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IoT + AI = Operations Intelligence: A new equation for a new world of data

In the equation IoT + X = Operations Intelligence, what role does artificial intelligence play as the X factor?

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A new world powered by the Internet of Things (IoT) demands a new computing paradigm and yesterday’s if-X-then-Y rules-based approach can’t handle today’s industrial complexities. 

IoT-embedded devices generate large amounts of data, but analyzing that volume of information using traditional algorithms can be overwhelming, like drinking water from a fire hose. We need more intelligent computing that can take on both volume and ambiguity — in context and in real time. Artificial Intelligence can be one of the special X factors in the equation, IoT + X = Intelligence, and it learns by example rather than by rules.

The IoT-AI dance

At first glance, it might appear that a rules-based process might work well enough for a solitary IoT-enabled device: monitor the temperature profile of a motor; if it overheats beyond a preset limit, turn it off. However, the real world in which such machines operate is much more complex and you need to parse interdependent signals at the edges to truly make sense of the data being fed to you. For example, air temperature, paint temperature, and humidity in combination may lead to warranty claims in complex combinations that exceed our ability to use traditional data science. AI can help. 

In that sense, AI is IoT’s ally. It tolerates ambiguity at the margins, meaning analysts don’t have to tie up precious capital resources just cleaning and formatting data so it can play well with existing algorithms. While it is popular to declare that the “garbage in, garbage out” theory holds true in data analytics, the good news is that AI can detect outliers and tolerate bad data up to a point, says James Jeude, Vice-President in Cognizant’s Digital AI & Analytics Strategic Consulting Group. “I believe that AI takes our best human thinking and allows us to duplicate it at scale and at low cost and push it into all corners,” he adds

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

IoT’s myriad touchpoints allow AI to prove its value, says Jeude. One of the many use cases of AI is proactive preventive maintenance. If you were to outfit every grocery store refrigerator with IoT sensors that measured current flow and temperature, AI could proactively predict compressor failure, delivering intelligence that can be acted on and saving revenues in the long run., catching failures well before the actual temperature rises and triggers an alarm.

“Humans have a limited ability to process complexity. IoT and AI are absolutely essential together to deal with that complexity challenge,” Jeude says.

Evolutionary AI

Previous iterations of AI founded on deep learning involved training algorithms on vast banks of test cases so the machines would lean on learned experiences to make informed decisions. Such AI is time and resource-intensive and doesn’t allow for flexibility at the edges. 

Evolutionary AI, on the other hand, allows for economical testing of corner cases. It factors in historical context, decision and output data before prescribing actions. “We can use evolutionary AI to drive iterations and pick the ones that are the winners and help us prune the losers,” Jeude says.

[Download]: Real Estate Manager Goes Digital

The very fact that IoT combined with AI creates intelligence is predicated on the fact that the cost of computing has decreased significantly. Equally important, Jeude points out, is that the ability to put decisions into effect has also become cheaper. Both have fallen by an order of magnitude every decade. “That IoT device can shut off a machine, call for repairs, flash warning lights, for a fraction of the cost,” he says.

IoT with AI delivers intelligence by processing volumes of data in real time and in context at complex scales humans can’t work with. With IoT and AI we are well-informed to make critical decisions right at the moment when they are needed the most. In today’s high-stakes digital landscape, that can make all the difference. Whether you’re working in retail, entertainment, manufacturing, finance, mining or countless other industries, IoT in concert with AI can deliver the transformational intelligence you need at scale.

[Download]: A New Approach to PLM

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Manufacturing

What you need to know if you’re attending MWC Los Angeles 2019

MWC Los Angeles 2019 is one of the newest and fastest growing events in the U.S. that brings together leading companies and influential experts from all sectors within the mobile technology industry to advance Intelligent Connectivity – a fusion of 5G, IoT, AI and Big Data.

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MWC Los Angeles 2019 is one of the newest and fastest growing events in the U.S. that brings together leading companies and influential experts from all sectors within the mobile technology industry to advance Intelligent Connectivity – a fusion of 5G, IoT, AI and Big Data.

