The Internet of Things (IoT) will make our operations more efficient by combining smart sensors, cameras, software, databases/business intelligence, and the Internet (primarily in the form of private clouds) together in diverse ways.

IoT is already evident in several places across the supply chain, but pundits are predicting that it will become so deeply part of our environment that it will become like electricity, something we are all but unaware of because we take it so much for granted. Supply Chain IoT will be present in many places:

  • People – Badges that let workers in to secure locations; sensors will monitor the health of our workers, particularly when they work remotely or in environments that can become hazardous.
  • Factories and warehouses – sensors that warn about everything from chemical spills, to prowlers, to broken water mains.
  • The Logistics Infrastructure – Smartphone apps are making navigation more efficient by detecting traffic congestion. “Smart systems” will warn about infrastructure problems that could impact local supply chain activities.
  • Products – Sensors on pallets, cases, and even individual products will become pervasive allowing for much more efficient demand-supply matching. Today, supply chain planning is top down; planners direct where goods will flow. There is a new vision of supply chain management and manufacturing known as Industrie 4.0 that is bottom up – products directing people (or even robots) on where they need to be taken and what processes remain to be performed on them.

David White of ARC recently published an article called “Analytics for the Industrial Internet of Things: Not Your Father’s BI” (available to ARC’s clients). He says that “business intelligence and analytics are due for a revolution” if they are to support industrial IoT applications. I’ve summarized a few of the more pertinent parts of his article.

When it comes to BI, the Industrial Internet of Things will shift emphasis to:

  • A Real-time Focus – BI has traditionally focused on the analysis of historical data. “Data analysis may occur as soon as it is captured, while the data is still ‘in-flight.’” Complex event processing engines become a core technology for analyzing multiple data streams simultaneously and generating alerts and alarms. Higher level predictive and prescriptive analytics will create larger frameworks that will allow diverse events to be correlated, quickly interpreted, and then acted upon in a timely manner.
  • A Rethinking of Data Storage Practices – The vast majority of IoT data will have only fleeting value. But companies need to think through this carefully and determine what data should be stored to help drive continuous improvement and what data can be almost instantly disposed of.
  • Business cases for determining the ROI of IoT Analytics – the business cases for IoT analytics will be very different than that of traditional BI.

“Collecting, storing and analyzing IoT data requires different processes, skills and technologies.” Acquiring those technologies and ‘growing’ the associated talent will become a key task for companies that want to use IoT to take their supply chain programs to the next level.