Last mile delivery logistics: accurate trip data summaries

For supply chain analysts, the final leg of the journey is often the most data-intensive. While trucks and vans navigate physical streets, operations managers navigate rows of timestamps, GPS pings, and status updates. In the world of last mile delivery logistics, success isn't just about getting a package to a doorstep; it is about accurately capturing, summarizing, and analyzing the data that defines that journey.

However, a significant disconnect exists between how tracking systems record data and how analysts need to report it. Tracking software typically exports data in a granular, transactional format—often called "tall" data—where a single shipment might generate dozens of rows representing every stop, scan, and zone change. Yet, for billing, driver pay, and performance analysis, you need a summary. You need "wide" data: one row per shipment, containing the start time, end time, origin zone, destination zone, and total distance.

Bridging this gap in standard spreadsheets is surprisingly difficult. This article explores why traditional tools struggle with this specific aggregation workflow and how using a tool like Quadratic can solve the "associated value" problem instantly.

The data challenge in last mile delivery logistics

Last mile delivery logistics is the process of moving goods from a transportation hub to the final delivery destination. It is the most expensive and time-consuming part of the shipping process, and it generates a massive digital footprint. Every time a driver enters a new zone, scans a barcode, or completes a drop-off, a database records a timestamp.

For a data analyst, this creates a structural problem. Your raw data export likely looks like a stream of events. A single Shipment_ID appears in 20 different rows. To calculate efficiency, you do not need 20 rows; you need a single summary line that tells you when the route started, where it started, when it ended, and where it ended.

This challenge scales in complexity alongside the operation. Whether you are managing a local courier fleet or dealing with the complex challenges scaling 4pl last-mile delivery networks in emerging markets like Nigeria health logistics, the data granularity remains a universal hurdle. As volume increases, the manual effort required to clean and pivot this data in Excel or Google Sheets transforms from a nuisance into a bottleneck that threatens operational visibility.

Why standard spreadsheets fail at trip summaries

Most analysts instinctively turn to a Pivot Table to summarize data. If you need the start and end times for a shipment, a Pivot Table works perfectly for the numerical timestamps. You simply group by Shipment_ID and ask for the MIN (Start Time) and MAX (End Time).

However, the workflow breaks down immediately when you try to identify the location associated with those times. This is known as the "Associated Value" problem.

The pivot table trap

If you drag the "Zone" column into the "Values" area of a Pivot Table to find out where the shipment started, the spreadsheet forces you to choose an aggregation method. You cannot simply ask for "the Zone in the same row as the Minimum Time." You have to choose COUNTA, MIN, or MAX.

If you choose MIN on a text field like "Zone," the spreadsheet returns the value that comes first alphabetically.

Consider a shipment that started at 8:00 AM in "Zone B" and ended at 5:00 PM in "Zone A."

* The Pivot Table correctly identifies 8:00 AM as the start time.

* The Pivot Table incorrectly identifies "Zone A" as the start zone because "A" comes before "B" alphabetically.

The result is a report stating the driver started at 8:00 AM in Zone A—a combination that never existed in reality.

The aggregation error

A similar issue occurs with distance. Tracking data often repeats the total trip distance on every row associated with that Shipment_ID. If you drag "Distance" into a Pivot Table, the default behavior is SUM. If a shipment has 10 status updates and covered 50 miles, the Pivot Table sums the distance 10 times, reporting that the driver covered 500 miles. While you can change this to AVERAGE or MAX to get the correct number, it requires constant vigilance. In high-pressure last mile delivery in logistics environments, these manual toggles are prone to human error.

The solution: summarizing shipment data in Quadratic

Quadratic offers a different approach by combining the familiar grid interface of a spreadsheet with the query power of SQL and Python. Instead of wrestling with Pivot Table settings that were designed for financial totals rather than logistical timelines, you can query the data directly to get the exact relationship you need.

In a recent use case, a logistics professional used Quadratic to solve this exact last mile delivery logistic workflow.

