Business Analytics in the Logistics Industry

Business Analytics in the Logistics Industry Page 1 of 8














Abstract .................................................................................... 2
Current State of Business Analytics..................................................... 3
Business Analytics in the Logistics Industry ........................................... 5
Conclusion .................................................................................. 6
References ................................................................................. 7
About the Author .......................................................................... 8
About L&T Infotech ....................................................................... 8



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Business Analytics in the Logistics Industry
A UT H OR : Na r e n d r a B h e n d e






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Abstract
Every business generates huge amount of data in the course of normal operations. This data is
often analyzed to gauge current performance and explore possible ways to improve it further.
This is done systematically and scientifically using business analytics which involves
diagnostic, predictive and prescriptive analysis.
However, the amount of data is ever increasing and is further boosted with the availability of
digital work place and technology allowing almost paperless offices through digitization. Data
generated through social media which is multiple times larger than what used to get
generated a few years ago adds to the quantity of data generated.
Earlier, organizations relied on standard sets of reports produced by their EDP (Electronic
Data Processing) departments. These reports were periodic, static in nature and mostly in
printed form. In the phase that followed, organizations set up MIS departments which were
responsible for data warehousing and business intelligence. Organizations used standard sets
of data values and a few statistical and quantitative techniques for analysis and decision
making.
The advent of sophisticated, state-of-the-art technologies and superior computing power has
changed the scenario. It is now possible to use explanatory and predictive modeling which
helps in better decision making. Business analytics has evolved considerably helping
businesses worldwide in identifying business opportunities, responding appropriately and
thereby benefiting from the results. With ever increasing competition and challenging
business conditions, the demand for business analytics is growing exponentially.
Most of the literature about business analytics focuses on supply chain and financial services.
Even though the travel industry is one of the most customer-centric industries, seldom do
travel companies put in efforts in business analytics. Business leaders still struggle to get
meaningful insights from available business analytics. Travel companies are yet to begin
focusing on identifying the ever changing customer preferences, and accordingly crafting
solutions that can make them preferred travel partners. Business analytics can be the most
beneficial tool for understanding customer preferences and getting timely answers to many
similar questions that require quick and informed decision making.
Challenges faced by logistic companies are unique and so are the solutions. Logistics
companies serve individuals as well as businesses, and one of their biggest challenges is
achieving on-time delivery and fulfilling service level agreements in the most cost-effective
manner. Global economic recession and changing patterns of the world economy have also
posed a great challenge to the logistics industry. In such turbulent times, business analytics
can help logistic companies determine specific drivers for driving performance. It can
pinpoint revenue leakage instances and improve decision making capability, thereby allowing
logistics companies to take timely corrective actions and achieve SLAs (Service Level
Agreements).
This paper describes the challenges faced by travel and logistics companies worldwide and
how possible business analytics solutions can help address them. It further describes various
use cases in the travel and logistics space.








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Current State of Business Analytics
Business analytics adoption level is highest in the securities and investment industry. This is
obvious considering the huge amount of financial data that this industry needs to function in
almost real time to drive the sensitive decision making process. Transportation finds the 6th
slot in business analytics adoption levels. Though this result indicates that business analytics
is used in some form by logistics organizations, it doesn’t state how widely business analytics
is adopted.












An IDC survey of over 2,700 IT managers shows that 91% of large enterprises (with over 5,000
employees) have implemented an analytics solution, while 83% of medium-sized enterprises
(with 500-5,000 employees) have done so. The same IDC survey of IT managers shows that
over half of the respondents have packaged rather than custom analytics solutions. The
reasons for this are the ease of use and broad availability of skills to maintain the solution.
Gartner's 2013 Magic Quadrant for Business Intelligence and Analytics Platforms Report
features the vendors in the BI (Business Intelligence) and analytics space. According to the
report, Gartner sees a shift from descriptive reports to building diagnostic analytics with
reasoning capabilities. Diagnostic analysis leads to predictive analysis to determine the trend
and finally the prescriptive analysis which suggests alternatives and helps in decision making.
As per Gartner, move towards "decentralization and user empowerment of BI and analytics" is
powering the trend.








