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“Bin picking is basically the Holy Grail of robotic automation,” says Scott Harms, president of MetalQuest Unlimited Inc. For a human, bin-picking is often non-ergonomic, especially when parts are heavy, leading to carpal tunnel and other repetitive-use injuries. It also tends to be dull work. “People that are doing it can get burned out relatively rapidly,” he adds. However, this task has proved challenging to automate because it requires 3D vision. And according to Harms, “Vision is the ultimate complexity when it comes to robotics.”

Harms’ company, MetalQuest, is not only a manufacturer of precision machined components with locations in Nebraska and Idaho, it’s an authorized internal FANUC integrator. The shop recently tackled the challenge of automating bin picking head-on by implementing its first 3D vision system from FANUC. According to Harms, integrating an automation cell with 3D vision is “more than twice as complex” as a 2D vision system, but automating this task has been beneficial to the company’s culture

As Industry 4.0 and Industrial Internet of Things (IIoT) concepts become real applications, they have generated many exciting conversations. One has centered around the integration of Information Technology (IT) with operations technology (OT). Large IT companies have actively promoted ideas like workload consolidation for businesses to optimize all kinds of processes and be more competitive in their respective industries. This excitement, boosted by some of the largest players in automation technology (AT) who are jumping on board, is well deserved. Greater system openness, real-time deterministic control with many-core processors, the incorporation of web technologies and machine learning, among other advances, are all possible through applying popular technologies to industrial applications.

The fully autonomous factory has always been an elusive vison that represents the culmination of the evolution of production automation. It is a manufacturing environment that is completely unmanned and run by AI systems directing machines and robotic production cells and lines. While this vision may become reality at some point in the future, it is unlikely that AI will fully enable this vision in the near term. However, it is clear that AI is already having and will continue to have a profound effect on manufacturing processes and the current smart manufacturing environment.

Thirty-four United States presidents are equipped with cameras and ready to prepare high-performance shock absorbers and suspension systems for installation onto vehicles in the performance and luxury auto market.

That might sound strange, but at Thyssenkrupp Bilstein’s Hamilton, Ohio, facility, the collaborative robots (cobots) that help with automation projects and part inspection are named after U.S. presidents. The manufacturer of shock absorbers and suspension systems made its first attempt at automation in 2016. Thirty-four cobots later, Bilstein has begun using its cobots to automate inspection processes throughout the facility to avoid human error during manual inspection.

Most people who work in the manufacturing sector relate to manufacturing data either from an IT perspective, an OT perspective or a business perspective. But what they require is a common industry perspective that understands how such data is enabling Smart Manufacturing initiatives and reshaping the industrial ecosystem for better collaboration and communication. In this article (and in the context of Smart Manufacturing), we will delineate a common understanding of manufacturing data and elucidate the various data types, data sources, methods of data exchange, data capture & ingestion technologies, and different ways data can be persisted and organized to seek business value.

Developing a well-thought-out data management approach is crucial for the success of your organization’s smart manufacturing strategy. Effective data management enables meaningful data gathering, storage, processing, and interpretation, while also facilitating the scalability of solutions across various plant environments, business units, and production lines. These capabilities are essential for generating actionable insights, making real-time decisions, and automating business operations in line with smart manufacturing objectives.

Without a solid data management strategy, challenges arise in digital projects, including a complex network of technologies, standalone solutions difficult to integrate, poorly planned digital infrastructure, and escalating costs. In a series of six articles, we will cover topics such as data collection, modelling, contextualisation, semantic structuring, integration, storage, analytics, data-driven decision-making, and the future of industrial data management.

Inputs and outputs (I/O) are fundamental to the world of computing, industrial control and data/information processing. Without them, industrial and electronic devices of all kinds would have no way of receiving data, and the user would have no way of manipulating or interacting with the data sources. Since they form the basis of our relationship with almost all the industrial devices we use, it’s important to understand how I/O modules function.

As a key component of industrial systems, I/O modules ensure connectivity and control of systems, processes and devices. They usually include analog channels, digital channels or a combination of both. Analog inputs can detect various signals including frequency, voltage or current. Digital I/O are used for low-level on/off signals. I/O modules that have analog inputs and digital outputs often incorporate an analog-to-digital (A to D) converter to process the signal.

The internet is the global system of interconnected computer networks that uses the InternetProtocol Suite (TCP/IP) to communicate between networks and devices. It is a “network of networks” that consists of private, public, academic, business, and government networks linked by electronic, wireless, and optical networking technologies. The internet grew out of research into computer networking carried out by the US Department of Defense during the 1960s, culminating in their first packet-switched system known as ARPANET. HTML is an example of a Markup Language; a text-encoding system consisting of a set of symbols inserted in a text document to control its structure, formatting, or the relationship between its parts. In this article we’ll discuss the Markup languages HTML, XML and JSON, and how they relate to the internet.

