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LNS REPORT – IOT And Big Data Analytics: How Manufacturing System Architecture Is Being Transformed – Author Matthew Littlefield

Digital transformation is the main topic and trend in mill products industries that gives manufacturing companies the opportunity to re-imagine their business.  LNS Research have delved into this topic in many areas with the latest being – IIOT AND BIG DATA ANALYTICS:  How Manufacturing System Architecture Is Being Transformed – Author Matthew Littlefield.  Click on the link to download the detailed report.


SAP’s HANA platform began as a processor of ‘Big Data’ but is now much more than that to enable companies to analyze the data as well as run their business process transactions. I wanted to review the LNS Research into the IIOT topic to see if we are on the right track.  Here are some key points from the paper:

Section 1 – State of the Market

  • – IIOT platform – Connectivity, Big Data Analytics, Application Development
  • – Companies now understand what IIOT is – 2015 44% did not know; 2016 19%
  • – Business case and funding are top two challenges to deploying IIOT technology

Section 2 – Understanding Digital Transformation

  • – LNS Digital Transformation Framework offers a systematic approach to undertaking simultaneous and interconnected IIOT initiatives
  • – Part of the framework is Operational Architecture emphasize need to have IT and OT groups collaborating with more formal process
  • – Solution selection should be made after the digital framework is decided by cross functional teams
  • – New model operational architecture – need to move to an expanded scope of Enterprise Architecture for managing ‘things’ with edge analytics and applications across the value chain of suppliers, internal operations, customers and products. Span applications and analytics environment that includes cloud/on premise and time series/structured/unstructured data types.  Also incorporates the four components of IIOT platform
  • – In LNS level 2 of Operational Architecture is to manage edge analytics and connectivity across devices and assets in operations ‘this is the area where most innovation and transformation is occurring.’

Section 3 – Adoption of IIOT Connectivity and Big Data Analytics

  • – Most companies combined plant data and big data analytics using IIOT for quality issues, manufacturing tolerances and unscheduled downtime.  OK to start here then add data from smart devices later.

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  • – 40% of companies think they already have analytics expertise but most are doing descriptive analytics and not prescriptive analytics with big data. LNS suggests more training is needed (SAP library of algorithms?)


Section 4 – Building the business case and recommended actions

  • – Companies should think of Big Data Analytics investments as a journey that is based on Operational Excellence maturity rather than a one off ROI calculation
  • – LNS Research recommends a 5 level approach to quantifying maturity
  • – Recommended actions:
    • Establish a digital transformation framework
    • Establish an operational architecture
    • Implement a business case journey for Big Data Analytics
    • Choose and initial use case for Big Data Analytics that aligns to a company’s pain points and/or competitive differentiation


I found the LNS Research report very useful to check how well SAP’s IOT vision to connect the enterprise with operational data aligns with industry experts.  Some of the key points from the SAP portfolio indicate close alignment:

  • – Architecture that supports cloud and on premise solutions on the HANA platform that works with structured and unstructured data
  • – SAP’s Manufacturing Integration and Intelligence provides enterprise connectivity to plant shop floor and MES
  • – SAP partners like OSISoft who have a pre-built HANA connector to move pertinent Big Data from the plant to HANA for advanced analytics such as predictive maintenance
  • – HANA analytics have a library of sophisticated algorithms from the KXEN acquisition that will help a company’s own experts to find meaning in their data

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      Author's profile photo Stefan Weisenberger
      Stefan Weisenberger

      Very interesting report indeed - with suprising results.

      For example, many respondents believe they already have strong analytics teams in place, but when looking at the algorithms currently used, advanced analytical capabilities such as machine learning are rarely used.

      I can also subscribe to the recommended actions, but I would put stronger emphasize on the organizational aspect. Very helpful concepts were described in a recent blog in the Harvard Business Review: "Figuring Out How IT, Analytics, and Operations Should Work Together".

      IIOT is just one aspect of digital transformation, although an important one in the asset intensive manufacturing industry. For mill products specifically, I would expect a different sorting in IIOT use cases with predictive maintenance and quality amont the top 3.

      Did I say that I would be interested in a drill down by industry?