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Three Common Misconceptions About AI in Manufacturing

Artificial intelligence (AI) and machine learning (ML) are creating quite a name for themselves in the manufacturing industry and for good reason. Both AI and ML are helping manufacturers use factory data to streamline operations, improve processes and make better business decisions.

But what’s the difference between artificial intelligence and machine learning?

Simply put, artificial intelligence is machine-generated intelligence that leverages concepts and tools from multiple fields, including computer science, cognitive science, linguistics, psychology, neuroscience and mathematics. Machine learning is a type of AI where machines absorb data and learn faster and more accurately than humans can.

Misconceptions About AI and ML in Manufacturing

Let’s look at the difference between Conventional AI and Intelligent AI and why using the proper tools is essential for successful overall equipment effectiveness (OEE) and improvements in manufacturing operations.

Many manufacturing operations are investing in AI for operational improvements. This article is designed to help dispel some common misconceptions about AI and speak to the many benefits early adopters of AI achieve.

Using the right tools for the right jobs helps manufacturers achieve success faster and deliver high ROI through early adoption.

AI and ML Misconception # 1: AI and ML are the same.

Artificial intelligence is a toolbox, and for a company looking to apply AI to digital transformation, it’s important to understand the distinction and benefits that can be achieved by each one.

Thinking about the tools a carpenter uses to build a house will help you understand “the right tools” for the job at hand. If a carpenter is going to build a house, they need multiple tools to be successful. For example, her raw materials might include plywood, shingles, nails, framing, metal flashing, etc.

The same ideas apply to AI. Depending on the job at hand and what you are manufacturing, you require the right AI tools for the job. Below are some examples of AI tools that can be deployed in manufacturing operations to help improve OEE in the categories of availability, performance and quality!

Without the proper skill sets and process domain knowledge for a particular industry, it won’t matter what tools are used or what data is accessed. To be successful, you need a combination of both.

Conventional AI vs. Intelligent AI

Let’s look at the difference between Intelligent AI and Conventional AI and why using the proper tools is important for successful OEE improvements in manufacturing operations.

AI has become a buzzword that has lost much of its meaning due to common misconceptions. To simplify, consider the following two categories of AI and definitions with examples to help distinguish the difference between both:

  • Conventional AI is a collection of technologies (tools) that use algorithms to programmatically simulate specific tasks often performed by human beings. This form of AI uses automated formulaic sequencing logic for problem-solving but doesn’t typically involve machine learning. Instead, these technologies focus on solving problems in which the behavior patterns can be laid as formulas.
  • Intelligent AI is engineered to learn and adapt to variability in processes, surpassing human intelligence for improved decision-making purposes. Intelligent AI is more advanced than Conventional AI because the algorithms learn and make better decisions as more data is consumed. In addition, because these technologies operate at incredible speed and accuracy rates, intelligent AI is much more advanced than Conventional AI when applied to complex manufacturing processes.

Many AI companies use Conventional AI for improved business intelligence (BI) purposes, but this requires a process engineer or operator to validate and act on the outcomes consumed through dashboards and reports. We call this reactive process control. It’s useful for things like root cause analysis but offers limited value for continuous manufacturing. Intelligent AI enables machines to quickly make highly accurate decisions so that parameter control changes can be applied automatically to continuous manufacturing environments without human intervention. This is often referred to as proactive process control or autonomous manufacturing.

With the Intelligent AI toolset, multitudes of machine learning techniques are applied depending on the task at hand. For example, building a roof and installing plumbing require different tools. In addition, machine learning improves manufacturing outcomes by combining powerful AI technology such as supervised and unsupervised reinforcement and deep learning systems.

Supervised machine learning algorithms use labeled data sets, beginning with understanding how the data is classified. Unsupervised models use unlabeled data sets and can figure out patterns and features from the data without preexisting categorization or explicit instructions. Reinforcement learning, on the other hand, uses an iterative approach.

Instead of being trained by a single data set, the system learns through trial and error and receives feedback from data analysis. With the power of faster data availability (real-time and streaming) and big data computational capabilities, deep learning applies specific “neural networks” algorithms. Neural networks are composed of decision nodes that more accurately train ML systems for unsupervised, supervised, and reinforcement learning tasks.

