Introduction
The logistics performance of your business is determined by two things: how quickly deliveries arrive and the geographical area you can deliver to. Delivery time measures the speed and reliability of your service, and the Service Area represents the geographical extent of your logistics capabilities. To better understand delivery routes, delivery commitment, and delivery costs while boosting customer satisfaction, you should measure the following two items.
Too many businesses still estimate delivery distances or delivery times based on guesswork or use manual processes for reporting. The result is often late deliveries, higher costs due to wasted fuel, and increased customer dissatisfaction. A more effective solution is to extract and analyze data from your existing logistics software applications, such as order management, fleet tracking (GPS), and customer records.
This guide provides step-by-step instructions for obtaining this information, cleaning the data, and ultimately creating actionable data for your business. Each section includes specific actions that you can take immediately to begin benefiting from your logistics operations. By the end of this guide, you will be able to implement a set of tools that enable you to make decisions about your logistics operations based on reliable data on delivery times and serviceable areas.
What Is Delivery Time and Service Area Data?
Delivery time data tracks the time from dispatch to drop-off and usually includes details such as pickup, travel, stop times, delays, and final delivery time. Service area data shows where you deliver—cities, zip codes, zones, and boundaries that define where you provide service.
Delivery time and service area data allow answering three crucial questions: How fast can I get there? Where do I provide service? Where am I slow?
Using delivery time, you can identify bottlenecks (e.g., traffic, loading delays, inefficient routes). Service area data can help you identify gaps, overlaps, and high-cost areas. Delivery time and service area data are typically stored across multiple systems. Your order management system will provide the promised order date, your fleet management system will track GPS and timestamp data, and your customer database will store addresses. Aggregating this information from multiple systems can create a comprehensive view of performance across all locations.
The most important reason to obtain accurate delivery time and service area data is to enable you to use the information for more efficient route planning, to enhance the reliability of your delivery promises, to decrease your fuel consumption, and to increase customer confidence in the service you provide. Having solid delivery time and service area data supports your strategy for expanding your service area or adjusting delivery fees for customers in high-cost areas.
When you have defined delivery time and service area data, you can make evidence-based decisions. When you don’t have a defined delivery time and service area data, making the right decision about them is a guess.
Where Can You Source Delivery Time Data?
Delivery time data is sourced from four primary channels. The first of these is the Order Management System (OMS), which tracks timestamps when orders are created, dispatched, and promised for delivery. Fleet and Telematics platforms track deliveries via vehicle telematics, including GPS pings, vehicle speed, stop duration, and arrival times. Driver applications provide real-time tracking of deliveries, e.g., “Out for Delivery, “” Arrived, and “Delivered.” Finally, third-party carriers offer an API for delivery that returns information on pickup and delivery scans.
To identify the complete set of systems that track a shipment, start with your OMS and any other systems that track a shipment (such as survey suppliers, fleet partners, etc.). The common timestamp fields to track in each system are: Order Created, Order Packed, Order Loaded, Order Departed, Order Arrived, Order Delivered. It’s imperative that, if you use multiple carriers’ event logs, you can retrieve them from all airlines in the same manner.
You can access your carrier scan data by exporting a CSV file, querying your database, connecting via the API, or scheduling an export. To continue optimizing your operations, it’s best to use either an API or a planned export.
When exporting or querying, be sure to document both the time zone and format of your time/date values to avoid confusion and errors.
Lastly, ensure you have established a formal data governance process. Limit access to sensitive information, anonymize data where appropriate, and document the meanings of your fields. It will reduce the risk of duplicate entries and ensure the delivery time metric reflects actual delivery times rather than partial records.
How Do You Map and Define Service Areas?
Service areas define the geographical zones you provide and are created by validating your delivery addresses for duplicates and fixing any errors. Once validated, convert the addresses to geographic coordinates (latitude/longitude) via geocoding.
Now that you’ve created your location map, you can group your locations by ZIP code, city, district, or Custom Polygon; most companies will make their delivery zones based on the travel time instead of the straight-line distance; for example, a 30-minute drive zone would provide much better results than a 10-mile circle; in this case.
After you’ve established your Service Areas, you’ll need to add the operational limitations; for example, where your deports are located, the hours your drivers work, the number of parcels your vehicles can accommodate, and any local regulations you are subject to will determine where you can effectively provide service; therefore, you should create a visual representation of all of this information on a map; you should use GIS or mapping tools to illustrate the density of your service areas and to identify any gaps or overlaps between the various zones.
Once you’ve defined your zones visually, document your Service Area Policies, including cutoff times for service, eligibility for same-day service, weekend coverage, and exceptions to these policies. It will enable you to convert raw geographic Information into a usable model of your Service Areas. By defining your geographic service areas accurately, you will be able to provide accurate promises, competitive pricing, and design the most efficient delivery routes.
How Can You Extract and Integrate Data Efficiently?
Automation and standardization are imperative to optimizing extraction methodology. Develop an inventory of core data attributes, including (1) systems; (2) data custody; (3) data fields; (4) data formats; and (5) update frequency. Subsequently, identify the mechanism for retrieving the information. Extracting real-time data may be accomplished via API connections, batch exporting via scheduled extracts, or querying an Internal database.
After retrieving your data, create a straightforward Data Pipeline. Ingest your unconfirmed data files, validate each field, and store them in a Central Repository or Analytics Framework. Establish uniform data identifiers (i.e., Order ID, Vehicle ID, Customer ID) to facilitate interrelatedness.
Once your data has been catalogued and cross-referenced, configure the information to produce Identifiable Metrics. Identifiable Metrics should include the Delivery Duration, On-Time Percentage, and Stopping Duration. Each data record should link back to a Service Area Zone so that you can analyse your data geographically and temporally.
