BIG Data & Hadoop Applications in Logistics

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com
Applications of BIG Data in
Logistics

Introduction to BIG Data
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management tools
or traditional data processing applications
Retail industries generate extremely high volumes of
data due to high churn of transactions & inventory.
These companies can utilize BIG Data insights to
ensure higher profitability.

Applications of BIG Data in Logistics
• Visualizing Delivery Routes
a) Monitoring traffic data
b) Aligning deliveries of two or more business units or companies
• Pinpointing Future Demand and Reducing Inventory
a) Contextual factors in demand forecast
• Simplifying Distribution Networks

BIG Data Applications in Logistics
Visualizing Delivery Routes
Big Data can be used to create flexible systems to optimize delivery routes based on histroical
traffic patterns, delivery priority and alignment to ensure maximum deliveries.
Driver routes can also be optimized based on real-time traffic patterns.
This also represents a great opportunity to optimize supply chain processes and reduce back-
end expenditure. Companies such as Fedex, BlueDart and DHL utilize BIG Data for these
purposes.
This also represents a great opporunity for E-commerce players to optimize delivery cycles and
power offerings such as same day delivery of products.
Get Started with BIG Data & Hadoop

Forecasting Demand and Reducing Inventory
Companies can look at vast quantities of fast-moving data from customers, suppliers and other
touch-points. They can combine that information with contextual factors such as weather
forecasts, competitive behaviour, pricing positions and other external factors to determine
which factors have a strong correlation with demand and then quickly adapt to the current
reality.
Companies that can forecase demand can optimize logistics. Thus, meeting customer demands
in an extremely agile manner.
As per research, companies that do a better job of predicting future demand can often cut
inventory & logistics cost by 20% to 30%. This leads to greater profitability.

Simplifying Distribution Networks
Logistics networks have evolved over time into dense webs of warehouses, factories and
distribution centres sprawling across huge territories.
BIG Data has enabled companies to solve intricate optimization problems that were never
uncovered in the past. Today, more variables, more scenarios and more analysis can be carried
leading to inclusive & optimized logistics networks.
In addition to optimization, companies can also avail a cost-reduction which based on industry
standards ranges from 10% to 20%, which is an extremely positive outcome.

Examples of BIG Data in Logistics
Amazon
 Amazon uses Big Data extensively top optimize its supply chain. It also has a patent for its algorithm for
‘anticipatory shipping’. This adjusts inventory to anticipate customer demand for specific products, in specific
locations during specific time-ranges. This algorithm is based on factors including previous purchase patterns and
consumer behavior on the website & social media.
FedEx
 FedEx has created a next generation, first-of-its-kind information service that combines a GPS sensor device and
a web-based collaboration platform: SenseAware. Originally used by the healthcare and life sciences industries
as a means to track high value and/or extremely time sensitive.
SenseAware attaches digital information to packages, providing:
a) Information about a shipment’s exact location
b) Notification when a shipment is opened or if the contents have been exposed
c) Real-time alerts and analytics between trusted parties regarding the vital signs of a shipment

© 2015 BlueCamphor Technologies (P) Ltd.
How Are Insights Derived from BIG
Data?
Say Hello to Hadoop Development :)

Introduction to Hadoop
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model.
It is an Open-Source Data Management technology with extensive storage and distributed processing.
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable Get Started with BIG Data & Hadoop

Why Should You Learn Hadoop?
• Hadoop is a modern BIG Data processing framework which utilizes distributed computing clusters.
• It can process, manage and store unstructured data with absolute ease.
• Due to its scalability & effectiveness, companies are heavily adopting Hadoop.
• It has become the standard for storing, processing and analyzing big data.
• BIG Data & Hadoop professionals are in extremely high demand.
• This is the first step to becoming a data scientist or data architect.

Reports – 2015
Here are some of the other Reports that convey the same message:
• “It is expected that Hadoop and Big Data Analytics will grow to around $13.9 billion from 2012 to 2017.”
(Markets and Markets Research Report)
• “The Data market currently with the fastest growth are Hadoop and NoSQL software and services.”
(Technology Research Organization, Wikibon)
• “The Big Data market will likely hit $23.8 billion with a 31.7% rise per year.” (IDC Report)
• “Almost 90% organizations have embarked on Hadoop related projects and thus Hadoop skills are in huge
demand.” (According to the Big Data Executive Survey 2013)
• “Companies hiring Hadoop professionals Analytics professionals obtain a 250 percent hike in their
salaries.” (According to Analytics Industry Report)
Gartner had predicted, “Hadoop will be in most advanced analytics products by 2015.” By far and large, this
prediction has turned out to be true. Hadoop is “The Technology” to harness Big Data

Predictions for 2015
According to the Forrester Report:
• A new data economy will arise, Hadooponomics. Due to its vast data prowess, enterprise adoption of
Hadoop will become compulsory. All those companies, which were unsure of adopting Hadoop till 2014,
are bound to adopt it in 2015
• The surge in demand for Hadoop job professionals will be fulfilled by Java professionals & other software
engineers who will upgrade their skills
• The creation of SQL-on-Hadoop options will further enhance usability & adoption
• Due to the introduction of Yarn (Version 2.0), the usage of Hadoop will go beyond analytics. It will
transform into an application platform giving birth to new frameworks in the Hadoop ecosystem
• New Hadoop sources will emerge from organizations like Oracle, HP, Tibco and SAP. Vendors such as Red
Hat, VMware and Microsoft will include Hadoop in their operating Systems

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Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and
Data Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive
Concepts
Module 8
Extending Hive and
HBase Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up
Discussion

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BIG Data & Hadoop Applications in Logistics

BIG Data & Hadoop Applications in Logistics

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