One way to think about the difference between Conventional AI and Intelligent AI is the hammer and nail vs. the nail gun. But, of course, both technologies can add value to a manufacturing operation. Still, if your goal is to accelerate OEE or improve product quality automatically, Intelligent AI will get you measurable results much faster.

How can the more sophisticated Intelligent AI tools help improve your operations?

Using power tools helps get the job done faster and with greater precision than manual tools. When it comes to cutting the plywood used for a roof would you choose a hacksaw or an electric-powered circular saw?

Intelligent AI can identify variability in complex manufacturing operations that either happens within tight production cycles or at very high rates of speed.

These require split-second decision-making followed by accurate recalibration recommendations and instantaneous parameter control setting changes.

Suppose your manufacturing operation is continuous and is highly sensitive to changes in machine conditions (i.e., temperature, pressure, machine speed and set-point accuracy). In this case, Intelligent AI is better suited for delivering proactive process control to your operations than Conventional AI.

If you are automating a task performed manually that doesn’t require a sophisticated machine learning model to perform, then Conventional AI may be a better fit.

AI and ML Misconception #2: AI is a proven technology that solves all complex manufacturing problems.

Conventional AI, which can be viewed as the manual tool in the toolbox (hammer), won’t be the end-all solution for solving your particular manufacturing complexities. AI models, just like the human brain, require education and training before they become proficient at problem-solving. Fortunately, Intelligent AI uses machine learning that ingests data more rapidly. As a result, its accuracy improves over time as more data gets processed and different conditions are encountered for training and retraining the models. In addition, measurable process improvement is achieved by applying the prescriptive parameter control setting changes in real-time.

Again, this is about using the right tools for the task at hand. If your primary objective is to improve product quality, then Intelligent AI, when applied, will get the job done faster and more precisely. But, just as a first-year medical student would be ill-prepared to perform brain surgery after taking his first anatomy course, even Intelligent AI requires thorough training by ingesting high volumes of data before it learns to apply the suitable model for the right conditions based on what the data is revealing. This includes some experimentation and repetitive model training before delivering accurate and automatic continuous quality improvement in many cases.

There will always be certain tasks and processes that are best left to humans to perform, especially those requiring human senses like taste and smell. However, even those categories are being explored by leading data scientists – stay tuned.

Without the proper skill sets and process domain knowledge for a particular industry, it won’t matter what tools are used or what data is accessed. To be successful, you need a combination of both.

AI and ML Misconception #3: AI can be used indiscriminately to solve any complex manufacturing problem.

If an AI company tells you, “just give me all your data, and our AI will solve your problems,” my advice is to run away as quickly as possible! Many AI companies fail to recognize just how complex and variable manufacturing conditions can be from production line to production line and from machine to machine.

The first step toward solving these complexities is acknowledging the underlying causes and using subject matter expertise and knowledge to address the issues. Understand that it’s not just “the data ” you must understand how the process works. Having SME inputs and incorporating them in your AI configurations is critical to success.

Furthermore, if your approach is to process “all the data” vs. “all the right data,” then your AI will struggle to decipher which data is essential and what process parameters are most closely associated with OEE improvements.

The “all the data” approach introduces noisy data into a process likely to deliver garbage results.

A wrench is useful when installing new plumbing in your bathroom, and hiring a plumber to help with the installation is a sound idea. However, hiring a plumber to build your roof with a wrench is a bad idea.

The point is that without an intimate level of domain expertise for your line of work, it won’t matter what tools you use or what data you access because you still need the proper skill sets and process domain knowledge to be successful.

