big data & hadoop applications in logistics

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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.

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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



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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 CharacteristicsFlexible

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




Why SkillSpeed?

Course Curriculum

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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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