Plans: Growth plan, Advanced plan.
Overview
The Order-to-Delivery Time Analytics report provides a comprehensive view of your order fulfillment performance. It helps you track the time it takes for orders to be processed and delivered to customers, identify potential delays, and optimize your operations for faster delivery.
What We’ll Cover
Get ready to explore your order-to-delivery data! In this guide, you’ll learn how to:
Access the Order-to-Delivery Time Analytics Report
Analyze Processing Time trends
Understand Processing Time Distribution
Understand Order-to-Delivery Time Distribution
Best Practices for Order-to-Delivery Time Analysis
Average Order-to-Delivery Time by Destination
Access the Order-to-Delivery Time Analytics Report
To access your Order-to-Delivery Time Analytics:
Navigate to the Analytics section in the Track123 App.
Select the Order-to-Delivery Time tab.
Apply filters by date range, carrier, and destination at the top of the report to refine your analysis.
Tip: Click Print if you’d like to download or generate an offline version of the report.
Processing Time trends
The Processing Time bar chart shows the time it takes for orders to move from the moment they are placed to the moment they are shipped.
What it shows:
Each bar represents the average processing time for orders created on a specific day.
The Y-axis indicates the number of days between Order Time and Ship Time.
Practical Use of the Processing Time Bar Chart
Short-term Monitoring (e.g., Last 7 Days):
You can view the processing speed of orders day by day to identify any abnormal fluctuations. For example, if the average processing time suddenly rises from 2 days to 4 days on a particular day, it may indicate temporary staff shortages, system issues, or a surge in orders.Medium to Long-term Trend Analysis (e.g., Last 30 or 60 Days):
By extending the date range, you can observe overall trends in order processing times. This helps evaluate whether operational improvements, such as adding packing staff or optimizing inventory management, are effective. A downward trend in average processing time indicates that the implemented optimizations are working.
Operational Decision-making:
Abnormal fluctuation days → Investigate the cause, such as delays or insufficient warehouse staff.
Trend analysis → Verify the effectiveness of process improvements and adjust warehouse scheduling or inventory layout.
Peak season planning → Allocate additional resources in advance to prevent delays in order processing.
Processing Time Distribution
The Processing Time Distribution shows what percentage of orders are shipped within specific time intervals.
Practical Use:
Quickly Assess Fulfillment Efficiency: Shows the percentage of orders shipped within different time intervals (0–3 days, 4–7 days, etc.), helping you see if most orders meet the target processing time.
Identify Bottlenecks: A higher percentage in longer intervals indicates potential operational issues such as staffing shortages, slow packing, or system delays.
Short-term Monitoring (Last 7–14 Days): Detect sudden spikes in slower order processing.
Medium/Long-term Trend Analysis (Last 30–60 Days): Evaluate the effectiveness of operational improvements; a shift toward shorter intervals indicates positive results.
Operational Decisions:
Adjust staffing or workflow based on slow intervals
Set realistic estimated delivery times
Benchmark performance across weeks or months
Order-to-Delivery Time
The Order-to-Delivery Time bar chart tracks the total time from order placement to successful delivery.
The Order-to-Delivery Time Distribution shows the percentage of orders delivered within specific time intervals.
Best Practices for Order-to-Delivery Time Analysis
Practical Use
1. Monitor short-term vs. long-term performance
Use the Bar Chart with a shorter date range (e.g., last 7 or 14 days) to spot sudden increases in average delivery times that may indicate temporary issues such as carrier delays, customs congestion, or warehouse bottlenecks.
Switch to a longer time frame (e.g., 30 or 60 days) to identify broader trends. For instance, if you implemented new warehouse staff scheduling or switched to a different carrier, you can see whether average delivery times have improved after that change.
👉 Example: If your 7-day data shows a sharp spike in delivery time while the 30-day trend remains stable, it likely means a temporary issue. But if both increase steadily, it may indicate a systemic delay that needs operational attention.
2. Combine carrier and region filters for deeper insights
Apply the Carrier filter to compare which logistics partners deliver more orders within your target timeframe.
👉 Example: If Carrier A delivers 80% of orders within 0–7 days while Carrier B only achieves 45%, you can identify Carrier A as your more reliable partner.Use the Destination filter to analyze whether specific countries or regions consistently show longer delivery times.
👉 Example: If orders to Germany often fall in the 8–11 day range while U.S. deliveries average 3–5 days, it could suggest customs or routing inefficiencies for that market.
3. Detect and investigate anomalies
Identify days in the Bar Chart with unusually high average delivery times and compare them with spikes in the Distribution Chart (e.g., more orders falling into the >11 days category).
Click into these outlier orders to examine their details and possible causes, such as poor route planning, customs clearance delays, missing tracking updates, or warehouse backlogs.
👉 Example: If October 5th shows an average delivery time of 12 days and the Distribution chart indicates a 15% jump in the >11 days range, it may be worth reviewing the carrier performance or warehouse capacity on that date.
4. Track the impact of operational or strategic changes
Compare performance before and after changes such as switching carriers, adjusting fulfillment center operations, or improving inventory placement.
The Bar Chart helps you visualize delivery speed improvements over time.
The Distribution Chart shows whether more orders are shifting toward shorter delivery intervals (e.g., from 8–11 days to 4–7 days).
👉 Example: After reorganizing warehouse inventory by shipping region, you might see the share of orders delivered within 0–7 days increase from 55% to 72%, confirming that the new layout improved operational efficiency.
Operational Decisions
1. Optimize carrier selection
Prioritize carriers that consistently deliver most orders within your target timeframe.
👉 Example: If Carrier X maintains 75% of orders within 0–7 days, while Carrier Y averages only 40%, it’s better to route high-priority shipments through Carrier X to ensure faster and more predictable delivery.
2. Adjust warehouse and fulfillment operations
Use both charts to identify when warehouse or shipping capacity becomes a limiting factor.
👉 Example: If delivery times spike every Monday, it might indicate weekend backlogs. You can assign additional packing staff or automate shipment labeling to smooth out early-week delays.
You can also analyze whether process improvements — such as reorganizing inventory placement or optimizing packaging workflows — actually result in shorter delivery times.
3. Improve delivery time consistency across regions
Compare average delivery times across countries or zones to pinpoint areas with longer transit durations.
👉 Example: If Southeast Asia orders show a consistent 3–5 day delay compared to other regions, collaborate with your carriers to explore alternative routes or customs pre-clearance programs.
4. Refine Estimated Delivery Dates (EDD)
Use real-world delivery data from both the Bar and Distribution charts to update your Estimated Delivery Dates.
👉 Example: If 85% of your U.S. orders are delivered within 5 days, you can confidently display an estimated delivery time of 3–6 days on your tracking page.
This builds customer trust, reduces “Where is my order?” inquiries, and improves post-purchase satisfaction.
Average Order-to-Delivery Time by Destination
This map visualizes Average Order-to-Delivery Time by Destination (days).
What it shows:
Colors indicate average delivery times, with darker shades representing longer delivery durations.
Allows you to quickly identify regions experiencing longer deliveries.
Practical use:
Adjust logistics strategy to improve delivery speed in slower regions.
Allocate resources for high-demand areas to reduce delivery time.
If you have any questions or feedback about the Order-to-Delivery Time Analytics report, please contact our support team.