Early Adopter OEE Benefits and Sustainability Gains with Applied Intelligence

Benefits early adopters can expect to achieve using AI-powered Applied Intelligence for their manufacturing operations:

Operational Improvement

  • Address Skilled Labor Shortage
  • Improve Product Quality
  • Increase Yield
  • Increase Throughput

Sustainability Improvement

  • Save Energy
  • Reduce Emissions
  • Reduce Chemicals Usage
  • Save Raw Materials
  • Decrease Water Usage

Tips on How to Get Started on an AI Project at Your Organization

Some manufacturers don’t want to bother with a complete overhaul of their manufacturing processes. Instead, the key to successful AI-powered manufacturing is early adoption. Tips for achieving early adopter success with artificial intelligence include:

  • Identify which aspects of OEE improvement are most important to your operations and force rank them:
    – Availability
    – Performance
    – Quality
  • Inventory your data and determine what data attributes are required for success:
    – What data sources do you have access to
    – Work with Process Engineers to find the “right data.”
    – At what frequency can data be accessed
    – Is the data tagged and categorized and time-stamped?
    – Is there data missing that is necessary to achieve the desired OEE improvements
  • Identify an aspect of your manufacturing process that often fails or processes that fall over and choose one, start small, build to scale, and move rapidly.
  • Work with the right AI tools appropriate for the job or task at hand – find an expert.
  • Experiment, test, refine results and automate what can be automated, then move on to the next problem to be solved.
  • Measure results, expand adoption, and scale across operations, then take credit for the wisdom and ROI you’ve delivered for your company.
Tom Tulloch ProcessMiner

Tom Tulloch

Chief Commercial Officer, ProcessMiner, Inc.

Passionately helping customers find new ways to monetize their data, Tom helps business leaders execute continuous process improvement objectives using applied artificial intelligence. A career spanning two decades of leadership and expertise in data, he is widely recognized as a data and emerging technologies expert, frequently asked to share his knowledge and expertise around new technologies shaping Industry 4.0 and the Digital Industrial Revolution.

Manufacturing’s Perfect Storm

US Manufacturing Supply Chain Unprepared for Record Surge in Demand

By: Tom Tulloch, Chief Commercial Officer, ProcessMiner™

Manufacturing is forecast to come roaring back in the second half of 2021, according to many leading indices. This is fantastic news for the US economy but poses significant challenges for the manufacturing sector due to unique complications brought on by the COVID-19 pandemic and resulting supply chain disruptions.

According to the IHS Markit and the Institute for Supply Management‘s activity data for February, both Purchasing Manager Index (PMI) readings registered at around 60.0, the second-highest readings in more than a decade (anything above 50.0 signals growth).

Add to this the US ISM Manufacturing Prices Paid Index surging to 86, its highest level in 17 years, and you have the makings of a “Manufacturing Perfect Storm.”

Great news for manufacturing, right?  Good problem to have, yes?  

Not so fast.

Unfortunately, many businesses are ill-prepared to keep pace with this forecasted V-shaped recovery. According to the IHS pricing index, US manufacturers are facing unprecedented cost pressure due to price increases in raw materials, rising fuel costs and inflationary pressure, which is likely to corner manufacturers into price increase challenges.

The other problem is the growing backlogs of new orders brought on by reduced operational capacity and new standard operating procedures (safety protocols) forced into manufacturing operations as a result of the COVID-19 pandemic.

Who will the winners and losers be in this “perfect storm?”

That depends.

For many manufacturers, innovation and ingenuity may help save the day, and the demand surge will lead to growth metrics that any wall street analyst or investor would celebrate. The mountain gets a bit steeper to climb for others who haven’t kept pace with Industry 4.0, SMART factory digital transformations.

The moral of the story is a much-deserved congratulations to the risk-takers and bold innovators who embraced Industry 4.0 technologies early on. Your early adopter status improves your odds of “making it rain” in this storm and it’s highly likely you will outperform your competitors who took a wait-and-see strategy.  Why?  Because you are now reaping many of the benefits brought about by connected factories, including:

  • Improved productivity
  • Better safety records
  • Lower scrap and waste
  • More efficient use of energy
  • Better product quality
  • Less downtime and higher OEE
  • Improved decision making

For those out there who are still on the fence, it’s not too late to get in the game and make those investments now. Perhaps you will survive this storm, but now is the time to reconsider your strategy and prepare for the next crisis looming just around the bend.

