Part 2 of 2 – Closing the Loop

There is a school of thought in manufacturing that quality inspection is not necessary to build a high-quality vehicle. When major programs are launched, the focus is on building the part and often the inspection systems are last in line for installation and commissioning. Additionally, when budgets are trimmed, inspection dollars are the first to be thrifted or re-allocated. When this happens, the plant engineers pay the price. They heavily rely on inspection data while refining the process and tooling during launch and later when they scramble to troubleshoot line stoppages. One way to ensure that everyone is served by your manufacturing strategy is to combine the build and inspection into the same operation. As we covered in our previous blog, Data Rich and Information Rich, Artificial Intelligence (AI) plays an important role in augmenting the automation of data analysis. This same concept applies to building parts using AI and machine learning to make adaptive robot guidance even more powerful for modern manufacturers.

The Rise of Robot Guidance Solutions (RGS)

In the late 1980s, Perceptron released the world’s first robot guidance system. The system used laser-based machine vision to measure the opening for a windshield then guided an industrial robot to center the windshield into the opening. When this system was installed, a whole new industrial capability was born. Today, from simple pick and place operations to complicated best fit panel loading, robot guidance is a mainstay of industrial manufacturing. As manufacturers strive to automate more operations to improve productivity, they can deploy robot guidance systems to maximize speed and ensure high quality in assembly operations. RGS has become a truly versatile tool for multiple industries.

Closed Loop Manufacturing

Robot guidance and quality work in concert throughout the modern manufacturing facility. One way these two technologies work in harmony is in closed loop manufacturing. One example of closed loop processing is what Perceptron termed “deck and check” when they began applying their technology to load automobile roofs and then subsequently verifying the dimensions of the roof ditches before the vehicle left the build operation. This breakthrough created an in-station process control (ISPC) strategy for loading roofs with higher dimensional quality and immediate verification of the quality of the part before releasing it from the welding station. The benefits of ISPC are significant. ISPC reduces the production line space and cost associated with installing a separate inspection station and ensures the point of discovery for a quality issue is early in the process before significant value has been added to the vehicle.

Adaptive Feedback and Control

One of the holy grails of machine vision for robot guidance has been true adaptive feedback control (AFC). With AFC, process and quality inputs are monitored and adjusted in real-time, creating a manufacturing process that responds to all the inputs to produce an assembly that is truly custom fit to the individual parts and process inputs. Add Machine Learning to this process and you could be on the doorstep of “lights out” manufacturing with an adaptive process that learns as it builds. Utilizing the networked data and analytical horsepower creates a feed-forward automation and a self-teaching manufacturing process. Harnessing this power could lead to a process that does much of the “heavy lifting” for us, while ensuring the highest quality parts at the required line rates.

All-Cash Transaction Values Perceptron at an Equity Valuation of Approximately$68.9 million
71% Premium to Equity Closing Price on September 25, 2020; 192% Premium to 2020 Low

PLYMOUTH, Mich., Sept. 28, 2020 — Perceptron, Inc. (NASDAQ: PRCP), a leading global provider of 3D automated metrology solutions and coordinate measuring machines, today announced that it has entered into a definitive agreement (or the “Agreement”) to be acquired by Atlas Copco, a world-leading provider of sustainable productivity solutions headquartered in Stockholm, Sweden, for $7.00 per share. The all-cash transaction values Perceptron at an equity valuation of approximately $68.9 million.

Under the terms of the agreement, Perceptron shareholders will receive $7.00 per share in cash for each share of common stock held. This consideration represents a premium of approximately 66% to the 30-day average closing share price of $4.22 as of September 25, 2020. The Board of Directors has unanimously approved the agreement and recommends that all shareholders vote in favor of the transaction. Harbert Discovery Fund, L.P., Perceptron’s largest shareholder with approximately 10.5% of the total shares outstanding, has signed a Voting and Support Agreement in favor of the proposed transaction. The transaction is expected to close during the calendar fourth quarter 2020, subject to customary closing conditions, including the receipt of shareholder and regulatory approvals.

“Since our inception nearly 40 years ago, Perceptron has grown to become a leading metrology brand, one recognized for its ability to provide advanced flexible automation and quality control solutions to a diverse mix of global customers,” stated Jay Freeland, Chairman and Interim CEO of Perceptron. “Atlas Copco recognized the long-term, unrealized value evident in our business, as reflected by a compelling cash offer at a significant premium.”

