筆記: (AWS re:Invent 2020 ZCW205) Connected Factory Solution drives Industry 4.0 success

Published:
  Lastmod: 2023-01-17
by Ernest Chiang

Abstract

對我這門外漢來說這場 Lightning Talk 是個拿取關鍵字與大分類之用，看看工業領域的用字遣詞，影片主要是將投影片唸過去。對已經在進行的朋友來說不需要看，對還沒進行的朋友來說可跳到最後面找適合的 partner 來談比較快。

有興趣的朋友可以考慮參閱這篇官方 blog 文章「Connected Factory Solution based on AWS IoT for Industry 4.0 success」，基本上內容相同，可以將時間拿去看其他場影片。

內容大綱

Topic

Connected Factory Solution drives Industry 4.0 success

Speaker

  • Pugal Janakiraman, AWS Speaker (WW IIoT GTM Specialist Lead, AWS)
  • Prashanth Adiraju, AWS Speaker (Sr. Mgr, AWS IoT Partner Acceleration Team, AWS)

Content

Introduction

  • Data drives manufacturing transformation
    • By consistently leveraging the value of industrial data, manufacturers can:
      • Reduce product development costs by up to 50%
      • Reduce operating costs by up to 25%
      • Increase gross margins by 1/3
    • Average impact customers are seeing:
  • Implementation challenges
    • Multiple devices (PLCs, SCADAs, RTUs)
    • Multiple protocols
    • OT integration into devices from applications does not scale
    • Lack of single source of truth of data
    • Inconsistent interfacing with OT
    • Systems for similar output
    • Scalability challenges
    • Note: OT = Operational technology
    • Multiple protocols (Note: sort of survey result, from high percentage to low)
      • None
      • Modbus
      • Don’t Know
      • CAN
      • Industrial Protocol (Ethernet/IP…)
      • OPC-UA
      • Profibus, Profinet
      • KNX
      • BACNet
      • EtherCat
      • IEC 60870, 61850
      • Other
      • DNP3
      • FOUNDATION fieldbus
      • Sercos
  • Undifferentiated heavy lifting for
    • Device connectivity
    • Certification of edge hardware
    • IT - OT integration and solution scalability
    • Data visualization across multiple form-factors including mobile devices
  • Vision
    • Enterprise-level visibility
    • Division- and plant-level visibility
    • includes
      • Ingest data to AWS (machine data, quality data)
      • Store data in a time series optimized data store
      • Model assets specify performance metrics for your equipment and processes
      • Visualize live and historical equipment data
      • Deploy ML/AI applications that optimize factory output, product quality, maximize asset utilization, and identify equipment maintenance issues
  • Use Cases
    • Root cause analysis
    • Predictive maintenance
    • Predictive quality management
    • Energy/sustainability solution
    • Digital Twin
      • a digital replica of a physical product, factory, or process
  • AWS delivery approach
    • Proof of value (PoV)
      • Deliverable: Production-ready MVP solution
      • indentification of a plant
      • Discovery workshop with plant
      • AWS PoV proposal
      • Solution prove-out
    • Maturity assessment and roadmap
      • Deliverable: Roadmap and Industry 4.0 maturity assessment report for all plants
      • Top business use cases identification and prioritization (press, forklift, welding, quality, etc.)
      • Maturity assessment of plants for Industry 4.0 readliness
    • Scale-out
      • Deliverable: Detail scale-out proposal for all plants
      • Deployment proposal
  • AWS IoT partner ecosystem for Connected Factory
    • Partner Solutions
    • Deployment partners
    • Edge applications
    • Qualified hardware
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