Big Data Analytics in Logistics Management: A Comprehensive Study

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AVN MITS6002 - RESEARCH STUDY
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Abstract
The logistics management is the most crucial task for the manufacturing companies to deliver
the things without any damage. This study discussed about the various logistics process and
its applications and various models that can be used in logistics management. The big data
analytics can be used for the logistics management. Today, all the data is stored and
transmitted over the internet so it created a difficult task for the data to store over the internet
here big data is used to lower the cost and enhancing the data storage capacity. For instance :-
big data in logistics is used increase the routing, manufacturing functions, have a track on
each and every task and also lowers the cost of logistics. The big data business analytics
(BDBA) and its applications are on logistics and supply chain management (SCA).
Furthermore, this study outlines the limitations of the study and how it can be make better.
Keywords: - big data, logistics management, methodologies
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Contents
Abstract......................................................................................................................................2
Introduction: -.............................................................................................................................4
Literature review........................................................................................................................5
Methodology..............................................................................................................................6
Bibliography...............................................................................................................................8
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Introduction: -
With the implementation of various technologies like the Internet of Things with web 2.0 and
many other technologies are implemented with big data and it gives a good response. Today,
all the data is transmitted or received over the internet includes the data like customer
information, what things customers demanding for, most search product, best-rated product,
customer contact information and it complies with various data positioning system such as
radio frequency-based identification tracking, mobile devices. In recent time, big data
analytics is designed in such a way to make decisions of real-time bases. This will result in
increased supply chain management and also lowers the cost of supply by automatically
making the chart of the logistics delivering process. The big data in logistics adjusts the
supply main according to the various factors like delivery location, delivery date, priority
customer and many more. The role of LSCM in big data analytics is to improve or enhance
the business. The use the digital information has done widely because of big data business
analytics (BDBA), it acts as an important business tool for organizing the data. It is of two
types:- big data and business analytics, where BD refers to the ability of the process
information with respect to the: - quantity, quality, and analytics state that the ability to
manage data by using various methods such as statistics and some mathematical operations.
BDBA plays a very important role in supply chain planning. It helps the entities in making
the supply chain decisions like production planning, inventory data, and logistics
management.
Source: [1]
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Literature review
Logistics is the process of managing the transportation process form the manufacturer
warehouse to customer home. It involves many activities which can be easily handled by bid
data. The main disadvantage is that the complexity of the big data and the data evolved from
its unstructured.
The manufacturing plants have the flow of various manufacturing materials, every stage
process, task completion, completed products and cost of production. In today’s time, all the
data is transmitted over the internet. In the supply chain, all the things are linked to it directly
or indirectly which makes the system complex. In BDBA, BD refers to the high quality of
dynamic data that overcomes the local process of data management and BA refers to the
technologies used in planning the management and operations. It gives many new
opportunities by arranging the whole data and making the decision on a real-time basis. For
any business organization to get success BDBA has a strategic application. SCA also plays a
vital role in the strategic chain, it works on changing the decision according to the market and
identifying and the second part is the various operational levels decisions which include the
planning, production and cost evolution. SCA is helped in improving the efficiency of the
organization. To win the competition in the market by providing the various kind of services
at low cost with high quality products, this can be done by SCA as it produces good quality
products by increasing the sale and lower the cost production.it also helps companies in
taking decision on a real-time basis in less time and it also results in achieving the goal in less
time. The information obtained by SCA show the lack of management of the company by
making proper design and this can be overcome by the design a proper model for the greater
efficiency.
Architecture or working model of logistics and supply chain operations
 Demand planning
As planning is the most basic and important part of any task to be done. Planning is
done in the initial parts of any program. The demand planning in logistics is used to
manage the process and plan the production of anything according to the demand of
the consumers. demand planning is required for the effective number of production, it
the first and the most difficult task, as all the production and cost depend on this
planning. This planning analyses the different users and their different demand based
on their taste. This method uses a time-series approach which is used in the short term
and mid-range.
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 Procurement
All the things such as payment method, most searched product, way of spending and
most trending product are analyzed to track the information of various products. The
SCA tells us about all these things and can get the data from by tracking the social
media sites, news and how much times the product is share. The analysis of data and
performance such as quantity, quality, delivery time and how much money is spent
makes the organization making the information.
