Multiple Authors
By: Douglas C. Fair, Scott A. Hindle

Today’s manufacturing systems have become more automated, data-driven, and sophisticated than ever before. Visit any modern shop floor and you’ll find a plethora of IT systems, HMIs, PLC data streams, machine controllers, engineering support, and other digital initiatives, all vying to improve manufacturing quality and efficiencies.

That begs these questions: With all this technology, is statistical process control (SPC) still relevant? Is SPC even needed anymore? Some believe manufacturing sophistication trumps SPC technologies that were invented 100 years ago. But is that true? 

We the authors believe that SPC is indeed relevant today and can be a vitally important aid to manufacturing. (SPC can be used outside of manufacturing, and to great benefit, but we keep our focus on manufacturing.)

As quality professionals and statisticians, are we biased in our view? Possibly. After visiting hundreds of manufacturing plants around the globe, though, and witnessing their unending manufacturing challenges and opportunities, the evidence is overwhelming: SPC is an important strategic tool in the quest for improved quality and reduced costs. We also postulate that SPC has more potential uses and benefits today than ever before.

John Davis’s picture

By: John Davis

Over the past decade, one of the biggest advances in enterprise resource planning (ERP) has been the ability to communicate and integrate with machines and external software programs to lower costs and increase efficiency. For example, BOM Compare software can reduce engineering costs and get jobs into production much faster by expediting the design-to-production process. Integrating ERP with nesting software can significantly lower material and labor costs, and reduce scrap, by automatically determining the most efficient way to cut parts on a piece of metal.

Mike Figliuolo’s picture

By: Mike Figliuolo

I’m going to take over the world! It’s really fun to say that. It’s even more fun to take action toward that goal.

Our world has gotten smaller. Way smaller. Globalization is an unstoppable trend. But as they say, the trend is your friend, so why not take advantage of it? I don’t care how big or small your business is—there are huge opportunities out there for you beyond the shores of your country (assuming yours has shores).

The real trick is understanding what globalization can do for your business, then figuring out how to exploit those opportunities. Let’s cover some simple notions around what the global opportunities are.

Going global is intimidating: It sounds huge. You need a passport and visas—and we know what a joy those processes can be. You need to understand new languages and cultures. And heaven forbid you have to take a coach class 14-hour flight across the ocean (trust me, it’s painful). OK, so get over it and dive in!

Ron Cowen’s picture

By: Ron Cowen

A single atom-thick sheet of carbon known as graphene has remarkable properties on its own. But things can get even more interesting when you stack up multiple sheets.

When two or more overlying sheets of graphene are slightly misaligned—twisted at certain angles relative to each other—they take on a plethora of exotic identities. Depending on the twist angle, the materials known as moiré quantum matter can suddenly generate their own magnetic fields, become superconductors with zero electrical resistance, or conversely, turn into perfect insulators.

Illustration depicts two bilayers (two double layers) of graphene that the NIST team employed in its experiments to investigate some of the exotic properties of moiré quantum material. Inset at left provides a top-level view of a portion of the two bilayers, showing the moiré pattern that forms when one bilayer is twisted at a small angle relative to the other. Credit: B. Hayes/NIST.

Mark Rosenthal’s picture

By: Mark Rosenthal

This all happened nearly three decades ago. Since then, the company has been through a series of mergers and acquisitions. Thus, the only thing I can be certain of is that things are different today—at least I hope so.

It was Tuesday afternoon of a traditional five-day kaizen event. Monday morning had been spent training the team on the basics of “just in time,” including some fundamental principles and a 1:1 flow simulation to demonstrate some of the possibilities.

Beginning midafternoon on Monday and continuing into Tuesday morning was “walk the process”—mainly looking at material flow, where things bunched up, and things that wasted people’s time (there was no shortage of that). By Tuesday afternoon the team was being guided through the process of developing a “vision”—a fairly idealized version of what would be possible with some changes.

The team I assisted was focused on the flow through an annealing oven. This was near the end of the process and was perceived as a bottleneck. The oven was designed as a long tunnel; individual plates of material could be placed on a conveyer at one end and would come out the other having spent enough time in the oven for the process to work. It was ideal for 1:1 flow, and that is what we were advocating.

