2. 2
22/10/2015
Big Data &
Logistics
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 …
3. 3
22/10/2015
Big Data &
Logistics
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
4. 4
22/10/2015
Big Data &
Logistics
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.
5. 5
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Big Data &
Logistics
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
6. 6
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Big Data &
Logistics
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
8. 8
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Big Data &
Logistics
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
9. 9
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Big Data &
Logistics
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
10. 10
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Big Data &
Logistics
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.
11. 11
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Big Data &
Logistics
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
USD 500 billion in value worldwide in the form of time and fuel savings, or 380 megatonnes of CO2 emissions saved
Urban multimodal transportation is one of the most complex and rewarding Big Data settings in the logistics sector. In addition to sensor data from infrastructure, vast amounts of mobility and social data are generated by smart phones, C2x technology (communication among and between vehicles), and end-users with location-based services and maps. Big Data will open up opportunities for innovative ways of monitoring, controlling and managing logistical business processes. Deliveries could be adapted based on predictive monitoring, using data from stores, semantic product memories, internet forums, and weather forecasts, leading to both economic and environmental savings
top four supply chain capabilities that Delotte found are currently in use form their recent study
Supply Chain Talent of the Future Findings from the 3rd Annual Supply Chain Survey
the following graphic provides an overview of 52 different sources of big data that are generated in supply chains
pne of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics.
An organization must set priorities, identify opportunities, and determine the value proposition.
shippers, big data can deliver value by helping them reduce shipping costs and increase service levels.
Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives. Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network. It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.