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IIoT deployments are moving fast up the maturity chain, from pilot projects to large scale implementations that are delivering real value. Below, we take a look at some examples and figures to map out where the IIoT market is currently, and what will shape the future of the industry.
‘Think big, start small, scale fast’ has been a tagline for digital startups for many years, but the story of the maturing Industrial Internet of Things (IIoT) market brings new meaning and perspective to the phrase. Initially tipped to be a runaway success, with a market worth $933.62 billion by 2025, the IIoT market has been off to a slower start than initially forecast.
Overcoming the Challenges
Complex integration procedures, with multiple new sensors and data streams generating false positives and requiring recalibration, technology standards competition, a fragmented market and lengthy cost/implementation periods have been key barriers in preventing IIoT adoption.
The journey from concept to full maturity has been plotted in many different ways over the years, including Gartner’s much-quoted ‘hype cycle’. Gartner’s Hype Cycle for Emerging Technologies in 2018 placed the IoT industry at the “peak of inflated expectations”, ready to crash down into the “trough of disillusionment” before beginning the plateau into maturity. The big question unanswered by Gartner was when the plateau would be reached.
The Beginning of Proof Of Value
Deloitte recently went on record to state that they believe IIoT is finally reaching maturity, and that their clients are now looking beyond proof-of-concept towards proof of value. Robert Schmid, chief IoT technologist at Deloitte provided an example of a plastic manufacturer client that planned to build a new production line to satisfy the demand for a specific product. By connecting a variety of processes with IIoT devices and overlaying analytics, Deloitte was able to help increase the manufacturer’s throughput by almost 10% and saved £20 million by not building a new manufacturing line.
Diverse Use Cases Emerge
Manufacturing turns out to be the tip of the iceberg in terms of active IIoT applications. Several other industries are gaining significant traction including oil and gas, mining, utilities and agriculture.
McKinsey reported that an anonymous top ten global energy company used IoT applications and devices as part of a broader program of process and technology upgrades. The program resulted in a 33% reduction in unit production costs over five years. According to the analyst firm, the enterprise saved more than $9 billion in capital costs. In addition, they deployed IoT analytic tools to assess drilling data, which resulted in increased yields from existing mature oil wells.
Utilities See the Value
Another early adopter, GE, has been developing renewable energy generation IIoT solutions. GE attaches sensors to wind turbine blades to finesse blade angles in order to maximize efficiency in changing winds. These sensors collect and feed the overall wind farm data into efficiency analysis tools. These tools help us to understand the economic loss from downtime for each turbine and how it could be used to drive maintenance schedules, enabling engineer time to be used more effectively.
An IIoT pilot to track water leaks launched recently in Kent, UK. South East Water partnered with Vodafone’s low power NB-IoT network to deploy digital water meters, sensors and acoustic loggers on underground mains water pipes in Kent. This will enable the system to ‘listen’ for escaping water within the network, determine when leaks have occurred and pinpoint a precise location. It’s worth noting that this pilot may not be entirely unprompted, as the UK utility watchdog Ofwat has demanded all water companies reduce water leakage by 15% by 2025.
Healthcare Data Analysis Delivers
Philips has also been actively piloting IoT in its healthcare devices for many years. The company has migrated from proof-of-concept towards proof of value as a result of analyzing IIoT data garnered from the firm’s ultrasound and CT scan machines. The data harvested by Philips showed that healthcare providers waste significant amounts of time recalibrating CT machines between the head and abdominal scans. This information was used to create scheduling software that ensures the number of recalibrations is minimized.
IIoT Maturity Beckons
In short, IIoT is maturing quickly, and while enterprise scale is clearly a factor in forging successful applications and value chains, these early successes should serve to anchor standards and blaze a trail for smaller enterprises and second-generation adopters alike. Another key factor will prove to be the network operators themselves, as they move from beta testing next-generation networks and into the active promotion of commercial packages based on them.
Think big, start small, scale fast. As technology continues to mature, IIoT is likely to prove very fast indeed.
Source: IoT For All
By AI Trends Staff
Many AI initiatives are loosely defined, lack proper technology and data infrastructure, and are often failing to meet expectations, according to a new report from Plutoshift on implementation of AI by manufacturing companies.
A supplier of an AI solution for performance monitoring, Plutoshift surveyed 250 manufacturing professionals with visibility into their company’s AI programs. Overall, the survey found that manufacturing companies are gaining experience while taking a measured approach to implementing AI.
Among the specific findings:
- 61% said their company has good intentions but needs to reevaluate the way it implements AI projects;
- 17% said their company was in full implementation stage of their AI project;
- 84% are not yet able to automatically and continuously act on their data intelligence, while some are gathering data;
- 72% said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of AI
- Only 57% said their company implemented AI projects with a clear goal, while almost 20% implemented AI initiatives due to industry or peer pressure to utilize the technology.
