How is Big Data used in Supply Chain Management? | Teradata

How is Big Data used in Supply Chain Management? | Teradata

Any enterprise supply chain, regardless of product or industry, directly produces a great amount of data. Much of it is massive volumes typically called "big data." These volumes are too large for traditional data processing applications to handle.

Modern organizations should consider this vast expanse of data an invaluable resource and implement best practices and tools that allow them to leverage it most effectively. The use of big data in supply chain analysis and management projects is integral for optimized planning, operational efficiency, production, order fulfillment, and customer satisfaction.

In the past, it might have been possible for businesses to focus most of their supply chain data analysis around internal data stored in systems like enterprise resource planning (ERP) and warehouse management system (WMS) platforms. But this is no longer a simple option for any enterprise-scale organizations that need integrated data from across the business and supply chain.

The biggest reason why traditional supply chain management practices are growing outdated is that supply chains themselves are more complicated and multifaceted than ever.

A hypothetical retail clothing franchise in the U.S. might:

This is a very common example of an apparel supply chain. For sectors like telecommunications and automotive or aerospace manufacturing, supply chains are far more complex.

All of the segments that make up an enterprise's supply chain operations infrastructure produce large quantities of structured and unstructured data, often in many different formats. Third-party supply chain partners, ranging from suppliers and logistics industry organizations to vendors, produce plenty of data on their own, and that too must be taken into account as part of the supply chain process.

Every piece of supply chain data can be valuable to the enterprise, including the largest data sets. Leveraging it requires the techniques of big data analytics: extraction, storage, processing, algorithm-based modeling, data integration, and thorough analysis.

Making the most effective use of big data in supply chain optimization and management requires an organized strategy, as well as support from leading-edge tech solutions like hybrid cloud architecture and a modern data analytics platform.

Big data has major value to deliver across all principal phases of the supply chain. Modern supply chain analytics techniques allow real-time analysis of various factors in each area.

Planning often benefits from analyzing big data more than other areas of the supply chain. Large data sets containing current and historical information on production levels, sales numbers, inventory volumes, and customer purchase histories are all critical to determining whether supply and demand are properly aligned.

Big data doesn't just tell organizations about the state of these market forces now and in the past. It enables enterprises to project how these forces may rise, fall, or plateau through the use of predictive analytics. Such projections will help companies budget for production, inventory management, logistics, and other costs.

For many businesses, procurement makes up a large share of their overall expenses. So they naturally want to reduce those costs whenever possible. Looking at various sets of big data can illuminate potential avenues for procurement cost-cutting.

It's critical to keep an eye on the manufacturing process in real time. Compiling big data helps track factors including resource availability—personnel, tools, materials, and space—and equipment effectiveness.

But supply chain teams can also go far more granular than that—for example, quantifying the speed of certain automated processes to determine if it has any connection to machine breakdowns. Using an Industrial Internet of Things (IIoT) deployment with sensors attached to machines is ideal for collecting this data.

Plenty of data points pop up as a product travels from the warehouse to a distribution hub and ultimately to the point of sale. Examples include time from warehouse shelf to package to vehicle, route efficiency, delivery speed, and accuracy. Collecting this data in real time alongside information on outside factors—weather and traffic patterns, accident rates, etc.—will help the supply chain team plan for better deliveries.

It's valuable to have visibility into costs associated with whatever product returns occur and why they happen. E.g., if people are returning non-defective products, analysts can project that demand for a product is dropping. Or they can determine whether a competitor has released an equivalent product that's becoming more popular, and alert the product development team to adjust its strategies. When returns are for defects, big data helps lead supply chain managers to the causes of those issues. Ultimately, this contributes to reduced return volume and costs.

Supply chain professionals are no strangers to challenges, and some of these may involve big data. This is how best to address them:

A closed-loop supply chain (CLSC), in which materials from products return to manufacturers for reuse, has become a common element of ethical corporation practices in industries such as packaging and food services. CLSCs sometimes make big data collection difficult because materials aren't in the same state and become harder to track. Leveraging technologies like the IoT and radio frequency identification (RFID) tagging can aid data collection in CLSCs.

Some areas of the enterprise vital to the supply chain may be behind on modern data management practices, meaning their records could be siloed. Data teams must encourage any departments with outdated approaches to implement and consistently use data sharing and data integration techniques to ensure data equity throughout the supply chain. Support from supply chain executives is invaluable here.

There isn't necessarily an established standard for analyzing supply chain big data, which may be confusing at first. Instead, it's often wise to use a variety of analysis techniques for comparative purposes—everything from regression and association-rule analysis to decision trees and k-nearest neighbor. Additionally, artificial intelligence (AI)—and particularly its subset machine learning (ML)—can accelerate supply chain analytics processes because they're well-suited to massive data sets.

Given the scope and vastness of supply chain big data, the scalability of the cloud makes analysis easier. Scalable, low-cost object storage in the cloud makes it easy to expand as needed. Also, the separation of cloud storage and compute operations helps reduce analytics costs.

Because big data in the supply chain has so many formats, it's ideal to use a comprehensive data analytics platform like Teradata Vantage to manage it. Vantage's superb integration, analysis, reporting, and visualization capabilities help bring big data to life for analysts, clearing a path for insight that's essential for creating effective supply chain strategies.

To learn more, read our report on the value of a dynamic supply chain.

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