ITECH7413 Big Data Technologies in Supply Chain Management Assignment Sample

Big Data Technology

Big data indicates the higher volume of data at a greater pace and a higher variety. Big data is the modern field in information technology that is responsible for predicting the needs and desires of a business. It can be defined as a novel generation of technologies and infrastructures intended to fiscally extract worth from extremely massive volumes of a broad amount of data, by facilitating greater pace capture, and analysis (Alexandru et al., 2016). It entails datasets which cannot be evaluated by the usual traditional data analysis tools further, it indicates the application of specified techniques and tools to manage massive datasets (Nasereddin, L-Khraishah, & Hakem, 2020).

Importance of Big Data in Supply Chain

As per Awwad et al. (2018), there is no doubt that the data generated is being progressively growing at a faster rate with the technological advancements across the organizations of the supply chain. Before the use of IT (Information technology), the information flow in the supply chain was documented in respect of physical documents. However, with the advent of IT tools, the preponderance of information flow related to the flow of resources is being accepted in form of digitally structured data. Additionally, it can be said that the volume of data gathered from several processes of the supply chain and the pace at which it is produced can be supposed as big data. Today, big data technology is assisting organizations in managing more responsive supply chains by analyzing market trends and customer perceptions. It is also enabling the prediction of supply chain linked activities strategically. In contemporary times, clients are more interested in getting real-time updates on product orders, its availability before purchasing it, and also to get access to the details of product manufacturing. In this regard, warehouse management can use big data to know the changes in consumer behaviour and the expectations of clients from the supply chain organizations (Sanders, 2016).

Benefits of Big Data in SCM (Supply Chain Management)

It is noteworthy that big data has a great potential for refining productivity and effectiveness and hence generating superior outputs (Acharya et al., 2018). The advantages of adopting big data technology in the operations of supply chain management are numerous and are illustrated below.

• It assists in recognizing supplier issues
• It offers more accurate and transparent operational information
• It also facilitates supplier modification and timely improvements
• It provides higher prominence throughout the supply chain
• It avoids recalls by giving early warnings about the faulty services and products.
• It refines the traceability of goods and services that is an essential feature of logistics
• The use of big data has enabled the organizations to lessen their inventory and supply chain risks
• It refines decision-making in entire SCM and makes precise decisions
• It has enables thorough evaluation of vendor performance in terms of client complaints, on-time service, vender profitability, and feedbacks.
• It assists to develop client buying behaviour by evaluating data from social media networks and other such websites.
• It produces more profitability by forecasting forthcoming consequences having a higher confidence level (Mohan, 2017).

Applications of Big Data in SCM

• ü Transparency inconsistency: Transparency indeed is one of the most essential features to be considered by clients, transporters, and carriers. In this respect, there are sensors attached in the vehicles to ensure transparency in the entire process. Sensors generate the data that can be utilized to evaluate the time of distribution and thereby evading any obstacle in the supply chain process (Arunachalam, Kumar & Kawalek, 2018).
• ü Delivery of subtle goods: It is noteworthy that the preservation of delicate products is a kind of big challenge for food manufacturers. Moreover, there is no need to worry about as the big data technology assists in holding the freshness of such kind of products by its diverse techniques. Again, it makes use of sensors at a truck that carries daily items and these are installed within the storage space in a bid to keep the record of temperature. Moreover, there is a technique called traffic condition prediction that certifies a greater quality of delicate products to be shipped and transported (Sanders, 2016).
• ü Route Optimization: It can be said that the optimization of routes assists in minimizing cost and evading late shipments. Big data is a technique that overcomes all the challenges faced by the company in terms of fuel costs, climate change, repair works, and so on. Big data technology does so by assimilating data gathered from sensors in automobiles, road conservation data, climate prediction, and more into a system to take effective decisions (Jain et al., 2017).
• ü The last mile of distribution can be accelerated: The last-mile of shipping indeed accounts for nearly 28% of entire delivery costs that are extremely ineffective. Moreover, the obstacles may arise like complexity in parking vehicles in metropolitan cities, enhanced delivery time, and so on. These challenges and obstacles are overcome with the usage of big-data technology as it facilitates the transporters to view the entire distribution procedure from start to end (Ranier et al., 2018).
• ü Mechanization of warehouses and the supply chain: The assimilation of big data with IT has an extremely higher potential to upsurge logistics efficiency. It results in the mechanization of the entire supply chain system. Moreover, numerous companies are incorporating such technologies like for example Amazon is making use of robots to hold the items from shelves and using automatic drones for delivering products.
• ü Refining forecasts on client needs: Organizations have to meet the tastes and preferences of the clients in a bid to retain them for long. Besides, there must be the complete fulfilment of orders of a company or else it will lose its reputation in the market. In regards to this, companies can leverage big data technology to get a complete understanding of the clients to forecast their needs, comprehend individual insights, and develop an exclusive brand experience (Awwad et al., 2018).

References for Big Data Technologies in Supply Chain Management

Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation, and decision making in the fashion industry. International Journal of Information Management, 42, 90-101.

Alexandru, A., Alexandru, C. A., Coardos, D., & Tudora, E. (2016). Big data: concepts, technologies, and applications in the public sector. Int J Comput Electr Autom Control Inform Eng, 10, 1629-1635.

Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges, and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.

Awwad, M., Kulkarni, P., Bapna, R., & Marathe, A. (2018, September). Big Data Analytics in Supply Chain: A Literature Review. In Proceedings of the International Conference on Industrial Engineering and Operations Management, 418-425

Jain, A. D. S., Mehta, I., Mitra, J., & Agrawal, S. (2017). Application of big data in supply chain management. Materials Today: Proceedings, 4(2), 1106-1115.

Mohan, S. (2017). Big Data: Transforming Logistics and Supply Chain. International Journal of Pure and Applied Mathematics, 117(20), 911-916.

Nasereddin, H. H., L-Khraishah, H. A., & Hakem, H. (2020). Big Data Technologies in Supply Chain Management: Opportunities, Challenges, and Future Trends. International Journal of Management, 11(6).

Ranieri, L., Digiesi, S., Silvestri, B., & Roccotelli, M. (2018). A review of last-mile logistics innovations in an externalities cost reduction vision. Sustainability, 10(3), 782.

Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.

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