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Posts tagged ‘Internet of Things’

Gartner’s Top 10 Predictions For IT In 2018 And Beyond

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

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73% of Executives Are Researching & Launching IoT Projects In 2017

  • Manufacturing-based IoT connections grew 84% between 2016 and 2017, followed by energy & utilities (41%).
  • 73% of executives are either researching or currently launching IoT projects.
  • The IoT platform market is expected to grow 35% per year to $1.16B by 2020.
  • B2B uses can generate nearly 70% of the potential value enabled by IoT.

These and many other fascinating findings are from Verizon’s State of the Market: Internet of Things 2017, Making way for the enterprise (16 pp., PDF, free, opt-in). The Verizon study found that the Internet of Things (IoT) gained significant momentum in 2016, with 2017 IoT investments accelerating. The majority of investments today are in IoT projects that are still in the concept or pilot phase, concentrating on tracking data and sending alerts. While easier to initiate and manage, the majority of pilots aren’t providing the depth of analytics data and insights IoT has the potential to deliver.

Key takeaways from the study include the following:

  • Manufacturing-based IoT connections grew 84% between 2016 and 2017, followed by energy & utilities (41%). Transportation and distribution (40%), smart cities and communities (19%) and healthcare and pharma (11%) are the remaining three industries tracked in the study who had positive growth in the number of IoT connections. The following graphic compares year-over-year growth by industry for the 2016 to 2017 timeframe.

  • Manufacturing is predicted to lead IoT spending in 2017 with $183B invested this year. Verizon’s study predicts that transportation and utilities will have the second and third-largest capital expenses in IoT this year. Insurance, consumer and cross-industry IoT investments including connected vehicles and smart buildings will see the fastest overall growth in 2017.

  • The IoT platform market is expected to grow 35% per year to $1.16B by 2020. From well-established enterprise service providers to startups, the platform market is becoming one of the most competitive within the global IoT ecosystem. The design objective of all IoT platforms is to provide a single environment for enabling API, Web Services and custom integrations that securely support enterprise-wide applications. Please see the post What Makes An Internet Of Things (IoT) Platform Enterprise-Ready? for an overview of the Boston Consulting Group’s recent IoT study, Who Will Win The IoT Platform Wars?
  • Improving the customer experience and excel at customer service by gaining greater insights using IoT leaders enterprises’ investment priorities. 33% of enterprises interviewed prioritize using IoT technologies and the insights it’s capable of providing to excel at customer service. 26% intend to use IoT technologies to improve asset management and increase Return on Assets (ROA) and Return on Invested Capital (ROIC). Consistent with how dominant manufacturing’s investment plans are for IoT this year, production and delivery capabilities are the top deployment priority for 25% of all businesses interviewed.
  • IoT has the potential to revolutionize pharmaceutical supply chains by drastically reducing drug counterfeiting globally. It’s estimated that counterfeit drugs cost the industry between $75B to $200B annually. The human costs of treating those who have been sold counterfeit drugs back to health are incalculable. IoT platforms and systems have the potential to drastically reduce the costs of counterfeiting, both on a personal impact and market standpoint. Drug manufacturers operating in the United States have until November 2017 to mark packages with a product identifier, serial number, lot number and expiration date, plus electronically store and transfer all transaction histories, including shipment information, across their distribution supply chains. Pharmaceutical manufacturers have a high level of urgency to make this happen and stay in compliance with the US Drug Supply Chain Security Act. IoT solutions are flourishing in this industry as a result.

73% Are Using Internet Of Things Data To Improve Their Business

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  • According to the Cisco Visual Networking Index, M2M connections will represent 46% of connected devices by 2020.
  • 95% of execs surveyed plan to launch an IoT business within three years.

These and many other insights are from the recently published Cisco Internet of Things (IoT) study, The Journey to IoT Value: Challenges, Breakthroughs, and Best Practices published on SlideShare last month. The study is based on a survey of 1,845 IT and business decision-makers in the United States, UK, and India. Industries included in the analysis include manufacturing, local government, retail/hospitality/sports, energy (utilities/oil & gas/mining), transportation, and health care. All respondents worked for organizations that are implementing or have completed IoT initiatives. 56% of all respondents are from enterprises.

Key takeaways from the study include the following:

  • 73% Are Using Internet Of Things Data To Improve Their Business. The data and insights gained from IoT are most often used for improving product quality or performance (47%), improving decision-making (46%) and lowering operational costs (45%). Improving or creating new customer relationships (44%) and reducing maintenance or downtime (42%) are also strategic areas where IoT is making a contribution today according to the Cisco study.

