Overcoming 5 Major Supply Chain Challenges with Big Data Analytics

Big data analytics can help increase visibility and provide 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.

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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).

Even if your organization is among the 83 percent who have yet to leverage big data analytics for supply chain management, you’re probably at least aware that mastering big data analytics will be a key enabler for supply chain and procurement executives in the years to come.

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. Leveraging big data, your supply chain organizations can improve your response to volatile demand or supply chain risk, for example, and reduce the concerns related to the issue at hand. It will also be crucial for you to evolve your role from transactional facilitator to trusted business advisor.

Leveraging master data management (MDM) at the scale of big data ensures that high quality and accurate data is driving your insights. MDM technology helps you explore the hidden relationships and gain insights that weren’t possible in the past.

Here are some examples how big data relationship management provides opportunities along the supply chain:

  • Discover and manage supplier relationships more effectively, and understand who is doing business with whom. While many vendors use big data to learn more about their customers, the most successful supplier managers will also use big data to better understand their vendors.
  • Create comprehensive supplier profiles, including data from external sources – such as Dun & Bradstreet – for financial, risk or performance metrics and provide risk managers with real-time analytics dashboards.
  • Better understand customers and their relationships with the company.
  • Learn how customers interact through different channels and offer better product recommendations.
  • Optimize inventory management and distribute products based on real-time demand.

Spend Matters recently published 5 data-driven supply chain challenges for 2016. Prioritizing the development of a big data analytics strategy will help your organization overcome these supply chain challenges:

1.     Better Predict Customer Needs and Wishes

Over 90 percent of dissatisfied customers will not do business with a brand that failed to meet their expectations (Source: customerthink.com). In the age of the customer, offering the right product, to the right person at the right time and place is key to gaining (or retaining) customer satisfaction and loyalty. Smart organizations will leverage big data to get a full 360-degree view of your customer to better predict customer needs, understand personal preferences, and create a unique brand experience.

2.     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.

3.     Better Assess Supply Chain Risk

Sixty-one percent of companies regarded as leaders in supply chain management consider supply chain risk management very important. Those same leaders also recognize the need for capabilities that provide greater visibility and predictability across their supply chains (Source: Accenture). Big data can help assess the likelihood of a problem and its potential impact, and support techniques to identify supply chain risk. Combining the analysis of historical data, risk mapping, and scenario planning can enable a risk management approach for early warning.

4.     Improve Supply Chain Traceability

Traceability is often directly linked to supply chain risk. For 30 percent of companies, traceability and environmental concerns continue as the biggest issues to watch for (Source: Ethical Corporation). Traceability and recalls are by nature data-intensive. Big data has the potential to provide improved traceability performance; it can also reduce the thousands of hours involved with accessing, integrating, and managing product databases that capture products that should be recalled or retrofitted.

5.     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). The ability to quickly 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 can have an impact on organizations’ 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.



  • http://customerthink.com/10-need-to-know-customer-dissatisfaction-stats/
  • Informatica Business Value Assessment “The Value of Trusted Data” 2015, Page 7
  • Accenture Global Operations Megatrends Study, “Don’t Play it Safe When it Comes to Supply Chain Risk Management”, Page 9
  • Ethical Corporation "Top Global Supply Chain Sustainability Trends 2015”
  • SCM WorldChief Supply Chain Officer Report”

Copyright © 2016 IDG Communications, Inc.