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)
3.5. Risk management
Any supply chain that is dealing with uncertainties in its decision-making processes is using risk management methods at some level. Risk is one of the consequences of a lack of information, and big data can be applied to reduce this lack of information. In the context of logistics processes, transportation risk can be defined as the deviation from the estimated delivery time. Big data analytics can be applied to predict these delivery time deviations, as well as prevent transportation risks such as missing cargo flights. Engelseth & Wang used big data analytics to manage the risks in long-linked supply chains. They used an analytical framework to mitigate the risks of a case study that looked at machine parts imported from China to Norway.
The establishment of big data analytics could resolve bargain issues between a supplier and a retailer. Tsao used big data analytics and game theory to show the way that a supplier and a retailer could determine the period in which to use their credit in order to minimize their risk of defaulting. A type of common risk is that of hazardous materials and waste in closed supply chains (supply chains with remanufacturers and recyclers). Big data analytics has also proven to be useful in recognizing powerful demand signals and minimizing the negative environmental impacts of remanufacturing.