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The State of the Union for IoT Intelligence

A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business.

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By Frank Antonysamy

Frank Antonysamy is Vice President of Cognizant’s Global IoT and Engineering Services

A digital transformation revolution in manufacturing is underway, and data is the primary currency paving the way for more efficient ways of doing business. Gone are the days when data analysis was left to Monday night quarterbacking by poring over static results. Today, thanks to a central wireless ecosystem which links relevant mobile devices, Internet of Things (IoT) connected machines and connected employees, data gathering and analysis in a smart factory is immediate and real-time optimization drives significant efficiencies.

But not all smart factories are created equal.

Given that enterprises are all on different points on the path to complete digital maturity, it helps to take stock of the state of IoT intelligence — where we are now and where we are headed — and what industrial organizations need to be successful.

Laying the foundation for intelligence

One of the key advantages of Industry 4.0 is the promise of the Internet of Things (IoT) or Industrial Internet of Things (IIoT). Sensors connected to raw materials, factory floor equipment and final products can relay information, over a Wi-Fi connection, about their health and usage patterns to wider enterprise asset management software and enterprise resource planning systems.

Equally important, stakeholders can access this data in real-time and on-demand. Companies can leverage this data to deliver insights across three channels:

  • Product intelligence
  • Service intelligence
  • Operational intelligence

There is significant overlap across these three pillars but their power to deliver a smart factory with new avenues for monetization, is revolutionary.

Here’s an overview of the IoT landscape with respect to its three core pillars of intelligence.

Case Study: Fast-tracking innovation with a PLM platform

Product intelligence shakes up traditional PLM

The smart factory runs on smart products whose intelligence can be leveraged to read the tea leaves of market demand. At its core, product intelligence is defined as intelligence derived from an intelligent (read…IoT-enabled) product. In the IoT world, traditional rules of product lifecycle management (PLM) no longer apply. Gone are the rinse-and-repeat cycles of concept, design, manufacture, marketing and sales. In traditional manufacturing, the ideation-sale stage took years if not decades and slight changes in market demand had a whiplash effect on the process.

IoT has rebooted the PLM conversation to move it away from the product and make it more about the customer. IoT-enabled products can now deliver intelligence post sales about how the product is being used (or not), how it is being disposed of, and a whole host of other downstream information. Such product intelligence is useful in two primary ways: as a method of refining the product to make it more agile and responsive to consumer needs (thereby leading to potentially more sales) and as new avenues of monetizing such product intelligence.

The future of product intelligence is a complete “closed-loop” product development, with real-time customer feedback woven into the process. It bears stressing that while customer focus groups and behaviors have always been part of the design and manufacture process, IoT has effectively compressed that time cycle and expanded the scale of parameters that might be considered — and monetized.

Service intelligence delivers customer-focused monetization

Monetization in the new smart factory landscape need not be restricted to product intelligence alone. Service intelligence, for example, is about delivering aftermarket intelligence in the form of added services to an existing or expanding customer base. A customer who buys Widget A from a manufacturing company might also be interested in understanding how to optimize the use of that widget for their own tailored environments.

[Download]: Designing Manufacturing’s Digital Future

While aftermarket services are not entirely new, the addition of IoT has the capability of delivering service intelligence on steroids. In the future, service intelligence providers will use IoT to tailor measurements of key performance indicators (KPIs) and delivery of data insights depending on exactly what the end customer is looking for. Tailoring service intelligence to the customer potentially leads to greater client stickiness. What’s more, IoT is capable of slicing and dicing intelligence for each and every customer, making the net results that much more insightful and leading to more bountiful monetization opportunities.

Operations intelligence squeezes the most out of machines

Monetization also comes from picking the low-hanging fruit in production processes. Arguably one of the best ways to squeeze the most out of IoT is to use it to increase manufacturing uptime. IoT is also favorably impacting the ability to fine-tune production processes by being able to connect, visualize and analyze data from a whole host of new players such as machines on the plant floor. RFID and computer vision layers also add to such intelligence.

IoT-embedded devices on the plant floor can spit out data that measures machine health, which can be fed into machine learning algorithms for predictive maintenance. If a rotor heats up past a preset temperature setting, for example, it can trigger the algorithm to send an alert to a plant worker or even proactively shut the machine down. Machine learning capabilities derived from IoT enhance KPIs such as manufacturing uptime.

[Download]: Creating a Digital PLM Platform to Promote Collaboration and Increase Efficiency

In the future, expect a move toward increasingly segmented manufacturing, possibly sliced and diced into ever smaller batches. Operations intelligence will allow manufacturers to segment the production process — and fine-tune each — to fulfill a variety of specialty orders at the same time.

What it takes to deliver on the promise of IoT

While IoT intelligence in its various forms promises a truly smart factory with a wealth of monetization opportunities, it needs a robust infrastructure to truly deliver. Elements of this winning infrastructure include, among others: a C-suite willing to address negative attitudes of incumbency; standardization of data aggregation and analytics processes such as machine learning; and future-proofing technologies through increasing reliance on open-source models.

