Big Data Analytics and IoT in Logistics

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1 Big Data Analytics and IoT in Logistics Anjali Goyal Principal Executive Director Accounts, Railway Board, Ministry of Railways New Delhi

2 Indian Railways and International Railways: Challenges and Responses Indian Railways- world s fourth largest railwaysame challenge as World's biggest American Railways Declining growth in coal carried Falling share in Passenger market Stagnant NTKMs Target: increasing train patronage Being more responsive to customer demand- forecasting expectations - designing services to match expectations Cost Optimization to reduces tariffs Optimal asset, fuel and crew utilization, - Improved Asset Reliability-therefore less capex & opex Maximizing PKMs and NTKMs to increase top line revenues New Revenue Streams- predicting consumer choices Role of Big Data, Data Analytics, IIoT

3 Response of - American Rail Roads- CSX Sensors on trains: real time monitoring, analysis, control and maintenance. -Precision railroading- Better Schedule Adherence -Better Asset utilisation Data stored and analysed on IoT platforms Big Data IIoT Railways Communication Hubs connecting Network control and Trains and Data -Improved operating efficiencies & safety -Point to point faster trains -Reduction in fleet requirement Real time machine to machine ( M2M ) communications- Signalling, train radios line side / trackside -Boost in returns

4 Preparing for Data Analytics and IoT on Indian Railways Needs Analysis By MoR and Zonal Railwaysunderstand technology and products on offer MoR to prepare With clarity on end objectives Master plan to adopt Big Data Develop infrastructure for IoT and Data Analytics Sensors, communication networks, data hubs, IT platforms and applications - for Railways data capture, storage and intelligent analysis.

5 Capacity Development Challenges Building Teams of young professionals trained in Python and R programming language-machine Learning, Data visualization, neural networks, Deep Learning and then into AI. Cost, Connectivity reliability and Security Safety-critical nature of trains-rigorous testing for fool proof reliability. Wireless connectivityprotect from data loss/breach and denial of service. Hacking and cyber security Will need time, efforts and investments. IR is just beginning to reap advantages of its data- We are just about beginning to scratch the surface.

6 Partnerships and symbiotic efforts of stakeholders Ministry of Railways Sharing Needs Analysis Patterns, correlations identified based on indepth knowledge of how train business operates and factors that dictate demandcounterpart teams with coding skill-sets Defining end objectives Big Data, Data Science experts Software solutions- for data analytics- existing IT platforms of IR Hardware Tech knowhowselection of appropriate hardware with Cost Optimization Analytical tools

7 Thank you

8 The Big Data Challenge Data analytics- Extracting meaningful, actionable information, with resource efficiency. Internet of Things- Industrial Internet of Things, Internet of Trains, Promise of fantastic benefits: predictive maintenance- minimal trains, no late trains, optimal asset, fuel and crew utilization, Cost optimization- infrastructure investment- Sensors, IT platforms, and all communication systems must meet end purpose of data processing Big Data and Data Analytics- What we seek Increased patronage of train travel and freight/parcel by train- an environment friendly shift Understanding, predicting- consumer choices, behaviour and expectations Enhanced productivity in Logistics-transportation, supply chain, warehousing Lower costs for customer Better service to satisfy expectations of quality, time sensitivity, reliability etc. Improved asset reliability, lower inventory costs, lower maintenance costs Forecasting demand

9 A few more International Experiences TTCI, USA: Analyzing Freight movement data to prevent accidentsapplying composite rules to Big Data- identifying pattern of factors causing failure- Predictive Maintenance. Railinc, AAR s rail data subsidiary -order of 100 Terabytes of data in active data repositories saving several man months and time on manual surveys. Siemens- Using Big Data to avoid trains running late Portix Logistic Software (PLS)- Predictive Analysis tools to forecast freight charges on container shipments Amazon- Predicting consumer demand using big data