MGT 5516
Final Project
Big Data
Analytics in the Supply of Logistics Infrastructure
Qihui SHI
Student ID: 53850628
Introduction
In industries through
the world, organizations recognize the needs to harness the ways to exploit big
data[1].
Admittedly, no other industries than logistics have benefited from the big data
technology for the competitive edge, as it is stated. That is the starting
point for us to explore the big data performance in the industry of logistics
with case study of DHL in assignment2. To further our in-depth understanding of
big data in the logistics as a complementary study, the project will go deeper
into the logistics infrastructure sector to illuminate how the big data
fundamentally change the industry landscape, and what opportunities will be
emerging accordingly with big data analytics integrated.
1. Industry profile
Logistics industry
covers a wide spectrum from infrastructure, forwarding, express/post, 3PL[2]s
and logistics fund. The logistics infrastructure sector is defined as the
provision of warehousing and supply chain management for logistics-related
customers including manufacturers, retailers and 3Pls. It is the platform basically
linking commodities activities, shedding a significant impact on overall
economic efficiency, either on a company basis or on a global scale. Given the
huge amount of information (volume) flowing at a higher speed (velocity) with
different kinds of data (variety) generated in the process, logistics is the
pioneering industry to embrace big data analytics for the sake of
cost-effectiveness, optimization and performance. Site selection, for instance, traditionally
was conducted based upon the analysis of site investigation ranging from vicinity
conditions such as traffic networks, industry segmentations in the market to
physical surroundings. Well-selected, or it might be a bad decision if key
element had been evaluated in an incorrect way.
With big data
analytics employed today, the approach to the site selections, disruptively
transformed, has been turning into a data-driven ecosystems, in which the
decision-making process is driven by big data analytics, rather than by the
previous spreadsheet in rows and columns.
2. Data-driven decision-making
'Big
data represents more a wide range of analytical technologies than simply the
ability to handle the large volume of data'[3]. In the logistics industry, there
are emerging innovative technologies applied making it possible for
organizations to utilize the big data for a smarter decision-making. Companies specializing
in providing warehouses to the target market are the pioneers in employing big
data analytics and the benefits they gain from the big data have been
tremendously outstanding. Being one of the functions big data is embedded with,
the decision-makings on configuring a warehouse now rely on the results generated
by analyzing the data on four key elements: the locations or site selection,
economy of scales, market projections and customers. These four factors are
interrelated rather than independent of each other, i.e., site selection,
economy of scale, market projection, and end-users analysis, in accordance with
the value chain from the project configurations, construction, operations and
customer relationships. To illustrate how the big data is applied in the decision-making
process of logistics warehouse industry, this section takes in-depth approach
focusing on the site selection process exclusively.
Site selection is a
complex activity in the process of initiating a project. Selection works used
to be functionally selected by professionals who have criteria in mind, and in
particular the know-how accumulated by years of practices, which is of course a
kind of data but not big enough at all, to make an initial recommendation. To
show their expertise in this regards, they used to ‘smelling’ the site and get
gut-feeling of the locations. What underpins their guts is the knowledge of
physical conditions such as soils, traffics, vicinity, among others.
Unstructured, those data are hardly being utilized to the full in terms of
value the data unleashed. More often than not, the data are biased in analyzing
and decision-making process. With the data analytics technology, their scopes
of work have been shifted to collecting, verifying and storing the related
data. In selecting site, the following data are required including traffic
flows of container trucks passing by the site, the physical conditions of the
site, industries-clustered within radius of three-kilos, the local master
planning in five years. It is a huge amount of work to verify those data yet it
is the prerequisite for effective data analytics. Take traffic flows for
example, it is a part of dataset typical easy said than done. How can the data
be accessed? How to verify the data? And what data are critical for
value-creation in analytics? Previously, it was outsourced to a consulting firm
but often it was not real time with very limited value. To conduct that part,
data team is working with highway management companies to extract the data only
about flows of container trucks, a determining factor for data analytics. Just
imagine how huge amount of data are generated each day at the toll-gate near
the site? And what value would be drawn by analyzing the data on a yearly
basis. Of course, the traffic data alone are far from being sufficient to
support the decision-making. A systematic approach is needed to integrate such
dataset as vicinity, urban planning, industries cluster, and even demographic
figures and facts, among others. Vicinity refers to the geological conditions
that determine the likelihood of site whether making the soil foundation is
cost-effective. Or any environmental damage might happen from the surroundings
of manufacturing for instance. Industry cluster refers to what kind of
industries are located in the target market, which substantially influences the
decisions on what kind of products are supposed to provide servicing the
industries there. Urban planning is the most tricky and unstructured part of
data in the whole process. Each city hall has a kind of master plan for urban development
for minimum five years to maximum of 15 years. However it is often adjusted
under each city administration given the difference in preferences of city
governors. Its master plan for traffic networks definitely exert big impact on the
site projections. A new highway under construction close to site signals a potential
value for customers’ operations. On the contrary, a termination of planned road
would likely result in the abort of a would-be good location.
From a broader view of
the whole picture, site selection is only part of the complex decision-making
process. If the site selection can be determined on a location basis, the
decisions on the project site are actually made in the global context given
that data other than location-related are impacted by the global economy trend.
Raw material prices for instance are the prime example for our case.
Combined together, the
foregoing dataset are already tremendously huge, and beyond the capability of
traditional analysis based upon the spreadsheet in columns and rows. Big data
technologies have provided solutions for organizations to capture the value. As such, logistics infrastructure are decided
not only on a local basis, or on a regional basis but on a global basis in an
age of globalization.
3. Emerging technologies and its disruptive
impact on business model
Though more and more
widely used across sectors in the logistics industry, big data analytics is
still at an evolving stage. Tough competitions drive continued innovation. The
market landscape is revolutionizing with new emerging technologies to create
new sources of data. Conversely, big data enable the market more efficient,
productive and transparent.
Google map is a useful
tool for users to go virtually through the streets but the data is not on real
time horizon. A real time mapping technology will help users to know the
operation ongoing at a warehouse site on a daily basis. Along with the data
generated by sensor embedded in the container trucks, the big market data will
be stored instantly and be analyzed for decision-making. Simply put, the
transparency of market data among supply and demand will help customers identify
where they can locate the required space while the owner can find where the
potential demand is. With that going, the business in logistics warehouse
industry conducted by taking advantage of information asymmetry will be
disrupted such as brokers. The brokerage fee is generated to the extent how
information is asymmetrical.
In a real world, a brand-new
innovative platform is emerging with combination of technologies such as mobile
devices, social media, search and real-time data, where information either on
supply or on demand could be searched out. They are providing a value-added
service by optimizing the selection time and process for customers and owners
as well, and gain profit through offline consulting services, leveraging on
their expertise such as configuration of space, and supply chain financing.
Customer loyalty is highly enhanced due to benefits shared by each
stakeholders.
4. Industry outlook in an age of globalized
digital world
Big data is at its
evolving stage and the potential for its value creation is still unclaimed. The
extent to which big data turn out big value in industries will be synergized by
the impact of globalization. It is true that decision-making make never be the
same, yet the data-driven decision-making in all industries will turn out more
accurate ultimately. For enterprises, it is necessary for them to shift the
strategic thinking from data-users to extract value from big data. It could not
be done in short time for all organizations. It is the tide that world move
toward. The best thing that we can do is to go with the tide. There will always
be risks ahead but it can be minimized with big data to our advantage.