When possible, automate your processes. Regularly update the information daily or several times a day, and keep track of any errors and missing fields so that they can be resolved. If in-house support is unavailable or too costly, consider using lighter ETL tools. These tools typically enable automation and streamlining without requiring significant engineering resources.
The primary goal is to create a single, reliable source of data that will be consistently updated for your dashboards, reports, and optimization models. This data source will form the foundation for all logistics-related decisions.
How Do You Clean and Validate the Data?
Logistics data can be challenging because of missing information. Cleaning up logistics datasets will provide honest and accurate information. Begin by reviewing the logistics dataset for missing timestamps, duplicate order numbers, and incorrectly formatted timestamps. Records with impossible values must be removed or flagged (e.g., if the delivery time is negative).
Standardize date and time formats and time zones. Align all timestamps to one standard. Identify invalid addresses (use geocoding). Remove all invalid addresses. For mismatched GPS location information, plan for noisy data by smoothing spikes and filling gaps between valid GPS location points with the nearest valid locations.
Compare sources by reviewing order management systems and driver app delivery confirmation notifications. If the differences are significant, this can indicate either scanning delays or system problems. Implement criteria to identify the most trustworthy field (OMS vs. app).
Develop basic quality metrics: percentage of records that are complete; average variance between OMS and app delivery confirmation times; and percentage of outlier records. Routinely review your quality metrics. Monitoring data quality is a process and should be considered as such, not a once-and-done.
Finally, document how your company defines “delivery time” (e.g., from dispatch to arrival at the recipient’s door). It eliminates confusion among teams. Clean, validated data allows you to measure your company’s performance based on the actual execution of the plan.
How Do You Analyze Delivery Time by Service Area?
Data is clean, enabling powerful analytical capabilities. All analyses start with calculating the average, median, and 90th percentile delivery times for each service area. Looking at these metrics together gives you an idea of both “typical” and “worst” performance.
Once you have these values, you can create a map of the results; the heat maps will show the slow zones, and the trend charts will show trends over time. Then you break down the causes of the delays by categories such as traffic, loading, customer unavailability, and distance. It gives you areas to improve delivery times.
You can measure your reliability by comparing promised times to actual delivery times. To do this, you need to find the zones where you constantly miss your promised delivery times. Those zones will likely require redesigning the route, adding additional drivers, changing your cut-off times, or some combination of these.
You will also want to segment by day of the week and hour of the day because some areas perform well in the morning but not during peak traffic. Adjust your schedule accordingly.
Finally, you should connect the costs associated with all your efforts by overlaying fuel consumption, overtime, and delivery failures. This way, you can see the financial impact of your delivery performance insights.
How Can This Data Optimize Routes and Capacity?
Use delivery time and service zone constraints to optimize savings through route planning based on several factors. Use clustering algorithms to group stops, reduce congestion, and optimize total routing for each driver.
Modify capacity in Zones with significant volume / low speeds for either service or routes. In addition to needing more vehicles and/or micro-hubs for high-volume or slow zones, you may also consider moving to fewer days for lower-density routes rather than providing them daily.
Modify the service you promise to attract customers; if you can deliver to an area within 24 hours 90% of the time, you should market “Fast” offerings. If you anticipate having trouble with continuous deliveries in an area, consider extending cut-off times or offering premium service rates.
Before you expand into new Zones, test pilot routes between each adjacent Zone and the zones you are considering rolling out to.
By developing dashboards to track on-time rate, delivery duration, and delivery costs by Zone, you will always have a continuous feedback loop to continually enhance service, routing, and customer demand, allowing you to create a more sustainable Logistics model.
What Are Common Challenges and How Do You Overcome Them?
To address data fragmentation, poor data quality, privacy and compliance, operational resistance, and scaling, organizations should invest in standard identifiers and integration tools to enable systems to communicate with one another. Validation rules, data audit, and clearly defined data ownership are necessary to address data quality.
To ensure privacy and compliance, organizations should anonymize customer data and restrict access to it based on employees’ roles. All aspects of an organization’s data practices should be documented. Another barrier organizations face is operational resistance; however, they can overcome this challenge by providing transparency into their definitions of new metrics and sharing quick wins (e.g., reduced overtime, improved on-time rates).
Lastly, organizations may struggle with scaling; however, organizations should start with a pilot area and gradually expand once they prove the value of their efforts. At the early stages of implementation, simple dashboards and rules often outperform more complex models. By anticipating these challenges and using practical solutions, organizations can establish a scalable, dependable system to extract delivery time and service area data.
Conclusion
Extracting delivery time and service area data provides companies with a strategic advantage in developing routes, meeting customer commitments, cutting costs, and scaling their business with confidence. This paper has established a framework for using this data to drive business by taking extracting delivery time and service area data and providing actionable intelligence from that information through the following steps: identifying and sourcing reliable data sources, defining service areas, creating one integrated dataset containing all of the data from all of the source datasets, performing analysis on the performance of each location by service type, and applying the results to improve routing and capacity planning.
Many companies don’t have the internal resources to extract and organize their data for use by back-end systems through data aggregation. Companies like 3i Data Scraping provide businesses with tools to collect and process data efficiently, improving business processes by providing access to high-quality, structured data. Partnering with the right data providers can lead to faster deliveries, fewer service failures, lower operational costs, and greater customer satisfaction for your company.
About the author
Mia Reynolds
Marketing Manager
Mia is a creative Marketing Manager who combines data-driven insights with innovative campaign skills. She excels in brand positioning, digital outreach, and content marketing to boost visibility and audience engagement.
