Introduction: and intelligent interfaces that can be

Introduction:

Everyone wants everything. Minimum lead
times, instant quotations, well-functioning pricing mechanisms, easier
documentation, impeccable demand forecasting and so on and so forth. A Lean, agile
or even a le-agile supply chain is the order of the day. But with its scope so
huge, not only in length but also in breadth, there is little transparency and
visibility in how the industry functions. Forecasts are made on guesstimates,
inventory is planned based on those forecasts, logistics are planned according
to how the inventory is managed, all this and so much more now spans all across
the globe. With all of the modules involved in this multi-billion-dollar
industry intertwined, there is little structure and logic to how things work. There
is a glut of data present in the industry which needs to be structured to be
used and make sense of. Processes that can be automated and intelligent interfaces
that can be developed. With the advancement of technology and emergence of new
players, there have been huge efforts bring organization to the supply chain
industry and speed up the processes involved. Going digital is the way forward
and in this article, we take a look at how technology is revolutionizing and
impacting the whole supply chain and logistics industry. We analyze cloud
computing technologies in the supply chain and solutions in the market to get a
sense of how digital SAAS supply chain operates and eases the industry pain
points.

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Reports:

It is extremely important to
leverage digital technologies in order to improve efficiency of digital supply
network. Accenture and the World Economic
Forum reports that
there is $1.5 trillion of value at stake for logistics players and a further
$2.4 trillion worth of societal benefits as a result of digital transformation
in the coming years (World Economic Forum, 2016). According to MHI’s 2017 annual survey on next generation supply
chains, 80% of respondents believe that the
digital supply chain will be the predominate model within the next five
years—with just 16% saying it’s happening today (Deloitte, 2017). Similarly, a 2016 survey from Capgemini found that 50% of those surveyed see
“digital transformation” as “very important,” yet only 5% were very satisfied
with their progress toward it.

Integrated planning and
execution, logistics visibility, procurement 4.0, smart warehousing, autonomous
and B2C logistics, prescriptive supply chain analytics etc. are some of
important areas of digital supply chain and supply chain is at the center of
the digital enterprise (Stefan Schrauf, 2016). Paper based supply chain
documents are increasingly being replaced by Electronic Document Management. Some companies are taking an
environment friendly steps, Capgemini provides digital supply chain expertise
in green logistics and carbon footprint reductions.

Analytics:

The growing tsunami of data is a boon to
businesses in the digital age. Limitless oceans of data, often reflecting
customer experience as it happens, have the potential to remake supply chains.
To put data growth in context: the world’s total digital data volume, which is
doubling every two years, stood at 4.4 zettabytes (trillion gigabytes) in 2013
and is projected to reach 44 zettabytes by 2020. (EY, 2016) With such amount of
data generated it is imperative for the players in the industry to understand
and utilize this opportunity. Supply chain analytics, be it descriptive,
prescriptive or predictive has become an integral part of the industry. Diagnostic analytics are used for root cause analysis (RCA),
online analytical processing (OLAP), and what-if analytics. They are commonly
applied for RCA of supply chain performance and data visualization. Prescriptive analytics allows optimization of transportation routes, factory scheduling
and inventory. Stochastic and deterministic optimization are both considered
prescriptive analytics functions, as is rapid re-planning optimization. Predictive analytics and machine learning
provides a platform to make utilize it to make decisions. Today, companies are
using different kinds of neural networks for predictive analysis. Different
time-series stochastic models are being built to analyze customer preferences
and forecast sales and other parameters to plan for the everchanging demand.

Financial data, demand data,
product data, manufacturing data, inventory data, weather, traffic, track and
trace data etc. are inputs for prescriptive analytics which helps in answering
the questions such as: What will be the effects of decision or situation? How
can an organization make a particular target to happen? Prescriptive analytics
help to predict scenarios at very minute levels of details. It helps to predict
the effect of shifting to a new supplier on product quality or even it can help
to decide whether introduction of new autonomous machine would increase safety
on the warehouse floor or not. The degree of detail to which companies can
predict the future depends entirely on the ability of companies to bring
together all the key elements of supply chain into an integrated whole. 

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