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
2.2. Sustainability
Big data can provide useful tools for manufacturers to perform their operations in a sustainable manner, keeping the environment better for future generations. Xu et al. showed how using the available big data on used products can increase the efficiency of remanufacturing systems and save more resources. Dubey et al. performed a field study and used the responses by 405 senior managers to develop a framework that could use big data to determine the most important factors for maintaining a sustainable manufacturing system. Lowering service costs, increasing the level of trust between stakeholders, respecting customers' privacy, and increasing data-sharing security are among the benefits that big data analytics may bring to sustainable manufacturing systems. In another study, Huang et al., developed a theoretical approach to demonstrate the application of big data analytics in the area of production safety management.
The application of big data analytics in Bosch Car Multimedia's (Braga-Portugal) organization reviews the challenges of collecting, integrating, storing and processing the data in a manufacturing environment. The Bosch organization study shows the potential opportunity that is created when the volume, variety, and velocity of data is used for sustainable innovations in a future manufacturing environment. In another article, the importance of risk management in developing sustainable manufacturing supply chains was studied. The paper showed that applying big data analytics in order to mitigate the supply chain's social risk can help improve social and economic sustainability.