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.
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