---
title: Useful tips to reduce the Carbon Footprint in your Last Mile
description: To cut CO2 emissions, as well as fuel bills, enterprises must analyse and implement critical features on their delivery operations...
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---

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# [Useful tips to reduce the Carbon Footprint in your Last Mile](https://blog.prophetic-technology.com/useful-tips-to-reduce-the-carbon-footprint-in-your-last-mile)

As the exponential growth of e-Commerce goes on, strategic players in the Last Mile delivering servi...

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Posted by [Dave Food](https://blog.prophetic-technology.com/author/dave-food) on Dec 24, 2019 5:05:15 PM

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As the exponential growth of **e**-*Commerce* goes on, strategic players in the **Last Mile** delivering service are challenged by the ever-increasing customer demands and higher volumes of transportation. This enormous activity demand the need for increased services from top transportation companies like FedEx, UPS, new regional **Last Mile** and **3PLs** providers as well.

However*, more vehicles* are equivalent to *more petrol spent* and consequently *more CO2 emitted,* aggravating the pressure on e-Retailers and logistics companies to finance and promote *efficient-technological advancements* and power-driven solutions. Sustainable Logistics researches support that investing in *greener* *trucks* is not the only way to minimise the carbon footprint of the **Last** **Mile**. 

To cut **CO2** emissions, as well as fuel bills, enterprises must analyse and implement critical features on their delivery operations, like:

- Adding new services and acquiring *local players* to boost up **Last-mile** delivery.
- To become *omnichannel* fulfilment, *inventory* and *labour* experts.
- To develop *visibility* to enhance delivery and inventory management functions.
- To be aware of the types of *vehicles in used*.
- To analyse *where* vehicles operate.
- The *topography* of *routes*.
- To *reallocate* delivery capacity.

**Coppel, case study (Tecnológico** **de Monterrey, México)**

One of the largest retailers in Mexico (Coppel) is running a project to achieve significant *cost* *savings* and *diminishing* **CO2** emissions, to implement a more largescale-program in the future.

This company has more than 1000 *retail stores* around the country and operates a *fleet of more than* 1000 **Last-mile** vehicles with *more or less 30,000 home deliveries daily*.  Its **Last-mile** delivery network makes it an ideal contender for research on *fuel efficiency*.

The company truck-fleet consist of a wide range of *vehicle models* and *age* that cope with variable road and traffic conditions. Its area of manoeuvre covers many different *topographies* varying among highly populated *urban areas* as to *rural communities*. 

It is challenging to track the *fuel efficiency* of individual vehicles or large-scale commercial carriers, the reason why they need to investigate more on *how* *to* *allocate* delivery fleets over diverse large-geographically areas. Coppel improved the environmental performance of its **Last**-**mile** fleet. 

**How do they tackle the problem?**

*They used Machine* *Learning* algorithms and models of road conditions based on *geospatial data* supply answers to these many problems:

- **Which** *road conditions* most affect fuel consumption.
- **How** *vehicle load* impacts fuel consumption, the weight of the vehicles, and more, to finally determine *which model perform the best*.
- To confirm how well the *analytical* results, *mirror* real-world **Last**-**mile** movements (home deliveries, distribution centres, and served regions around the country.
- To use the study to help Coppel improve the *environmental* *performance* of its **Last**-**mile** fleet. 

**Insightful about the study:**

- It finds out which cluster has the *highest* *impact* on CO2 emissions.
- High vehicle utilisation or even overloading means *no significant effect* on fuel consumptions.
- The *type* and *age* of the vehicle produce a notable *effect* on the diesel burn rate (vehicles over eight years old have the most significant emissions factors.)
- If a specific vehicle does better on certain routes, then it could *be* *reassigned* according to the variations in performance and reduction in fuel consumption.

**Further comments**: *transportation* is the primary source of *greenhouse gas emissions*, and is *responsible* for considerable *carbon* *footprint*; e-commerce volumes could drive this tendency *even* *higher*.

*Matching* up vehicles *to* *the* *area* of operation can drive *fuel savings* and *CO2 reductions* without significant investments in green technology delivering service. Logistics and transportation professional should not ignore the potential of their *existing* **Last**-**mile** operation to improve *environmental* *performance.*

 

**Dave Food**

[Prophetic Technology](https://www.prophetic-technology.com/)

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