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Three Real-World Examples Depicting the Correct Application of Data Analytics in Logistics

Logistics Tech Outlook | Wednesday, June 15, 2022

The interactive Power BI supply chain dashboards provide critical business insights on temperature threshold, driver performance, and customer behavior to assist teams in making informed decisions.

FREMONT, CA: Data Analytics is about analyzing large amounts of data. The term "data" refers to large amounts of information gathered from various sources, whereas "analytics" refers to using power tools to extract useful information from the collected information. The supply chain and logistics industries have made extensive use of data analytics. However, until recently, the industry was reliant on obsolete machines, equipment, and processes. This hampered productivity while also contributing to the company's decline.

Three Examples of Using Data Analytics Correctly in Logistics.

Visibility of the Supply Chain

Data analytics is one such technology that aids in the real-time tracking of products and machinery. Vehicles can be managed and monitored, and shipments can be tracked from the manufacturing phase to the last mile distribution of goods. Improved delivery and shipment status are the results of continuous device monitoring. This enhances supply chain efficiency and creates an environment in which leaders and stakeholders can gather supply chain information more quickly and efficiently. The supply chain management solution, which included a mobile app, improved communication between fleet managers and drivers.

Analytics Predictive

Predictive analysis is one of the major implications of data analytics in logistics. Companies can now study and analyze machine behavior patterns to detect anomalies. As a result, organizations control the behavioral changes that cause machines to malfunction. In addition, businesses can use predictive analysis to detect and respond to events like weather changes more effectively.

Furthermore, predictive analytics is essential for maintaining demand-supply balance. Shippers can effectively generate reports on consumption and forecast demand using historical data and existing models. This speeds up delivery while reducing waste.

Optimization of Routes

Determining the most efficient route from point a to point b is known as route optimization. This shortens the time to deliver a package while improving system efficiency. The same can be said for optimizing logistics routes. The collection of information from various sources resulted in a large amount of data. GPS, weather, fleet information, and delivery schedules are all fed into the system, predicting the best delivery route.

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