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  • IT executives often see IoT initiatives as more successful (35%) than their line-of-business counterparts (15%). With IT concentrating on technologies and line-of-business users focused on strategy and business cases, the potential exists for differences of opinion regarding IoT initiatives’ value. The following graphic provides an overview of how stark these differences are.

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  • Engaging with the IoT partner ecosystem in every phase of a project or initiative improves the probability of success. The most valuable phases to engage with ecosystem partners include strategic planning (60%), implementation and deployment (58%) and technical consulting or support (58%). The following graphic provides an overview of most and less successful organizations by their level of involvement in the IoT partner ecosystem.

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  • Only 26% of all companies are successful with their IoT initiatives. The three best practices that lead to a successful IoT implementations include collaboration between IT and business, the availability of internal and external partnerships to gain IoT expertise; and a strong technology-focused culture.
  • 60% of companies believe IoT projects look good on paper but prove more complex that expected. This finding underscores how critical it is for IT and line-of-business executives to have the same goals and objectives going into an IoT project. Being selective about which integration, technology, and professional services partners are chosen needs to be a shared priority between both IT and line-of-business executives.

2017 Is The Year Integration Enables Industry 4.0 Growth

  • industry-40-landscape35% of companies adopting Industry 4.0 predict revenue gains over 20% in the next five years.
  • Data analytics and digital trust are the foundations of Industry 4.0.
  • Cost-sensitive industries including semiconductors, electronics, and oil and gas are the most focused on adopting Industry 4.0, with 80% of companies in these industries saying it is one of their top priorities.

The recent article by Boston Consulting Group (BCG), Sprinting To Value In Industry 4.0, provides insights into how real-time integration between enterprise systems is an essential catalyst for Industry 4.0 growth. Industry 4.0 focuses on the end-to-end digitization of all physical assets and integration into digital ecosystems with value chain partners encompassing a broad spectrum of technologies. BCG surveyed 380 US-based manufacturing executives and managers at companies representing a wide range of sizes in various industries to complete the study.

Industry 4.0 Is  At An Inflection Point Today 

Having attained initial results from Industry 4.0 initiatives, many manufacturers are moving forward with the advanced analytics and Big Data-related projects that are based on real-time integration between CRM, ERP, 3rd party and legacy systems. A recent Price Waterhouse Coopers (PwC) study of Industry 4.0 adoption, Industry 4.0: Building The Digital Enterprise (PDF, no opt-in, 36 pp.) found that 72% of manufacturing enterprises predict their use of data analytics will substantially improve customer relationships and customer intelligence along the product life cycle. Real-time integration enables manufacturers to more effectively serve their customers, communicate with suppliers, and manage distribution channels. Of the many innovative start-ups taking on the complex challenges of integrating cloud and on-premise systems to streamline revenue-generating business processes, enosiX shows potential to bridge legacy ERP and cloud-based CRM systems quickly and deliver results.

There are many more potential benefits to adopting Industry 4.0 for those enterprises who choose to create and continually strengthen real-time integration links across the global operations. Recent research completed by Boston Consulting Group and PwC highlight several of them below:

  • Manufacturers expect to gain the greatest value from Industry 4.0 by reducing manufacturing costs (47%), improving product quality (43%) and attaining operations agility (42%). 89% of all manufacturers see an opportunity to use Industry 4.0 to improve manufacturing productivity. Reducing supply chain costs (37%), enabling product innovation (33%) and attaining faster time-to-market (31%) are the next level of benefits manufacturers expect to attain. The following graphic provides an analysis of where manufacturers see Industry 4.0 having the greatest impact on their organizations.

 industry-40-image-1

  • Manufacturers are gaining the greatest value from Industry 4.0 by creating pilot projects that create flexible, agile real-time platforms supporting new business models with real-time integration. Industry 4.0’s focus on enabling end-to-end digitization of all physical assets and integration into digital ecosystems relies on real-time integration to succeed. For manufacturers in cost-sensitive industries, the urgency of translating the vision of digital transformation into results is key to their future growth. The more competitively intense an industry, the more essential real-time integration

