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How Big Data Analytics Can Improve Demand-Supply Cycle in Retail?

Big data has found diverse application in different industries. From healthcare to automotive, there would hardly be any sector that cannot benefit from big data. A few years ago, in a seminar, I was asked about how big data can enhance the supply and demand chain in retail industry and it took me a while to answer that question.

This motivated me to write about big data uses in the retail industry, specifically about the supply and demand cycle.

The Data that Backs the Proposition

In November 2017, a renowned research and consulting company, published a report on the importance of analytics in supply chain management. The report included result of a survey in which participants were asked about the future of analytics in retail and the strategy they would use to embrace the digital mediums. The results showed that 94% of respondents agreed to digitalization being an important factor in supply chain.

While, this shows that companies are accepting digital means and methods like big data analytics, this also points out to the importance of having a strategy. Almost 50% of the respondents did not have a strategy in place.

The Logical Reasoning

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.
  • Reduce Investments: Retail chains operate on an investment first model in most of the cases. They usually have an upfront investment followed by returns as and when the product sells. Big data analytics can help you create strategies that will optimize production and inventory processes; thus, increasing the time frame of your return on the investment.
  • Greater Clarity: In a supply-demand cycle, many different individuals and teams are involved in sourcing a product, marketing it, selling it, etc. Big data analytics increases the visibility of data throughout teams. The team involved in sourcing gets to see the data that the marketing team or the sales team has. This empowers sensible decision making.

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.

 

About the Author

James Warner is a business intelligence analyst at NexSoftSys.com with excellent knowledge on Hadoop and big data analysis, data warehousing/data staging/ETL tool, design and development, testing and deployment of software systems from development stage to production stage with giving emphasis on object oriented paradigm.

 

 

 

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Comments

  1. Its a good insight into the big data analytics. Demand forecasting is a challenging task that could benefit from additional relevant data and processes. It also helps supply chain managers to make more informed decisions for their organization.

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