How Data Analysis Improves Logistics

Cameron Writt, of Bentonville, Arkansas, has varied experience as a standup comedian and data analyst. In his current work as a data analyst at WCI in Bentonville, Cameron Writt harnesses bulk sales and logistical data to identify new sales patterns, track retail price changes, and uncover opportunities for shipping.

Data analysis is crucial for logistics companies as big data and advanced technologies become prevalent. Logistics companies use data analytics to gain insights on supply chain operations and add to their bottom line. Through data analytics, companies can enhance their supply chain visibility by monitoring products and shipments promptly and optimizing operations.

Data analytics helps logistics managers determine the optimal route for their fleets with GPS, weather tracking, and comparative fleet data. This informed approach to route optimization enables timely, secure product delivery.

Logistics companies also employ data analysis to enhance their inventory management. They establish optimal reorder points for individual items to guard against running out of stock, which can lead to customer defection. Data analysis also allows companies to curb overstocking costs, such as storage, personnel, and spoilage.

Data analysis can help companies manage their warehouses by tracking the movement of products and combining that information with sales data to decide where to store each item. Companies can assign a unique code, called a stock-keeping unit (SKU), to each product for internal tracking of stock levels.