“After careful consideration, our Board of Directors came to the conclusion that a sale of the Company to Atlas Copco would be the optimal outcome for all shareholders and Perceptron employees,” continued Freeland. “As a respected, well-capitalized organization with global reach, Atlas Copco is an ideal fit for our company. Atlas Copco’s leadership position across a broad array of industrial markets, combined with a growing presence in the machine vision space, will allow them to fully leverage our technology to the benefit of existing and new customers, all while realizing economies of scale with the potential to support growth. We are excited by the opportunities that lay ahead for our combined organizations and recommend that Perceptron shareholders vote in favor of the Agreement and the transaction.”

Perceptron engaged XMS Capital Partners, LLC as its financial advisor, Dykema Gossett PLLC as its legal advisor and Vallum Advisors LLC as its financial communications advisor on this transaction.

ABOUT PERCEPTRON®

Perceptron (NASDAQ: PRCP) develops, produces and sells a comprehensive range of automated industrial metrology products and solutions to manufacturing organizations for dimensional gauging, dimensional inspection and 3D scanning. Products include 3D machine vision solutions, robot guidance, coordinate measuring machines, laser scanning and advanced analysis software. Global automotive, aerospace and other manufacturing companies rely on Perceptron’s metrology solutions to assist in managing their complex manufacturing processes to improve quality, shorten product launch times and reduce costs. Headquartered in Plymouth, Michigan, Perceptron has subsidiary operations in Brazil, China, Czech Republic, France, Germany, India, Italy, Japan, Slovakia, Spain and the United Kingdom. For more information, please visit www.perceptron.com.

SAFE HARBOR STATEMENT

Certain statements in this press release may be “forward-looking statements” within the meaning of the Securities Exchange Act of 1934, including our expectations regarding the possible effects of the COVID-19 pandemic on general economic conditions, public health, and global automotive industry, and the Company’s results of operations, liquidity, capital resources, and general performance in the future, the potential impact of COVID-19 on our customers generally and their plans for retooling projects in particular, our fiscal year 2021 and future results, operating data, new order bookings, revenue, expenses, net income and backlog levels, trends affecting our future revenue levels, the rate of new orders, and our ability to fund our fiscal year 2021 and future cash flow requirements. We may also make forward-looking statements in our press releases or other public or shareholder communications. Whenever possible, we have identified these forward-looking statements by words such as “target,” “will,” “should,” “could,” “believes,” “expects,” “anticipates,” “estimates,” “prospects,” “outlook,” “guidance” or similar expressions. We claim the protection of the safe harbor for forward-looking statements contained in the Private Securities Litigation Reform Act of 1995 for all of our forward-looking statements. While we believe that our forward-looking statements are reasonable, you should not place undue reliance on any such forward-looking statements, which speak only as of the date made. Because these forward-looking statements are based on estimates and assumptions that are subject to significant business, economic and competitive uncertainties, many of which are beyond our control or are subject to change, actual results could be materially different. Factors that might cause such a difference include, without limitation, the risks and uncertainties discussed from time to time in our periodic reports filed with the Securities and Exchange Commission (the “SEC”), including those listed in “Item 1A. Risk Factors” of our Annual Report on Form 10-K for our fiscal 2020. Except as required by applicable law, we do not undertake, and expressly disclaim, any obligation to publicly update or alter our statements whether as a result of new information, events or circumstances occurring after the date of this report or otherwise. The proposed merger is subject to certain conditions precedent, including regulatory approvals and approval of the Company’s shareholders. The Company cannot provide any assurance that the proposed merger will be completed, nor can it give assurances as to the terms on which such proposed merger will be consummated.