Tom Tulloch ProcessMiner

Tom Tulloch

Chief Commercial Officer, ProcessMiner, Inc.

Passionately helping customers find new ways to monetize their data, Tom helps business leaders execute continuous process improvement objectives using applied artificial intelligence. A career spanning two decades of leadership and expertise in data, he is widely recognized as a data and emerging technologies expert, frequently asked to share his knowledge and expertise around new technologies shaping Industry 4.0 and the Digital Industrial Revolution.

Bringing Industry 4.0 to You

For most of us, the building years of our lives were shaped by the books we read. A generation acquired its knowledge caressing through dry books with stenciled alphabets. From learning our ABCs to Shakespeare’s sonnets, Industrial Revolution ensured that its role in shaping world history through its machinery, chemicals, steam and more, is kept alive and documented via its own production of printing machines.

The Industrial Revolution paved the way for the life we know today and far surpassed the era of simplistic conveyor belts and heavy manual surveillance. Production lines employ machinery and humans alike. Industries have always stepped up with technological advancement and thereby use a plethora of devices designed and produced to meet specific tasks on factory grounds. Now is an era of warehouse robotics and sensors which not only aid in increasing manufacturing throughput but also capture different types of data, in every step of the production.

Over the years, technological advancement has mostly been through digitization, and while it gradually dropped its ones and zeros, computations became faster, devices shrunk, and we entered an era when a human trait like intelligence can also be synthesized artificially. With quintillion bytes of data generated every day, it became crucial to understand and track the data that matters, and it gradually led to the emergence of data science to look for patterns and meanings.

How can present-day industries step up with such digital advancements and thrive on it?

Say Hello to Industry 4.0

The ever-growing demand, supply-chain functioning and pressure to ensure timely deliveries keep manufacturing industries on their toes. Hence, it is essential that every process of the production cycle contributes to minimum loss, maximized production, and function like clockwork.

Most industries are already equipped with devices and sensors which, apart from functioning for which they are built also capture and send data from one unit to another. Industry 4.0 uses an existing framework of these networks of devices and sensors and gives then an AI advantage.

It uses the principles of digital automation and data exchange to make manufacturing factories function smartly. Referred now as the fourth manufacturing revolution, Industry 4.0 is the next generation of computerization which not only lets devices communicate but also provides real-time monitoring and thereby optimization.

With ProcessMiner, industries can onboard this change with ease. The SaaS-based platform picks relevant data from the start of the process to the end-quality product employs machine learning techniques to train models based on the data received and figure out the complex but relevant relationships between quality parameters. After this slow and steady phase, the customer’s dashboard is ready for easy to grasp information and charts in real-time. Moreover, the models are adaptive and evolve as per the changes done in factories. With a trained model in place, customers get recommendations based on the real-time data in their respective dashboards.

Let’s look this up in detail with an industrial case study.

Empowering Paper Industry via Real-time Predictions and Recommendations

From pulp to high-quality packaging paper, paper industries use numerous sensors across the manufacturing unit which capture real-time data every 15–30 seconds. The end goal is to ensure that paper quality, often measured in terms of their strength parameters like Mullen, STFI or Ring Crush is intact while ensuring the least usage of raw materials and generate maximum paper reels.

The platform captures relevant data from sensors during the process, and of finished quality paper of a particular grade. This process is repeated for as many grades of paper as defined by the customers and the AI model is continuously trained. Our customers define the strength parameters relevant for a particular grade of paper prior to model training and the process is then repeated for multiple grades. The model takes in data and mines for underlying complex relationships across various process data that contribute to paper quality, thereby creating a model that is not over-simplistic and hence more accurate with predictions.

All the needed data like strength parameter, paper grade, paper reel throughput is available on the customer dashboard. Over time, the model learns and defines “target ranges” for variables like Mullen/STFI/Ring Crush, raw materials weight, etc. These target ranges are the learned optimal values at which the paper production was the highest with minimum loss.

The quality of the paper reel produced is paramount in the whole process. A trained AI model in place takes real-time sensor data and makes predictions for strength and variable parameters that are bound to paper reel quality. Further, if the quality parameters deviate from their ideal values, the AI model also makes recommendations to bring them back to their desired limits.

