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Until recently, software-defined networking (SDN) technologies have been limited to use in data centers — not manufacturing floors.

But as part of Intel’s expansive plans to upgrade and build a new generation of chip factories in line with its Integrated Device Manufacturing (IDM) 2.0 blueprint, unveiled in 2021, the Santa Clara, Calif.-based semiconductor giant opted to implement SDN within its chip-making facilities for the scalability, availability, and security benefits it delivers.

“Our concept was to use data center technologies and bring them to the manufacturing floor,” says Rob Colby, project lead. “We’ve had to swap the [networking infrastructure] that exists, which is classic Ethernet, and put in SDN. I’ve upgraded a whole factory from one code version to another code version without downtime for factory tools.”

Aside from zero downtime, moving to Cisco’s Application Centric Infrastructure (ACI) enabled Intel to solve the increasingly complex security challenges associated with new forms of connectivity, ongoing threats, and software vulnerabilities. The two companies met for more than a year to plan and implement for Intel’s manufacturing process security and automation technology that had been used only in data centers.

“This is revolutionary for us in the manufacturing space,” Colby says, noting the cost savings from not taking the factory offline and uninterrupted production is a major financial benefit that keeps on giving. 

That ability to upgrade the networking infrastructure without downtime applies to downloading security patches and integrating tools into the production environment alike, Colby adds.  

“Picture a tool being the size of a house. One of our most recent tools is a $100 million tool, and landing a tool of that size involves a lot of complexity, after which I have to connect it so it can communicate with other systems within our infrastructure,” Colby says. “[Having SDN in place] makes landing tools faster and the quality increases. We’re also able to protect it at the level we need to be protecting it without missing something in the policy.”

Bringing SDN to the factory floor

The project, which earned Intel a 2023 US CIO 100 Award for IT innovation and leadership, has also enabled the chipmaker to perform network deployments faster with 85% less headcount.

Colby says it took a couple of years for the partners to build the blueprint and begin rolling out the solution to existing factories, including rigorous offline testing before beginning.

The migration required no retraining of chip designers in the clean room but some training for those in the manufacturing facilities. “We really went above and beyond to make it as seamless as possible for them,” Colby says. “We’ve recently been testing being able to migrate them over to ACI on the factory floor without any downtime. That will accelerate our migration for the rest of the factory floor.”

The collaboration with Cisco enables ACI to be deployed for factory floor process tools, embedded controllers, and new technologies such as IoT devices being introduced into the factory environment, according to Intel.

It was “clear that we needed to move to an infrastructure that better supported automation, offered more flexible and dynamic security capabilities, and could reduce the overall impact when planned or unplanned changes occur,” Intel wrote in a white paper about its switch to SDN. “The network industry has been trending toward SDN over the last decade, and Intel Manufacturing has been deploying Cisco Application Centric Infrastructure (ACI) in factory on-premises data centers since 2018, gaining experience in the systems and allowing for more market maturity.”

Moving ACI to the manufacturing factories was the next step, and Colby cited Sanjay Krishen and Joe Sartini, both Intel regional managers, as instrumental in bringing SDN to Intel’s manufacturing floor.

The broad view of SDN in manufacturing

There are thousands of semiconductor companies globally, mostly in Taiwan. Yet the US Government CHIPS and Science Act of 2022 has incentivized more semiconductor manufacturing on US soil, and it is taking root.

“The use of cellular and WiFi connectivity on the factory floor has enabled these manufacturers to gain improved visibility, performance, output, and even maintenance,” says IDC analyst Paul Hughes.

“For any industry, software-defined networking brings additional scale and on-demand connectivity to what are now connected machines (industrial IoT),” Hughes says, adding that this also provides improved access to the cloud for data management, storage, analytics, and decision-making. “SDN allows networks to scale up securely when manufacturing activity scales and ensures that all the data generated by and used by machines and tools on the factory floor can move quickly across the network.”