- 17% said their company implemented AI projects because their company felt pressure to utilize this technology from the industry
- 60% said their company struggled to come to a consensus on a focused, practical strategy for implementing AI
Among its conclusions, the report stated, “To truly utilize data, manufacturing companies need a data infrastructure and platform that is designed around performance monitoring for the physical world. That means gaining the ability to take data from any point in the workflow, analyze that data, and provide reliable predictions at any point. Right now, few companies report these full capabilities and would rethink their direction.”
Plutoshift CEO and Founder Prateek Joshi stated in a press release about the survey, “Companies are forging ahead with the adoption of AI at an enterprise level. Despite the progress, the reality that’s often underreported is that AI initiatives are loosely defined. Companies in the middle of this transformation usually lack the proper technology and data infrastructure. In the end, these implementations can fail to meet expectations. The insights in this report show us that companies would strongly benefit by taking a more measured and grounded approach toward implementing AI.”
Biggest Players Investing and Gaining Valuable Experience with AI
Another way to gauge how AI is or will penetrate manufacturing is to examine what the biggest players are doing. Siemens, GE, FANUC, and KUKA are all making significant investments in machine learning-powered approaches to improve manufacturing, described in a recent account in emerj. They are using AI to bring down labor costs, reduce product defects, shorten unplanned downtimes and increase production speed.
These giants are using the tools they are developing in their own manufacturing processes, making them the developer, test case, and first customers for many advances.
The German conglomerate, Siemens, has been using neural networks to monitor its steel plants and improve efficiencies for decades. The company claims to have invested around $10 billion in US software companies (via acquisitions) over the past decade. In March of 2016, Siemens launched Mindsphere, described as an “IoT operating system,” and a competitor to GE’s Predix product. Siemens describes Mindsphere as a smart cloud for industry, being able to monitor machine fleets throughout the world. In 2016, it integrated IBM Watson Analytics into its tools service.
Siemens describes an AI success story with its effort to improve gas turbine emissions. “After experts had done their best to optimize the turbine’s nitrous oxide emissions,” stated Dr. Norbert Gaus, Head of Research in Digitalization and Automation at Siemens Corporate Technology, “our AI system was able to reduce emissions by an additional ten to fifteen percent.”
Siemens envisions incorporating its AI expertise within Click2Make, its production-as-a-service technology. It was described in an account in Fast Company in 2017 as a “self-configuring factory.” Siemens envisions a market where companies submit designs and factories with the facilities and time and handle the order would start an automatic bidding process. The manufacturer would be able to respond with the factory configuring itself. That’s the idea.
GE’s Manufacturing Software Strategy a Work in Progress
GE, which has had fits and starts with its software strategy, has over 500 factories worldwide that it is transforming into smart facilities. GE launched its Brilliant Manufacturing Suite for customers in 2015. The first “Brilliant Factory” was built that year in Pune, India, with a $200 million investment. GE claims it improved equipment effectiveness by 18%.
Last year, GE sold off most of the assets of its Predix unit. An account in Medium described reasons for the retrenchment, including a decision to build a Predix cloud data center, and not recognize the competition from Amazon, Microsoft, and Google. Another criticism was that Predix was not known to be developer-friendly. Successful platforms need developer content, and developers need support from a community.
GE’s software strategy in manufacturing is a work in progress.
FANUC Has Invested in AI
FANUC, the Japanese company producing industrial robotics, has made substantial investments in AI. In 2015, Fanuc acquired a stake in the AI startup Preferred Networks, to integrate deep learning into its robots.
In early 2016, FANUC announced a collaboration with Cisco and Rockwell Automation to develop and deploy FIELD (FANUC Intelligent Edge Link and Drive). This was described as an industrial IoT platform for manufacturing. Just a few months later, with NVIDIA to use their AI chips for their “the factories of the future.”partnered with NVIDIA to use their AI chips for their “the factories of the future.”
FANUC is using deep reinforcement learning to help some of its industrial robots . They perform the same task over and over again, learning each time until they achieve sufficient accuracy. By partnering with NVIDIA, the goal is for multiple robots can learn together. The idea is that what could take one robot eight hours to learn, eight robots can learn in one hour. Fast learning means less downtime and the ability to handle more varied products at the same factory.train themselves. They perform the same task over and over again, learning each time until they achieve sufficient accuracy. By partnering with NVIDIA, the goal is for multiple robots can learn together. The idea is that what could take one robot eight hours to learn, eight robots can learn in one hour. Fast learning means less downtime and the ability to handle more varied products at the same factory.
KUKA Working on Human-Robot Collaboration
KUKA, the Chinese-owned, Germany-based manufacturer of industrial robots, is investing in human-robot collaboration. The company has developed a robot that can work beside a human safely, owing to its intelligent controls and high-performance sensors. KUKA uses them; BMW is also a customer.
Robots that can work safely with humans will be able to be deployed in factories for new tasks, improving efficiency and flexibility.
Source: AI Trends