This document discusses the potential of big data in logistics. It notes that big data in logistics is characterized by large volumes of diverse data from both structured and unstructured sources. Applying advanced analytics to big data can provide greater insights across supply chains, enabling more efficient routing, inventory control, and issue resolution. Key challenges to realizing big data's potential in logistics include identifying appropriate business cases, gaining data sharing between stakeholders, and developing data science expertise in the logistics field.

1. Logistics and
Big Data
Germán Herrero
Head of Transport Sector
Research and Innovation, Atos Spain

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Big Data is not only about size
Data diversity matters
BIG
DATA
Traditional
Structured
Data
+ =
BIG SIZE
SOCIAL
OPEN
REAL TIME
MULTIMEDIA
LINKED/SHARED
UNSTRUCTURED
“EXHAUST”
SIZE matters here, but, as in many other areas in life, there is more …

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The 3+ V’s of Big Data
▶ 1. Volume (lots of data Zettabytes)
▶ 2. Variety (complexity, dimensionality)
▶ 3. Velocity (fast data)
+
▶ 4. Veracity (truthfulness, curation)
▶ 5. Venue (location)
▶ 6. Vocabulary (semantics)
▶ 7. Variability …
From “Understanding Big Data” by IBM

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Nothing new under the Sun?
The concept IS NOT new, but technology and tools ARE
▶ …but having data bigger requires different approaches:
– techniques, tools, architectures
▶ …with an aim to solve new problems
– …or old problems in a better way.

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Potential
▶ 10% efficiency gain = 100 B€ Cost Savings (EU)
[ALICE ETP “Alliance for Logistics Innovation through Collaboration in Europe]
▶ IDC at BDVA research agenda -Transport & Logistics ranked 3rd in spending in
data products/services - 500 B€ Data Market

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Big Data potential
▶ Big data and advanced
analytics are being integrated
into optimisation tools,
demand forecasting,
integrated business planning
and supplier collaboration &
risk analytics at a quickening
pace.
Source: Deloitte Supply Chain
Talent of the Future Findings
from the 3rd Annual Supply
Chain Survey

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BIG DATA IN LOGISTICS
WHAT THE
HELL IS FOR

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Big Data in Supply Chain
management
▶ Big data is providing supplier networks with greater data accuracy, clarity, and
insights, leading to more contextual intelligence shared across supply chains.
▶ The scale, scope and depth of data supply chains are generating today
is accelerating, providing ample data sets to drive contextual
intelligence.
▶ Source: Big Data Analytics in Supply Chain Management: Trends and Related Research. Presented at 6th
International Conference on Operations and Supply Chain Management, Bali, 2014

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Big Data in Supply Chain
management
▶ Big data is providing supplier networks with greater data accuracy, clarity, and
insights, leading to more contextual intelligence shared across supply chains.
▶ The scale, scope and depth of data supply chains are generating today is
accelerating, providing ample data sets to drive contextual intelligence.
▶ Plotting the data sources by variety, volume and velocity by the relative level of
structured/unstructured data, it’s clear that the majority of supply chain data is
generated outside an enterprise.
▶ Forward-thinking manufacturers are looking at big data as a catalyst for greater
collaboration. Enabling more complex supplier networks that focus on knowledge
sharing and collaboration as the value-add over just completing transactions.
▶ Advanced analytics forecasting, demand planning, sourcing, production and
distribution. And the benefits extend across the chain:
– Real time issues can be resolved faster, leading to better customer and supplier
relationships
– Inventory control can be optimized
– Assets can be used more efficiently
– Supply chain security – illegal/fraud trade

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Big Data in Delivery
▶ Fleet managers are already using routing SW to provide drivers efficient routes
to save gas and money
– But there is still some slack…
▶ New techniques in geoanalytical
mapping and fast analysis of
millions of data points lend new
meaning to the phrase “data
visualization.” Instead of
making piecemeal changes,
teams of data analysts centrally
monitor routing patterns and
readjust entire networks
▶ Traceability are by nature data-
intensive, making big data’s
contribution potentially
significant.

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Conclusions
▶ In the next years, Logistics will be defined by a massive increase in contextual
data — from precise, up-to-the-minute weather forecasts, to door-to-door
tracking across all oceans and remote territories of the planet
– Heterogeneous amounts of data
▶ Novel application scenarios for increasing efficiency, sustainability and user-
friendliness of transport and logistics services
▶ Relevance/Impact: high, as confirmed by IDC
▶ Challenges:
– Appropiate business cases – Business aspects are key
• set priorities, identify opportunities, and determine the value proposition
• Fear of losing data from logistic stakeholders
– Right questions are as important as right answers
– Data scientists in Logistics

12. Thank you
Atos, the Atos logo, Atos Consulting, Atos Worldline, Atos Sphere,
Atos Cloud and Atos WorldGrid
are registered trademarks of Atos SE. January 2015
© 2015 Atos. Confidential information owned by Atos, to be used by
the recipient only. This document, or any part of it, may not be
reproduced, copied, circulated and/or distributed nor quoted without
prior written approval from Atos.