A brief idea about Big data
In modern digital world
data is a boon to industries. The volume and variety of data being generated
using computers are growing at a rapid pace in every two years. Mostly
unstructured data generated through mails, blogs, Twitter, Facebook,youtube, and other sites are called big data. It is possible to analyze this massive data collection to discover
patterns in the data that have many applications in industries. Rajaraman (2016) defined big data
as a term that is often used to describe such data
that has high volume, high velocity, and may be of high variety. Moreover, it
requires modern technologies to capture, store, and analyze such a huge amount of
unstructured data. Many researchers and industrial practitioners agree that this massive amount of data creates new opportunities and
hence many organizations wish to understand and enhance their big data
analytics capability to make better use of their unstructured data and apply it
in a judicious way.
Source: Rajaraman,
V. 2016. "Big Data Analytics." Resonance
21 (8): 695-716. doi: 10.1007/s12045-016-0376-7.
The need for Big data in Supply chain
Recently the amount of
data produced from end-to-end supply chain management practices has increased
significantly making it difficult for supply chain professionals to handle such a massive amount of data. Data analytics techniques can be used by supply chain
professionals to capture, organize, and analyze unstructured data and give a valuable insight to supply chain industries.
The application of big data in the supply
chain industry helps in demand forecasting, inventory management, transportation management, and human resource management.
Supply Chain Analytics
The term supply chain
analytics is used to define the application of big data analytics in supply
chain management and can be divided into descriptive, predictive, and
prescriptive analytics. Descriptive analytics focuses on what has happened
in a current scenario and why. Various types of real-time
information reporting technologies like RFID and GPS are used to generate data
for such type of analysis. The statistics collected are useful to highlight
total inventory, average money spent per customer, and fluctuations in yearly
sales. Further, predictive analytics is more concerned with the question of
what will happen by exploring data patterns using statistics and simulation. It
can be used to forecast customer behavior and sales pattern. Finally, prescriptive
analytics explores the question of what should be happening and how to make the
best decision. This type of analytics is complex to administer however if
implement effectively this technique can help in optimize production, scheduling, and inventory control.
Role of big data in solving issues of the Supply chain industry
As maintained by Singh Jain et al. (2017) supply chain management has entered an era of internetwork competition. Now it's not brand vs. brand competition but whole supply chains are competing against each other. Supply chain industries are facing several primary and secondary issues that need to be addressed in getting an edge over their competitors. Primary issues deal with reducing operational costs and overall inventory costs. Whereas secondary issues are concerned with improving customer service, risk management, and fast product delivery.
Source: "Application of Big Data in Supply Chain Management." Materials Today: Proceedings 4 (2, Part A): 1106-1115. doi: https://doi.org/10.1016/j.matpr.2017.01.126
Big data help industries
to reduce the complexity of information and analyze the data in such a way so
that it becomes easier for managers to make effective business decisions. Big
data has enabled firms to adopt supply chain analytic techniques to optimize
their operations. Supply chain analytics not only enable companies to
effectively manage their operations but also assist them in formulating long
term business strategies. If a supply chain firm wishes to adopt a push strategy
then they can make use of analytical tools to develop an algorithm that can
accurately forecast demand patterns of customers in a particular season and
using those data investment can be done to maintain the inventory. Hence
companies can reduce their excess inventory cost. Supply chain analytics also helps in analyzing the changing global
market conditions and facilitate demand management. Use of technologies like
RFID and GPS are used to gather real-time information about goods that are
transported from suppliers to manufactures and using these data algorithms is formulated to know the competency
of suppliers. Big data has improved the
decision making capability of
procurement professionals who now can
easily segment their suppliers based on their needs.
The philosophy of
customer-focused supply chain has come into reality with the advancement of
data analytic techniques. Supply chain analytics enables firms to know the
specific needs of their customers' and companies can customize the products
according to the demands of the customer . Electronic commerce along with big
data has transformed the modern business pattern. Now, suppliers and online
retailers can predict customers' demand more clearly and they can serve their
customers in a better way. Manufacturing decisions are also based on the
statistics generated by analytical tools and hence the variable cost of companies
gets minimized. In spite of several advantages
there are few limitations that restrict the application of big data in supply chain
industries. Organizations should develop policies in favor of big data.
Companies can consult the third parties to provide the necessary technical skills to
their employees so that they can work better with digital analytical tools.
Lack of sufficient investment and a shortage of skilled professionals are common
barriers to the proper implementation of big
data analytics in manufacturing supply chains. However, the future of supply chain
companies will depend on the extent of digitalization and it will be
difficult for firms to survive if they
will not be able to make full use of Big Data.
by Divya Shekhar