industry-40-image-2

  • Investing in greater digitization and support for enterprise-wide integration is predicted to increase 118% by 2020 in support of Industry 4.0. 33% of manufacturers surveyed report they have a high level of digitization today, projected to increase to 72% by 2020. The leading areas of these investments include vertical value chain integration (72%), product development and engineering (71%), and customer access including sales channels and marketing (68%).
  • New product development and optimizing existing products and services are the greatest areas of growth potential for analytics and Big Data using Industry 4.0 technologies and integration strategies through 2020. Industry 4.0 is revolutionizing the use of analytics and manufacturing intelligence, setting the foundation for greater optimization of overall business and control, better manufacturing, and operations planning, greater optimization of logistics and more efficient maintenance of production assets and machinery. By better orchestrating these strategic areas, manufacturers are going to be able to attain levels of accuracy and responsiveness to customers not achievable before.
  • Globally, manufacturing enterprises expect to gain an additional 2.9% in digital revenues per year through 2020, with digitizing their existing product portfolios (47%) leading all other strategies, further underscoring the need for real-time integration. Introducing an entirely new digital product portfolio is the second most common strategy (44%) followed by creating and offering new digital services to external customers (42%). Just over a third (38%) plan to create and sell big data analytics services to external customers.

 

Integration Will Accelerate Internet Of Things, Industrial Analytics Growth In 2017 

  • internet of thingsEnabling real-time integration across on-premise and cloud platforms often involves integrating SAP, Salesforce, third-party and legacy systems. 2017 will be a break-out year for real-time integration between SAP, Salesforce, and third party systems in support of Internet of Things and Industrial Analytics.
  • McKinsey Global Institute predicts that the Internet of Things (IoT) will generate up to $11T in value to the global economy by 2025.
  • Predictive and prescriptive maintenance of machines (79%), customer/marketing related analytics (77%) and analysis of product usage in the field (76%) are the top three applications of Industrial Analytics in the next 1 to 3 years.

Real-Time Integration Is the Cornerstone Of Industrial Analytics

Industrial Analytics (IA) describes the collection, analysis and usage of data generated in industrial operations and throughout the entire product lifecycle, applicable to any company that is manufacturing and selling physical products. It involves traditional methods of data capture and statistical modeling. Enabling legacy, third-party and Salesforce, SAP integration is one of the most foundational technologies that Industrial Analytics relies on today and will in the future. Real-time integration is essential for enabling connectivity between Internet of Things (IoT) devices, in addition to enabling improved methods for analyzing and interpreting data. One of the most innovative companies in this area is enosiX, a leading global provider of Salesforce and SAP integration applications and solutions.  They’re an interesting startup to watch and have successfully deployed their integration solutions at Bunn, Techtronic Industries, YETI Coolers and other leading companies globally.

A study has recently been published that highlights just how foundational integration will be to Industrial Analytics and IoT. You can download the Industrial Analytics Report 2016/17 report here (58 pp., PDF, free, opt-in). This study was initiated and governed by the Digital Analytics Association e.V. Germany (DAAG), which runs a professional working group on the topic of Industrial Analytics. Research firm IoT Analytics GmbH was selected to conduct the study. Interviews with 151 analytics professionals and decision-makers in industrial companies were completed as part of the study. Hewlett-Packard Enterprise, data science service companies Comma Soft and Kiana Systems sponsored the research. All research and analysis related steps required for the study including interviewing respondents, data gathering, data analysis and interpretation, were conducted by IoT Analytics GmbH. Please see page 52 of the study for the methodology.

Key Takeaways:

  • With real-time integration, organizations will be able to Increase revenue (33.1%), increase customer satisfaction (22.1%) and increase product quality (11%) using Industrial Analytics. The majority of industrial organizations see Industrial Analytics as a catalyst for future revenue growth, not primarily as a means of cost reduction. Upgrading existing products, changing the business model of existing products, and creating new business models are three typical approaches companies are taking to generate revenue from Industrial Analytics. Integration is the fuel that will drive Industrial Analytics in 2017 and beyond.

Internet of Things

  • For many manufacturers, the more pervasive their real-time SAP integration is, the more effective their IoT and Industrial Analytics strategies will be. Manufacturers adopting this approach to integration and enabling Industrial Analytics through their operations will be able to attain predictive and prescriptive maintenance of their product machines (79%). This area of preventative maintenance is the most important application of Industrial Analytics in the next 1 – 3 years. Customer/marketing-related analytics (77%) and analysis of product usage in the field (76%) are the second- and third-most important. The following graphic provides an overview of the 13 most important applications of Industrial Analytics.

Internet of Things

  • 68% of decision-makers have a company-wide data analytics strategy, 46% have a dedicated organizational unit and only 30% have completed actual projects, further underscoring the enabling role of integration in their analytics and IoT strategies. The study found that out of the remaining 70% of industrial organizations, the majority of firms have ongoing projects in the prototyping phase.