 Production
It’s the process of making things after the planning is done. It enables the makers to
calculate the cost of production and also where to store the products. After the
production, managers have to decide where to store the products by calculating how
much space it is used by the products, managers help in improving productivity. SCA
helps in calculating the waste material and how it can be utilized, it also helps in
deciding the next process.
 Inventory
All the data is entered into the ERP system, the data is the type of production, cost
calculation, how much products are sold. SCA provides a well-organized library for
the materials. It also tells us which things are empty and which things are needed in
the future, it tells the brief history of the production of material
 Logistics
Logistics generates a large amount of data such as shipping, address data. Here Big
data is used in RFID tags for tracking the shipment. Its work is the distribution of the
goods in a given to the concerned person. Logistics data is generated in every task and
is upload to the site which helps in tracking the data. Various methods are used in
calculating the shortest path and the least time for the delivery of the goods
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Methodology
1. Simulation-based logistics
System dynamics(SD), Agent-based modelling(ABM) and discrete-event
simulation(DS) are some simulation methods. DS is the standard technique for the
supply chain simulation. In the SD model, a top-down approach is used in the supply
chain it is a useful process to overcome the complex process. In ABM based process
uses a bottom-up approach from every agent.
Source: [2]
2. Last-mile logistics.
It refers to the last step of the delivery process form the warehouse center to the
customer delivery address. The last mile has a distance of 70- 120 kilometers. This
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model allows customers to get their products early by paying an additional fee. In
urban cities, delivery is tough because of traffic and parking. This is an intelligent
model by making a better service and also this model directly affects social activity. It
mostly saves the 25% cost of the transmission.
Some possible ways to provide this service more effectively: -
 Delivery companies should have pickup points so that users can pick up their
products.
 The drone can be used for the delivery of parcels.
 Better routes are used to reduce the time and cost too.
Source : [3]
Some examples of big data in logistics: -
 The last mile goods should be dispatched first, as it takes a longer time to deliver
 Goods should be a track on every stage so that the user can get the updates and should
know where the products reached.
 Routes or transportation should be changed if traffic is there.
 Goods that are sensitive should be sent with good protection and service, and a handle
with care sticker is used for the indication of sensitive goods
 The warehouse should be automated which reduces the time and also reduces the cost
and also arranges the goods in a well-designed form.
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Bibliography
[1] T. Sakai, K. Kawamura, and T. Hyodo, "Evaluation of the spatial pattern of logistics
facilities using urban logistics land-use and traffic simulator.," Journal of Transport
Geography, vol. 74, no. 1, p. 145–160., 2019.
[2] M. Kirch, O. Poenicke and K. Richter, " RFID in Logistics and Production –
Applications, Research and Visions for Smart Logistics Zones.," Procedia Engineering,
vol. 178, no. 1, p. 526–533.
[3] G. Wang, A. Gunasekaran, E. W. T. Ngai and T. Papadopoulos, "Int. J . Production
Economics Big data analytics in logistics and supply chain management : Certain
investigations for research and applications. Intern.," Journal of Production Economics,
vol. 176, no. 1, p. 98–110., 2016.
[4] M. Awwad, P. Kulkarni, R. Bapna and A. Marathe, "Big Data Analytics in Supply
Chain : A Literature Review," vol. 4, no. 1, pp. 1-5, 2018.
[5] S. R. Golroudbary, S. M. Zahraee, U. Awan and A. Kraslawski, "ScienceDirect
ScienceDirect Sustainable Operations Management in Logistics Using Simulations
Sustainable Operations Management in Logistics Using Simulations and Modelling : A
Framework Decision Making in Delivery and Modelling : A Framework for Decision,"
Procedia Manufacturing,, vol. 30, no. 1, p. 627–634., 2019.
[6] H. B. Rai, S. Verlinde and C. Macharis, "Case Studies on Transport Policy City logistics
in an omnichannel environment . The case of Brussels.," Case Studies on Transport
Policy,, vol. 7, no. 2, p. 310–317., 2019.
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