ADDITIONAL INFORMATION AND WHERE TO FIND IT

This communication does not constitute an offer to buy or sell or the solicitation of an offer to buy or sell any securities or a solicitation of any vote or approval. In connection with the proposed merger, the Company plans to file relevant materials with the SEC, including a proxy statement on Schedule 14A. Promptly after filing the definitive proxy statement with the SEC, the Company will mail the definitive proxy statement to each shareholder entitled to vote at the annual or special meeting relating to the proposed merger. This communication is not a substitute for the proxy statement or any other document filed or to be filed by the Company with the SEC in connection with the proposed merger. INVESTORS AND SHAREHOLDERS ARE URGED TO CAREFULLY READ THE PROXY STATEMENT (INCLUDING ANY AMENDMENTS OR SUPPLEMENTS THERETO AND ANY DOCUMENTS INCORPORATED BY REFERENCE THEREIN) AND ANY OTHER RELEVANT DOCUMENTS IN CONNECTION WITH THE PROPOSED MERGER THAT THE COMPANY WILL FILE WITH THE SEC WHEN THEY BECOME AVAILABLE BECAUSE THEY WILL CONTAIN IMPORTANT INFORMATION ABOUT THE PROPOSED MERGER AND THE PARTIES TO THE PROPOSED MERGER. The definitive proxy statement and other documents relating to the proposed merger (when they are available) can be obtained free of charge from the SEC’s website at www.sec.gov.

PARTICIPANTS IN SOLICITATION

The Company and certain of its directors and executive officers and certain other members of management and employees may be deemed to be participants in the solicitation of proxies from shareholders of the Company in connection with the proposed merger under the rules of the SEC.  Information regarding the persons who may, under the rules of the SEC, be deemed participants in such solicitation in connection with the proposed merger will be set forth in the proxy statement if and when it is filed with the SEC.  Information about the directors and executive officers of the Company may be found in the Company’s definitive proxy statement for its 2019 annual meeting of shareholders, which was filed with the SEC on September 27, 2019.  These documents can be obtained free of charge from the source indicated above. To the extent holdings of such participants in the Company’s securities are not reported, or have changed since the amounts described in the proxy statement for the 2019 annual meeting of shareholders, such changes have been reflected on Initial Statements of Beneficial Ownership on Form 3 or Statements of Change in Ownership on Form 4 filed with the SEC. These documents may be obtained free of charge from the SEC’s website at www.sec.gov or the Company’s website at www.perceptron.com. Additional information regarding the participants in the proxy solicitation and a description of their direct and indirect interests, by security holdings or otherwise, will be contained in the proxy statement and other relevant materials to be filed with the SEC when they become available.

Contact:

Investor Relations
investors@perceptron.com

Part 1 of 2 – Data Rich and Information Rich

One of the long-standing challenges of managing a 100% data sample is being data rich but information poor. In today’s modern manufacturing facilities, the fast cycle times and large data sets can often paralyze the people responsible for managing the overall quality of the parts produced. This data overload is compounded as companies lose skilled workers to retirement or attrition. The talent and experience gaps create a very reactionary culture in many facilities where the human resources can only react to critical quality alarms. Plant floor personnel have less and less time to be proactive and have even less time to perform the data analysis required to search for trends in common cause and special cause variation data. Advances in machine learning and artificial intelligence can aid modern manufacturers by automating the analysis for users and eliminating the reactive approach to quality spills.

Automatic plant floor analytics

It is now a reality for companies to leverage breakthroughs in data analytics to identify root cause faster. Tools like Argus from Perceptron act as “engineers in a box” crunching countless calculations behind the scenes while production runs uninterrupted. Now the large data sets get processed in real-time, with the software searching for correlations, upstream and downstream contributors to process variation, and special or common causes to inform quality professionals with suggestive answers to the problem, instead of merely pointing out there is an issue.

Data rich environments are perfect for machine learning

The amount of data generated during every production cycle in a manufacturing plant can be staggering. The data is often pristine with absolute accurate systems measuring in the 100-micron range every second, every cycle. Incorporating a machine learning layer is as simple as purchasing a software package and database that can handle all the data inputs in real-time. From there, the machine learning models will take over, looking for trends in the data based on existing measurement data and CAD information to notify operators of what is happening to their process and product. This is in stark contrast to the traditional methodology where plant personnel are alerted that there is a problem, not what the problem is or how to potentially solve it.

Use machine learning and AI to augment not replace

At the end of the value chain it is still people that perform most of the corrective actions based on the information provided by these new proactive systems. Think of the efficiencies to be gained in facilities if the workers can skip the hours or, in some cases, days of data analysis required to solve problems. Now the system does the analysis and provides the guidance on where to go first: Station 5 and locating pin. Valuable human resource time gets re-allocated to problem solving and you become data rich and information rich, which is a major win-win for manufacturing.