Each year, HMS Networks presents their analysis of the industrial network market, focusing on new installed nodes within factory automation globally. As an independent supplier of solutions within Industrial ICT (Information and Communication Technology), HMS has a substantial insight into the industrial network market. The 2023 study includes estimated market shares and growth rates for fieldbuses, Industrial Ethernet and wireless technologies. In the 2023 study, HMS concludes that the industrial network market continues to grow and that the total market growth in 2023 is expected to be +7%, confirming the continued importance of network connectivity in factories.

With so much news about the struggles manufacturing companies face in making the digital transformation, the challenges involved may seem insurmountable. Especially when we see statistics indicating that 70+% of digital transformation projects fail. Despite the inherent difficulties, it’s impossible to avoid the need to digitally transform your operations to remain viable in a digital business world.

Geoffrey Jackson, senior director of process services and technology, GAF.

If anything about making a digital transformation successful has been found, it’s that a clear yet flexible plan is necessary. But what such plans should look like is a lot less clear in these early days of industry’s digital transformation.

One of the best descriptions I’ve encountered about the lessons learned during a company’s digital transformation was delivered at the 2023 Industry of Things World event in San Diego by Geoffrey Jackson, senior director of process services and technology at GAF, a manufacturer of roofing and waterproofing materials.

Industry 4.0 adopters in factories around the world recognize that industrial data is gold. More users and systems want access to this data in real time to convert it into valuable information they can act on to predict machine failure, prevent downtime, improve product quality, and meet sustainability goals. IDC projected there will be 41.6 billion IoT devices in the field generating 79.4 zettabytes of data by 2025. These devices include machines, sensors, cameras, and industrial tools. It’s an overwhelming volume and variety of data. How can companies leverage it effectively?

Today, technology is quickly changing manufacturing from an industry of mass-produced goods to customised products. The ultimate business goal—and pillar of 21st century competitiveness—is for manufacturers are to make the right product, deliver it to the right customer for the right price. Ideally, the product will also offer greater utility or customer appeal at a higher level of sophistication.

The Industry 4.0 vision adds intelligent manufacturing systems to the fundamental processes of fabrication and assembly. In this vision, “Digital Transformation” represents a fully connected manufacturing environment. All equipment in this environment is online, intelligent, and capable of making decisions with varying degrees of autonomy.

Earlier articles present ideas, tools, and approaches to Industry 4.0 that ASEAN member countries and other nations are beginning to take. What’s the point? Where do the resources, tools, and ideas come together? And for what purpose? This article highlights one of the critical pillars of Industry 4.0–the smart factory.

In my last blog on Data Contextualization, we talked about data contextualization at various levels in the OT Ecosystem architecture, particularly in the Operational Data Lake. LNS Research is increasingly hearing the term “Data Hub” used to describe the processing, conditioning, contextualization, persistence, and access functionality so key to data management and one’s ability to scale the New OT Ecosystem. Questions we frequently hear are:

“Are Data Lakes and Data Hubs the same thing?”

“If not, then do they have to be combined as shown in the green section of Figure 1 below?”

Is a Unified Namespace the same thing as a Data Hub?

What the heck is a Unified Namespace anyway?

While I am not an IT architect, my intention is to get a common sense handle on the subjects to guide customers and properly position solutions available in the market. I won’t cover every possible technical detail here, but hopefully, my approach will make sense, especially to OT people who must increasingly live in an OT world full of IT. Let’s start with some basic definitions.

The fact that digital transformation and Industry 4.0 are changing manufacturing operations is no secret, nor is the fact that skilled manufacturing talent is increasingly harder to come by. With Deloitte research projecting that 2.4 million manufacturing jobs will be unfilled by 2028, the question becomes: What talent and skills do manufacturers need in order to succeed in the factory of the future?

Here we look at five manufacturing jobs and how they are expected to evolve. We highlight the critical new skills needed for these jobs that manufacturers will need to cultivate in order to better recruit, train and upskill workers.

A common thread across all of the jobs below is a trend in “human capabilities”—or soft skills—becoming just as important as technical and manufacturing skills. For example, as artificial intelligence (AI) reduces the need for humans to be involved in lower-value, manual work, workers increasingly need to be able to take on new responsibilities in areas such as problem-solving, communication, interpretation, design thinking and customer engagement.

If you are reading this article, you are most likely a consumer of wireless technology—at least in your personal life. In the industrial space, wireless has been utilized for dozens of years for the transmission of data from personal devices or fixed assets and based upon a specific job function or where wired communication was not feasible or desirable. However, the proliferation of smartphones, tablets, and Industry 4.0 drivers such as digital twins, augmented reality, gamification, and the Industrial Internet of Things (IIoT) has created opportunities for expanded industrial wireless solutions and capabilities with a much broader range of users and use cases. This includes everyone from shop floor users and geographically dispersed asset management teams to the top floor where executives are realizing that wireless isn’t only a technology platform; it offers technology solutions delivering safety, efficiency, and cost reductions with positive impact to the bottom line.

