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Business Value of IoT and
Machine Learning in Logistics
Francis Cepero
Director Vertical Markets
2
B2B Partner in
Cloud, IoT, ML and Security
+1500 Customers
+500 international IoT
customers
Gartner MQ IoT Services 2020...
3
6 Classic Problems in Logistics
Asset profitability impacted
from inefficient planning and
operation (idle times).
Asset...
4
Scenarios Quality Control Maintenance Operations
Descriptive -
what happened?
• Quality Monitoring
• Testing Process
• M...
5
Predictive Analytics for Rail Logistics
Predictive Analytics future – integrating for success
Level 0: Prepare for busin...
6
Real Time Digital Logistics: Connected Assets and Connected Planning
Intelligent Mobility 2021 - BigML - A1D
IoT Solutio...
IoT Integrated Asset Mgmt
8
Target: Manage all type of assets and tools centrally - at a much lower cost
Locomotives/Heavy
Machines/Passenger Trains...
9
Integrated Asset Management and IoT
Do this: EAM for Integrated Maintenance and Asset Optimization
• Modern enterprise a...
Real life examples
IoT and ML on the Edge
11
Optimal Pantograph Configuration – based on real train data ???
Right
Left
12
Optimal Pantograph Configuration – and.. why exactly?
13
Can we understand complex data streams? Can we predict them?
ML predicts the fR_Mean with an excellent accuracy
14
ML Workflow
1. ML task: predict fR_Mean (a number) → Regression (supervised learning) with metric R2 to quantify predic...
15
Integrate IoT and ML with modern Asset Management Systems to add value. Many
important scenarios beyond predictive main...
Francis Cepero
Director Vertical Markets
francis.cepero@a1.digital
+436646636921
+491721411028
Contact us!
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Intelligent Mobility: Business Value of IoT and ML in Logistics

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BigML’s partners, A1 Digital, introduce how the Internet of Things and Machine Learning can bring business value in Logistics.

Speaker: Francis Cepero, Head of Vertical Market Solutions at A1 Digital.

*Intelligent Mobility 2021: Virtual Conference.

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Intelligent Mobility: Business Value of IoT and ML in Logistics