As more semiconductor manufacturing springs up in the US, the use of SDN also “becomes one of the key steps in digital transformation where, in this case, a semiconductor manufacturer can collect, manage, and use data holistically from the factory floor to beyond the network edge,” says Hughes, whose most recent survey, IDC’s 2023 Future of Connectedness Sentiment, shows that 41% of manufacturers believe that the flexibility to add/change bandwidth capacity in near real-time is a top reason for SDN/SD-WAN investment.

The survey also showed that 31% of manufacturers say optimized WAN traffic for latency, jitter, and packet loss is another top reason for SDN/SD-WAN investment and is considered very important for managing factory floor equipment in real-time.

Intel has deployed SDN in roughly 15% of its factories to date and will continue to migrate existing Ethernet-based factories to SDN. For new implementations, Intel has chosen to use open source Ansible playbooks and scripts from GitHub to accelerate its move to SDN.

Intel certified Cisco’s ACI solution in time to deploy in high-volume factories built in Ireland and the US in 2022 and for more planned in Arizona, Ohio, New Mexico, Israel, Malaysia, Italy, and Germany in the coming years, according to the company.

Intel’s core partner on the SDN project is confident the benefits will continue to have a sizable benefit — even for a company of Intel’s size.

“The biggest benefit is that SDN helped Intel complete new factory network builds with 85% less headcount and weeks faster through the use of automated scripts,” says Carlos Rojas, a sales and business developer who worked on the project. “Automation and SDN enable better scalability and consistency of security and policy controls, and the ability to deploy micro-segmentation, improving Intel’s security posture and reducing attack surfaces.”

CIO 100, Manufacturing Industry, Networking, SDN

Posted on Posted in Digital transformation, General topics Tagged with , , , , , , , ,

By Bryan Kirschner, Vice President, Strategy at DataStax

In their 2020 book Competing in the Age of AI, Harvard Business School professors Marco Iansiti and Karim Lakhani make some bold predictions about the winning enterprises of the future.

These organizations, which they refer to as “AI factories,” build a “virtuous cycle between user engagement, data collection, algorithm design, prediction, and improvement,” unlocking new paths to growth as software moves to the core of the enterprise.

A little more than two years after the publication of their seminal work, data gathered from IT leaders and practitioners lend a lot of credence to Iansiti and Lakhani’s hypotheses — particularly those regarding the kind of technology architectures and strategies that engender success with AI.

The AI factory

Successful AI companies — think Apple, Netflix, Google, Uber, or FedEx — build innovative applications and, as they scale, start the flywheel of data, growth, and improvement spinning by gathering ever-growing amounts of real-time data, accessing it instantly, and tuning their predictions.

User experiences become more personal and intuitive; key decisions can be made nearly instantaneously; and predictions can occur in real-time, empowering a business to improve outcomes in the moment.

This unlocks new paths to growth: in the authors’ words, as AI factories “accumulate data by increasing scale (or even scope), the algorithms get better and the business creates greater value, something that enables more usage and thus the generation of even more data.”

For more traditional firms to achieve this kind of success requires a host of changes in both their operating models and technology profiles.

Open-source software and AI success

The State of the Data Race 2022 report is based on a survey of over 500 IT leaders and practitioners that delved into their organizations’ data strategies.

For the purpose of this analysis, responses were divided into three groups:

those where both AI and ML are already in widespread deploymentthose where AI and ML are at most in the pilot phase or early daysthose in between these two extremes, characterized as being in “limited deployment”

The study assumed the organizations with AI/ML widely in production provide useful information about the evolving shape of the “AI factory” and looked for differences across the three stages of maturity.

Iansiti and Lakhani wrote that AI factories will evolve “from a focus on proprietary technologies and software to an emphasis on shared development and open source” because the competitive advantage they enjoy comes from data they accumulate — not the software they develop in-house.

The survey data backs this up in spades. A strong majority of each of the three AI/ML groups considers open-source software (OSS) at least “somewhat” important to their organization (73%, 96%, and 97%, respectively, ordered from “early days” to “wide deployment”).

