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
5.2. Advantages
Big data analytics can help companies better understand their business needs. Many companies plan their business growth model according to a demand boom, which may be the result of business growth in general. However, they should consider that a change in market growth may leave them with several empty warehouses and idle manufacturing plants. Big data enables these firms to predict the market direction and plan development strategies based on this analytical information.
Using big data enables companies to simulate a digital model of an entire manufacturing process. Collected data from the customers can be used to improve the marketing and sales processes. Clustering the customers into various groups, providing service according to the needs of each group, and using customers' data to target where/when to advertise these new products are the other applications of big data in marketing.
The importance of big data analytics in improving customer loyalty is not negligible. Most customers would be loyal to the company which provides them a high-quality service the first time. Companies can use big data analysis to predict customers' needs and satisfy those needs in order to make them loyal customers.
Optimizing activities which are out of the normal boundaries of the company - such as supplier selection or technological adjustments - require a high level of information-sharing between the stakeholders in a supply chain. Sharing information has always been an obstacle in supply chains, but big data technological developments can help simplify and speed up this process.
From a logistical point of view, big data can be applied in order to forecast delivery times or optimize delivery routing by using traffic, weather, and drivers' information. Another possible application is to use the products' information in making inventory management and sales decisions.