Why is Data Analytics Suitable for Logistics Businesses?

Why is Data Analytics Suitable for Logistics Businesses?

Data analytics helps improve operational processes, reduce costs, increase profits, and facilitate the management of complex operations such as warehousing, picking & packing, routing, or even delivery. With these intricate processes, we should incorporate data analysis into decision-making and increase accuracy through in-depth data analysis, which can be applied in various aspects as follows: 

  1. Reduce transportation costs: Data analysis can be instrumental in selecting travel routes and controlling transportation costs. For example, a transportation company used analytics software to choose routes for product shipment. As a result, the company could save 10% on fuel, reduce delivery time by 5%, and decrease unnecessary delivery trips by 20%. Businesses can reduce both direct and indirect costs: 

    • Direct costs: Data analysis allows selection of fuel-efficient routes or avoidance of traffic jams, reducing costs and enabling faster parcel delivery (e.g., fuel, truck driver wages). 

    • costs: This includes late delivery penalties, lost or misdelivered goods. 

  1. Warehouse management: Data analysis helps analyze and plan warehouse space allocation suitable for product storage and track product information such as lost or expired goods to prevent future problems promptly. 


5 Steps to Applying Data Analytics in Logistics Businesses

Referencing Maslow’s Hierarchy of Analytics from Davoy company, logistics companies can apply data as follows

Level 1: Data-Driven Decision –  Using data to assist in making various decisions in line with the business and comprehensively monitoring issues in each department to enable sustainable growth. 

          Examples of using data to make decisions include: 

    • Customer Discounts: If a customer requests shipping discounts, we can use data on each customer’s sales volume to determine and calculate the most appropriate discount. This allows us to create marketing strategies that attract consumers and increase sales. 

    • Warehouse Space Management: By identifying which items or product types are often shipped together, we can analyze the data to allocate warehouse space more efficiently. Storing related items in the same area reduces costs and time for picking or shipping nearby products. 

    • Delivery Route Optimization: Efficient delivery routes can be improved by analyzing data, which helps reduce delivery time and costs. 

Level 2: Integrated Data : Consolidating data from various company systems into one place, such as creating a data warehouse, allows for a comprehensive, clear, and in-depth view of the business.

         Examples of consolidating data include: 

    • Warehouse and Shipment Data: Analyzing warehouse and shipment data together. For instance, when expanding warehouse space, data analysis can guide decisions on where to open a new warehouse based on which products sell well. 

    • Customer Data Analysis: Understanding customer behavior through data analysis helps forecast customer demands and develop delivery services accordingly. 

Level 3: Automated Data Automated data : within the Data Pipeline system enables logistics businesses to perform analyses and operations more efficiently. Continuously updated dashboards eliminate the need for ad-hoc updates.  

** For Davoy, creating a dashboard is viewed as an important step in data analysis.

          Examples of automated data management include: 

    • Tracking System: Collecting data on the location and status of goods in transit. This data feeds into the Data Pipeline system, allowing immediate analysis or retroactive checks for areas of improvement. 

    • Warehouse Dashboard: Monitoring data within the warehouse, including quantities of goods entering and leaving, for effective real-time warehouse planning and organization. 

Level 4: Real-Time Analytics :  Real-time data analysis using Machine Learning or AI unlocks the full potential of real-time analytics.

          Examples of Real-Time Analytics Real-time data include:

    • Product Storage Recommendations: When new products enter the system, an AI system can analyze data and recommend suitable storage areas in the warehouse. 

    • Transportation Route Management: Analyzing traffic data, weather, and routes to calculate the most time and cost-efficient delivery routes each day. 

Level 5: Analytics Edge Real :  Applying in-depth data analysis with Machine Learning or AI provides a competitive advantage. 

          Examples of Analytics Edge Real data analysis include: 

    • Business Models: Developing models to identify strengths, weaknesses, and new opportunities for business growth. 

    • Delivery Efficiency: Analyzing traffic data, weather, and traffic conditions to optimize transportation routes for faster delivery compared to competitors. 

    • Customer Database: Forecasting demands, offering appealing products and services, and building brand loyalty through customer behavior data analysis. 

An interesting example of using data in logistics is inventory management.

One of the challenges in logistics is avoiding having too much or too little inventory stock. Leveraging data to analyze information can help manage inventory by monitoring stock levels, demand, and replenishment cycles. This helps reduce inventory management costs, improve service levels, and avoid stockouts or excess inventory.

          One example of applying data to a logistics company is the story of the shipping giant UPS, which successfully achieved massive cost savings by using data and data analytics to find the most efficient delivery routes. 

          UPS discovered that if drivers avoided left turns and instead chose routes that only required right turns, it would significantly reduce delivery time and distance. When multiplied by UPS’s fleet of over 55,000 trucks, this strategy could save an enormous amount of fuel costs and delivery time. 

          Combining the experience of UPS drivers with data collection technology and route analysis, they developed the ORION (On-Road Integrated Optimization and Navigation) system. This system helps calculate and plan the most efficient delivery routes by avoiding left turns as much as possible. 

          Finding the optimal delivery route is a classic problem known as the “Traveling Salesman Problem”, which is highly complex. As the number of delivery points increases, the number of possible routes grows exponentially, making it impossible for humans to find the best route on their own. 

          UPS’s use of Data-Driven Logistics impressively solves the problem of finding the most efficient delivery routes. It can save costs and increase profits for the company tremendously, demonstrating the potential of applying data and advanced analytics to logistics operations. 

 Reference from



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