Big data is not just limited to the Silicon Valley, but it has
transformed the operation of most large-scale industries. Through predictive
analytics, big data has made it possible to determine how a series of factors
can potentially impact a company’s business. When it comes to supply chain
management (SCM), dozens of variables define its success, and predictive
analytics can make a significant difference. While each element of the supply
chain is the focus of a distinctive management activity, as much as 90% of the
supply chain demands some level of forecasting.
How then, can predictive analytics help you be at the top of your game
as a supply chain manager? Besides aiding in cost reduction and improving
customer behaviour, it can help you avoid risky ventures by forecasting future
trends, thus keeping you a step ahead in running a business that is more
accurate and profitable than ever before.
Until recently, supply chains had
mostly relied upon quantifiable factors like statistical models and performance
indicators. However, with the introduction of big data, predictive analytics
has now started revolutionizing the industry by analyzing unstructured, rapidly
growing datasets in real-time. Investing in data science
courses can thus help supply chain managers to
make better-informed decisions about their overall processes. The SCM of a
particular product can be impacted by a variety of factors like machinery,
vehicles, weather condition, workforce, etc. As a result, SCM experts have been
working relentlessly on how each supply chain can be optimised to drive maximum
profitability.
In a white paper published by the
Journal of Business Logistics, it was
stated that big data analytics could have a variety of prospective applications
in Supply Chain Management which when applied, can help industries in
outperforming their competitors. When there are a dozen variables that can
directly impact operations, here is how predictive analytics can make a
difference in the SCM workflow:
Making sense of massive volumes of data:
When it comes to large quantities
of data, predictive analytics becomes indispensable as it not only makes it
easy to collect data but also computes it efficiently to generate relevant
insights. The various processes associated with a supply chain produce enormous
amounts of data, and while easily accessible, this data is hardly useful when
arranged into small samples. With predictive analytics, SCM professionals can
create their high-performance computational systems that are tailored to the
workflow of their organization. Once the operational data is arranged and
analyzed on a global scale, it gives forth several kinds of patterns. These
patterns can then be successfully used to predict several instances like the
rise and fall in demand for a product, the number of items required for
inventory management, etc.
Help avoid risky ventures:
The high degree of accuracy
provided by predictive analytics enables SCM managers to identify fraudulent
activities, thus helping them identify safer alternatives instead. It can also
be used to focus on areas that had been previously neglected, to later provide
a measurable boost to the company’s sales figures. For instance, an inventory
management database only depicts a list of all the raw materials required for
manufacturing. On the other hand, an inventory management system powered by
predictive analytics can precisely predict which items of the inventory need to
be replenished based on the history of their individual usage. Predictive
analytics classifies data into patterns that are relevant for an organization’s
computations. These patterns can thus be used to recognize different facets of
the supply chain like data reusability, refinement of datasets, and operational
analysis at each stage of operation.
Forecasting future trends:
By running computations on data
collected from sources like customers, suppliers, and partners, predictive
analytics can predict future trends. By analyzing these data using advanced
analytical tools, SCM experts can pinpoint complex intricacies of consumers’
demand and can thus determine potential variations in supply and demand. For
instance, a retail SCM professional can use predictive analytics to examine
historical transactions to predict a possible surge in demand. A supply chain
that is driven by analytics can also forecast structural shifts in the economy.
These insights can then be used for planning inventory and production processes
in a manner that can increase production and decrease spending on unnecessary
items.
Save time and reduce cost:
One of the biggest reasons behind
the spur of predictive analytics in SCM is that it helps professionals to reduce
the time and expense of computations involved significantly. According to a
study performed by Accenture Global Operations, the
incorporation of big data and predictive analytics in a company’s operations
can lead to a 4.25x improvement in the delivery time, and a 2.6x increase in
the efficiency of the overall supply chain. Data-driven evaluations can thus
significantly simplify previously complex processes. For instance, earlier it
used to take SCM experts a significant amount of time to come up with practical
solutions to operational problems, and even they involved human errors
associated with calculation and evaluation. With predictive analyses, however,
SCM professionals can quickly implement the advanced algorithms that are
tailored to their specific needs and are free of error-causing inaccuracies.
Further, even when an unexpected event takes place, it can be rerouted again
within a few minutes, thus making sure that only accurate predictions have been
made.
Aids in improving customer experience:
SCM personnel can also use
predictive analysis to enhance their company’s customer service significantly.
Since predictive analysis tools analyze the performance of the supply chain
both as a whole and as separate links in the chain; professionals can now determine
precisely which aspect of the supply chain can be worked upon to improve the
customer experience. Further, if a problem happens to pop up in the workflow,
professionals already have the in-depth knowledge that enables them to pinpoint
the cause of the problem and thus come up with an immediate solution. Thus,
predictive analytics analyses customer reviews and behaviour that helps
professionals to make the potential changes for performance improvement.
When it comes to SCM, big data
and predictive analytics have just started to make an impact in the field.
Process management techniques enable every company to come up with relevant
information on various processes in the production lifecycle. SCM professionals
can thus use predictive analytics to improve their processes significantly,
thus giving them a competitive advantage over their peers.
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