Presented by Intel

For the past decade, manufacturers worldwide have pursued a vision of industrial transformation by combining smart, autonomous, connected technologies to reinvent everything from production efficiency to product customization. There are now more opportunities than ever to leverage advances in connectivity, analytics, robotics, factory automation, IoT products, AI, computer vision, and edge computing to boost speed-to-market, service, and innovative new business strategies.

Today, new forces are accelerating this global shift. Key among them: unavoidable market changes brought on by the global pandemic; global supply chain flow disruptions; a workforce demographic in major flux; and the quickening, exponential rise in data available from sensors and other manufacturing technology. “In a time of major disruption, companies are recognizing and embracing the opportunities to significantly transform,” notes Christine Boles, vice president of the Internet of Things group at Intel.

There’s another big driver: new technologies and industry alliances are helping manufacturers meet rigorous demands. New processors tailored for IoT and edge, with a new focus on open source software, reference models, stronger partnerships, and ecosystems, are helping organizations handle new challenges and opportunities. Together, they make it easier and faster to tackle key tasks, including scaling and converging flexible workloads, creating phenomenal AI vision apps, enabling real-time operations, and distributing processing and collection to the edge.

Here’s a closer look at the new landscape, and what to look for in a solution.

The impact of pandemic disruption

Pre-COVID-19, the global market related to industrial transformation was projected to surpass $300 billion by 2023 — a 384% rise in just five years. Today, volatility is an ongoing fact of business. Leadership is facing new pressure from all sides — supply chain disruption, changes in customer demand, and loss of workforce. So, it’s no surprise that many observers predict that adoption of these technologies and processes appear to be accelerating rapidly.

“The pandemic accelerates the need to quickly respond to changes in demand, and highlights the need for a production shift,” Boles says. “This evolution is helping companies weather the storm with next-generation technologies that build resilience and agility into business processes.”

According to McKinsey, 39% of industry leaders are now leveraging a nerve-center, or control-tower, approach to increase end-to-end supply-chain transparency, while around 25% are fast-tracking automation strategies that address the problem of worker shortages. The new normal is also spurring leaders to adopt quick-win solutions that protect workers and keep business on track, such as sensors that can monitor employee health and enforce social distancing policies, and digital tools that enable remote collaboration.

There’s a new focus on prioritizing manufacturing agility and flexibility to respond to changing conditions.

Some 93% of manufacturing and supply-chain professionals are planning to turn their focus to the resilience of their supply chain, and 90% are looking to support their digital assets by investing in the right talent.

The human factor

In fact, the success of this transformation depends heavily on how well companies adapt to the rapidly changing workforce. Harnessing automation, a defining feature, will require higher-skilled positions and technical talent.

In many places, experienced workers are leaving. At the same time, a new generation of employees is arriving with new expectations, skills, talents, and workstyles. “You have knowledge leaving, and you have a new workforce coming in that has grown up digitally,” says Boles. “They have different expectations of how they interact with the equipment they operate or manage.”

Investing in industrial transformation technology is a way of also tapping in to this new skilled, highly valuable workforce. Businesses need to be more effective at identifying, attracting, and retaining the talent they require as technology replaces manual labor, which in turn creates brand new positions and opportunities for these employees.

But even as companies source new talent, Boles says they’re recognizing the need to leverage the expertise of their current workforce — retraining them for higher-value, tech-based roles that require new skills — and tapping into long-term employees’ comprehensive understanding of the industry.

The convergence of generations and the need to harness the wide curve of knowledge, learning, and ability are both a driver and critical success factor for organizations seeking to unlock the transformative power of the shift now taking place.

The need to use data better, especially in real time

In many ways, this industrial transformation is all about data and, increasingly, real-time data.

IDC predicts that up to 70% of enterprises will run varying levels of data processing at the IoT edge by 2023. Researchers also forecast that by 2025, 55.6% of all data will come from IoT devices, such as retail devices, industrial equipment, digital signage, medical implants, and other connected things. The two are deeply connected.

“It’s about building better products and better companies,” says veteran tech analyst Jack Gold. “It’s about having workers do more by knowing what’s actually going on in your business and analyzing the data at every point of your business. Data is the lifeblood of the process.”

