BIG DATA TRANSFORMING TRANSPORT AND LOGISTICS

WHITE PAPER

ALC Summit 10 May 2018

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Overview

About the White Paper

This paper is the result of a presentation by Sameer Babbar of SVB Group at Australian Logistics Council at

Melbourne Convention and exhibition center on 10th May 2018. It focusses on number of areas where big data

is playing a pivotal role in the transport and logistics industry. Author wishes to acknowledge the effort of

all the directors, partners, clients for the efforts which has enabled him to present the information.

If you are looking for the answers to the following questions, please read on:

What is changed in the world that is causing rapid transformation in transport and logistics?

What is big data?

What are the different V’s of big data?

What is the benefit of big data in solving last mile challenge?

How does transparency in big data usage help everyone?

Why do we need to look at end to end efficiency of system?

How does address management help?

What is the key impediment of big data implementation in transport and logistics?

About the Author

Sameer Babbar

Though leader in leveraging technology, innovation and use of big data in helping clients make critical decisions in cost reduction and

business revenue uplift. I am passionate about creating true value for my clients. I have continually offered fresh perspective and new

insights to my clients to solve their complex business challenges in the simplest possible way.

Organisations that have helped in the transport and logistics

include

• BHP

• Coastwatch • VicRoads

• Maunsell McIntyre • ORTA

• GTA Consultants

BE, ME, MBA(MBS), GAICD

SVB Group

Melbourne, Victoria, 3000, Australia

Tel +61 4 1948 2269

Email sbabbar@svbgroup.com.au

website www.svbgroup.com.au

About the Company

SVB Group

SVB Group is an Australian strategic analytics organisation that offers leading edge solutions that enable businesses and

governments to make strategic, tactical, well-informed and effective decisions that lead to greater commercial returns and

well engaged customer experience.

The company achieves this through a combination of technology,

deep industry experience and a culture focused on delivering fast,

measurable and meaningful client outcomes.

SVB Group enables clients to interpret, manage and act upon the

complex and unique relationships between strategy and wide range of data sources, in order to deliver operational efficiencies,

sound governance and competitive advantage.

SVB Group also offers a wide range of software, tailored and open

source tools that enhance efficiencies and enable businesses and

governments to align the top down strategy to bottom up data

that that is emerging at a rapid rate.

SVB Group Pty Ltd

Level 27, 101 Collins Street

Melbourne, VIC, 3000, Australia

Tel +61 3 9653 6441

Email info@svbgroup.com.au

Website www.svbgroup.com.au

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Image Courtesy: Shutterstock

Exec Summary

Is big data the new oil?

A century ago, the oil barons became the master of universe. Data, particularly big data is equally transformational. Though unlike oil, data does not deplete or cause ecological imbalance. It can be repurposed easily.

It is a currency that is helping individuals and originations to explore the information that is out there and turn it into a commercial outcome. It is a key ingredient to create value in the information age.

The discussions in the document will relate to what is big data and how it is transforming the world in general and logistics and transport industry in particular.

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What is big Data?

Glimpse of history

Starting from smoke signals, an oldest form of real-time

communication that could travel at the speed of light from

point to point, we now have transcended to the world where

every person able to communicate with other person with

various modes at the speed of light. Information is being

generated and consumed at an astounding pace.

In 1998 John Mashey, a PhD from Pennsylvania state

University, spoke of Big data. He worked with Silicon Graphics

and was involved in special-effects in Hollywood. He used a

simple and shortest term to convey that the boundaries of

computing kept advancing.

October 1997 Michael Cox and David Ellsworth published

“Application-controlled demand paging for out-of-core

visualization” in the Proceedings of the IEEE 8th conference on

Visualization. They started the article with “Visualization

provides an interesting challenge for computer systems: data

sets are generally quite large, taxing the capacities of main

memory, local disk, and even remote disk. We call this the

problem of big data. When data sets do not fit in main memory

(in core), or when they do not fit even on local disk, the most

common solution is to acquire more resources.” It is the first

article in the ACM digital library to use the term “big data.”

There are number of individuals who have been instrumental

in popularizing the term –“Big Data”.

