Analysis of Overpayments for B2B Courier Services using Python

Analysis of Overpayments for B2B Courier Services using Python

One important aspect of using courier services is accurately analyzing the charges you incur as a business. This includes looking at things like weight, volume, distance travelled, mode of transportation used etc.

Accuracy in this area is key because it ensures that you are not overpaying for your courier services and helps you budget accordingly. You can undertake this analysis yourself or hire a third-party service provider who can do it for you. And trust me when I say this; taking the time to ensure accuracy in your B2B courier charge analysis will definitely pay off in the long run!

So, I recently came across an interesting analysis technique that uses Python to identify overpayments made for B2B courier services. Essentially, one can scrape invoice data and then use Python libraries such as Pandas and Numpy to run various statistical tests on the data in order to identify trends and anomalies.

By applying outlier detection algorithms and special functions one can easily detect if there are overpayments being made beyond a certain threshold. Moreover, this technique can be fine-tuned by incorporating more variables like volume of goods shipped, distance travelled, delivery time etc to arrive at a more nuanced understanding of courier costs. Overall, this is a relatively simple but powerful method of identifying potential cost savings in businesses that rely heavily on courier services!

In this article I want to share with you how I analyzed the slab delivery of one construction company (let’s call it ABC) and found that they heavily overpay their couriers.


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