In the past few years, there is a lot of buzz around ‘Big Data Analytics’ owing to its tremendous potential in providing valuable insights for better supply chain management.

Big Data Analytics has touched almost all the functions of supply chain management such as network optimization, demand forecasting, logistics planning, inventory management, supplier collaboration, customer service and risk management.

Why is it difficult to apply big data analytics in supply chain management inspite of its great usefulness?

Challenges in Using ‘Big Data Analytics’ & rectification

Big data is characterized by 3Vs: Volume (Amount of data generated), Velocity (The speed with which data is generated) and variety (formats in which data is generated). The collection, storage and aggregation of all the data generated at a higher speed from the various sources in the unstructured formats, are the biggest challenges in exploiting big data analytics for supply chain improvement. Moreover, big data analytics demands sophisticated systems and technology to process the data. Other challenges include the inadequate knowledge of data types to be used, proper handling of analytics tools and transformation of operations as per insights gained by the big data analytics.

In parallel, they need to develop the tools and capabilities to overcome the above challenges in order to harbor the advantages of big data analytics in its true sense.

Some Examples of Big Data Analytics in Supply Chain Management

Amazon: Monitors and controls the inventory of around 1.5 billion items, present in its 200 fulfillment centres with the help of big data analytics.

Wal-Mart: Uses big data analytics to handle around a million customer transactions per hour. It had asked their suppliers to implement radio frequency identification (RFID) systems for tagging of the shipments. As a result, the generation of data is increased to 100-1000 times of data generated from conventional bar code systems. It imports data from various databases for further analysis.

UPS: Redesigned their global logistics network after the deployment of telematics (set of technologies to analyse information from the vehicle) in its cargo segment.

Conclusion

The era of ‘Big Data Analytics’ in supply chain management has just begun and there is a long way to go. The businesses have to adopt the technologies for the deployment of big data analytics sooner rather than later to address their supply chain challenges. The early adapters will gain a competitive edge in harnessing the big data analytics to boost the ir supply chain performance.

CGN Global has a vast experience in improving various supply chains of manufacturing, consumer goods and logistics companies with the help of big data & analytics.

References

http://www.supplychaindive.com/news/what-Big-Data-supply-chain-application-primer/435865/
https://scholarspace.manoa.hawaii.edu/bitstream/10125/41314/1/paper0165.pdf
https://www.forbes.com/sites/louiscolumbus/2015/07/13/ten-ways-big-data-is-revolutionizing-supply-chain-management/#2f598e1869f5
http://www.industryweek.com/supply-chain-big-data
http://www.smartdatacollective.com/larry-alton/463019/big-data-analytics-and-changing-face-supply-chain-management
http://esg.adec-innovations.com/resources/newsletters/june-2016-sustainability-issues-that-businesses-can-capitalize-on/big-data-analytics-and-the-evolution-of-the-supply-chain/
https://www.researchgate.net/publication/270506965_Big_Data_Analytics_in_Supply_Chain_Manage-ment_Trends_and_Related_Research

What we found interesting

https://www.investcalgaryregion.ca/blog/how-to-use-big-data-supply-chain-analytics