Big Data
Broadly speaking, Big Data refers to large volumes of data, both structured and unstructured. It can be analyzed for insights and applied in machine learning (ML) projects, predictive modeling, and other advanced analytical applications. Big Data analysis uncovers hidden patterns, unique correlations, and critical insights that wouldn’t be reachable with traditional data analysis.
Examples of Big Data include data found in business transaction systems, customer databases, medical records, Internet click logs, mobile applications, and social networks.
But what is the difference between traditional data and Big Data?
Big Data in the supply chain: why is it important?
In the context of international logistics, interest in Big Data is resurrecting. In a research published by Production, 60% of interviewers claimed that they use Big Data applications to supply chain management.
However, does your supply chain need Big Data? It expands the info set for analysis beyond the traditional internal data stored in corporate resource planning and supply chain management systems. For instance, throughout the supply chain, point-of-sale info, inventory data, production volume data, weather conditions, and social data. Also, other unconventional data points can be analyzed to suggest end-to-end supply chain improvements.
1. Improving operations and reducing costs
From suppliers to manufacturers, including distributors, carriers, freight forwarders, retailers, and consumers; all these participants in the logistics process interact with each other generating large data sets. These ones can be used for optimization projects. Therefore, Big Data analytics is able to improve. : Demand forecasts, reduce safety stocks, generate optimal delivery plans and reduce the cost of uncertainty.
For example, Big Data analytics help minimize delivery delays by analyzing GPS data. As well as weather and traffic data, to optimize delivery routes. United Parcel Service (UPS) uses an internal dynamic route optimization system that has helped it reduce the number of wasted miles on delivery routes, all thanks to the value of Big Data advances.
2. Strategic business development
3. Improved customer experience
When Big Data is effectively used, it can dramatically increase customer satisfaction by enabling companies to identify their customers’ preferences or pain points. Besides, it can be valuable data that is difficult to obtain directly from the consumer.
As a result of this application, companies are able to analyze social media, mobile, and web data to learn how customers use their products. In more innovative cases, other companies have explored the use of unified systems to monitor on-shelf stock levels.
Conclusion
At Grydd, we harness the power of big data to transform your logistics operations. By addressing the root causes of bad forecasting with predictive analytics, real-time tracking, and process automation, Grydd ensures you have the tools needed to adapt and excel in a dynamic supply chain landscape.
and boost your Supply Chain Management
