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)
3.3. Sustainability
Another application of big data analytics in supply chains can be the optimization and adjustment of the operations based on sustainable objectives. Big data can improve the environmental, financial, and operational management of the supply chain in order to help combat climate change. Badiezadeh et al. developed a network data envelopment analysis with big data in order to help assess the performance of sustainable supply chain management. Open access to big data can facilitate innovation, create resilient supply chains, and improve the performance of the distribution network. Liu demonstrates how applying big data analytics to the targeted advertising of products can reduce the carbon emissions in a supply chain.
It is worth noting that using big data analytics is not beneficial in all supply chains. There are several barriers that may interact together and prevent big data analytics from establishing a sustainable system. Cheng et al. considers a sustainable supply chain with a manufacturer and a retailer and shows that the proficiency in big data analytics depends on the service level adopted by the retailer. Available big data from transportation and logistic provider companies can be used to satisfy delivery requirements while also keeping in mind carbon emission constraints. In another study, the application of Big data analytics in enabling the resilience of supply chains after disasters was studied by Papadopoulos et al., where they used the example of Nepal to prove their analysis.