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Big Data and the Supply Chain


BigDataBlogImageGoogle “Big Data” and your top results will be from the leading names in enterprise IT: Oracle, IBM, Intel, McKinsey and the like. It’s a hot topic with tremendous business-driving potential. Big Data is loosely defined as huge, complex data sets that require powerful analytical tools and the industry at large has described three dimensions: volume, velocity, and variety. Recently, there’s a lot of discussion about a “fourth V” – veracity, which speaks to the difficulty businesses have in deriving value from their information. These “Vs” are key to understanding what’s new about Big Data.

  • Volume: There is so much of it—12 terabits of tweets per day for example. 
  • Velocity: The data may need to be analysed in real-time, such as with financial transactions that can happen instantaneously.
  • Variety: The data can be generated from an ever-growing list of sources like social media, text, images, sounds, sensor readings, RFID and many more.
  • Veracity: Businesses simply don’t trust much of the information they have on hand to make business decisions. This challenge is compounded by the other 3 Vs.

The volume of big data is ever increasing—every day we create 2.5 quintillion bytes of information. So with this increasing amount of data in the eco system, the question is what do we do with it and can it be useful to assist in improving the supply chain?

Some of the core areas of Big Data that relate specifically to the supply chain according to Oracle are:

  • Web Logs – Customer shopping patterns, order data, browsing information. 
  • Trailer Tags – Tracking data on trailers or shipping containers including transit times, temperature logs and security data.
  • Pallet/Case Tags – Travel and dwell times for individual pallets or cases.
  • EOBR’s (Electronic on-Board Readings) – Vehicle transit times including driver hours and productivity. 
  • Mobile Devices – Application usage by employees, partners and customers.
  • Social Platforms – Social media information on the popularity of products and profiles of users. 

These sources are above and beyond the more traditional sources of supply chain data which include:

  • EDI – Order information, goods receipt data 
  • Planning Data – S&OP including suppliers and customer plans
  • Financial Transaction Data – Purchase orders, payment details

Well-planned analysis of big data represents a huge opportunity to optimize supply chains by giving more detailed information than ever before at every aspect of the supply chain. By harnessing the data, supply chain improvements can provide big business benefits

  • Products demand can be better forecasted 
  • Changes in demand will be picked up more quickly improving supply and inventory
  • Bottlenecks in materials procurement, assembly, and transit can be identified and better managed
  • Products can be further customized and targeted at specific groups or individuals

In order to take advantage of the opportunities, a key competency will be how to manage the data flows through the supply chain utilizing ERP systems and more importantly automating processes to effectively analyse the data. We’ll look at advancements in information technology within the supply chain in future posts.



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