Let’s Reveal Manufacturing

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Predictive vs. Proactive Control Of The Plant Floor

The process of deciding which ai solution is best for your company is becoming

increasingly more difficult as the number of new tech companies offering new solutions

increases. Nevertheless, the sooner you decide, the better. Typically, the biggest hurdle to

overcome is the decision to get started! There are different approaches to applying ai to

the day-to-day production operation, so it is important to understand the options before

deciding. One of the fundamental criteria when evaluating Industry 4.x solutions is the

difference between predictive analytics and proactive analytics.

The primary focus of many ai applications is currently predictive maintenance, or the

ability to predict, based on a complex array of variable, when a machine is likely to fail.

The ability to predict accurately enables maintenance organizations to schedule required

maintenance in advance of machines failing during scheduled production hours. With

accurate predictions, maintenance activity can be scheduled during non-production hours

dramatically improving machine uptime and production throughput. The challenge

however is including enough inputs for the mathematical probability of failure to be

trustworthy. For example, if feed-rate, speed, and time are the inputs available for

calculating probabilities when in reality temperature and pressure are the critical

characteristics, accurately predicting when a failure is likely to occur becomes highly

suspect at best. Replacing a $30,000 motor that hasn’t failed yet based on ai that is

probability-based is a highly stressful decision! With the worst part being no way to

verify whether or not the prediction is accurate and true. Proactive analytics on the other

hand takes an entirely different approach and yields a more certain outcome.

Proactive analytics use a fact-based, data-driven approach to predict machine failures.

Available data gathered from sensors mounted to machines or directly from the PLC

(programmable logic controller) running the machine is used by both approaches. The

difference being one approach uses a highly complex statistical engine to calculate the

probability of failure, while the proactive approach monitors a real-time stream of data

from each of the inputs individually. Each individual machine parameter can be

compared to its design target for optimal performance with ai-driven algorithms that

determines when it’s time to schedule a proactive maintenance activity and notifies

appropriate maintenance personnel. Knowing the difference between these two

approaches will equip buyers and decision-makers to challenge assertions made by sellers

about their predictive maintenance and machine-learning capabilities, and depending on

the answers they receive, be in a much better position to compare solution providers

capabilities leading to a much more intelligent defensible decision once a selection is

made. When implementing any new technology, always be sure to include change-

management as an essential requirement for each deployment.

You can find an example of a proactive maintenance solution at www.trumbleinc.com or

get answers to your questions by emailing curious@revealfactory.ai to explore these

topics further.

Jeffrey Trumble is the creator of REVEAL, a patented enterprise software solution

developed by Trumble Inc. offering proactive control of product quality, and machine

performance, through a single application.

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Revolutionizing the Automotive Industry: How REVEAL® Technology is Tackling Recalls and Warranty Claims

The automotive industry is highly competitive, with its manufacturers aiming to produce high-quality products at the lowest possible cost.

The biggest challenge?

Warranty claims.

Warranty claims and recalls come with enormous financial consequences. Warranty claims are requests made by customers for the repair or replacement of faulty product, with the cost of these claims ranking number one in the list of contributors to the cost of poor quality (COPQ).

Warranty Week reported in 2020 that manufacturers had approximately $29.8 billion in warranty reserves. This is how much manufacturers have set aside to cover warranty claims. Warranty claims are one of the most significant expenses in the automotive industry.

In 2020, according to J.D. Power, the average warranty claim cost for a new vehicle in the United States was $443 in parts and labor per claim. Another example is the electronics industry. Approximately $4.4 billion in warranty reserves were held by the top 20 electronics manufacturers in 2020, according to Warranty Week.

Reducing warranty claims requires critical initiatives:

Use high-quality materials and components. If a manufacturer uses low-quality wiring when manufacturing electronic devices to cut costs, for example, warranty claims will likely far outweigh the apparent savings.
Leverage the Industrial Internet of Things (IIoT) to improve outdated quality control systems. Manufacturers can reduce warranty claims by implementing more robust quality control systems made available by the ongoing digital transformation that is well underway.
Challenge your quality control assumptions. Activities such as inspection, testing, and audits are helpful, but are they enough?
Explore emerging technologies designed to take quality control to the next level. Proactive control and defect prevention is now possible.
Look for systems capable of detecting emerging issues. Correcting issues before they disrupt the manufacturing process is the right application of AI, IIoT, and many other new and emerging technologies designed to serve the manufacturing industry.

An example of this is REVEAL® Plant Floor Intelligence®. A patented enterprise software solution developed by Trumble Inc., introducing the industry to the power of proactive quality control. As the name suggests, this technology reveals process variability and other emerging threats in the manufacturing process early enough to allow for corrective action before a loss occurs.

By leveraging the Cloud, the Industrial Internet of Things, and artificial intelligence, REVEAL® makes intelligent decisions based on the real-time state of the manufacturing process itself.

Combining this with a lightning-fast communication module that is smart enough to target only the appropriate problem solvers, creates a proactive quality control environment worthy of investigation. In your pursuit to find the right solution for your plant, be sure to ask about these topics before you decide.

One last note to consider. Every implementation of REVEAL® technology includes a change-management service as an essential part of each deployment.

Don’t underestimate resistance to change!

When implementing any new technology, be sure to include change management as an essential requirement with each deployment.

Learn more about REVEAL® by emailing customersuccess@trumbleinc.com or send your inquiries to curious@revealfactory.ai to explore these topics further.

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Welcome to 'Let's REVEAL® Manufacturing' —a Podcast show dedicated to exploring plant floor intelligence, revealing new insights on industry 4.0 and investigating how all of this works together with state-of-the-art AI technology to make the plant floor work smarter. Saving time, money, and headaches and increasing that bottom-line profit!

For more on this subject see our blog: HERE