Since data is the lifeblood of IoT, enterprises need to ensure that they don’t get mired in the data lake — that the data they’re working with is clean and structured, relevant to the KPIs they want measured, and fed to algorithms in a consistent format. Once data is clean and uniform, smart factories can leverage IoT to feed machine learning algorithms that learn from the data and eventually deliver an almost lights-out production stream.

Since the future of intelligence also involves its monetization — vendors up and down the digital supply network will pay for insights — it will be important to connect stakeholders to the central nervous system of the smart factory in new ways. Customer service agents (or even customers themselves) for example should be able to see where product orders are in the production process and fine-tune their forecasts accordingly. IoT delivers transparency to all stakeholders — within reason, keeping intellectual property concerns in mind.

[Download]: Advancing Smart Manufacturing Operations Value with Industry 4.0

IoT in manufacturing is not limited to the production floor either. IoT sensors in warehouses can detect when supplies are going bad, when inventory is low and beef up accordingly. Remote weather events that can affect vendor delivery can trigger automated backups. The IoT-driven smart factory touches many processes and products much beyond the plant floor.

Until true digitization from start to finish is a total reality, companies are figuring out stop-gap measures that will leverage the promise of IoT. A “nerve center,” which serves as a central repository for data gathering and analytics can serve to overcome the problem of data connectivity across locations and devices.

The ripple effect from IoT intelligence is not limited to the manufacturing floor alone. By placing the digital core at the center, it reshapes processes up and down key constituencies such as supply chain and asset management.

How tomorrow’s tech might impact IoT intelligence

IoT is already being incorporated in the smart factory of today. Tomorrow, expect acceleration with respect to monetizing closed-loop product intelligence, an increased focus on the customer through service intelligence and using operations intelligence by improving businesses processes on the way to a truly smart factory.

The road is expected to get even smoother with the advent of 5G technology which will decrease latency of IoT for edge computing devices. 5G will deliver even faster access to data in real time which will make real-time analysis even more accurate. The technology has special ramifications for production processes where time is of the essence. Devastating machine shutdowns can be averted in split seconds by machine learning algorithms fed through 5G connections from IoT-enabled equipment. This means smart factories of faster computing speeds and greater agility. The state of the union for IoT intelligence is strong and only expected to grow stronger as new technologies such as 5G make data competencies that much more robust.

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How AI can be used to mitigate the climate crisis

87% of respondents to a BCG survey said that advanced analytics and AI is a helpful tool in the climate change fight.

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AI for climate crisis
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Wildfires. Floods. Drought. 

Climate change is, as the Intergovernmental Panel on Climate Change (IPCC) says, “widespread, rapid, and intensifying.” Its effects are reaching just about every region on earth. Of course, human influence is a key cause — and it’ll be human actions that have the greatest potential to help curb its most damaging effects. 

So where does digital transformation and technology fit into all this?

According to a new report from AI for the Planet and knowledge partner Boston Consulting Group (BCG), AI is one tool in our back pocket that is “uniquely positioned” to help with our efforts at adaptation and resilience — both with immediate responses to specific crises and long-term management and planning.

“Due to its capacity to gather, complete, and interpret large, complex datasets on emissions, climate impact, and more, it can be used to support all stakeholders in taking a more informed and data-driven approach to combating carbon emissions and building a greener society,” explain the authors. “It can also be employed to reweight global climate efforts toward the most at-risk regions.” 

Related reading from BCG: Why AI Needs A Social License

And the experts overwhelmingly agree. A recent ​​BCG global survey of both public and private sector climate and AI leaders found that 87% of respondents said advanced analytics and AI is a helpful tool in the climate change fight. Among private sector leaders, 67% said they want governments to further step up their support of AI in the climate change fight.

The survey also found that 43% of organizations say that they have a vision for using AI in their own climate change efforts.

With respect to obstacles — a common digital transformation issue — 78% of respondents cited low access to inside or outside AI expertise as an obstacle. 77% reported a lack of ready-made AI solutions. And finally 67% noted a lack of organizational confidence in AI data and analysis.

How can it work?

According to the report, leaders find that reduction and measurement of emissions is where AI can really create business value. Looking at the specific ways AI can be used for the trifecta of mitigation-adaptation-resilience, the technology can measure emissions on both micro and macro levels, and can help bolster hazard forecasting (eg. rising sea levels or extreme weather). 

AI can also be applied to broader research and modeling. One example from the report is personalized tools that measure an individual’s carbon footprint.

One important thing to remember? As the authors explain here, “Note that AI is not the solution itself, but a tool to help leaders and citizens make informed judgments about how to tackle climate challenges.”

Read the full ‘How AI Can Be a Powerful Tool in the Fight Against Climate Change’ here.

