Big Data and 21st century Supply Chains

Data is everywhere and manufacturing companies today are collecting increasingly massive amounts of data with the help of digital technologies.

New strategies, improved skills and more powerful tools are needed to make sense of that data and crunch the numbers, and find useful insights that are buried in the data. This situation is elevating the importance of Big Data analytics as a critical business capability.

To share few statistics about the amount of data:

  • More than 90% of data in the world today has been created in the last two years, with 80% of it being unstructured, such as images, audio, video, social media, web pages and emails.
  • 1.8 trillion gigabytes of new data were created in 2011.
  • Data is expanding at a rate that doubles every two years.
  • By 2020, the digital universe will be 40 trillion gigabytes.
  • Most U.S. companies have at least 100 terabytes stored.
All companies understand the importance of big data and acknowledge that data analytics of the huge digitized data can help their supply chain process, but the challenge is how to implement it. However, the increased understanding of big data analytics is leading to action, and is becoming a reality.

The trend to implement analytics is on its way and companies have serious plans to incorporate role of analytics in their supply chains.

Optimized supply chain– i.e. delivering the right amount of product to meet market demand while minimizing production, inventory and transportation costs–is a smoothly functioning, comprehensive proposition sought after by all companies. Advances in data analytics, combined with proliferation of data acquisition mechanisms and huge volume of data points, is generating a plethora of possibilities for improving efficiencies in this integrated view of the supply chain.

Earlier, most of the companies sought methods to centralize data to help run their businesses via ERP systems. Now, the concentration is shifting to analytics in to effective decisions with respect to predict customer demand, supplier availability, inventory management, delivery route, etc.

Big Data extends the ability to respond, to predict and, in some cases, even recommend subsequent action, based on insights retrieved from these sources. This takes companies a step ahead in increasing -efficiency in the supply chain. Consequently, the focus is evolving from “supply to replenish” through “supply to forecast” to “supply to prediction based on dynamic pattern analysis”.

Big Data analytics capabilities even have the capacity to reduce supply-side disruptions. For instance, process industries have plant control systems that capture thousands of data points a second. With Big Data techniques, it is possible to proactively adjust parameters in order to improve yield and reduce waste. By identifying potential bottlenecks ahead of time, planners now can account for alternative scenarios and maximize payoff. Moreover, this capability can be used to predict, prevent, or even adapt to equipment failures in transportation, logistics, and warehousing.

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