There are numerous advantages of using big data analytics in a retail setting. However, today, we are focusing only on the demand and supply chain. Let’s first look at a few ways big data analytics can help.
- Predictive Analysis: A supply chain is based on predictions that rely on previous performance of a products, seasonal demand, etc. Retailers stock products that will be sold off within a stipulated time. Big retailers usually maintain records of the products they have, the shelf life, the time to sale, etc. This data can be analyzed using big data analytics strategies.
- Product Tracking: The retail sector has experienced a boom and major industry players have gone far from manual tracking of goods acquired and good sold. With big data analytics, retailers can use digital copies to track a product in terms of the demand, returns, damages, discount offered, etc.
- Real-Time Insights: No matter what supply chain model you use, real-time insights can be useful especially when you have to take instant decisions. For example, if you have to discontinue a product because of compliance issues or repeated returns, you might need real-time data. This is just one example and there might be many more such cases where real-time insights matter.
With big data entering all mainstream industries, analytics is surely going to be different and not limited to projections and predictions. The supply chain in retail is a small example of the vast applicability of big data. The benefits that it offers in terms of investments, efforts, decision making, etc. are all attraction points for retail businesses who are sooner or later going to adopt big data analytics to completely rule the market or at least have a considerable market share.
This article originally appeared on insidebigdata.com. To read the full article, click here.