Role of Big Data in the Supply Chain

Advanced big data analytics is defined as the scientific process of transforming raw data into useful information for making better decisions.

Currently, the amount of data produced and communicated over the Internet is gargantuan and rising constantly, thereby creating challenges for the organizations that would like to reap the benefits from analyzing this data. But the analyses of this data is more than worthwhile as it has the potential to provide unique insights into, market trends, customer buying patterns, and maintenance cycles, as well as into ways of reducing costs and enabling more targeted business decisions.

Big data enables you to quickly model massive volumes of structured and unstructured data from multiple sources. For supply chain management, this can help increase visibility and provide deeper insights into the entire supply chain (Source: Computer World).

Big data analytics also increases visibility and provides deeper insights into the supply chain. Leveraging big data, supply chain organizations can improve the way they respond to volatile demand or supply chain risk–and reduce concerns related to the issues.

Sixty-four percent of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations (Source: SCM World). Ninety-seven percent of supply chain executives report having an understanding of how big data analytics can benefit their supply chain. But, only 17 percent report having already implemented analytics in one or more supply chain functions (Source: Accenture).

Embedding big data analytics into day-to-day supply chain operations improves decision making: Companies that embed analytics in their operations have faster and more effective reaction time to supply chain issues than those that use big data analytics on an ad-hoc basis (47 percent versus 18 percent according to Accenture).

Improve Supply Chain Efficiency

Cost efficiency, cost reduction, and spend analytics will continue as top business priorities in supply chain management. Embedding big data analytics in operations leads to a 2.6x improvement in supply chain efficiency of 10 percent or greater(according to Accenture).

[Source: Accenture]
[Source: Accenture]


Agility – Improve Reaction Time and Order-to-Cycle Delivery Times

Ninety percent of companies say that agility and speed are important or very important to their business (Source: SCM World). Being able to rapidly and flexibly meet customer fulfillment objectives is rated the second most important driver of competitive advantage across all industries. Embedding big data analytics in operations will positively impact upon an organization’s reaction time to supply chain issues (41 percent) and can lead to a 4.25x improvement in order-to-cycle delivery times (according to Accenture).

Improve Supply Chain Traceability

The current saga with the Samsung Note 7 being recalled highlights the critical value of supply chain traceability. For 30 percent of companies, traceability and environmental concerns continue as the biggest issues to watch for (Source: Ethical Corporation). Traceability is an extremely data-intensive venture. But big data analytics can provide improved traceability performance; it can also reduce the countless hours involved with accessing, integrating, and managing product databases that dictate what products should be recalled or retrofitted.


The increasing importance of big data and its decision making value for the supply chain can in no way be understated. Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains. This has the potential to create larger profit margins and give rise to more informed decision-making. Big data also gives all those in the value chain a chance to forecast when stock will be needed, reducing the amount of idle stock on hand maximizing their profitability.



Computer World Overcoming 5 Major Supply Chain Challenges with Big Data Analytics by Antonnia Rener:

Ethical Corporation „Top Global Supply Chain Sustainability Trends 2015”

Big data analytics in logistics and supply chain management: Certain investigations for research and applications by Gang Wang & co.