Big Data Analytics in Supply Chain Management
Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.
Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.
3. Big data in supply chain management (other than manufacturing and logistics)
3.1. Strategy development
Management's decision to use big data analytics in a company would be a strategic one. Management commitment positively affects the level of accepting big data analytics in a company. Using big data analytics lets the manager have access to analysis which based on dynamic data, and can make the supply chain more competitive. Applying big data analytics to planning, decision making, and supply chain coordination and control can improve the preparedness, alertness, and agility of the supply chains in question. The aspect of value creation by big data has not been studied often in supply chain management literature. Therefore, Brinch studied the value discovery, creation, and capture that can be achieved using big data analytics in a business supply chain.
All benefits considered, there still is not enough empirical research that applies big data analytics to supply chain management, so there is a lack of ability to adopt an informed strategy just by comparing different methods. Investing money in the required hardware and software to apply big data analytics may affect the strategies of a supply chain. As for training, our educational system can train a good number of data scientists, but may have ignored the managerial abilities of these data scientists in many cases. As a consequence, converting the available data into applicable knowledge which can mitigate supply chain risks is still an obstacle for many supply chains.