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.
2. Big data in manufacturing systems
2.1. Operations improvement
A number of studies show that big data analytics can improve the entire operational performance in manufacturing systems. Yadegaridehkordi et al. developed a hybrid approach to study the effect of the adoption of big data analytics on manufacturing companies' performance. Popovič et al. showed that big data analytics' capability, along with organizational readiness and certain design factors, could enhance a business's performance. In another study, Guo et al. applied data visualization and machine learning algorithms to better inform the operations manager of the product's market situation. Some other applications of big data analytics in manufacturing systems are shown by implementing big data analytics in a manufacturing company and using big data to improve the trading performance of emitting companies.