  1. 1. Business Value of IoT and Machine Learning in Logistics Francis Cepero Director Vertical Markets
  2. 2. 2 B2B Partner in Cloud, IoT, ML and Security +1500 Customers +500 international IoT customers Gartner MQ IoT Services 2020 Corporate Group 100% Subsidiary of A1 Telekom Austria Group, Part of America Móvil 200 Employees 3 locations in Central Europe: Vienna, Munich and Lausanne A1 Digital in a nutshell Intelligent Mobility 2021 - BigML - A1D
  3. 3. 3 6 Classic Problems in Logistics Asset profitability impacted from inefficient planning and operation (idle times). Asset availability impacted by maintenance and down time Lack of visibility on the daily operations, few data enabled metrics or key performance indicators SERVICE OPERATIONS Lack of optimized usage of assets, integrated planning, route planning No scale due to lack of operational capacities. Service level compliance issues and associated penalties. Inability to adapt to customers growing demands CUSTOMER ENGAGEMENT Few insights through data analysis (e.g. machine learning models for demand and supply chain) Strong dependency on manual repetitive tasks. Lack of automated and optimized planning capacities to attend growing demand. MANUAL PLANNING ASSET OPERATION OPTIMIZATION DEMAND VISIBILITY Intelligent Mobility 2021 - BigML - A1D
  4. 4. 4 Scenarios Quality Control Maintenance Operations Descriptive - what happened? • Quality Monitoring • Testing Process • Monitoring & Evaluation • Detect Quality Loss • Equipment Monitoring • Performance Analytics • Maintenance Analytics • Equipment Failure RCA • Operations Monitoring • Process Mining • Operator Behavior • Operation Failure RCA Predictive - what will happen? • Early Defect Detection • Yield Quality Predict • Predict Failures • Estimate remaining useful life (RUL) • Predict Failure Impact • Predict Activity / Setup Times • Predict Production KPI(s) • Demand Forecasting • Supply Chain Disruption Prescriptive - what to do? • Process Parameter Recommendation for Quality Improvement • Self-calibrated testing • Reduce Failure Cost • Reduce Failure Rate • Repair Recommendation • Optimize Maintenance • Start parameters optimization • Failure Rate Reduction • Fuel/Energy Reduction • Equipment Scheduling and Dynamic Dispatch • Operations Recommendation Opportunities for compound improvement with IoT/ML Smart Assets Use Cases (sense/predict/react) RCA: root cause analysis
  5. 5. 5 Predictive Analytics for Rail Logistics Predictive Analytics future – integrating for success Level 0: Prepare for business impact Select Pilot Use cases, Collect data, select partners, align on platforms, run first PoCs, first Business Cases, test highest value with minimal risk. Level 1: Subsystems: Predictive analytics is executed at the subsystem /edge level Use Cases: wheel bearing damage, flat spots, weight detection, exhaust filter, pantograph Level 2: Asset Management: Condition of the cars / devices Use Cases: Detect anomalies and combine them with asset data. Condition/Predictive Maintenance of single asset types Level 3: Operations Management Use Cases: Multimodal logistic, predictive demand, supply chain simulation, predictive repair cycles, scheduling, optimizing shifts, call centers, secondary assets Level 4: Strategic Management Use Cases: Strategic Investment decisions based on capacity utilization, market analysis, economic activity (mega/macro/micro cycles), strategic decision support systems Platform based approach: sustainable and repeatable impact at low costs. Avoid lock-in. Achieve economies of scale, scope and skills at business, technical and commercial levels Enterprise Systems ERP EAM SCM CRM LPS external … DWH BPM Strategic Initiatives Innovation Programs Continuous Improv. PDCA Balanced Scorecards Quality Control Programs Operations Mgmt Platforms of differentiation Systems of records ML Models ML Datasets Clusters Assoc. sources Anomaly IOT Device Management Real-Time Analytics Data Visualisation Integration Device Connectivity Storage Platform based predictive applications
  6. 6. 6 Real Time Digital Logistics: Connected Assets and Connected Planning Intelligent Mobility 2021 - BigML - A1D IoT Solutions + Advanced Logistic Planning Source: A1Digital/MathITLogistics real-time management on assets and shipments real-time modelling of transportation networks
  7. 7. IoT Integrated Asset Mgmt
  8. 8. 8 Target: Manage all type of assets and tools centrally - at a much lower cost Locomotives/Heavy Machines/Passenger Trains Rolling Stock/Freight Cars Other Vehicles/Forklifts/ Containers/… Machines Rail Tracks/Switches/ Railroad Crossings Tools, Workshop Equipment, Spare Parts, … Facilities Shunting/Logistics/ Disposition Humans, Experts, Teams Infrastructure/CheckPoints/ Visual Detection/Damage Recognition Intelligent Mobility 2021 - BigML - A1D
  9. 9. 9 Integrated Asset Management and IoT Do this: EAM for Integrated Maintenance and Asset Optimization • Modern enterprise asset management (EAM) system to improve processes and customer service • Identify opportunities to reduce asset maintenance expenses with better visibility into costs • Manage planned and corrective fleet maintenance more efficiently • Fast transition (six-month) • Fast integration with IOT based solutions EDGE ML PROCESS ML
  10. 10. Real life examples IoT and ML on the Edge
  11. 11. 11 Optimal Pantograph Configuration – based on real train data ??? Right Left
  12. 12. 12 Optimal Pantograph Configuration – and.. why exactly?
  13. 13. 13 Can we understand complex data streams? Can we predict them? ML predicts the fR_Mean with an excellent accuracy
  14. 14. 14 ML Workflow 1. ML task: predict fR_Mean (a number) → Regression (supervised learning) with metric R2 to quantify prediction accuracy 2. Variety of ML models for Regression e.g Decision Tree (DT), Random Forest (RF), Neural Networks etc 3. Each model has several tuning parameters e.g #nodes for a DT, #Trees for RF 4. We examined systematically (AutoML)~ 30 models with all possible parameter combinations and compare R2
  15. 15. 15 Integrate IoT and ML with modern Asset Management Systems to add value. Many important scenarios beyond predictive maintenance … Key take-aways for Connected Logistics 1 2 3 IoT enabled Asset Management and Logistics Planning will create additional business value in your organization and improve your customer acquisition costs. IoT = Team sport: Partnership between business and tech experts always helps! Intelligent Mobility 2021 - BigML - A1D
  16. 16. Francis Cepero Director Vertical Markets francis.cepero@a1.digital +436646636921 +491721411028 Contact us!

BigML’s partners, A1 Digital, introduce how the Internet of Things and Machine Learning can bring business value in Logistics. Speaker: Francis Cepero, Head of Vertical Market Solutions at A1 Digital. *Intelligent Mobility 2021: Virtual Conference.

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