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Gartner describes leaders as "Leaders are vendors that are reasonably strong in the breadth
and depth of their BI platform capabilities and can deliver on enterprise-wide
implementations that support a broad BI strategy. Leaders articulate a business proposition
that resonates with buyers, supported by the viability and operational capability to deliver on
a global basis."
Vendors are increasingly moving up in the report which indicates the increasing maturity
acquired by the platforms.







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Business Analytics in the Logistics Industry
Business analytics helps logistics and distribution companies improve the speed, accuracy and
reliability of goods movement. Business analytics can immensely benefit most players in the
logistics ecosystem such as shippers, 3PLs, 4PLs, freight forwarders, ocean shipping, trucking,
rail, air cargo, airports, sea ports, rail terminals and regulatory authorities too.
A logistics service provider organization generates huge amount of data at different data
capture points. It includes ordering data, cargo-tariff data, scheduling data, charging & billing
data, asset management, fleet mapping, maintenance, storage area and allocation data and
RF integration data. Though most of the logistics organizations already have this data in their
data stores, they use it only for operational purpose and do not know what to do with the
archived data.
Business analytics can bring out the real value of this data which can lead to improved supply
chain efficiencies and customer satisfaction at reduced costs.
Cost Savings Analytics
The biggest scope for cost reduction in logistics is in the area of fleet maintenance. Logistics
organizations have always struggled to reduce fleet maintenance costs. Traditionally,
organizations maintained preventive maintenance schedules which helped them achieve
reliability. However, business analytics can go miles ahead in determining optimal schedules
for preventive maintenance. This approach helps organizations avoid unnecessary
maintenance while ensuring necessary maintenance right on time. This helps maximize asset
life at a much lower cost.
In case of break-downs, business analytics can quickly perform repair/replace comparison and
suggest the best cost-effective way. It can also perform root-cause analysis and help reduce
repetitive failures. The biggest advantage of business analytics is in the field of predictive
maintenance. Real-time big data analysis and rule-based artificial intelligence algorithms help
suggest maintenance, thus avoiding field failures.
Labor Planning Analytics
Business analytics can effectively facilitate resource allocation. It can enable scheduling with
correct matching of routes to fleet to driver. It allows optimization of resources by reducing
idle time between trips. It can also suggest overtime/outsourcing possibilities to improve
efficiency at reduced costs. Business analytics can be further leveraged by organizations to
understand driver sentiments based on analysis of their conversations and organizational
behavior patterns. This analysis can be effectively used by HR departments to improve
productivity and efficiency, and retain staff.
Warranty Analytics
Business Analytics can also help transportation companies maximize benefits from warranty.
Manufacturers are expected to service equipment free of cost during warranty periods.
However, non-availability or poor quality of data become major bottlenecks in warranty
claims process. In such a scenario, predictive analytics can provide early warning signals
which help transportation companies to service assets even before they fail. This saves cost
as well as ensures timely service under warranty. One challenging example of warranty
analytics is in the rail industry where warranty of moving stocks is involved. There is a very
limited time window to inspect, identify defects and service. Predictive analytics saves time
and provides timely maintenance information so that moving stocks can be serviced just in
time.