Internet of things

  • Business Intelligence (BI) tools, Predictive Analytics tools and Advanced Analytics Platforms will be pivotal to enabling industrial data analysis in the next five years. Business Intelligence Tools such as SAP Business Objects will increase in importance to industrial manufacturing leaders from 39% to 77% in the next five years. Predictive Analytics tools such as HPE Haven Predictive Analytics will increase from 32% to 69%. The role of spreadsheets used for industrial data analytics is expected to decline (i.e., 27% think it is important in 5 years vs. 54% today).

Internet of Things

  • The Industrial Analytics technology stack is designed to scale based on the integration of legacy systems, industrial automation apps and systems, MES and SCADA systems integration combined with sensor-based data. IoT Analytics GmbH defines the technology stack based on four components inclouding data sources, necessary infrastructure, analytics tools, and applications. The following graphic illustrates the technology stack and underscores how essential integration is to the vision of Industrial Analytics being realized.

Internet of Things

  • Industrial Internet of Things (IIoT) and Industry 4.0 will rely on real-time integration to enable an era of shop-floor smart sensors that can make autonomous decisions and trade-offs regarding manufacturing execution. IoT Analytics GmbH predicts this will lead to smart processes and smart products that communicate within production environments and learn from their decisions, improving performance over time. The study suggests that Manufacturing Execution System (MES) agents will be vertically integrated into higher level enterprise planning and product change management processes so that these organizations can synchronously orchestrate the flow of data, rather than go through each layer individually.

Internet of Things

Internet Of Things Will Replace Mobile Phones As Most Connected Device In 2018

  • abstract, background, banner, telecoms, communication, innovation, concept, design, icon, internet of things, internet, computer, innovate, innovative, ball, circle, sphere, circular, social, data, access, wireless, connection, pattern, global, world map, networking, hexagon, circuit, electric, electronics, microchip, power, gradient, blue, vector, illustration, logo,Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021.
  • By 2021 there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions.
  • Worldwide smartphone subscriptions will grow at a 10.6% CAGR from 2015 to 2012 with Asia/Pacific (APAC) gaining 1.7B new subscribers alone.

These and other insights are from the 2016 Ericcson Mobility Report (PDF, no opt-in). Ericcson has provided a summary of the findings and a series of interactive graphics here. Ericcson created the subscription and traffic forecast baseline this analysis is based on using historical data from a variety of internal and external sources. Ericcson also validated trending analysis through the use of their planning models. Future development is estimated based on macroeconomic trends, user trends (researched by Ericsson ConsumerLab), market maturity, technology development expectations and documents such as industry analyst reports, on a national or regional level, together with internal assumptions and analysis.In addition, Ericsson regularly performs traffic measurements in over 100 live networks in all major regions of the world. For additional details on the methodology, please see page 30 of the study.

Key takeaways from the 2016 Ericcson Mobility Report include the following:

  • Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021. Ericcson predicts there will be a total of approximately 28B connected devices worldwide by 2021, with nearly 16B related to IoT. The following graphic compares cellular IoT, non-cellular IoT, PC/laptop/tablet, mobile phones, and fixed phones connected devices growth from 2015 to 2021.

Internet of Things Forecast

  • 400 million IoT devices with cellular subscriptions were active at the end of 2015, and Cellular IoT is expected to have the highest growth among the different categories of connected devices, reaching 1.5B connections in 2021. Ericcson cites the growth factors of 3GPP standardization of cellular IoT technologies and cellular connections benefitting from enhancements in provisioning, device management, service enablement and security. The forecast for IoT connected devices: cellular and non-cellular (billions) is shown

IoT Connected Devices

  • Global mobile broadband subscriptions will reach 7.7B by 2021, accounting for 85% of all subscriptions. Ericcson is predicting there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions by 2021 as well. The following graphic compares mobile subscriptions, mobile broadband, mobile subscribers, fixed broadband subscriptions, and mobile CPs, tablets and mobile routers’ subscription growth.

mobile subscription growth

  • Worldwide smartphone subscriptions will grow at a 10.6% compound annual growth rate (CAGR) from 2015 to 2012. Ericcson predicts that the Asia/Pacific (APAC) region will gain 1.7B new subscribers. The Middle East and Africa will have smartphone subscription rates will increase more than 200% between 2015–2021. The following graphic compares growth by global region.

smartphone subscriptions

  • Mobile subscriptions are growing around 3% year-over-year globally and reached 7.4B in Q1 2016. India is the fastest growing market regarding net additions during the quarter (+21 million), followed by Myanmar (+5 million), Indonesia, (+5 million), the US (+3 million) and Pakistan (+3 million). The following graphic compares mobile subscription growth by global region for Q1, 2016.