But ratings of “very” important closely track AI/ML maturity: 84% of companies with AI/ML in wide deployment describe OSS this way (22%of “early days” organizations do, and this jumps to 46% of those with AI/ML in limited deployment).

Perhaps even more striking, organizations not using OSS are a tiny minority (1%, 1%, and 7%, ordered from “wide deployment” to “early days”). But a majority of those with AI/ML in wide deployment (55%) join companies like The Home Depot in having a company-wide mandate for use of OSS.

Real-time data and AI

Consider the AI leaders mentioned above. These companies have assembled technology infrastructures that enable instantaneous changes and decisions based on real-time feedback. Relying on day-old data and batch processing to update the routing of a package to ensure on-time delivery just doesn’t cut it at FedEx.

So, it isn’t surprising that Iansiti and Lakhani report that AI factories lean into real time. “The top enterprises … develop tailored customer experiences, mitigate the risk of customer churn, anticipate equipment failure, and enable all kinds of process decisions in real time,” they say.

Much like with OSS, findings from The State of the Data Race point to real-time data (and the technology architecture that enables it) as a matter of core strategy for the AI leaders. The substantial use of this correlates with AI maturity: 81% of companies that have broadly deployed AI/ML say real-time data is a core strategy. Forty-eight percent of organizations with limited AI/ML deployment describe it as a core strategy; the figure was 32% for companies in the early stages of AI/ML.

But among the advanced group, a full 61% say that leveraging real-time data is a strategic focus across their organization (four times that of organizations in the early days, and more than twice that of those with limited deployment). And 96%of today’s AI/ML leaders expect all or most of their apps to be real time within three years.

This makes sense: as an enterprise intentionally rewires its operations to make the most of AI/ML, it becomes especially important to eliminate any arbitrary architectural barriers to new use cases that require “speed at scale” anywhere in the business.

Today’s OSS as-a-service ecosystem makes that possible for everyone, freeing the future organization to make the most of its unique customer interactions and datasets.

Uniphore: A case study in real-time data, AI, and OSS

Uniphore helps its enterprise customers cultivate more fruitful relationships with their customers by applying AI to sales and customer service communications. The company relies on real-time data to quickly analyze and provide feedback to salespeople upon thousands of customer reactions during video calls.

“We have about fourteen different AI models we run in real time to coalesce the data into something meaningful for our clients,” says Saurabh Saxena, Uniphore’s head of technology and VP of engineering. “Any kind of latency is going to have a negative effect on the real time side.”

“Without the ability to process data in real-time, our solution really wouldn’t be possible,” he adds.

To get “the speed they need,” Uniphore relies on open-source Apache Cassandra® delivered as a service via DataStax (my employer) Astra DB. Its performance and reliability are key to ensuring Uniphore’s system is something every salesperson is motivated to rely on in order to be more effective in the moment.

But winning adoption among line staff points to another of Iansiti and Lakhani’s insights on the implications of AI for senior management. As the latter explained in a 2021 interview, “AI is good at predictions” — and predictions are “the guts of an organization.” Senior executives need to constantly ask, “Do I have data now to improve my prediction power — my accuracy, my speed?”

As Uniphore points out, sales forecast accuracy is something most sales leaders are concerned about. As a knock-on effect of using Uniphore’s tools, quantitative data on sentiment and engagement can flow into sales forecasts without the need for more staff time. In addition to the direct uplift that sellers experience, forecasts improve– — management to spend their time on more important things, like investing for growth, with greater confidence.

This closes the loop on Iansiti and Lakhani’s insight that AI factories can unlock a more powerful operating model over and above the benefits of individual use cases and point solutions.

Building an AI factory

Organizations that leaned into the insights in Competing in the Age of AI may have stolen a march on their competition. Judging from our survey data, they’ve been amply rewarded for doing so. The good news is that they’ve proven best practices for success — and the tools you need to accelerate your own progress on the journey to becoming an “AI factory” are ready and waiting.

Learn how DataStax enables AI-powered apps

About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Artificial Intelligence, IT Leadership