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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

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GPU manufacturer Nvidia is expanding its enterprise software offering with three new AI workflows for retailers it hopes will also drive sales of its hardware accelerators.

The workflows are built on Nvidia’s existing AI technology platform. One tracks shoppers and objects across multiple camera views as a building block for cashierless store systems; one aims to prevent ticket-switching fraud at self-service checkouts; and one is for building analytics dashboards from surveillance camera video.

Nvidia isn’t packaging these workflows as off-the-shelf applications, however. Instead, it will make them available for enterprises to integrate themselves, or to buy as part of larger systems developed by startups or third-party systems integrators.

“There are several of them out there, globally, that have successfully developed these kinds of solutions, but we’re making it easier for more software companies and also system integrators to build these kinds of solutions,” said Azita Martin, Nvidia’s VP of retail.

She expects that demand for the software will drive sales of edge computing products containing Nvidia’s accelerator chips, as latency issues mean the algorithms for cashierless and self-checkout systems need to be running close to the checkout and not in some distant data center.

In addition to tracking who is carrying what items out of the store, the multiple camera system can also recognize when items have been put back on the wrong shelf, directing staff to reshelve them so that other customers can find them and stock outages are avoided, she said.

“We’re seeing huge adoption of frictionless shopping in Asia-Pacific and Europe, driven by shortage of labor,” said Martin.

Nvidia will face competition from Amazon in the cashierless store market, though, since while Amazon initially developed its Just Walk Out technology for use in its own Amazon Go and Amazon Fresh stores, it’s now offering it to third-party retailers, too. The first non-Amazon supermarket to use the company’s technology opened in Kansas City in December.

Assessing cost control

The tool to prevent ticket switching is intended to be integrated with camera-equipped self-service point-of-sale terminals, augmenting them with the ability to identify the product being scanned and verify it matches the barcode.

The cost of training the AI model to recognize these products went beyond the usual spending on computing capacity.

“We bought tens of thousands of dollars of products like steak and Tide and beer and razors, which are the most common items stolen, and we trained these algorithms,” said Martin.

Nvidia kept its grocery bill under control using its Omniverse simulation platform. “We didn’t buy every size of Tide and every packaging of beer,” she adds. “We took Omniverse and created synthetic data to train those algorithms even further for higher accuracy.”

Beer presents a particular challenge for the image recognition system, as it often sells in different-size multipacks or in special-edition packaging associated with events like the Super Bowl. However, the system continues to learn about new product formats and packaging from images captured at the checkout.

While implementation will be left up to retailers and their systems integrators, Martin suggested the tool might be used to lock up a point-of-sale terminal when ticket switching is suspected, summoning a member of staff to reset it and help the customer rescan their items.

Nvidia is touting high accuracy for its algorithms, but it remains to be seen how this will work out in deployment.

“These algorithms will deliver 98% accuracy in detecting theft and shutting down the point of sale and preventing it,” she said.

But that still leaves a 2% false positive rate, so CIOs will want to carefully monitor the potential impact on profitability, customer satisfaction, and frequent resets to prevent ticket switching.

A $100 billion problem

A 2022 survey by the National Retail Federation found that inventory shrink amounted to 1.44% of revenue — a relatively stable figure over the last decade — and in 2021, losses due to shrink totaled almost $100 billion, the NRF estimated.

Of that, survey respondents said 26% was due to process or control failures, 29% due to employee or internal theft, and 37% due to external theft.

But Nvidia suggests that its loss prevention technology could eliminate 30% of shrinkage. That, though, would mean it could prevent four-fifths of all external retail theft, even though in addition to ticket switching, that category also includes shoplifting and organized retail crime activities such as cargo theft, and the use of stolen or cloned credit cards to obtain merchandise.

Plus, potential gains must be weighed against the cost of deploying the technology, which, Martin says, “depends on the size of the store, the number of cameras and how many stores you deploy it to.”

More positively, Nvidia is also offering AI workflows that can process surveillance camera video feeds to generate a dashboard of retail analytics, including a heatmap of the most popular aisles and hour-by-hour trends in customer count and dwell time. “All of this is incredibly important in optimizing the merchandising, how the store is laid out, where the products go, and on what shelves to drive additional revenue,” Martin said.

Artificial Intelligence, IT Strategy, Retail Industry