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Dow Chemical envisions the future of manufacturing

Dow Chemical, one of the world’s biggest chemical producers, is taking a leadership role in the digital transformation of its industry.

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Despite its foundation in the pure science of chemistry, the chemicals manufacturing industry doesn’t exactly conjure high-tech images when people think of what goes into making chemical products.

And yet, the chemicals industry is poised to be the poster child for the very high-tech Industry 4.0 revolution, which takes existing manufacturing processes, and infuses them with digital DNA, thanks to the IIoT.

Dow Chemical, one of the world’s biggest chemical producers, is already taking a leadership role in the digital transformation of its industry. “We have significant amounts of data from our instrumentation and process sensors to use with the new analytics and deep-learning technologies,” Billy Bardin, Dow’s Global Operations Technology Center director, told Chemical Engineering.

Related: Stepping into Digital with IOT – 14 Cases

Dow, like many other chemical companies, has been using sensor tech for decades, but the IIoT represents an entirely new model for how data from these sensors becomes part of the company’s end-to-end process. Not only does the IIoT offer optimization of the production process, it can improve efficiency, while reducing both energy consumption, and operational cost.

Safety — a key consideration given the stakes — can also be improved. Many chemical producers, including Dow, are still manufacturing at facilities that date back 50 years or more. Modernizing these plants is a constant effort, but with the advent of the IIoT, gains in situational awareness accompany the gains in efficiency and productivity.

[Download] Stepping into Digital with IOT – 14 Cases

Recently, the company enlisted the help of Schneider Electric to digitize its Carrollton, KY processing plant, giving teams better data visibility for pumps, valves and motors. The roadmap also includes the addition of Schneider’s HART devices to enable operations and maintenance teams to remotely view equipment health or thresholds for valves in order to manage them better, according to Automation World. The improvements in preventative maintenance this data enables are key to better employee safety, as well as protecting the environment.

Better efficiency, cost savings, and greater safety? Strong arguments for better chemistry through digitization.

[Download] Stepping into Digital with IOT – 14 Cases

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Manufacturing

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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Manufacturing

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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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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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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