Do you know the impact of big data analytics on supply chain management? In recent years, the volumes of Big Data have reached incalculable levels for the analytical management of a more demanding Supply Chain. There are many possibilities offered by big data to predict, in the short and medium-term, the risks in operations and to optimize supply chain planning.
- To improve existing processes by focusing on current business needs and challenges.
- To create products and services to launch new value propositions.
The Possibility of Segmenting Suppliers
- Greater ease in evaluating supply channel options.
- The most effective way to win in sync with suppliers.
- A very effective tool when preparing for the negotiation.
- The starting point for mitigating capacity constraints.
- An indispensable resource for inventory optimization.
- The source of knowledge that allows the redesign of processes.
- An objective instrument to carry out the analysis of the workforce.
- A way to find more cost-efficient alternatives for the transport function.
- This means that makes it possible to optimize vehicle maintenance.
- The tool allows the micro-segmentation of customers.
- a valuable asset when it comes to predicting fluctuations in demand.
- The only way to delve into customer behavior.
- The greatest ally of the inventory function, by optimizing both prices and assortment.
The Importance of Using Big Data Analytics in Supply Chain Management
Now, let us learn about the relationship between Data and Big Data Applications in the Supply Chain Management?
There are many reasons why we need an objective extraction of information that allows us to make the right decisions in our business. It is a way of streamlining movements in the supply chain to keep up with the immediacy that customers demand in the delivery of their orders. To predict customer behavior, strengthen the relationship of companies with their customers by improving their offers and ensuring optimal delivery, etc.
In logistics, applications for data analysis in inventory management, forecasting, and transportation routes are required. In warehouses, to monitor stock levels, as unstructured and messy data can provide alerts when merchandise replenishment is needed, as well as to analyze and collect data from point-of-sale systems, order books, and shipping information.