Big Data Analytics in Supply Chain Industry

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:

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