Mobile subscriptions Q1

  • 90% of subscriptions in Western Europe and 95% in North America will be for LTE/5G by 2021. The Middle East and Africa will see a dramatic shift from 2G to a market where almost 80% of subscriptions will be for 3G/4G. The following graphic compares mobile subscriptions by region and technology.

Mobile technology by region

  • Mobile video traffic is forecast to grow by around 55% annually through 2021, accounting for nearly 67% of all mobile data traffic. Social networking traffic is predicted to attain a 41% CAGR from 2015 to 2021. The following graphic compared the growth of mobile traffic by application category and projected mobile traffic by application category per month.

mobile video traffic

  • Ericcson also provided mobile subscription, traffic per device, mobile traffic growth forecast, and monthly data traffic per smartphone. The summary table is shown below:

summary table

10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The ten ways machine learning is revolutionizing manufacturing include the following:

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014

Sources:

Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.

Roundup Of Internet of Things Forecasts And Market Estimates, 2015

  • Internet of things forecastCisco predicts the global IoT market will be $14.4T by 2022.
  • IC Insights predicts revenue from Industrial Internet IoT spending will increase from $6.4B in 2012 to $12.4B in 2015.
  • IoT in manufacturing market size is estimated to grow from $4.11B in 2015 to $13.49B by 2020, attaining a CAGR of 26.9%.

With the potential to streamline and deliver greater time and cost savings to a broad spectrum of enterprise tasks, opportunities for Internet of Things (IoT) adoption are proliferating. It’s encouraging to see so many industry-leading manufacturers, service providers, software and systems developers getting down to the hard work of making the vision IoT investments pay off.

Forecasting methodologies shifted in 2015 from the purely theoretical to being more anchored in early adoption performance gains. Gil Press wrote an excellent post on this topic Internet of Things By The Numbers: Market Estimates And Forecasts which continues to be a useful reference for market data and insights, as does his recent post, Internet Of Things (IoT) News Roundup: Onwards And Upwards To 30 Billion Connected Things.

Key takeaways from the collection of IoT forecasts and market estimates include the following:

ABI Research market estimates; Internet of Things market forecast

1`4 4 Trillion IoT Internet of Things market forecast

green graphic IoT Internet of Things market forecast

software BI; Internet of Things market forecast

  • IC Insights predicts revenue from Industrial Internet of Things spending will increase from $6.4B in 2012 to $12.4B in 2015, attaining a 17.98% CAGR. IC Insights predicts the Industrial Internet will lead all five categories of its forecast, with Connected Cities being the second-most lucrative, attaining a 13.16% CAGR in the forecast period. The research firm segments the industry into five IoT market categories: connected homes, connected vehicles, wearable systems, industrial Internet, and connected cities. Source: IC Insights Raises Growth Forecast for IoT.

revenue for IoT systems; Internet of Things market forecast

  • Manufacturing (27%), retail trade (11%), information services (9%), and finance and insurance (9%) are the four industries that comprise more than half the total value of the projected $14.4T market. The remaining 14 industries range between 7% percent and 1%. The following graphic based on Cisco’s analysis of the IoT market potential by industry and degree of impact. Cisco predicts Smart Factories will contribute $1.95T of the total value at stake by 2022. Source: Embracing the Internet of Everything To Capture Your Share of $14.4 Trillion, white paper published by Cisco.

top four industries IoT; Internet of Things market forecast

  • Intel Capital, Qualcomm Ventures, Foundry Group, Kleiner Perkins Caufield & Byers (KPCB), Andreessen Horowitz, Khosla Ventures, True Ventures and Cisco Investments are the leading IoT investors this year. Intel Capital is investing in a broad base of IoT-related technologies, encompassing 3D body-scanning and biometric sensors, wearable sand IoT infrastructure startups. Source: The Most Active VCs In The Internet Of Things And Their Investments In One Infographic. 

mot active IoT investors; Internet of Things market forecast

  • Vodafone’s latest Machine-to-Machine (M2M) study found that 37% of enterprises have projects targeted to go live in 2017. Vodafone defines M2M as technologies that connect machines, devices, and objects to the Internet, turning them into ‘intelligent’ assets that can communicate. M2M enables the Internet of Things. The following graphics compare M2M adoption trends from 2013 and 2015 and by industry. Source: 2015 Vodafone M2M Barometer Report (free, opt-in reqd., 36 pp.).