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One-third of organizations face ransomware attacks at least weekly

Ransomware threats are on the rise. Does your organization understand the financial risks and how to mitigate damages?

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Is your organization ready for a ransomware attack? What if you were hit weekly, or even daily?

According to Menlo Security’s 2022 Impacts: Ransomware attacks and preparedness report, one-third of organizations face ransomware attacks at least weekly, while 9% deal with attacks more than once a day. They also report that in the last 18 months, 53% of respondent organizations have been the victim of a ransomware attack.

Ransomware attacks are on the rise — especially ‘Highly Evasive Adaptive Threats’ or (HEAT). As explained by Menlo, “a HEAT attack is a class of cyberthreat that leverages web browsers as the attack vector and employs various techniques to evade multiple layers of detection in current security stacks.” 

It’s also important to note that the rise in remote work has also increased potential entry points, opening up a whole new range of vulnerabilities.

Additional findings from Menlo’s survey, conducted among 500+ IT security decision makers at US- or UK-based companies with more than 1,000 employees, include:

  • 41% of respondents said they worry about ransomware attacks evolving beyond their team’s knowledge and skillset.
  • 39% worry about attacks evolving beyond their company’s security capabilities.
  • 46% of respondents are most concerned about the risk of employees ignoring the advice of corporate security, clicking on links or attachments with malware.
  • The average estimated cost of a ransomware attack is $326,531.00. Insurance payouts brought this up to an average of $555,971.00.

“Security professionals are coming under increasing pressure as organizations face an unprecedented number of highly sophisticated threats like ransomware,” explained senior director of Cybersecurity Strategy at Menlo Security, Mark Guntrip, in a press release.

“On the frontline of cyber defense, they are often coping with huge amounts of stress, worrying about what employees are doing, their team and whether they are getting the right support internally, so it’s no surprise they are prioritizing the business over job security.”

Should ransomware demands be paid?

On the one hand, it seems quite risky. On the other hand, you want your data back. 

Menlo found that 32% of respondents worry about the risk of paying a ransomware demand and not getting their data back. Two-thirds did say they would pay a demand, while 27% say they would never pay a demand.

Ultimately, though, who pays? 31% of respondents would leave it to their insurance company to pay. Almost one in five think the government should pay up.

A big factor here? Preparedness, Guntrip adds.

“Do you have the right processes and strong backup in place? If so, you won’t need to pay it. If, however, your organization is unable to function as normal, access data or the damage is likely to bring down the business, that’s when you need to re-evaluate your options.”

All in all, the report emphasizes that with advancing ransomware technology — including ransomware-as-a-service (RaaS) readily available to low-level criminals — making sure your security infrastructure is able to stop attacks is key.

Access the full report from Menlo, including featured insights, here.

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Mexico to use underwater drone in search for trapped miners

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A rescuer works at a flooded coal mine in northern Mexico where 10 workers are trapped
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Rescuers will deploy an underwater drone as part of efforts to save 10 workers trapped for five days in a flooded coal mine in northern Mexico, authorities said Monday.

The device provided by the navy has a high-resolution camera and light to identify possible obstacles without putting lives at risk, civil defense national coordinator Laura Velazquez said.

Work continued to pump water from the mine in Agujita in the northern state of Coahuila to make it safe enough for rescuers to go inside.

The military said that it was hoped rescuers would be able enter to one of the shafts in the middle of this week if the water level drops to 1.5 meters (around five feet).

The mine shafts descend about 60 meters and the water inside the one that rescuers plan to go into was 19.4 meters deep, down from more than 30 meters initially, officials said.

“We’re hurrying to remove the water so that the rescuers can enter,” said President Andres Manuel Lopez Obrador, who visited the site on Sunday and urged intensified efforts to save the miners.

Around 300 liters were being pumped out each second, he told reporters back in Mexico City.

“Everyone has faith. No one is thinking about anything other than the rescue,” he said.

Authorities said the miners had been carrying out excavation work when they hit an adjoining area full of water.

Five workers managed to escape from the crudely constructed mine in the initial aftermath of Wednesday’s accident, but there has been no contract with the others.

With each passing hour relatives were becoming increasingly desperate and more reluctant to talk to the media.

Authorities reinforced a security cordon around the mine, about 1,130 kilometers (700 miles) north of Mexico City.

Several hundred soldiers and other personnel, including six military scuba divers, are taking part in the rescue effort, according to the government.

The Attorney General’s Office said on Sunday that it asked the labor ministry to provide information on safety inspections carried out at mines in the area to determine the cause of the accident.

Coahuila, Mexico’s main coal-producing region, has seen a series of fatal mining incidents over the years.

Last year, seven miners died when they were trapped in the region.

The worst accident was an explosion that claimed 65 lives at the Pasta de Conchos mine in 2006.

Only two bodies were retrieved after that tragedy and the families have repeatedly urged the Mexican authorities to recover the others.

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