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Gartner Top 10 Strategic Technology Trends For 2016 Define The Future of Manufacturing

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Oct

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Gartner Top 10 Strategic Technology Trends For 2016 Define The Future of Manufacturing

Jet engine production made possible by manufacturing execution systems ibasetAt the Gartner Symposium/ITxpo held October 4 – 8th in Orlando, David Cearley, Vice President and Gartner Fellow, released the company’s Top Ten Strategic Technology Trends for 2016. What makes this latest series of top ten strategic technology trends so fascinating for manufacturers is the emergence of Digital Mesh, Smart Machines and an entirely new IT reality comprised of an Adaptive Security Architectures, Advanced System Architectures, Mesh App and Service Architectures and Internet of Things (IoT) Architecture and Platforms. These three strategic areas unify the ten trends Gartner defines as significant for 2016.

How Algorithms And Smart Machines Will Revolutionize Manufacturing

Gartner predicts that algorithms will have a galvanizing effect across and within enterprises and implies this will extend to value chains, defining the future of business from an interconnection and relationship standpoint. Algorithms will increasingly be used for automating background tasks that are completed by smart machines.  General Electric’s Predix platform, IBM’s IoT Foundation and several other cloud-based IoT platforms are already making progress on transforming the vision of algorithm-based or smart machines into a reality in global manufacturing.  Taken in this context, Gartner’s top ten strategic technology trends for 2016 are directly applicable to the future of digital manufacturing. A graphic illustrating the top ten strategic technology trends for 2016 is shown below.

top ten technology trends 2016

Gartner Top 10 Strategic Technology Trends For 2016

A summary of the top 10 strategic technology trends for 2016 are:

  • The Device Mesh – Gartner defines the device mesh as including mobile devices, wearable, consumer and home electronic devices, automotive devices and environmental devices — such as sensors in the Internet of Things (IoT). What’s so compelling about this trend from a manufacturing standpoint is the potential to capture quality, cost, time-to-market and most importantly – customer feedback – during each phase of a product or service’s journey through the value chain of a business. Accuracy, agility, time-to-market and quality will all drastically improve as a result of the device mesh becoming more commonplace.
  • Ambient User Experience – Having a digital mesh as a foundation, the next step is to create a unified, seamless user experience across all applications. This is partially attainable today with cloud-based platforms. Digital manufacturing’s growth will be predicated on how well an ambient user experience can be delivered to the shop floor.
  • 3D Printing Materials – The growing range of 3D-printable materials will drive a compound annual growth rate of 64.1 percent for enterprise 3D-printer shipments through 2019. These advances will necessitate a rethinking of assembly line and supply chain processes to exploit 3D printing. “3D printing will see a steady expansion over the next 20 years of the materials that can be printed, improvement in the speed with which items can be printed and emergence of new models to print and assemble composite parts,” said Mr. Cearley.
  • Information of Everything – Everything in the digital mesh produces, uses and transmits information. This information goes beyond textual, audio and video information to include sensory and contextual information. Information of everything addresses this influx with strategies and technologies to link data from all these different data sources. Capturing this data and putting it into a manufacturing strategy context can save thousands of hours a year by better optimizing manufacturing operations.
  • Advanced Machine Learning – In advanced machine learning, deep neural nets (DNNs) move beyond classic computing and information management to create systems that can autonomously learn to perceive the world, on their own. The explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomic. DNNs automate these tasks and make it possible to address key challenges related to the information of everything trend. DNNs (an advanced form of machine learning particularly applicable to large, complex datasets) is what makes smart machines appear “intelligent.”
  • Autonomous Agents and Things – Machine learning gives rise to a spectrum of smart machine implementations — including robots, autonomous vehicles, virtual personal assistants (VPAs) and smart advisors — that act in an autonomous (or at least semiautonomous) manner. While advances in physical smart machines such as robots get a great deal of attention, the software-based smart machines have a more near-term and broader impact.
  • Adaptive Security Architecture – The complexities of digital business and the algorithmic economy combined with an emerging “hacker industry” significantly increase the threat surface for an organization. Relying on perimeter defense and rule-based security is inadequate, especially as organizations exploit more cloud-based services and open APIs for customers and partners to integrate with their systems. Gartner says IT leaders must focus on detecting and responding to threats, as well as more traditional blocking and other measures to prevent attacks. Application self-protection, as well as user and entity behavior analytics, will help fulfill the adaptive security architecture.
  • Advanced System Architecture – The digital mesh and smart machines require intense computing architecture demands to make them viable for organizations. Providing this required boost are high-powered and ultraefficient neuromorphic architectures. Fueled by field-programmable gate arrays (FPGAs) as an underlining technology for neuromorphic architectures, there are significant gains to this architecture, such as being able to run at speeds of greater than a teraflop with high-energy efficiency.
  • Mesh App and Service Architecture – Monolithic, linear application designs (e.g., the three-tier architecture) are giving way to a more loosely coupled integrative approach: the apps and services architecture. Enabled by software-defined application services, this new approach enables Web-scale performance, flexibility and agility. Microservice architecture is an emerging pattern for building distributed applications that support agile delivery and scalable deployment, both on-premises and in the cloud. Containers are emerging as a critical technology for enabling agile development and microservice architectures. Bringing mobile and IoT elements into the app and service architecture creates a comprehensive model to address back-end cloud scalability and front-end device mesh experiences.
  • Internet of Things Platforms – IoT platforms complement the mesh app and service architecture. The management, security, integration and other technologies and standards of the IoT platform are the base set of capabilities for building, managing and securing elements in the IoT. IoT platforms constitute the work IT does behind the scenes from an architectural and a technology standpoint to make the IoT a reality. “Any enterprise embracing the IoT will need to develop an IoT platform strategy, but incomplete competing vendor approaches will make standardization difficult through 2018,” said Mr. Cearley.

Source: Gartner Identifies the Top 10 Strategic Technology Trends for 2016.  Press Release Announcement, October 6, 2015.

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