Matthew Greenwood’s picture

By: Matthew Greenwood

It’s no secret the automotive sector is racing to find ways of tapping the potential of generative artificial intelligence (GenAI) to design and build the next generation of vehicles. This technology has promise, from redefining manufacturing processes to helping carmakers design smarter, safer, and more efficient vehicles.

Much of the time, proprietary automotive innovation is kept under lock and key as a critical competitive advantage. But recently, Toyota has shared the development of a new tool that enables designers and engineers to collaborate more efficiently and easily.

GenAI is a type of artificial intelligence that doesn’t just focus on processing data. It uses advanced machine learning techniques—particularly deep learning—to generate new content. The technology could help carmakers optimize designs and structures, producing lighter, more aerodynamic, and more fuel-efficient vehicles. However, GenAI is still in its infancy and has encountered challenges when evaluating complex variables such as manufacturing limitations and detailed safety regulations.

Sabine Terrasi’s picture

By: Sabine Terrasi

Robots do monotonous workflows and less pleasant, repetitive tasks with brilliance. Combined with image processing, they become “seeing” and reliable supporters of humans. They’re used in quality assurance to check components, help with assembling and positioning components, detect errors and deviations in production processes, and increase the efficiency of entire production lines.

An automobile manufacturer is taking advantage of this to improve the cycle time of its press lines. Together with the automaker, VMT Vision Machine Technic Bildverarbeitungssysteme from Mannheim, Germany, developed the robot-based 3D measuring system FrameSense for the fully automatic loading and unloading of containers. Pressed parts are thus safely and precisely inserted into or removed from containers. Four Ensenso 3D cameras from IDS Imaging Development Systems provide the basic data, and thus the platform for process automation.


The actual workflow that FrameSense is designed to automate is part of many manufacturing operations. A component comes out of a machine—here, a press—and runs on a conveyor belt to a container. There it’s stacked. As soon as the container is full, it’s transported to the next production step, e.g., assembly into a vehicle.

Wael William Diab’s picture

By: Wael William Diab

Artificial intelligence (AI) is everywhere—and that’s something to marvel at. AI is powering everything from advanced web searches to social media recommendations and video game design. But it could do infinitely more.

AI has the potential to revolutionize our societies and economies. Discussions about the future of AI tend to focus on the risks; but issues around data bias, lackluster transparency, and privacy are in fact driven by unscrupulous use of the technology, not the technology itself.

We’ll never realize the benefits of AI by solely focusing on the negatives. An AI-positive future is possible, but we need to actively pursue it. If we approach AI with a positive mindset, placing societal needs such as ethics and sustainability at the heart of its development, then we can unlock its full potential.

The promise of AI

Imagine if the advances we’ve seen in AI during the last year had happened even half a decade ago. Could it have accelerated the development of coronavirus vaccines? Could it have averted the global economic downturn we experienced this year? Questions and potential scenarios like these lend credence to the argument that it could be unethical not to develop AI.

Ramūnas Berkmanas’s picture

By: Ramūnas Berkmanas

Imagine a manufacturing world where machines seamlessly collaborate with artificial intelligence (AI) to ensure flawless quality inspection. It’s a future that holds immense potential for revolutionizing the industry.

Major manufacturers like FANUC, ABB, and KUKA AG, alongside specialized cobot producers such as Techman Robot, Automata, Franka Emika, and Universal Robots, are driving this innovation.

According to a Grand View Research report, the collaborative robot market was valued at $1.23 billion in 2022 and projected to exceed $11.04 billion by 2030.

Let’s delve deeper into the future of manufacturing, where cobots and AI converge for unparalleled quality inspection.

Why quality inspection matters

Quality inspection is crucial in manufacturing. It ensures that products meet the desired standards and customer expectations. Automated inspection, driven by advancements in inspection technology and machine learning integration, plays a vital role in achieving high-quality control and defect detection.

Harish Jose’s picture

By: Harish Jose

Today I’m looking at some practical suggestions for reducing sample sizes for attribute testing. A sample is chosen to represent a population. The sample size should be sufficient to represent the population parameters such as mean and standard deviation. Here we’re looking at attribute testing, where a test results in either a pass or a fail.

The common way to select an appropriate sample size using reliability and confidence level is based on the success run theorem. The often-used sample sizes are shown below. The assumptions for using binomial distribution hold true here.

The formula for the success run theorem is given as:

n = ln(1 – C)/ ln(R), where n is the sample size, ln is the natural logarithm, C is the confidence level, and R is reliability.

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