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Don’t forget the human factor in IoT’s service intelligence equation

Overlooking the human element of IoT can leave money on the table.

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Automation is a powerful lure for businesses investing in the Internet of Things (IoT). Smart devices feeding real-time data to algorithms can find hidden problems, identify efficiencies and circumvent human error. In short, smart automation can save companies a lot of money. But the IoT conversation shouldn’t focus exclusively on automation. 

Overlooking the human element of IoT can leave money on the table. Empowering employees with effective access to intelligence can improve customer service and differentiate a company from its competitors.

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

For Phanibhushan Sistu, good service intelligence relies on a robust data infrastructure for employees. Sistu is AVP of engineering and IOT solutions at Cognizant. He says that while many companies have already invested in connected devices, “not all of that information is available to the person who is going to a location from the service point of view.”

To illustrate his point, Sistu uses a telecommunications provider as an example of what’s possible. 

This type of business often relies on a fleet of full-time and contracted service technicians who prioritize a daily list of house calls. A pre-IoT business may provide these technicians with a description of a problem as called-in by the customer, but little else. Diagnosis doesn’t really start until the truck pulls up, and anyone who’s heard “I don’t have the right part for this” knows the flaws in this system.

“Their front-end employees cover multiple locations, which comes with a cost,” Sistu says. 

“Somebody goes in to fix a problem or install something, then doesn’t learn until they arrive that they don’t have the right equipment or that a problem was misdiagnosed.”  

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A modern telco with properly managed data can track how customers are using their hardware, index common complaints and analyze how its different hardware products perform. Put into the hands of front-line staff, this becomes a competitive advantage.

A connected employee can “dynamically manage situations as they change,” Sistu says. Smart data can tell service techs whether other customers in an area have had similar complaints or how a customer’s usage might have affected the product. Even seeing other technicians nearby through real-time tracking can make getting parts easier. By bringing the right diagnostic tools and replacement components, service calls can be resolved faster and more effectively.

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And accessible data infrastructure can do more than save costs, Susti says. It can also be a revenue generator. 

“Maybe as an enterprise, I have different levels of services I provide — diamond, gold, platinum or whatever. On the fly, I may decide to redirect my technician to attend to a diamond customer because my [service-level agreement] for them is more rigorous. It’s about dynamic planning, dynamic optimization.”

Of course, Sistu says these principles extend to a wide range of business sectors that have front-line staff dealing with customers post-purchase — from manufacturing to medicine. 

Now that IoT has extended customer success management further beyond the date of purchase, companies must ask how customers are experiencing a product’s “life service.” With proper data infrastructure, long-term service agreements can shift from cost centres to selling points.

“I believe this kind of experience always commands some premium,” Sistu says. “People probably don’t mind paying a few extra pennies or dollars for a better experience.”

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Manufacturing

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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What you need to know if you’re attending AVEVA World Summit

AVEVA World Summit is where the most innovative industrial executives from around the world gather for an exclusive opportunity to network with 400 global digital leaders across diverse sectors. 

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AVEVA World Summit is where the most innovative industrial executives from around the world gather for an exclusive opportunity to network with 400 global digital leaders across diverse sectors. 

The summit is an opportunity to discover how these leaders and Cognizant — a Platinum Sponsor — are transforming the entire asset and operational lifecycle.

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.

Aveva World Summit takes place September 16-18, at Marina Bay Sands, Singapore.  

One session to highlight? “Digital Transformation in Hybrid Industries,” featuring Cognizant’s VP of IoT and Engineering services, Frank Antonysamy. This session will examine the benefits of digital transformation, and addressing challenges through a sustainable platform that can adopt best practices, continuous improvements, and grow with the business.

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IoT + Digital Twin = Operations Intelligence: An Equation that Delivers Useful What-If Scenarios

In the equation IoT + X = Operations Intelligence, what role does a digital twin play as the X factor?

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Measure twice, cut once. This basic premise, that it would be advantageous to visualize outcomes before you act, forms the backbone of an entity known as the digital twin. This approach is particularly useful in today’s high-stakes industries such as manufacturing, construction, mining and more. Measuring twice and cutting once allows enterprises to tweak every aspect of the production process to maximize revenue.

The digital twin enables companies to envision what-if scenarios for various operating conditions in the virtual world before it affects processes in the real world. The more fully the digital twin avatar is fleshed out, the more accurate its predictions. This means enterprises need IoT (the Internet of Things) to color in the picture completely. IoT helps the digital twin realize its full potential to deliver operational intelligence. 

The promise of the digital twin

A digital twin is a replica, described by data, of physical assets, processes and systems that helps organizations understand, dissect, predict and optimize their performance. It can combine design and engineering details with operating data and analytics about anything from a single part to multiple interconnected systems to an entire manufacturing plant.

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

If you need to describe a physical asset (say a motor running on a shop floor) through data, you need that motor to both generate data and make that data easily accessible. This is where IoT falls into the picture: it “sensorizes” a variety of physical machines and brings them into the digital twin conversations, says Vivek Diwanji, senior director of technology at Cognizant. IoT-enabled embedded devices can then transmit data about their health under a variety of operating conditions and channelize that information through an Internet connection from shop floor to enterprise resource planning (ERP) software. 

Layered possibilities

A digital twin is about different perspectives – essentially comprised of many layers that are progressively overlaid with more detailed data input. The level of detail depends on the insights you’re looking to derive. If you need to know when a vehicle tire is going to wear-out, all you need to measure is temperature and air pressure. Long-term durability intelligence on the other hand, also needs to measure ambient conditions, daily operation numbers, road type and more.

[Download]: A New Approach to PLM

A lack of common IoT standards across industries makes the data difficult to gather, but that conversation might change with the advent of 5G, Diwanji predicts. For now, digital twin is a powerful tool that enables companies to deliver field services, conduct smart operations and evaluate product development outcomes before investing millions into the pipeline.

By 2020, 30% of Global 2000 companies will be using data from digital twins to improve product innovation success rates and organizational productivity, according to IDC. They can realize gains of close to 25%. And IoT is a key player in that equation to deliver such operational intelligence.

“Digital twin is an application that leverages IoT. The very definition of a digital twin necessitates that a digital model is running in conjunction with a physical model. That connection, between the physical and the digital, happens through IoT,” Diwanji says. “IoT is really the backbone of the digital twin.”

[Download]: Real Estate Manager Goes Digital

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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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