The event is an opportunity to discover how leaders like Cognizant are looking into the future of connections, content and commerce.

To help you prepare, here is a selection of articles, case studies, ebooks, and clips from Cognizant, discussing digital transformation:

  • Learn Cognizant’s 4 key success factors to Industry 4.0 transformation. For starters, lead with strategy, not the technology. Watch the video here.
  • AI, Machine Learning, and IoT are ensuring the efficacy and efficiency of one of the most demanding engineering projects in the world. Learn how Cognizant helped Norwegian offshore engineering firm Kvaerner adopt these digital technologies.
  • The promise of Industry 4.0 is compelling, but for many traditional manufacturers, the reality is less than ideal. In this new Cognizant report, find examples of manufacturers that are navigating the shift.
  • It’s all about speed: Insight from Cognizant on how 5G will transform the business sector and create a leadership race for data intelligence.
  • Not all smart factories are created equal: Cognizant takes stock of the state of IoT intelligence, and what industrial organizations need to ensure both digital maturity and success. Read more here.
  • The many touchpoints of IoT connectivity allows AI to really shine, and prove its value to manufacturers in the form of proactive preventive maintenance, says James Jeude, VP in Cognizant’s Digital AI & Analytics Strategic Consulting Group, in this piece.
  • The human factor in IoT intelligence is key: Connected employees can “dynamically manage situations as they change,” explains Cognizant’s AVP of engineering and IOT solutions Phanibhushan Sistu.
  • The leap to IoT is a necessary one for your organization. This Cognizant ebook looks at 14 such businesses that jumped confidently into the digital future.
  • It took less than 12 weeks for Cognizant to implement an IIoT platform for a leading global industrial manufacturer. Get the case study here.
  • Without the duo of IoT and the Digital Twin, your organization is living in a black-and-white outlined world, in terms of operational intelligence. Color it in, get more accurate predictions, and fully realize potential.

One session to highlight? “5G and IoT – Understanding the Relevance to Your Business,” featuring Senior Director, Cognizant Digital Business, Prashanth Bhushan. This session will examine how the impending mass availability of 5G networks is set to mark a major step forward in extending 5G applications to various vertical industries and fields beyond traditional telecom and mobile internet.

MWC Los Angeles 2019 takes place October 22-24, at the Los Angeles Convention Center. 

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Manufacturing

IoT + PLM = Product Intelligence: An Equation that Delivers Monetization Opportunities

In the equation IoT + X = Intelligence, what role does product lifecycle management play as the X factor?

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Consumers are fickle. 

Today’s hot must-have can easily fade into obscurity tomorrow. Most products don’t even make it out to the playing field, and instead languish on the sidelines. The B2B world is not much different. Thanks to the Internet of Things (IoT) however, manufacturers are gaining a better peek at the product lifecycle beyond their distribution warehouse and leveraging that intelligence to their advantage.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

PLM matters

More often than not, manufacturers make educated guesses at what consumers want. Sure they rely on surveys and calibrated focus groups but too often they’re still skating on thin ice with data snapshots. Product manufacturers rely on past sales records, future trend forecasts and hope design comes up with a nifty new gadget. They then execute strategic marketing and finally keep fingers crossed for best outcomes. 

The problem with such an iterative pattern is that the process is more or less opaque once the product leaves the manufacturer. Today’s product lifecycle management delivers much-needed transparency to the trail a product follows not just from concept and design and sales but on to how the consumer actually uses and disposes of it. 

And it does so through IoT. An IoT-embedded device relays product information to the mothership long after it has left the point of sale so manufacturers can gain insight into usage patterns and other data. While PLM has always followed the trail of breadcrumbs that products leave, IoT now allows for a longer and more useful trail. 

[Download]: A New Approach to PLM

IoT + PLM

“With IoT live product feedback has become possible. The [data] gap that existed between when we have sold a product and when someone discards that product…that gap has now been connected by IoT,” says Jagmeet Singh, Director, Connected Products, at Cognizant. 