The industry of today — and tomorrow — uses unprecedented access and new sources of data in strategic ways. To work most efficiently and effectively, experienced and newer employees alike need access to data and analysis at every point of the manufacturing process. To stay competitive in the marketplace, industry needs to embrace the powerful, actionable insights that data offers in real time on the floor, and over time when it comes to decision-making in strategy

Data is essential at every step, from ideation, prototyping, and development to production, logistics, and innovation. It impacts maintenance, asset utilization, collaboration between employees and assets, quality control, and more. Unfortunately, Gold estimates, 90 to 95% of data acquired in a company never gets used. “If you’re only processing 10% of your data, what’s the other 90% not telling you about your business?” he asks.

That’s why, even pre-pandemic, a foundational motivation for many was the need for timely, actionable information. In particular, says Intel’s Boles, “Industry leaders are looking to capitalize on the data that edge inference and AI closer to operations can bring.” They’re realizing the need for more real-time visibility, whether it’s camera data looking for production line defects or worker safety, or local data waiting to be extracted and analyzed.

During the pandemic, such capabilities become even more urgently crucial. Manufacturers now must manage disrupted global trade and supply chains, break down silos to improve end-to-end visibility, pivot quickly into new markets, and bring together demand forecasting, supply planning, production planning, logistics planning, sales, and operations. And data, of course, fuels the AI and machine learning algorithms that help organizations respond to a rapidly changing world more effectively.

All this requires next-generation platforms that deliver edge-specific, IoT-enhanced data to help address manufacturers’ need for data, Boles says, and to address specific manufacturing environments and the convergence of workloads. Bringing those workloads together in one system is what new specialized processors help address, bringing that capability together: The ability to have edge inference, with AI capabilities, to act upon vision workloads and provide control.

Next-gen industry solutions

The challenges of embracing and deploying practices, processes, and systems are daunting enough in “normal” times. Coupled with the new market pressures above, they can appear doubly so.

Fortunately, top vendors are introducing a spate of technologies, services, and solutions to streamline key tasks and enable new capabilities — specialized next-generation chips optimized for IoT and edge, open development platforms, reference models, and vertical industry solutions, to name a few.

What does a state-of-the-art next-gen solution look like?

According to Boles, it’s key that companies understand the need to implement a complete set of future-forward solutions that address the specific environments into which they’re being implemented. Also crucial is the ability to bring together edge inference and AI capabilities, and the ability to act upon those inputs in real time.

Next-gen platforms require powerful compute, from both the CPU as well as the graphics, and the ability to support a full range of operating systems, hypervisors, and tool chains to support real-time industrial features at the edge. Also crucial: a flexible development framework that can bring leading-edge technologies and capabilities from IT to IIoT, often on industry-standard platforms.

At the system level, a must-have in industrial infrastructure is time-sensitive networking to allow coordination across distributed workloads in a deterministic way in low-latency forms. Because different operations require widely varying levels of latency, the ability to finely tune latency on the platform is key. Boles notes that Intel has built these features into their next-generation products with the Intel Atom x6000E series processors and the 11th-gen Intel Core processor for the IOT space

But, she adds, it’s not enough just to have the silicon platform. Software reference designs help ecosystem partners build solutions with edge insights, and control workloads to accelerate their development time.

For example, Intel Edge Controls for Industrial lets companies integrate real-time deterministic compute, standards-based connectivity, and IT-like management with operational technology (OT)-like predictability. By achieving multi-vendor interoperability, businesses can progress from more rigid, fixed-function controls to a flexible microservices architecture with portable applications. This leads to easy management and fast adoption of new, best-in-breed solutions.

Similarly, Intel Edge Insights for Industrial enables easy AI deployment via a set of integrated capabilities, including data ingestion, processing, and transmission. The optimized edge analytics improve product quality, operational performance, prediction of downtime, and automated operational flows. “The reference software and related technologies enhance vision analytics and predictive maintenance and AI capabilities,” says Krish Kupathil, head of Hi-Tech and Digital at QuEST Global, “helping us create the frontier for our customers.”

A final key factor: Developing vital software elements in open source communities. Doing so gives every ecosystem partner a better chance to innovate. Using industry standards and an open source approach maximizes the flexibility and economics of deploying systems to achieve their goals. Analyst Gold says the strength of Intel’s long-established relationships in these ecosystems give the company a distinct market advantage over rivals.

2020 has underscored just how quickly companies need to change — and just how agile they need to be in a fast-paced world of unpredictable, dramatic twists. MIT estimates that the optimal mix of industrial transformative technologies could save a large company up to $16 billion “as the traditional value chain will pivot toward hyper-personalized experiences, products, and services driven by innovative business models that result in new sources of revenue.” With those kinds of benefits, winners know to keep driving ahead.

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