Big Data Analytics and the Evolution of the Supply Chain

Companies today are nearly overrun with massive amounts of data about their customers, suppliers, and even potential markets thanks to evolving digital technologies. While it’s commonly said that information is power, if you don’t make proper use of that data, it’s all but useless. The adage holds true when it comes to crunching numbers to benefit a crucial business capability such as the supply chain.

A recent Accenture study of more than 1,000 senior global executives found that, while 97 percent understood how Big Data could benefit their supply chain, only 17 percent reported implementing any of the findings. Here are some of the ways that Big Data can identify opportunities and revolutionize supply chain management in nearly any organization.

Big Data Analytics

A previous story on Tech.Co noted that the amount of data collected globally is growing at a rate of about 59 percent per year. Big Data analytics is the process by which analysts study massive quantities of data with the goal of identifying patterns and other useful information that can lead to better, and more efficient, business decisions. Big Data analysis was first used 20 years ago for marketing and sales purposes with great success.  Today, companies are taking Big Data analytics to the next level and applying it to company operations, in particular to their various supply chain challenges.

Modern-Day Supply Chain Challenges

Leveraging Big Data on the supply chain level allows companies to improve their response to volatile demand and to reduce supply chain risk. The insights that Big Data analytics provides into the depth of the supply chain often creates efficiency and allows companies to overcome ongoing threats.  Spend Matters recently shared its “5 Data-Driven Supply Chain Challenges to Overcome in 2016.”  Here are those challenges and how Big Data analytics can help to meet them.

  • Better Anticipation of Customer Needs  – According to, more than 90 percent of dissatisfied customers won’t return to a brand that failed to meet their expectations.  Providing superior service and prompt delivery the first time is now critical for businesses and Big Data analytics can help companies both understand customer desires and predict demand levels.
  • Improved Supply Chain Efficiencies – Successful businesses understand that efficiency translate to better bottom line numbers.  According to Accenture’s study, using Big Data analytics in operations can lead to an increase in supply chain efficiency of 10 percent or greater.
  • Improved Assessment of Supply Chain Risk – Accenture also reports that industry leaders recognize the importance of supply chain risk management, with a majority realizing the need for both more visibility and predictability in their supply chains.  Big Data analytics makes use of a combination of historical data, scenario planning, and risk mapping to assess potential problems and provide management with an early warning system.
  • Better Supply Chain Traceability – According to Ethical Corporation, 30 percent of companies surveyed report that traceability in the supply chain is one of their biggest issues.  Big Data solves many problems related to traceability and also reduces the hours involved in accessing and managing product databases should a product recall ever occur.
  • Better Agility and Reaction Time in Volatile Markets – Being able to react to market conditions quickly and fulfill customer expectations is critical.  These can both be supply-chain dependent, and Accenture’s study reports that companies making use of Big Data analytics in operations are five times more likely to report shortened order-to-delivery cycle times.

Revolutionizing Supply Chain Management with Big Data Analytics

In addition to addressing the particular supply chain management challenges that we just listed, Big Data has the potential to revolutionize business operations and results in many ways.  Several more include:

  • Big Data analytics can be used to optimize delivery networks using geoanalytics.
  • Using Big Data, businesses can get a better idea of how their supply chain decisions affect the company’s bottom line and its particular financial objectives.
  • Big Data can allow companies to deepen their relationships with suppliers, with more detailed vendor profiles, and even identify opportunities for expanding business.

Big Data Use for Predictive Lead Time Solutions

Have you missed delivery schedules in the past due to occasional stock-outs from suppliers? Do you have supplies that show up sooner than expected that you’re forced to store? Any of these issues, and a host of others, that involved suppliers can cost your business time and valuable clients. Big Data analytics makes use of massive stores of historical data about your suppliers to measure performance, predict future behavior, and make smart business decisions. Predictive lead time technology (PLT) applies big data solutions to supply chain management and eliminates the guess-work in production scheduling.

Companies that make use of Big Data analytics can quickly begin addressing their greatest supply chain challenges. Those that do not may miss out on huge efficiency gains and on the chance to grab a competitive advantage.

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Written by:
Andrew Armstrong is a technology enthusiast, business owner, and digital marketing strategist based in the San Francisco Bay Area. A graduate of UC Berkeley in 2003, Andrew enjoys Cal Football games, experimenting with new technology, and chasing around his toddler son with his wife.
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