CONNECTED LOGISTICS Denis SENPERE Inspirage Europe Vejle, October 2 nd 2018 1 1 2017 Inspirage / Oracle All rights reserved 1 2017 Inspirage / ICP Solution / Oracle All rights reserved Courtesy Alstom / Amtrak
CONNECT ANALYZE PREDICT INSPIRAGE VISION FOR IoT Predict Assets, Factory & Fleet Performance AI based Failure Prediction Models Self Correcting Systems Proactive Maintenance Analyse Assets, Factory & Fleet Performance Historical Data Analysis Anomalies Digital Twin and Augmented Reality Connect Assets, Factory & Fleet Upstream Integration with Devices Real-time, streaming analytics Downstream Integration with Business Systems 5
ORACLE IoT STANDARD APPLICATIONS Asset Monitoring Production Monitoring Fleet Monitoring Connected Worker Service Monitoring for Connected Assets Monitor assets, their health, utilization & availability Manufacturing equipment & production line monitoring & prognostics Monitor shipments, fleet vehicles, driver behavior and costs Enhance worker safety through monitoring of workers and environment Automate asset monitoring and customer service to enhance customer experience Internet of Things Cloud Enterprise (Platform) Connect Analyze Integrate Learn Copyright 2017, Oracle and/or its affiliates. All rights reserved. 6
INSPIRAGE IoT SOLUTIONS BASED ON ORACLE Asset Monitoring IoT Lab Use case for Asset Monitoring, 2 Devices Temperature sensor and Pressure Sensor used in lab and integrated with IoT CS to send real time data. 2-Asset Network Associated multiple attributes for each assets Production Monitoring Real-time Shop Floor visibility on Plant Operations and Production Line out-put Condition Monitoring of Production Line to Diagnose Production Anomalies across Plants / Lines / Machines Recommend Predictive and Prescriptive Maintenance based on Predictive Analytics on Plant resources / machines Fleet Monitoring Cargo Monitoring Monitoring of individual cargo conditions Fleet Monitoring Geo Position, Shipment info, Gate events, Route, Speed with mobility extensions 7
ENABLER ENABLER ASSET MONITORING SOLUTION USE CASES SUMMARY Asset Full: In-House Asset Full: Remote Asset Health Monitoring Predictive Analytics Breakdown Maintenance Feedback to Product Design Preventive Maintenance Collaboration with Planning Asset Less: Monitor Asset Health Monitoring Predictive Analytics Asset Less: Maintenance Breakdown Maintenance Preventive Maintenance ENABLERS Equipment Sensor IoT Cloud Service IoT Asset Monitoring Equipment Sensor IoT Cloud Service IoT Asset Management Maintenance Cloud Visual Builder Cloud Service 8
Information Needed Business Needs CONNECTED FLEET MONITORING Where is my Truck? Distance to Destination? When will it be delivered? Was it deviated from the plan? How Many deliveries pending Is my cargo in good and safe condition? Can I plan next trip / actions? Geo Position Shipment Info Route Traffic Speed Cargo Condition Gate Events 10
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PRE-BUILT IoT ASSETS Process Flows 13
PRE-BUILT IoT ASSETS Process Flows Configuration Handbook 14
PRE-BUILT IoT ASSETS Process Flows Configuration Handbook Integrations 15
IoT CUSTOMER CASES 17
USE CASE FOR PORT Asset Full : In-House Asset Health Monitoring Predictive Analytics Asset Parameters Heavy Duty Crane: Hydraulic Oil Pressure Lubricant Oil Temperature Oil Level YARD TRUCK DUMPER TRUCK TOWER CRANE Mid-Range Yard Truck: Engine Oil Temperature Average Engine Oil Temperature Tire Pressure Location 18 ASSET HEALTH MONITORING PREDICTIVE ANALYTICS
VIDEO USE CASE FOR PORT 20
AUTOMATED MATERIAL MOVEMENT WITH IN-PLANT TRACEABILITY 1 2 3 4 5 RFID Tag Storage Location-1 Storage Location-2 IoT sensor (Load Sensor) RFID reader Load Sensor + GSM Sensor + RFID reader IoT sensor (Load Sensor) RFID reader WHA T Material Movement Mobile Apps Dashboard on IoT Platform 1 2 Picking 3 4 Transfer 5 Auto-identify Material Picking and Picker Auto-calculate Units Picked Notify Stakeholders Auto-validate Material & Quantity Automated System Record Exception Management In-Transit Material display on the Dashboard Put Away Auto-identify Put away Auto-validate Material & Quantity Automated System Record Exception Management & Notify Stakeholders HOW Parameter Monitored : Weight and SKU Sensor Type : IoT Sensor (Load Sensor) and RFID Technology : IoT, SCM Cloud, Mobility, RFID Parameter Monitored : Sensor Type : Technology : Weight, SKU and Position IoT Sensor (Load Sensor), GSM Sensor and RFID IoT, SCM Cloud, Mobility, RFID Parameter Monitored : Sensor Type : Technology : Weight and SKU IoT Sensor (Load Sensor) and RFID IoT, SCM Cloud, Mobility, RFID 21
MATERIAL TRACKING WEIGHT BASED AUTO PICKING AND RECEIPT REGISTER THE DEVICE IN SIMULATOR 22 22
SOLUTION VALUE PROPOSITION Smart Production with Automated Material Transfer and Improved In-Plant Traceability 60%- 70% Reduction in Data Entry Time 1 20%- 30% Reduction in Labor Cost 2 Real-Time System of Records Proactive Exception Handling Real-Time in-plant Tracking of Critical Items Intelligent System Guided Controls Control Towers To Manage Operations 1. Real-Time Information of Critical High Value Items 2. Minimal Manual entry of data from Job Card Effort Reduction 1. Picking Wrong Item: Auto Alert 2. Picking Wrong Quantity: Auto Alert 3. Material moved out of Handler Bin : Auto Alert with Location identification in the Process Exception Dashboard 1. Material Movement Dashboard 2. No Misplaced items leading to Lost Inventory 3. Higher Inventory accuracy leading to improved Fulfillment rates 1. Material delivered to Incorrect Work Center: Auto Alert 2. Minimizes multiple Picker trips for the same Material against a Job (Incorrect Item, Qty.) 1. Minimizes Picker Route Deviation 2. Picker Efficiency Data which can be linked to Compensation Benefits 1 http://www.assemblymag.com/articles/92976-ensuring-traceability-in-electronics-assembly 2 The 2015 MHI Annual Industry Report by Deloitte and MHI For a large logistics organizations 150,000 pallets for In-Plant movement 6 minutes of staff time per pallet 2 Man-Years of labor savings 28
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