Big data is about 10 V’s and an A

© Sameer Babbar

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Every time you hear of big data you will hear about the V’s ( to keep the palette consistent), though these V’s continue to increase for the purpose of this discussion the following will be suffice. • Volume: We are now creating more than 40 Zettbytes of data (1024bytes) • Velocity: Speed and direction from where the data is coming is rapidly increasing. It is happening because of sheer increase

in volume and connectivity. • Variety: Type of datasets very significantly. From traditional documents, data bases, to semi-structured, unstructured data,

gps locations, social media pictures. IOT. Combining them together creates a whole new meaning. • Veracity: The noise, abnormality or bias in data. • Variability: Data where the meaning is constantly changing. For example, same words may present a different context. This is

damn good (creates a new meaning) • Value: The value that can be created by use of data. This can be subjective based on data workflows and knowledge, skills and

technologies available to create such value. • Visualisation: Picture is worth a thousand words and when it comes to data how the static, or dynamic data can be used to

tell a story in a coherent, conceivable and a communicable way. • Virality: How does the data spread among other users and applications. • Viscosity: How difficult it is to work with the data. • Validity/Volatility: How long the data is valid and how long it should be stored. In the real time world what happened a month

ago may not have relevance for analysis today. • Analytics: Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns,

unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decision

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Data Sources:

There is a large and ever-increasing number of data sources. As the sensor quality, accuracy and types have rapidly increased we are

inundated with data sources. There is immense value to be created by the use of this by leveraging knowledge, skills and experience

and augmenting machine learning and artificial intelligence in the process.

It is interesting to note that a substantial amount of data that is generated end up in proprietary databases and repositories. Significant

value can be created if it available for greater good. It is also good to see new data sharing paradigms and entrepreneurial models are

emerging which are continually helping data sharing.

Examples:

• Weather Patterns: Research suggests that bad weather leads to planned journeys being rescheduled, rerouted to cancelled. This consequentially leads to increased congestion, accidents, delays, pollution and at human level more mental stress and unhappiness.

• Connected devices: Convergence of wireless connectivity with automated driving capability has held to CAV or connected and automated vehicles. These will lead to enhanced road safety, efficient transport and increased productivity.

• Smart Phones: Besides locating the device and helping organisations calculate the level of congestion and speed of vehicles on the road, the smart devices are helping drivers to sign on, sign off, suggest break periods. Organisations can easily track staff and their movements. Assist with navigation. Get the delivery sign off. Giving a ready history of jobs and reports and keeping the organisational data in sync and transparent.

• RFID: Tool to reduce errors and inefficiencies. Help with increased efficiency on loading, tracking and delivery of cargo. Reduced loss of cargo through theft and mishandling, Reduced fleet maintenance. Safe and secure freight transport with electronic manifests.

• Camera’s (in vehicle, roadside, others): Logging the video while driving is helping keep a record for safety and insurance. Roadside cameras are helping to create a safe environment for drivers. There are cameras that are also helping record the street view and other assets alongside of the road.

• Sensors in the road: Magnet loops buried near the road intersections are already being used to control the lights, understand the speed and type of vehicle and congestion levels.

• Autonomous devices: Players like Google, Tesla and Uber are heavily invested in research and development to power autonomous logistics. It is

akin to railroad in the years past. • Routes: Routing is a complex issue in logistics. When

you have a fleet of vehicles moving around, narrow deadlines, variable traffic conditions, constraints that drivers have, constraints that vehicles have (turning radius) and constraints the road has (can’t park) Superimpose events, school time peak hours, work hours, holiday traffic, weather, seasonality, routing becomes a complex issue for all organisations. There are number of free, open source commercial solutions however this is one of the nagging issues in the industry.

• Places A wide range of data providers have a very good repository of points of interest in the world. These are key locations with total number of records exceeding 100million.

• GPS Using satellites, and sensors we can identify location of our assets at high degree of precision.

• ….and many more

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What are some benefits of using big data in transport and logistics?

