Top 3 Applications of Big Data for Supply Chain Management Operations

Big data is no longer for marketing and sales only; it has enormous applications. Supply chain management is one of the sectors which could reap huge benefit from data science.

Traditional ways of supply chain management and Enterprise Resource Planning (ERP) have changed. Its quoted over Forbes that 80% of the manufacturers are handling their business network activities through big data and cloud-based technologies.

Rapid product lifecycles need speed and manufacturers relying on old Enterprise Resource Planning (ERP) are stupid. Reason? Lack of ‘forward-thinking’ approach. Companies are depending heavily on business models with rapid product launches and big data analytics.

Accuracy, speed and quality are the core parameters which is why businesses need big data integration into their processes. Here are top 3 applications of big data in supply chain management, based on 'how this industry can get maximum out of data analytics'.

Accuracy, Speed, and Real-Time Tracking

Real-time analytics offered by data helps companies to track deliveries of their products to end customers. Barcodes input processing, global positioning system devices, road network data, traffic sensor data, vehicle data etc. allows the logistics manager to optimize their delivery scheduling. Moreover, companies can automate delay notices to customers, give them real-time tracking delivery status updates.

Suppliers Network Management

A manufacturer could get a good sales margin if he uses mix of suppliers rather than producing the product with all the infrastructure. Big data facilitate in a way that it allows companies calculate mathematical models and forecast their margins which otherwise is a complex process.

Data can estimate additional costs because of the delivery speed from different suppliers, long-term contract cancellation costs, and even the supplier reliability. Further, data stats evaluate performance predictions of different suppliers.

Optimize Pricing Models

Consumer data extracted from internal and external sources used as a key for preparing pricing models. Predictive analytics which is one of the forms of big data could be used for determining or forecasting demand of the particular product at varying price points. Companies can use data for soft launches and checking the customer feedback.

Another useful application of big data is the sales forecast through pricing. Businesses can use dynamic pricing for increasing revenue in the phase of high market demand.

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