How Can Big Data Analytics Enhance the Demand-Supply Cycle in Retail
Updated: Feb 12
There has virtually been no sector in the market today that has been left untouched by the wave of big data analytics. Every process of a business has been automated and made efficient. One such process which is an essential part of every e-commerce industry is the optimization of the
In 2017, a report was published about the importance of analytics in supply chain management by a renowned research and consulting company. The report included a survey where people were asked their opinion on the future of analytics in retail. The results showed that almost 94% agreed to digitalization being paramount to the growth of the industry, but 50% had no idea how they could embrace this change in their work and company.
Role of Big Data Analytics in Demand-Supply Cycle
Let’s tackle one process at a time and see how big data can help in enhancing the Demand-Supply Cycle in retail-
1. Predictive Analysis
When it comes to optimizing demand and supply, predictive analytics is the holy grail in retail.
It uses the previous position of products in the market to determine how the commodities are going to perform in the next season. It takes into account the performance of the product, seasonal demand etc. to give a prediction that will help the retailers how much of the product to keep in stock, to discount and at what time. Big retailers often keep a record of all their products and then use that data to make analytical strategies.
2. Product Tracking
With the heavy influx of big data-based decision making and other advancements in technology, retailers can now resort to digital footprints of each product in terms of demands, returns, damages, discounts offered etc. and use this that to come up with marketing strategies that not only help in profits but also reduce wastage.
3. Real-time insights
It doesn’t matter which supply-chain model you are following. Real-time insights are your new best friend when it comes to instant decision making. For example, if you see a product having high rate of returns or having compliance issues, then its best to discount it. Note this type of insight is only possible if you have access to real-time data.
4. Reduce investments
Most retail chains operate on an investment first model. They usually have an upfront investment which is followed by returns as and when they happen i.e. when the products sell. Big data analytics can help a retailer create strategies that will help optimize the inventory and product process, in the process increasing the time frame of your return on investment.
5. Greater clarity
The supply-demand chain doesn’t run on one person or variable. It is depended on a number of factors and people. Various teams who are involved in sourcing, selling, marketing etc. comprise of this scenario. Big data serves a great purpose of increasing the visibility throughout the teams.
The team involved in sourcing the product can see the data marketing or the sales team has.
This empowers sensible decision making.
Looking for a way to integrate big data analytics more seamlessly with your supply chain?
Contact us at Datahut, your big data experts.