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.