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)
3.4. Food supply chains
The lack of information in food supply chains can bring about huge costs in the forms of deterioration and waste. Accordingly, using the information excluded from big data analytics is gaining more attention from the decision makers in food supply chains. Big data analytics has applications in agricultural supply chain management, farm management, food sustainability assurance, consumer demand management, new product development, and food safety. Ji & Tan considers five major benefits of using big data in food supply chain management: 1- data sharing over supply chain echelons; 2- doing experiments to find frauds and anomalies in the supply chain; 3- accurate clustering of customers in order to target the marketing of each cluster; 4- developing automated algorithms to support the decision-making processes; and 5- developing new products, services, and business models.
Big data analytics was used by Liu, 2017 to develop new e-commerce methods for marketing fresh products with a short shelf life by keeping in mind the critical aspects of humidity and temperature. In another research paper, Mishra et al. used social media big data to determine factors that influence customers' beef purchasing decisions. They believe that the available unstructured big data in social media can help businesses to design their supply chain to be more consumer centric. Big data applications have also been developed for cold chains (temperature-controlled supply chains). However, there is an important lack of understanding regarding what data in a cold chain should be collected, and what is the appropriate method to collect and analyze that data.