1. Solving the last mile challenge

Image Courtesy: Shutterstock

I will attribute C grader (in college for the work that resulted in FedEx) Fred Smith of FedEx for popularizing the hub and spoke

model though originally pioneered by Delta air in 1955.

Though it brings economies of scale (aggregation and disaggregation at hubs) and scope (spokes are easy to operate and can help

create new routes) the last mile may cost up to 28% of the supply chain.

Some of the aspects where big data helps in streamlining and reducing the cost of last mile delivery

• Checking out parking near a location,

• guiding the vehicle to park and paying for exact usage.

• When and where the delivery can be made

• keeping the constraints like pets, alarm systems or signatures needed in consideration.

Cool Examples

• appyparking which helps you pay for exact usage in parking

• UPS’s my choice where you can plan and play with your choices for delivery

Proximity and route to delivery address – Google maps, Apple Maps and less known public domain open street maps have some

good tools built around them.

Service providers can automatically collect information once the delivery has been made including signatures, if required put a

time stamp and gps location update the system in real time confirming to the addresses via email. Australia post has started using

this mechanism recently however this has been in use around 2001 in various forms.

.

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2. Transparency of information

© Sameer Babbar

All the information on products and services can now at fingertips of provide and consumer.

We can monitor the temperature of frozen transport. Control the speed limits on the road from a control room.

All the information from sensors, tags, GPS enabled data, MAC identifier from phones with their location is now available in real

time. There is increased transparency of information and a diminishing probability of hiding errors and omissions.

All data goes through interpretation and reinterpretation so the information on the data structures, context of data creation,

metadata and information on collection systems are vital.

The new sources of data can be praising or giving bad name to a

business particularly in social media. There can be complaints on

the product, delivery, response time, shipping error,

mismatched bundling, scheduling etc. There is an electronic

follow economy being created.

With this heightened level of transparency, there is a pressing

need to understand and monitor it and exhibit behaviors that

acceptable. Companies have loyalty programs where they share

information and feedback about their customers in forums with

others. The feedback loops are rapidly increasing while the

room for error is reducing, leading to trust.

Trust thus leads to teamwork and partnership thus successful outcomes in the game and increasing CLV (increased loyalty, less

churn, prediction of disruption, increased information on specific customer preferences and ability to tailor the services to the

category of one) and better outcomes for the entire ecosystem.

All data goes through interpretation

and reinterpretation so the

information on the data structures,

context of data creation, metadata

and information on collection systems

are vital

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3. Route Optimisation:

Image courtesy: Google

The journey between two points can have a number of constraints, shortest, fastest, scenic, avoiding accidents travelling through different segments on the road to different waypoints in sea or air.

Time and location of vehicle is known via magnetic loops on the road or by location a phone on the network. This gives rate of movement of vehicles gives an understanding of speed, direction, which in turn gives an idea on the level of congestion on the road.

Modern aircrafts, trains, trams and buses can tell their time of arrival with a great degree of precision. INRIX, TomTom, Google are collecting phone locations to calculate speed and congestions from smartphones. Google had acquired WAZE which was using crowdsourcing for understanding traffic.

Simplistic model to find travel parameters of a vehicle. © Sameer Babbar

Using various optimization techniques, efficient routes are picked up to achieve the desired business outcome. Organisations are optimizing fleets to get better efficiency out of the same number of vehicles. Routes can be optimized in real time for example in companies like DHL to bypass congestions or accidents. Models can be easily built to optimise resource utilization.

It also helps in amortization of investments in warehouses, distribution centers and purpose-built infrastructure, to ensure that accessibility is optimum.

It is now helping in planning purposes for example to compare with planned route versus travelled route to calculate efficiencies in the planning process.

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UPS for example decreased their fuel consumption by 10m gallons and delivered 350000 more packages by avoiding left turn (in US in 2016)

4. End to end efficiency

Given any system there is always room to improve. There are three key measures of service – speed, cost and quality of service.

Speed can be tuned to near real time, cost to zero or negative (getting customers to do a part of whole of job) and enhanced customer experience by constantly innovating dimensions of service (Kim and Mauborgne’s Blue Ocean Strategy dwells deeper into it.

In any case end to end efficiency of the entire value chain is considered.