The workflow

The user started with raw data containing columns for Shipment_ID, Timestamp, Zone, and Distance. The goal was to group the data by Shipment_ID to find three distinct data points:

1. The earliest pickup time and the specific Zone where it happened.

2. The latest dropoff time and the specific Zone where it happened.

3. The single, distinct distance traveled.

The method

In Quadratic, the user did not need to write complex helper columns. They utilized a SQL query directly within the spreadsheet cell. By using window functions or DISTINCT ON logic (depending on the specific SQL dialect used), the user could sort the data by time per shipment and select the first row for the pickup details and the last row for the dropoff details.

The result was a dynamic summary table. If the raw data updated, the summary updated instantly. There was no risk of "alphabetical" sorting errors because the query specifically requested the Zone associated with the minimum timestamp, preserving the row's integrity.

Handling "mixed aggregation" without formulas

The core of this problem is "mixed aggregation." You are trying to perform a mathematical function on one column (finding the minimum Time) while preserving the relational data of another column (keeping the Zone intact).

In traditional spreadsheets, solving this requires "formula hell." You typically have to combine INDEX, MATCH, and MINIFS functions. You first calculate the minimum time, then use MATCH to find the row number of that specific time and ID combination, and finally use INDEX to retrieve the zone. This method is computationally heavy, brittle, and difficult to audit. If you are looking for top logistics data analytics solutions for last-mile delivery optimization, relying on fragile formula chains is a liability.

Quadratic handles this natively through code. A Python script or SQL query can group the data and extract the first and last records based on time logic in a single step. This ensures that the "Start Zone" reported is always the zone where the "Start Time" actually occurred, eliminating the risk of mismatched data points.

Validating the data for CDL and fleet compliance

Accuracy in these summaries is not just about operational efficiency; it is a compliance requirement. Inaccurate data aggregation can lead to significant real-world consequences.

If a spreadsheet error sums up the distance column and reports 500 miles instead of 50, it distorts fuel efficiency calculations and can incorrectly inflate driver pay. Furthermore, accurate start and stop times are critical for maintaining valid logs for a commercial driver's license (CDL). Hours-of-service regulations are strict, and presenting data that implies a driver was in a specific zone when they were actually elsewhere can flag audits.

Competitors in the space are already leveraging advanced analytics to tighten their operations. When analyzing cdl last mile competitors logistics delivery performance, the companies that win are the ones that trust their data. Using a tool that guarantees data integrity allows operations managers to focus on route optimization rather than double-checking spreadsheet formulas.

Conclusion: better data for better logistics

Effective last mile delivery logistics is about more than vehicles and drivers; it is about the data that directs them. The transition from raw, "tall" tracking logs to accurate, "wide" shipment summaries is a critical step in visualizing performance.

Standard spreadsheets force analysts to compromise, accepting "alphabetical minimums" or risking aggregation errors. By moving this workflow into Quadratic, logistics professionals can ensure that every report reflects the physical reality of the route. The result is better compliance, accurate pay, and a clear view of network performance.

If you are tired of writing VLOOKUP arrays or debugging broken Pivot Tables, it is time to upgrade your toolkit. Try Quadratic for your next logistics report and experience the difference of data tools built for modern complexity.

Use Quadratic to Summarize Trip Data per Shipment

• Transform raw, tall data into actionable, wide summaries: Easily convert granular tracking data (many rows per shipment) into single-row summaries for billing, driver pay, and performance analysis.
• Accurately associate zones with start and end times: Overcome the "associated value" problem by precisely linking the correct origin and destination zones to the earliest pickup and latest drop-off times, eliminating alphabetical errors common in traditional spreadsheets.
• Prevent aggregation errors for critical metrics: Consistently report correct distances and other numerical data by querying distinct values, avoiding common summing mistakes that inflate figures and distort analysis.
• Simplify complex data transformations with code: Use native SQL and Python directly within the spreadsheet grid to perform advanced aggregations (like window functions) without resorting to brittle, formula-heavy workarounds.
• Ensure data integrity for compliance and operational trust: Generate reliable, auditable reports that accurately reflect trip details, supporting CDL hours-of-service regulations, fuel efficiency calculations, and precise driver compensation.
• Automate updates for dynamic logistics reporting: Create summary tables that instantly reflect changes in raw data, eliminating manual recalculations and ensuring real-time visibility into last mile performance.

Ready to simplify your logistics reporting? Try Quadratic.