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.
5. Conclusions
5.3. Directions for future studies
Both the literature and a review of companies' experience in the area of using big data analytics in manufacturing shows that the number of applied case studies are more than the number of theoretical publications. It is likely that researchers will develop more novel applications for big data in manufacturing systems, such as developing methods that can obtain high-quality solutions using less time and money.
Big data has been widely used for predictive studies in the literature, but there are not many prediction error measurement studies in big data. More precisely, beyond simply the quality of the input data, the accuracy of big data analysis is significantly affected by the quality of the model used to analyze the data. We still have a way to go regarding developing measures which can determine the accuracy of a big data analysis method.
The current studies regarding big data applications in supply chain management are mostly theoretical and conceptual, and there is a noticeable shortage of studies of analytical models. Moreover, the existing analytical models mostly study big data applications in modeling sustainability. Therefore, there is still a gap in the application of big data regarding optimizing operations (such as logistics and procurement) in a supply chain.
There are some study directions in big data that can significantly improve the performance of logistics systems: 1- Developing an efficient collaboration among all the decision makers, transporters, retailers, and door-to-door delivery service providers; and 2- Applying cloud-based services in smart transportation systems and integrating them in an online planning framework in order to provide a connection between vehicles, traffic managers, and the final customers.
Finally, a good research topic to follow would be to find out if analyzing a sample of a big data set could give us the same quality results as the basic big data analytics. Design of experiment methods can be used for categorizing big data into different groups in order to save time regarding analysis.