Taking information intelligence to the next level – Part 2

Our next blog will discuss how you can use information intelligence to automatically solve problems through proactive ‘machine learning’ and part routing.

Want to know more about how Perceptron uses machine learning? Contact us at info@perceptron.com.

Perceptron Receives New Order to Support Upcoming Electric Vehicle Launch

EV Battery Expertise, Best-In-Class Metrology Solutions and Local Technical Support Results in New Customer Win

In our last blog we discussed automation as it relates to metrology. That post focuses on the data collection side of the quality equation. Putting sensors on robots to monitor in real-time was just one area where automation could be deployed to benefit the manufacturing plant. Now with the full arrival of Industry 4.0 through machine learning, automation does not only have to be reserved for data collection, it can also be used for data analysis. Engineers in modern manufacturing facilities can rely on advanced analytical tools to send them the answer, eliminating the time it takes to solve the process variation puzzle manually.

The large amounts of data available in modern, connected factories help engineers keep their processes stable and in control, but there is a time cost associated with managing that data. The time it takes to figure out what database to pull data from, time to pull reports, and the time to analyze and compare everything adds up quickly. The time spent only grows if the problem gets more involved. This time is critical, especially if a process issue or production stoppage is the reason the engineer started the data mining and analysis work to begin with.

Every minute of data mining and analysis could be one minute of lost production which equals lost profit. Some companies even track a metric called Non-Value-Added Activity (NVAA) which is the amount of time dedicated to work done that does not produce parts. An argument can be made that all the time mining and analyzing process data can be put directly in the NVAA cost bucket. However, without data, how can an engineer know where to start to fix a production issue?

The solution is to leverage machine learning to complete the analysis in real-time, and provide answers, not just data, to the engineer. Utilizing tools that enable aggregation of information, visibility without excessive keystroking or mouse clicking, and the answer, instead of just a report, will shorten time to root cause, reduce NVAA, and ultimately reduce loss.

Machine learning and plant floor analytics

As the connected factory grows and joins the internet of things (IoT), it has become possible to apply technology to eliminate the time associated with traditional data acquisition and analysis. Machine learning can be used to create a comprehensive set of rules that automate the analysis, creation of charts, and can send the information directly to the correct person to fix the issue. Think of it as a dimensional engineer in a box.

Improvements in other technologies that enable continuous real-time analytics powered by machine learning are more affordable and available to the factory floor than ever before. Lower-priced data storage, increased connectivity of machines, and high-speed computing are also catalysts for this change in manufacturing. Now instead of pushing alerts and charts, the software can push answers through email clients, text messages and even smartphone mobile apps, enabling engineers to spend their time fixing issues instead of analyzing data.

Providing an intelligent IT automation platform that analyzes the process data that is collected, and sends answers creates efficiency on the plant floor, where effort can be transitioned from data collection and analysis to fixing issues at a controlled and manageable pace, instead of reacting to production losses in a frenetic and unpredictable fashion. For example, Perceptron’s software product, Argus, provides engineers 3-D visuals of product build issues, not just charts. answers, not just charts. The software uses traditional, time-tested tools like statistical process control (SPC) and robust real-time metrology data combined with high speed computing, powerful algorithms, and artificial intelligence to enable a truly proactive approach to process management.

Challenges of proactive process control

Enabling proactive control does have its challenges. One of the first things that needs to be addressed is getting the plant personnel to trust the answers coming from the system without having to rely on their own ability to interpret the data. This new paradigm can make engineers and front-line personnel feel like they are not providing as much value without using their know-how to analyze the data. However, these skilled workers can now pivot from data collectors and analyzers to process improvement specialists. These newly empowered and informed personnel can focus on taking the answers to the plant floor to make immediate improvements to their tooling that can be immediately validated with their in-line gauges and automated analytics. The potential for continuous process improvement and world-class quality is limitless.

Next steps

The ability to harness machine learning for proactive process control is already here. Once the right technology is identified and deployed, all it takes is a change in a plant’s culture to fully leverage the efficiencies created by machine learning and automated process control.