We are a constantly connected information generation. The amount of data that is being created and stored on a daily basis is growing exponentially but are businesses mining and analyzing this information to glean useful insights?

Big data is a relatively new term that is used to describe the large volume of data (whether structured or unstructured) that flows in and out of any given business every day. The volume of data can seem overwhelming but the focus should be on how that information can be harnessed.

Analyst Doug Laney describes big data as having:

  • Volume. In the past, storing and accessing and analysing this large amount of information was very difficult but the right analytics software has made this possible.
  • Velocity. The speed at which data comes in makes the ability to view and assess data in real-time a high priority. RFID tags, sensors and smart metering assist with this level of visibility.
  • Variety. Data is being generated in many different forms from a myriad of data points such as connected devices, GPS records, as well as information gathered from supply chain software like ERP and WMS, websites, and even social media. According to recent research there are in the region of 50 different sources of data that might be collected through supply chain sources.

This complexity makes it necessary to use high powered analytics to cross-reference and correlate this data to give new, previously untapped insights for smart decision making.

Effective big data analytics provides many opportunities within the supply chain such as:

  • Monitoring customer’s buying habits and discovering how to give them better service offerings.
  • Understanding and managing supplier relationships more effectively.
  • Creating comprehensive supplier profiles for financial, risk or performance metrics.
  • Optimising inventory management through sourcing and distributing products based on current demand.
  • Real-time data collection can be used to assist with transport operations routing, scheduling, labour and equipment allocation by assessing current and past operations and taking traffic, weather, and mapping information into account.
  • Through tracking and visibility, root causes of failures and defects can be determined and avoided in future.
  • Identifying and keeping up with new trends that indicate change in demand.
  • Forecasting algorithms can automatically give alerts when resupply of stock is needed.
  • Improving traceability performance in order to cut the time usually spent on managing databases of faulty products that need to recalled or retrofitted.

Utilising big data analytics in your operations can give you the ability to quickly and efficiently meet customer expectations, giving your business the competitive advantage.

 

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