Despite increasing signs of overlap between SCADA (supervisory control and data acquisition) and MES (manufacturing execution systems), the two automation mainstays are more likely to co-exist as a tightly knit couple rather than re-emerging as a single powerhouse manufacturing platform. 

SCADA systems tend to the sub-second communications work of devices such as PLCs and sensors to monitor and control the nitty-gritty machinations of shop floor equipment and workflows. MESs operate at a higher level, serving as a control center for oversight of production scheduling, quality control, and traceability, among other critical operational aspects. 

The inflection point of a curve is the point where the curve takes a considerable change in trajectory. The change can be either upward or downward.

The same is true with business performance. To move in an upward trajectory from an inflection point, organisations need to make certain strategic, decisive choices. In today’s world, technology driven transformation powered by Industry 4.0 technologies is one such preferred choice. Progressive organisations have made this choice to come up with new business models, enhance customer experiences, build smart products and services, and transform operations. ACG started its Industry 4.0 journey in 2017 with focus on transforming operations.

Enterprises have increased their adoption of digital manufacturing during the COVID-19 pandemic – and this trend is not expected to slow down as people return to work. However, driving successful digital transformation within industrial organizations requires not only having the right technology but also achieving a step change in operational productivity. Even before the pandemic, people’s day-to-day roles were changing at all levels – and the accelerated pace of digitalization as a result of the COVID-19 pandemic will only make these transitions more extreme. When not properly planned for or communicated, these changes can often be resisted, meaning costly transformation initiatives could slow down or fail. 

Research from market insight provider IoT Analytics has revealed that making edge computing systems "smart" by integrating intelligent tools is a key driver of the technology’s continued growth. Edge analytics is a major enabler of an intelligent edge solution, broadening the scope of its use cases by enabling low latency, high-volume data actions. Here, Johan Jonzon, co-founder and CMO of low-code streaming analytics platform Crosser, explains the important role of edge analytics in Industry 4.0.

The demand for more flexible and cost-effective operations has long been at the top of the agenda for manufacturing companies. A 2020 survey conducted by industrial automation provider Yokogawa revealed that 48% of respondents valued productivity as a key focus in their digitalization strategies, while 40% regarded operational efficiency as their main objective. The need for automated processes and technologies has been increased further by the pandemic as a means to keep operations flowing.

Today’s manufacturing industry is making an inevitable transition from tradition automation to a fully connected and agile future by applying new digital industrial technologies such as AI/ML, digitalization, IoT, robotic automation. Industry 4.0 is a transformative change from computerized operations to a state where all systems, human and machine, are connected, driving better decisions at operational and strategic levels leading to the next leap in productivity, safety, and quality of industrial operations.

The type of IoT connectivity an organization uses is critical to the success or failure of a new IoT project. Considerations such as the length of battery life, network coverage and cost all come into play when an organization needs a connectivity mechanism.

Since Kevin Ashton invented the concept of the internet of things in 1999, the number of IoT devices available has grown from zero to around 27 billion. Today, connectivity options include proprietary and cellular wireless transceivers that broadcast data to IoT devices a few feet or a few miles away and satellites that transmit data from orbit.

As a result of the Industry 4.0 transformation, production floors, supply chains, and manufacturing business systems are being increasingly retrofitted with advanced technology, from IoT devices to robotics. This smart infrastructure not only streamlines laborious workflows, but also is a source of real-time information — the lifeblood of modern data-driven manufacturing.

Successfully applying machine-generated insights to minimize downtime, improve product quality, and optimize resource management, many manufacturers nevertheless give low priority to the issues of data management, centralization, and compliance. Due to this neglect, enterprises might miss the opportunity to enhance their decision-making, overcome persistent big data challenges, and overlook IT security risks with no security testing in place.

Let’s explore how smart manufacturing companies can benefit from adopting a solid data governance strategy.

I’ve been thinking a lot about manufacturing’s prioritization of digital transformation recently. This year is shaping up to be the year that many manufacturers take the plunge and start digitizing their operations. From what I’m seeing, the acceleration is real. The technologies are mature and the need is apparent. It’s exciting.  

But as manufacturers start new digital projects or expand their ongoing efforts, I think there’s one topic that’s not getting enough attention. Namely: manufacturing’s long tail. 

So in this post I want to help you see how focusing on manufacturing’s long tail of operational challenges can make your projects more successful.  

Digital transformation and customer experience go hand in hand. These 100 statistics show the growth and importance of digital transformation, its impact on customer experience and digital challenges and opportunities for the future.

When it comes to production, horizontal integration has come to refer to well-integrated processes at the production-floor level as well, while vertical integration means that the production floor is tightly coordinated with higher-level business processes such as procurement and quality control.

In this article we explore how Industry 4.0 has further amplified the importance of horizontal and vertical integration, making them the very backbone on which the Smart Factory is built.

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