Description
Due to the growing share of on-line shopping nowadays, an additional sales channel for companies has come up. Internet sales have become an essential part of retail business in recent years. Consequently, the volume of traffic caused by delivery services has increased rapidly with the success of e-commerce. Likewise, the delivery market slowly transforms from a mainly B2B market to a B2C one (e.g. Drone delivery). The final track of the supply chain – home delivery to a customer – is called “Last Mile”. The “Last-Mile” of a delivery poses significant logisticalcal challenges, especially regarding the increasing customer expectations, such as "same day delivery" or "exact time delivery" which leads to the decreasing time available for planning. Furthermore, the “Last mile” has a huge effect in traffic of commercial vehicles in cities. The Last Mile Delivery (LMD) accounts for a major part of the costs involved in a delivery. A research of Capgemini Research Institute showed that the costs of LMD account 41 % of the overall supply chain costs (Jacobs, Warner et al., p. 20).

Figure 1 - Distribution of overall supply chain costs (Jacobs, Warner et al., p. 20)
In the reality of LMD, challenges like a small or single order compared to deliveries to stores, many constantly changing geographically dispersed locations (compare deliveries to stores) etc. must be faced. The goal is to improve the efficiency of LMD, to minimise costs incurred, improve safety to minimise the impact on traffic as well as minimise the environmental impact. To improve the quality of life in the affected areas, the LMD should become environmentally friendly and emission-free (noise and emissions), the volume of traffic should be reduced to prevent illegal parking, collisions and stressful congestions.

Congestion, air quality, collisions and illegal parking are all ills affecting the quality of life of citizens. The accessibility of inner-city locations is becoming more and more limited for cars and trucks in contrast delivery services are growing especially in these dense inner-city areas.
There are several solutions to solve these problems that reduce pollutant emissions, lower the impact on traffic, improve safety and make LMD more efficient.
Driving Factors
City Context
1. Size of the city:
Main difference of rural and urban areas is the population density. According to a study by McKinsey, within Germany 24 packages per person and year are ordered. As a result, the package density depends on the size of the city – the bigger the population density, the bigger the package density. Moreover, a study by Bundesnetzagentur shows that 2016 the largest number of packages got delivered in Berlin – the most populated city in Germany (bundesnetzagentur.de).
- Operational hours / number of vehicles:
- Large cities: larger number of vehicles and drivers or more time
- Smaller cities: small number of vehicles is enough
- Regulation:
- Medium size and large cities are more likely to restricted access to the inner city
- Cities without a city centre have no need to restrict access
- Package density
- Large cities: due to the large package density the distances to be covered are shorter and the number of packages higher
- Smaller cities: little package density and number of packages
2. Local stores:
The number of ordered packages depends on the offer of local stores and the distance customers need to travel to get there. This is also related to the size of city and the location the customer lives
3. Age structure of the resident
The age structure of a city also influences the number of parcels and thus the parcel density. The following figure shows the percentage of online buyers in the different age groups:

based on de.statisia.com
Supporting Factors
- High package density: Large number of ordered packages per area
- Support of local authorities: eg. by limit access to parts of the city to make smart solutions for LMD unavoidable
- Simple handling: User-friendly use of the alternative method of deliver – no or little difference in the order
- Smart data analytics: Use of data-driven applications optimizes route and operations while creating additional revenue streams