Businesses use cloud technologies to analyze the data that IoT-embedded products spit out and reap a whole host of benefits. Manufacturers access instant feedback about feature design – in an age of user-centered design, this is a big deal – and price points. Product design now needs fewer reboots and manufacturers can produce with more confidence.

The introduction of IoT and associated data analytics expanded the use of Product Data as a Service (PDaaS), where larger sets of metadata about products are now available to be harvested, right from raw materials used, to manufacturing processes to quality control and beyond.

Simply by following the IoT trail (and obtaining all-important user permission), manufacturers can monetize information about product usage to secondary vendors who might benefit from related information. A computer manufacturer, for example, can sell valuable information about the heat exchanger, the battery, the Bluetooth connection to satellite providers who can use this as intelligence for their own sales and marketing promotions.

IoT breathes new life into PLM extending the line beyond the four walls of the warehouse to the real world where live data from every user shapes future product iterations.

IoT + PLM = Customer intelligence. With sharper data based on usage patterns, manufacturers can anticipate customer needs better and deliver they want. The result is more revenue and less waste. IoT lets PLM realize its full potential.

[Download]: Real Estate Manager Goes Digital

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IoT + big data analytics = operations intelligence: An equation that draws a better picture

In the equation IoT + X = Operations Intelligence, what role does big data analytics play as the X factor?

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Global pressures to decrease energy consumption and the speed of business are accelerating the call for new ways of delivering efficiencies. 

What if a fridge could tell us that the food it stores was going to spoil in a day? What if buildings could cut down heating costs by selectively turning down the heat depending on occupancy? For a fridge to “talk” in this way, it would need a way of measuring the parameters that indicate data spoilage. The Internet of Things (IoT) delivers just that.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

With IoT, everyday “things” such as refrigerators, machines, warehouses, televisions, washing machines, broadcast data about their health through outfitted chips and sensors. So large is this data deluge, that the IoT market is expected to grow at an astonishing 24.7% and reach $1111.3 billion by 2026.

Big data analytics

We need big data analytics to harvest this torrent of IoT information in useful ways. Simply spitting out data is not enough, big data analytics help companies make sense of the data and thereby deliver intelligence.

The classic definition of big data is that it presents in three “Vs”: large volumes, variety and high velocity. So the concept of big data is not new, even if this set of parameters has evolved since the early 2000s when it first took root. The introduction of IoT, however, has increased the number of sources which contribute to the data dialog. Whether or not these data points contribute to a symphony or merely create a cacophony depends on data analytics.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

“Big data provides context to a world in which IoT is working,” says James Jeude, Vice-President and Practice Leader of Cognizant’s Digital AI & Analytics Strategic Initiatives Group. “As an industry we have solved the volume problem [of big data], where today’s big data really matters is that it delivers on variety. It is variety that really matters [for intelligence] at this point.” 

Take the example of a refrigerator in a grocery store. To know when the compressor is about to break, the store can measure both the refrigerator temperature and the current input. The higher the electric current flowing to a refrigerator to maintain the same temperature, the more likely it is to fail. These two indicators might be the canary in the coal mine but IoT and Big Data allow a variety of additional efficiencies. Measuring the gases emitted by the food can be a window into when the food might spoil; measuring outside temperature and humidity might tell us acceleration patterns of food spoilage. The number of times a grocery store refrigerator is opened and closed is also a useful parameter to measure.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

The promise of big data analytics is that it allows a whole new range of IoT variables to participate in the conversation. 

IoT facilitates information gathering from more sources so everyone who contributes to a situation gets a hand at the table. Data analytics leverages this IoT data to deliver a more comprehensive picture of the situation. The net analysis is more nuanced and more valuable. Indeed IoT + Big Data Analytics = Valuable Intelligence. 

With IoT and big data analytics, you no longer  have to picture an elephant by merely touching its ears and tail. You can now access more touchpoints to visualize the whole animal.

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1 download. 14 Case Studies.

Download this report to learn how 14 companies across industries are demonstrating the reality of IoT-at-scale and generating actionable intelligence.

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