adoption of M2M 2013 2015; Internet of Things market forecast

adoption of M2M by industry; Internet of Things market forecast

growth iot; Internet of Things market forecast

  • New connections to the Internet of Things (IoT) will grow from about 1.7B in 2015 to nearly 3.1B in 2019. IoT applications will also fuel strong sales growth in optoelectronics, sensors/actuators, and discrete semiconductors, which are projected to reach $11.6B in 2019, attaining a CAGR of 26% during the forecast period. Source:  IC Insights Internet of Things Market to Nearly Double by 2019.

New Connections Internet of Things; Internet of Things market forecast

Internet of things forecast Microsoft; Internet of Things market forecast

McKinsey Institute Internet of Things; Internet of Things market forecast

  • IDC predicts that by 2018, 40% of the top 100 discrete manufacturers will rely on connected products to provide product as a service. 55% of discrete manufacturers are researching, piloting, or in production with IoT initiatives. By 2017, 50% of manufacturers will explore the viability of micrologistics networks to enable the promise of accelerated delivery for select products and customers. 65% of companies with more than ten plants will enable workers on the factory floor to make better business decisions through investments in operational intelligence. Source: IDC Manufacturing Insights Report courtesy of Cognizant, Transforming Manufacturing with the Internet of Things May 2015

 

5 Insights & Predictions On Disruptive Tech From KPMG’s 2015 Global Innovation Survey

  • cloud computing survey 215% of U.S. tech leaders see biotech/digital health/healthcare IT as the most disruptive consumer-driven technology in the next three years.
  • 13% of U.S. tech leaders predict data and analytics will be the most disruptive enterprise technology in three years.
  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/machine-to-machine (M2M) (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.

These and many other insights are from the fourth annual 2015 Global Technology Innovation Survey released via webcast by KPMG last month. KPMG surveyed 832 technology industry business leaders globally, with the majority of being C-level executives (87%). Respondents were selected from a broad spectrum of businesses including tech industry startups, mid- and large-scale enterprises, angel investors and venture capital firms. For an in-depth explanation of the survey methodology, please see slides 6 and 7 of the webinar presentation. The goals of the survey include spotting disruptive technologies, identifying tech innovation barriers and opportunities, and tracking emerging tech innovation hubs.

The five insights and predictions from the report include the following:

  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/M2M (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.  U.S. tech leaders predict biotech/digital health/healthcare IT (15%), data and analytics (14%) and cloud computing (14%) will be the three most disruptive technologies over the next three years.  Chinese tech leaders predict artificial intelligence/cognitive computing (15%) will be the most disruptive technology impacting the global business-to-consumer (B2C) marketplace.

tech driving consumer technologies

  • The three most disruptive technologies predicted to drive business transformation in enterprises over the next three years in the U.S. include cloud computing (13%), data and analytics (13%), and cyber security (10%). Japanese tech leaders predict artificial intelligence/cognitive computing will have the greatest effect (23%), and 14% of Chinese tech leaders predict the Internet of Things/M2M (14%) will have the greatest impact on business transformation in their country.  The following table compares global tech leader’s predictions of which technologies will disrupt enterprises the most and drive business transformation over the next three years.

business transformation

  • Improving business efficiencies/higher productivity, and faster innovation cycles (both 20%) are top benefits tech leaders globally are pursuing with IoT strategies. The point was made on the webinar that in Asia, consumers are driving greater adoption of IoT-based devices to a richer contextual customer experience. Greatest challenges globally to adopting IoT is technology complexity (22%), lack of experience in the new technology or business model (16%), and both displacement of the existing tech roadmap and security (both 13%).       

IoT in the enteprrise

  • Analytics are most often adopted to gain faster innovation cycles (25%), improved business efficiencies and higher productivity (17%) and more effective R&D (13%).  The greatest challenges are technology complexity (20%) and lack of experience in the new technology or business model (19%),

data and analytics KPMG Survey

  • Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%).  IoT/M2M is also expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%).  The following graphic compares tech leader’s predictions of the industries with the greatest potential revenue growth (or monetization potential) in the next three years.

Emerging Tech IoT monetization

 

Sources:

Tech Innovation Global Webcast presenting the findings of KPMG’s 2015 Global Technology Innovation Survey

KPMG Survey: Top Disruptive Consumer Tech – AI In China, Healthtech In U.S., 3-D Printing In EMEA

 

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