Peter Reynolds' Most Read IoT Blogs of 2015

By Peter Reynolds

Category:
Industry Trends

My process automation perspective may be showing through in these blogs for industrial IoT. Having spent time deploying automation and information systems in Oil & Gas downstream, I am keenly aware of the technology disruption brought by cloud computing, IT-OT convergence and the use of big data in manufacturing operations. Below is a summary of my most read blogs during 2015. The first three blogs listed below were distinguished to be among the top 10 most read out of the entire compilation of industrial-iot blogs published in 2015.

Please feel free to comment or send me a note with your thoughts about these.

  1. OSIsoft – SAP Partnership Deepens SAP’s Predictive Analytics at the Plant Floor. For many process manufacturing companies (and nearly all oil  & gas companies), SAP is the core system for back office, financials, and capital planning. From an internet of things perspective, the addition of real-time data to SAP's HANA Analytics platform will only strengthen the opportunity to deepen the use of SAP applications at the plant level. SAP recognizes that success in the predictive analytics market not only requires streaming (real-time data) but also the process data archives that are found within a process historian. SAP's partnership with OSISoft allows SAP to enhance its offering and scale quickly with SAP partners building on HANA while OSISoft may also realize the expanded use of sensor data (and more PI tags) across a wider variety of process applications, especially for predictive maintenance.

  2. The Importance of the GE Predix Pilot for RasGas and GCC. The ability to securely and cost-effectively integrate industrial big data and analytics into the cloud can not be understated. Gulf Coast Countries (GCC) are more conservative when considering IT and cyber security than peer industries in other parts of the globe. Rasgas critical assets in Qatar are now integrated into the Predix cloud (currently, this is Amazon Web Services public cloud). This initial step for this industrial manufacturer will pave the way for other GCC countries (and others) to follow suit.

  3. Does Predictive Analytics Need More Than Historian? The ability to stream real-time data and use process history data across manufacturing applications is not new. The ability to efficiently solve a manufacturing problem by combining streaming process data, with historical archives and unconventional data sources and unstructured data that is spread throughout the plant is new. Maintenance and Engineering staff may not have to become data scientists, but they may work alongside one. The convergence of IT and OT technology is coming of age and manifesting in the way enterprise analytics will make use of process historian data.

  4. Internet of Things: Have we been doing this for 25 years? Building on what many automation companies have been saying, I read Emerson's Peter Zornio Forbes interview with great interest. From the automation point of view we have done a fantastic job deploying sensors to plant assets and connecting these to plant information architectures, but to quote ARC's Team Shea-  Smart Sensors Are the “Tip of the Spear” for IIoT. Industrial IoT outcomes and benefits are completely different from what plant automation have been doing for the past 25 years. Let's keep adding cost-effective wireless sensors, but forget the DCS and PLC - we have already done a great job with that. IoT sensors may also bypass the DCS and PLC's and go straight to the cloud for a greater purpose.

  5. Schneider Electric’s Internet of Training and Simulation Things Kudos to Scheider Electric's SimSci team to figure out how to expand the use of process simulation. First redesign the platform to make it easy for owner operators to transition from process design to process optimization of the plant  - and then train operators. Second stick it all in the cloud, so it is safer and easier to deploy at customer sites. Schneider plans to make simulation available to universities from the cloud and simplifying the licensing aspects. There is also no IT hardware or infrastructure to maintain which engineers quite self-sufficient.

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