The role of big data in the retail supply chain

By admin on 13/08/2016Comments Off on The role of big data in the retail supply chain

The role of big data in the retail supply chain

Big data isn’t only about the amount of information involved; it’s also about the ability to process and analyse it from multiple angles. It’s a buzzword yes, but it’s also a new era; we’ve moved from electronic data processing to the information technology age. It means all that data hidden in archives and basements can now be made available for a business to process. All of it.

However, big data isn’t a magic bullet. But there are some real benefits to be had, and not just in the most talked-up arenas of marketing and online business. There are also impressive gains to be made in traditional operations, such as in supply chain management, that can benefit from:

1) Increased data transparency and faster access to data and calculations

2) Faster and more detailed performance monitoring and exception identification

3) Faster and more accurate decision making using automated algorithms

4) Faster and more accurate analyses

There is a wide range of technology that handles big data, but to truly be able to leverage and analyse vast amounts of data, the right technology should have additional capabilities built in; such as columnar database and in-memory computing.

There are big data databases that offer a columnar layout which have the ability for millions of data sets to be compressed efficiently and calculations to be done in-memory, within a computer’s random access memory (RAM). This allows huge quantities of data to be processed up to 100 times faster than with comparable systems. The use of advanced algorithms and analytics can turn data into a powerful resource for providing better forecasting & replenishment, dynamic snapshots of a business in real-time and instantaneous illustrations of the impact of supply chain decisions.

Advanced Supply Chain Management (SCM) software offers retailers in-memory computing capable of processing information for tens of thousands of SKUs across major multinational chains, with the capability of forecasting years into the future. For example, the replenishment team can analyse the options for a forthcoming promotion by running hypotheticals for all the possible permutations. Drawing on historical data for the promoted product, or in its absence on an appropriate substitute reference product, forecasts can be run for each store, location, discount and season combination. The results, which are generated in seconds, can then be analysed to gather insight in areas such as profits made during the promotion, or the ongoing sales uplift. These SCM solutions can also flag potential bottlenecks and demand peaks in time for organisations to take action.

What remains key, is that any “big data” technology used within operations and logistics needs to be able to handle vast data flows with speed and agility, that allows working with big data to be intuitive, straightforward and useful. The technology needs be able to evolve at the rate a business changes.