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Embedded Analytics
Transportation management systems (TMS) require a lot of inputs along with fine tuning of
configuration parameters for effective functioning. With business analytics of relevant data,
the performance of TMS can be continuously monitored and results can be fed directly into
TMS resulting in automatic fine tuning of TMS. As this happens in almost real time, optimal
performance can be obtained.
Other Applications
There are numerous other possibilities of using business analytics in logistics, including:
 Selection of a profitable mode
 Selection of a profitable route
 Load building and consolidation
 Freight charging and billing
Conclusion
With technology advances and increasing digitization, organizations today generate more and
more data, but struggle to gain maximum benefits from it. Business analytics is a scientific
and systematic way to apply sophisticated quantitative and statistical analysis and predictive
modeling to obtain tangible business results. Business analytics can be immensely beneficial
to the travel and logistics industry. More and more businesses prefer packaged rather than
custom analytics solutions. Gartner’s 2013 Magic Quadrant for Business Intelligence and
Analytics Platforms Report features SAP, Oracle, Microsoft and TIBCO Spotfire as some of the
leaders in this area.
In the travel industry, business analytics can be used to obtain meaningful predictive insights
through data collection from a number of data sources such as Travel Management Companies
(TMCs), Global Distribution System (GDS) providers, call centers, user interfaces, travel
agencies and individual carriers.
Sales and marketing analytics can help organizations in the travel industry understand
behavior patterns of their customers. This information helps organizations analyze trends and
customer preferences – their likes & dislikes and sentiments. This can then act as a powerful
tool for devising strategies, uncovering hidden sales opportunities with the right decision-
making strategies, and also, in identifying unknown risks in market segments.
Social media analytics can help organizations explore meaningful information from a huge
amount of irrelevant data. It can provide information in the form of level of satisfaction,
factors responsible for the level of satisfaction – on-time arrivals, involuntary denied
boarding, mishandled baggage, quality of food, etc. This information can help the carrier
work on a corrective action plan and improve the rate of satisfaction.
Customer buying patterns analytics helps in creating differentiation, targeted products,
campaigns, promotions and discount offers. Predictive product pricing helps in dynamically
and intelligently changing pricing, and thereby maximizing revenues, reducing losses by
optimizing capacity utilization. Some other possible use cases of business analytics are
demand/sales forecasting, inventory management, revenue leakage, cancelations, Customer
Life Time Value (CLTV) and loyalty, and multi-channel campaign optimization.
Business analytics in the logistics industry can help save costs by way of predictive
maintenance. It suggests optimal schedules for preventive maintenance and can quickly






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suggest repair/replace options in case of break-downs. Labor planning analytics help in
maximizing resource utilization with improved productivity and efficiency.
Warranty analytics helps organizations in take maximum benefits out of warranty conditions
including timely replacements. Embedded analytics turbo charges transportation management
systems by feeding back the output data, thereby automatically fine-tuning the system for
maximizing performance.
Business analytics can also be deployed for selection of a profitable mode, selection of a
profitable route, load building and consolidation, freight charging and billing, etc.

Abbreviations and Acronyms
CLTV Customer Life Time Value
TMC Travel Management Companies
GDS Global Distribution System
TMS Transportation management systems
SLA Service Level Agreement
EDP Electronic Data Processing

References
2012 IDC Manufacturing Insights
Gartner's 2013 Magic Quadrant for Business
Intelligence and Analytics Platforms









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About the Author
Narendra Bhende is a BE (Bachelor of Engineering), MBA (Master of Business Administration)
and a PMP
®
(Project Management Professional) with 18+ years of rich IT experience. As a
Delivery Head with a Fortune 100 company, Narendra has managed the organization’s regional
development center and has provided IT services for regional IT across platforms like
Mainframe and mid-range systems - Sun Solaris. He has also offered support for various
applications like ERP, E-commerce and EDI across various geographies – ANZ, Southeast Asia,
India and China. Narendra has a decade of experience in the supply chain domain which
includes areas of Distribution, Logistics, Inventory (MM), OTC(Order to Cash), P2P (Procure to
Pay), Credit and E-commerce. He was part of a number of transition and transformation
initiatives and is a recipient of a ‘Global Award of Excellence’ for his contribution.

About L&T Infotech
L&T Infotech is a wholly-owned subsidiary of the USD 14 billion Larsen & Toubro (ranked 9
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L&T Infotech’s horizon is filled with the promise of new and cutting edge offerings in the
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