customer needs and a dynamic supply chain. They can then dominate their market as a more flexible and consistent low-cost producer with solid quality and delivery to dominate their markets.

Need Data to Improve Company

To reach these levels of better production capacity and low cost production the key is people must have access to data about machines and how they’re operating… uptime and downtime, productivity of the machine, quality of the products it creates, it’s running condition, how the operator is running the machine, etc. With this data companies can then monitor production lines and pieces of equipment, find issues, and make improvements.

Downtime

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

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

Downtime is one of the biggest “killers” in production efficiency. It leads to material loss, resource loss, uptime loss, and capacity loss. Additionally, it reduces a company’s ability to deliver product to quality standards which leads to missing promised delivery dates, and lost trust with a customer. In the end it all results in higher costs and less revenue.

Benefits of Tracking Downtime

Tracking downtime gives you the data you need to both find root causes of downtime, and make improvements to users, machines and processes. When downtime improves then production capacity increases, costs of product are reduced, revenue increases and margins increase.

Downtime Study

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

      • 46% companies couldn’t deliver services to customers
      • 37% lost production time on a critical asset
      • 29% were completely unable to service specific equipment

They also found that downtime in a factory affects 62% of its productivity with scheduled resources and equipment. These numbers indicate unplanned downtime is common and has a big impact on companies.

Downtime Fallacy

Companies who don’t track downtime of their machines may believe they know what the equipment downtime is. However, they are often off by as much as 50%. Without the actual data to measure and analyze, many companies are not seeing the whole picture, and taking all information into account when measuring downtime – and this can greatly impact their business.

Why Should I Calculate OEE Manually?

OEE (Overall Equipment Effectiveness) is a valuable KPI for how well machines on the plant floor are being utilized. (More information on what OEE is can be found in the Intro to IoT blog post) It is often thought to be a complicated value to calculate, and sometimes it can be. However, it can be greatly simplified. Given the value and the potential simplicity it is recommended that operations staff including their supervisors and managers should know how the calculation works, what OEE is, and how to use it effectively.

Why is OEE Valuable?

OEE has a direct correlation to the revenue the machine provides the manufacturer. If the machine isn’t utilized well, i.e., if it’s down or if it’s producing poor quality parts that cannot be sent to a customer, then the machine isn’t generating as much revenue as it otherwise would.

It’s also valuable because breaking the value down into is parts can help hone in on the cause of issues with a machine. It is made up of three separate percentage values: Availability, Performance, and Quality. One can look at the component values and determine where to start their analysis to find issues with the machine.

Why Calculate Manually

Why should operators and related staff calculate OEE manually? Two reasons:

  1. To understand what OEE really means and what it’s made up of, and
  2. To get a solution calculating OEE in place when an automation solution isn’t available.

Understanding OEE: One can only understand another person’s perspective when they’ve walked a mile in their shoes. In the same way a person can only understand a concept once they’ve recreated it themselves and worked with it. The nice thing is that OEE, or at least some parts of it, can be calculated easily on paper.

Automation isn’t available: In many manufacturing companies OEE will likely be calculated automatically by various systems such as IoT software platforms (these are the solutions we often implement). These solutions are becoming more predominant as companies look for more ways to pull valuable data from their machines to complete…all under the moniker of Smart Manufacturing. However, some companies may want to start utilizing OEE before budgets can be made available for these solutions. So, why not start when pen and paper!?

How to Calculate Manually

A deeper discussion of the calculation of OEE can be found in this How OEE is Calculated blog post. So we won’t cover the details here.

To calculate OEE all you need is pen, paper, maybe a clipboard, and diligent effort to record and calculate the data regularly. It is also helpful to have Excel available. Don’t need anything more fancy than that.

You can start with Availability, one of the components of OEE. Put the paper, pen, and clipboard close to the machine to measure. Then every house have users mark what happened with that computer regarding it’s availability: If it was up and running at normal speed mark a “G” for Green (can include planned downtime if appropriate). If the machine experienced some setup or adjustment time during that hour, mark a “Y” for Yellow. If the machine was down, mark “R” for Red. You could also use colors pens/markers and/or whiteboard.

Then count the total number of G’s, Y’s, R’s. Subtract the total number of Y’s and R’s from the total number of G’s to get the number of hours the machine was available to run. Count the total number of hours over all on the paper no matter the condition for the scheduled operating time. Then divide available time by the scheduled operating time and you get the Availability Rate % value.

Do this for each day, record the results in Excel, maybe even create a simple chart, and you’ll start to see a trend for where the OA (Overall Availability) for the machine is. If you do this for multiple machines you can then get some solid, objective data on the status of the various machines, and which might need some attention.

You can also repeat this process recording similar data for Performance and Quality.

How is OEE Calculated?

OEE (Overall Equipment Effectiveness) provides an indication for 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 it’s 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 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 product 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 for 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 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 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 it’s 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 it’s 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 similar fashion and have similar demands.

Comparison of OEE across different lines and plant 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 it’s true potential.

OEE is made up of three parts, each which are 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 parts can be further broken down as follows:

Availability (up-time 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?

This is very valuable because the utilization of an asset 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.

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 their 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 it’s 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 which 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 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 the 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 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 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 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.

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.