Importance of big data in the business environment of Amazon

Supply chain management and logistics are the crucial part of the business processes. It is the logistics and the supply chain management that manages the distribution, storage, transportation and packaging as well as delivery of the items. Big data plays an important role in managing the logistics and supply chain management (Ghosh 2015). This article is aimed to highlight the importance of big data in supply chain and logistic taking the special case of Amazon.

Big data helps to provide the information regarding the global flow of the goods and services (Jeske et al. 2013). Big data helps to collect and process the voluminous data like the demography and statistics of traffic through the fleet of vehicles. It also  improves the tractability performance by reducing the time to access, integrate and manage the product’s database (Hazen et al. 2014).

Importance of big data in retail

Big data enables the retailers to know which merchandisers must be stocked in a particular location and the items to be placed in the stores. Big data helps in optimising the inventory and thus preventing stock out incidents. Moreover it helps to understand the needs of customers before they shop (Gantz & Reinsel 2012). About 5 ZB of data was generated by e-commerce websites like Myntra, Amazon, Jabong and ebay in 2015. The following trend shows growth in the demand of the big data by customers as well as in business in coming years (Padgavankar & Gupta 2014).

Growing demand of Big Data (Source: Ghosh 2015)
Growing demand of Big Data measured in ZB (Source: Ghosh 2015)

Importance of big data in e-commerce

Amazon is one of the most popular companies that take leverage of big data in the supply chain management. Amazon has moved to big online market, offering more than 480 million products (Zhu & Liu 2014). Big data has helped the online retailer to judge the demands of customer present in different locations by identifying their previous purchases. Apart from the items searched by customers for shopping  online, time spent while e-shopping are also used by Amazon to estimate the consumer demand. It segments its customers based on their interests and purchases. Amazon sells its products or items based on trends and demography of its customers.

Changes posed in Amazon before and after the adoption of big data

Importance of big data was realised  by Amazon and it start focusing on it. It was found that retailers of Amazon who had started using big data in their business have experienced 60% growth in their business margins and also 1% improvement in productivity of labour from the past 6 years. Below statistics show the growth in the amazon revenue from past two years because of the adoption of big data or Amazon web services in Amazon.

Impact of big data on the revenues of Amazon
Impact of big data on the revenues (in millions) of Amazon

It was found that the total revenue of Amazon has increased by 4.7 million dollars during 2013- 2015 (Carvalho & Marden 2015).

By using the big data recommendation engine, it was found that Amazon analysed customer data coming from 152 million plus accounts and it was found that big data helped in generating 29% sales (Polonetsky & Tene 2013). Furthermore big data helps to identify the real time risks while processing and integrating the data thereby detecting online fraud in Amazon. According to global payment technology, Amazon has made an improvement of 132% in detecting fraud (Nelson 2016) in online transactions.

Big Data streamlining order management system of Amazon

Before big data came into picture, Amazon faced a lot of issues in analysing consumer trends. It took about 10-15 days for Amazon to analyse the buying behaviour of consumers (Richards et al. 2014).  Also lot of time was consumed in integrating and processing the data. Due to the emergence of big data, low effort with minimum time is required to integrate and analyse the data. Different vendors of Amazon use different softwares for managing their order system. Two of the mostly used softwares by Amazon for order management are discussed below:

Atmosol

Some of the vendors of Amazon like Tupperware, Nick, Red bull, South Park, Wrigley, Russell Athletic etc. makes use of the software Atmosol. A customer can fulfill the order, manage inventory and interact with the software system by leveraging the Amazon Web-store platform for processing their e-commerce needs. To keep the site in top operating condition it makes use of custom tools of productivity which are Order Management System and Inventory Management System.

Order Management System is the software package of atmosol’s software for processing the orders of customers. This system helps to transfer the data from the systems of these vendors to the Amazon Web-store system. Through this software, enterprises transfer the order shipment details to Amazon Web-store. The software makes use of Amazon API in order to execute functions. To manage the data related to the Amazon Web-store Inventory, Inventory Management System is used. Similar to the Order Management System, Inventory system acts as a bridge between the vendor’s system and Amazon Web-store System. Furthermore the software helps in updating and managing the warehouse’s inventory level to Amazon Web-store (Chandler 2016).

Solid commerce

Solid Commerce is the software used by the Amazon vendors to keep their order management system simple, accurate and compare prices of similar offerings. The software helps the vendors to integrate with popular marketplaces so that vendor can view the product information for each site. As the details of the product’s quality is already provided within the Amazon Web-store, listing of that product is a piece of cake. Consequently, solid commerce software allows vendors in updating Amazon Web-store listings. It would automatically update new quantity, price and information about the product to Amazon Web-store. In addition it also allows the vendors to automatically re-price their product price listing and also helps to manage the variations in listings (Management 2016).

References 

  • Atmosol – chandler, A., 2016. Atmosol Everything Ecommerce. Atmosol, p.1. Available at: https://webstore.atmosol.com/amazon-webstore-order-processing-inventory-management-integration.html.
  • Carvalho, L. & Marden, M., 2015. Quantifying the Business Value of Amazon Web Services. IDC White Paper, pp.1–15.
  • Gantz, J. & Reinsel, D., 2012. THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digi tal Shadows, and Biggest Growth in the Far East. Idc, 2007(December 2012), pp.1–16.
  • Ghosh, D., 2015. Big data in logistics and supply chain management – A rethinking step. International Symposium on Advanced Computing and Communication, pp.168–173.
  • Hazen, B.T. et al., 2014. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, pp.72–80. Available at: http://www.sciencedirect.com/science/article/pii/S0925527314001339.
  • Jeske, M., Grüner, M. & WeiB, F., 2013. importance of Big Data in Logistics. DHL Customer Solutions & Innovation, (December), pp.1–30.
  • Management, O., 2016. Solid Commerce. Amazon Inventory, p.1. Available at: http://solidcommerce.com/.
  • Nelson, P., 2016. Fraud Detection Powered by Big Data. Search Technologies, p.1. Available at: http://www.searchtechnologies.com/fraud-detection-big-data.
  • Padgavankar, M.H. & Gupta, S.R., 2014. Big Data Storage and Challenges. International Journal of Computer Science and Information Technologies, 5(2), pp.2218–2223.
  • Polonetsky, J. & Tene, O., 2013. Big Data & Privacy: Workshop Paper Collection. The Board of Trustees of the Leland Stanford Junior University, 25, pp.1–122. Available at: http://www.futureofprivacy.org/big-data-privacy-workshop-paper-collection/.
  • Richards, D.A., Melancon, B.C. & Ratley, J.D., 2014. Managing the Business Risk of Fraud : A Practical Guide. , pp.1–80.
  • Zhu, F. & Liu, Q., 2014. Competing with Complementors : An Empirical Look at Amazon.com. Working Paper 15-044, pp.1–37.

Indra Giri

Senior Analyst at Project Guru
He completed his Masters in Development Economics from South Asian University, New Delhi. His areas of interest includes various socio development issues like poverty, inequality and unemployment in South Asia. Apart from writing for Project Guru he loves to travel and play football in his spare time.
Indra Giri

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