It is this disruption/innovation that is driving the changes in the industry. All these changes are helped by data that is now available on fingertips.

Some interesting business models are emerging for example

Quicargo combines trucks with empty spaces with nearby businesses in real time.

Staxxon folds empty shipping containers to transport them efficiently (taking only one fifth of space)

Australian startup Passel matches, instore buyers with online buyers who live in the vicinity of instore buyers. Thus, allowing instore buyers to do the parcel drop off on the way back with extra bit of cash along the way.

5. Automation

Warehousing and distribution centers that have high volume of repeat operations use. automatic storage and retrieval that leads to time and cost efficiency and precision.

In a manufacturing environment tooling, work in progress, buffer environment (ambient, cold, freezer clean room) is automated.

Amazon is using KIVA robots for picking and packaging at its large warehouse. KIVA is a company that Amazon has acquired in 2012. Once deployed in all its fulfilment centers the cost saving will approximate $800m.

Dell, Swatch, BMW use data and automation to mass customize the orders for its clients.

Amazon Prime Air is a delivery system designed to get packages to customers in less than 30 minutes using unmanned aerial vehicles – drones. (Cruising below 400 ft, carrying wt below 5 pounds, guided by gps and real time data).

Cost Speed

Quality

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6. Address Management

There was a time when we used to mail the greetings and deliver the parcels ourselves every Christmas. Now send the messages ourselves (electronically) and get the services from delivery companies. Not knowing the correct address now throws a big challenge to someone who is delivering your parcel.

To put some context 76% of the undelivered mail is due to outdated or incorrect address so verification of the address is vital for correct and timely delivery. This can be done at the upstream end when the order is being accepted by a delivery company.

In Australia an eight-digit delivery point identifier is used for customer mailing bulk letters (DPID). This is a machine-readable barcode that improves the speed and accuracy of deliveries. The mechanism used here is to match a customer address to postal address file which has a record of all deliverable address in Australia using a software called AMAS called address matching and correction software.

This software ensures validating the address at upstream end.

Many organizations around the world are using address standardization and management to ensure it is formatted to the hierarchy in vogue in a country and ensure speed and accuracy of delivery.

What is the key challenge using big data in transport and logistics?

Policy Creation/Impelementation Policy impact

Policy presents itself as one of the key challenge areas in big data in general and transport and logistics in particular.

Policy creation/implementation is top down while the impact of policy changes is observed all around. The change of pace of technology is so profound that it confronts policymakers.

Trying to stay in play with the innovation curve is perplexing as the direction and differentiation that is yet to emerge is hard to predict. This can result in huge costs for policy makers.

The contrary staying behind the curve once the technology is bedded down risks information to be used and abused in many possible ways whilst policy makers play a catchup.

To compound the issues, it is difficult to predict the possible ways in which the data can be shared and used when the future has not been invented yet.

There are some interesting ways to discuss the resolution of this challenge, which is a separate discussion author can shed light on.

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What is the road ahead of using big data in transport and logistics?

The future of digital and big data in supply chain is very bright

Big data continues to help and improve the logistics and supply chain which will be one of the key determinants towards creation of

• Smart Cities

• Emergence of new business models and disruption of existing models which is already underway.

• Rapid adaptability and new opportunities to become better, cheaper and faster, allowing nimble organizations to go through hyper growth.

• Decision making to continue to be enhanced and automated.

• New ecosystems to be created.

• Customer experience to play a pivotal role in defining the measure of success. • Advanced analytics will be able to mitigate adverse events even before they occur.

• Smart contracts, traceability and authentication will become prime drivers of use of block chain in the supply chain.

I wish you good luck in transforming the world around you.

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For more information please contact:

.

Disclaimer: The whitepaper is general in nature and should only be used as a general guide. Authors, its directors, partners clients or

associates are not responsible for any damages caused by use of the information contained here.

© 2018 SVB Group Pty Ltd. All rights reserved

Sameer Babbar SVB Group

+61.3.9653 6441 +61.4 1948 2269

Sbabbar@svbgroup.com.au

www.svbgroup.com.au