Benefits of ProcessMiner’s Industry 4.0 Platform

  1. Tailor-made for your manufacturing type: Each factory is unique in its own design of production line and sensor placements. Our dashboard is designed to reflect the numbers that are relevant to the type of factory and its relevant data.
  2. Adaptability: Manufacturing industries undergo constant changes that can range from a change of raw materials to the degradation of machine parts. As a result, the trained models from older sensors run the risk of being irrelevant for real-time predictions and recommendations. ProcessMiner’s platform has identified this hurdle and hence designed the AI model to be self-adaptable with new sensor data. Over time, data from old sensors become obsolete and the model trains itself with new data.
  3. Accuracy: The model under training is fed with different kinds of data from different sensors across the process and in different types. As such, the data dimensionality is complex and hence difficult to find relationships between process parameters that matter. Our platform leaves no data unturned that can lead to the end goal — more paper, less waste. This complexity makes predictions super reliable and accurate.
  4. Informed decision making: ProcessMiner’s dashboard narrows down the complex and computation heavy processing of data to give relevant information related to the paper reel quality. The additional mechanism of real-time predictions and recommendations to maintain paper reel quality lets our customers make data-driven decisions.

Industries are evolving. The AI drift in digitization is here. With superior digital technological growth in place, it is a matter of time that industries would plug this growth and use it to manufacture goods more meaningfully. With access to a handy, real-time dashboard, and intelligent information, industries can thrive with reduced costs, reduced waste of raw materials, and maximized production outputs.

Originally posted on Medium.

Variability Reduction: Why Important To Manufacturers?

A routine issue faced by most manufacturers is their process variation. Variability in the process can wreak havoc on product quality and customer satisfaction. Also, it has a severe impact on revenue, cost, and margins.

In the highly competitive manufacturing market, the champion (industry) will be the one who has a strategy to mitigate this variability.

Focus on Variability Reduction Strategy

Variability in the manufacturing process is the difference between the produced quality measure and its target. High variability leads to either waste or excess production costs.

Unfortunately, due to their stochastic nature, the process variability in manufacturing systems is unavoidable.

But it is controllable and with the right strategy, can be minimized.

For example, think of paper manufacturing. An important paper quality measure is thickness. Due to high process variability (as shown in Figure 1), sometimes the thickness quality is less than the lower specification limit determined by the customer — resulting in a loss of sales.

To avoid the loss, operators often overfeed the machine with more wood fiber. This pushes the process mean upwards resulting in higher production quality (see Figure 2).

However, this strategy is extremely costly due to the overconsumption of raw materials. Therefore, the right strategy is to reduce process variability. As shown in Figure 3 below, reducing the variability reduces the out-of-limit instances. It further allows us to tighten the production target range (product specification limits) to further save material cost, waste production, and increase the throughput.

However, the major challenge in reducing process variability is an operator’s inability to measure the product quality at all times. Most manufacturers perform quality tests at the end of the production cycle time or in long-time intervals (e.g. 45 mins or more in the mills we have worked at). In paper mills, the operator takes a sample from a reel of paper at its completion and then proceeds to test it in a lab while the manufacturing process continues. Following this, based on the lab results, the operator makes changes in the process accordingly. However, if the quality test fails, we have then already lost a batch of product.

This lag in the lab results can be very frustrating, which results in both an issue and an opportunity for all manufacturers. Why? Because the results from the lab tests are used to adjust the process settings. The subsequent adjustments assume that all variables involved in the process will remain constant and consistent with the variables recorded at the time of the test. However, the reality is that most of them, for example, speed and temperature, will most likely change.

This causes the operator to either always chase changes in the quality variables by making continual adjustments in the dark, or to set the controls to a certain “recipe card” and wait for the next lab test. There must be a better way to manage this process. Viewing the reel of the paper example above as an opportunity can help us address the lag issue with a new, different, and more effective process.


