Man in front of a screen of data

12 Big Data opportunities for supply chain innovation

29th January 2020

Harnessing the power of Big Data is vital for supply chain management. But what are the concrete opportunities that Big Data presents?

What does Big Data mean for supply chain management?

Information is the driver of corporate decision making on strategic, tactical, and operational levels. Companies in the supply chain must have access to up-to-date, accurate, and meaningful information. That’s why harnessing the power of Big Data is vital for supply chain management. But what are the concrete opportunities that Big Data presents?

Researchers at the University of Kassel in Germany spoke to 20 experts from a leading global company in the management consulting industry who identified the opportunities and challenges that Big Data analytics presents for supply chains.

Here are the top 12 opportunities that they found.

Big Data enables enhanced discovery, access, availability, exploitation, and provisioning of information within companies and the supply chain. It can enable the discovery of new data sets that are not yet being used to drive value.

Better modelling enabled by Big Data analytics allows for more accurate decisions, continuous productivity improvements through automation, leaner operations, and optimized servicing through predictive analytics.

Big Data can increase supply chain visibility and transparency through real-time control, and multi-tier (process, decision, and financial) visibility irrespective of data location. According to the experts in the study, the impact of enhanced corporate information availability on the visibility and transparency of the supply chain represents the second most relevant opportunity on the corporate level.

With Big Data, companies are able to react quicker to changing market conditions, made possible through visibility and a deeper understanding of their information-enriched ecosystem. This can increase real-time responsiveness to customer needs and changing market conditions, reduce time-to-market, and increase the robustness of supply chains.


Big Data analytics can enhance customer segmentation, allowing for better scalability and mass personalization. It can improve customer service levels, enhance customer acquisition and sales strategies (through web and social), as well as enabling customization of delivery.

Big analytics can provide insights for product launch and release planning, and increased granularity of planning levels allows for optimized, shorter planning cycles.

A wide variety of data streams can aid innovation and product design. These include utilizable product usage data, point-of-sales data, field data from devices, customer data, and supplier suggestions to drive product and process innovation.

Big Data can reduce long-term costs, increase ability to invest, and improve understanding of cost drivers and impacts.


Big Data can enable better integration and collaboration within supply chains. Adopting cross-functional integration and collaboration approaches with key partners can build a culture of trust, leading to higher levels of information sharing, and helping to optimize across the whole supply chain ecosystem.

Product traceability enabled by data leads to lead-time reduction, for example by in-transit processing of goods. It can enable real-time rescheduling, route planning, re-routing and road-side service planning.

Vital information becomes more transparent and available at a much higher frequency as parties cooperate and share Big Data insights across the supply chain. This allows shortening of planning cycles and the operation of planning with higher levels of granularity, leading to more efficient inventory management practices, ultimately resulting in optimized inventory stocks.

Big Data can enhance risk evaluation, aiding continuity management at industry and supply chain level to reduce the impact of disruptions.