Below are a few questions worth considering:

  1. What if the quality could be predicted every 30 seconds instead of every 45 minutes or more?
  2. What if process variables like speed, temperature, raw material, etc. are incorporated into providing real-time predictions on the product quality?
  3. What if real-time recommendations are sent to operators and production supervisors to achieve quality targets and reduced variability? What if it leads to a closed-loop?

Today, companies with Industry 4.0 technologies, like ProcessMiner Inc. in Atlanta, Georgia, are working with manufacturers to layer their real-time predictive analytics and AI solutions on top of complex manufacturing processes to reduce variability, increase profit margins, and improve customer satisfaction.


The results are staggering but how does it work?

In most cases, ProcessMiner can utilize existing sensor technology and historian architectures to drive data to their secure cloud-based analytics platform. This means no development or hardware changes for the manufacturer. Additionally, ProcessMiner brings industry expertise to the manufacturers’ process. With this platform, there is no need to hire additional data scientists and process engineers for deployment.

The ProcessMiner solution is constantly ingesting data to deliver clear quality predictions coupled with real-time recommendations on process changes. This ensures production quality is consistently delivered, and variability is reduced through a comprehensive yet easy-to-understand user interface that delivers timely data to both the operator and supervisor.

You might be asking“Could the ProcessMiner solution work for our manufacturing process?” The simple answer is, “Yes, most likely it will.”


Questions to consider when looking at a real-time predictive analytic solution:

  1. Do you have quality measures clearly defined and are there existing sensors that collect data along with your manufacturing process?
  2. Are you unsatisfied with your ability to meet those quality standards?
  3. Is there a financial impact on your organization if quality measures are not consistently met? Have you determined what that financial impact is?
  4. Have you encountered barriers in developing or researching a machine learning/predictive analytics/AI solution?
  5. Have you invested in “predictive maintenance,” and looking for a solution that predicts and improves quality?

If your answers to any or most of these questions are yes, your team may benefit from ProcessMiner’s advanced AI to reduce variability in your production processes.

Originally posted on Medium.

The Importance of Autonomous Manufacturing Amid the COVID-19 Outbreak

All manufacturers are adjusting to the new normal and with “Social Distancing” in full swing companies are making more aggressive investments in technology to achieve autonomous manufacturing. 

This approach accomplishes a couple of important objectives; one, it alleviates the demands on human intelligence for SMART Factories to operate, and two, it improves the overall efficiency of operations.

The Role of Artificial Intelligence in SMART Factories

Continuous manufacturing operations are like commercial jet-engines; they operate 24X7X365 and so things like machine down-time and the efficient use of assets are critical for success.

Now more than ever, companies need to take advantage of the benefits that new technologies can deliver to their businesses.

“AI applications boil down to doing more with less and eliminating human errors in manufacturing processes.”

Higher levels of automation including robotics, IoT and AI, are the new technologies helping to deliver orders on time with less reliance on human intervention.

These new investments are changing the way companies think about how they engage their customers, empower their employees, and optimize their operations.

Most manufacturing businesses have already made investments in SCADA systems, IoT devices and robotics, but to realize the full potential of those investments they need to put the data they are collecting to work.  This is where Artificial Intelligence comes in.

Initially, AI in smart machines was used to make recommendations about repetitive manual tasks that operators would perform.  Significant advancements have been made, and the shift from assisted intelligence has transformed into autonomous intelligence where machines are making recommendations that humans now trust.  

Turnkey AI platforms are now closing the loop between their recommendation engines and PLC systems so that operators don’t have to act on those recommendations leaving them to focus on more mission-critical tasks.  It boils down to doing more with less and eliminating human errors in the manufacturing process.

SMART Machines On the Shop Floor Now a Reality

The use of big data and AI will continue to drive operational benefits in all areas of operations, such as more efficient usage of raw materials, lower energy usage, and improved product quality.  With the right foundations in place and with access to the right data manufacturers will see AI make informed decisions at each stage in the production process in real-time.

As AI adoption becomes more mainstream, manufacturers stand to save millions and disruptors stand to gain the most from these new advancements.  Be a disrupter and get on board with AI for manufacturing now. Laggards will forever be playing catch-up and worse yet lose ground to their competitors.