How Megacity Distribution Models Are Changing Urban Logistics.Connect
“Megacities” are cities with 10 million or more inhabitants and they represent a huge market for goods and services. At the same time, they also present some difficult logistics challenges, such as inadequate, traffic-clogged road systems.
In order to overcome these obstacles, companies need to develop more efficient last-mile supply chains in these densely populated urban centers.
The good news is that many companies already possess the tools they need to streamline the delivery of products to big city customers.
Logistics Challenges with Megacities
There are 31 megacities on the planet, and the United Nations projects that the number will reach 41 by 2030, accounting for a population of some 453 million people.
Most megacities are located in emerging economies. In addition to congestion, carriers that deliver goods in these urban centers must navigate complex traffic planning regulations and the demands of many small retail outlets called nanocenters. With limited back room space, nanostores need to be restocked frequently. In Mexico City, for example, some 60% of the city’s nanostores maintain only one to two days of inventory.
Using data to improve supply chain efficiencies
Companies can address some of these challenges by using data they already collect, coupled with distribution network modeling techniques to improve the planning and execution of urban delivery services.
For example, they can combine GPS data generated by smartphones carried aboard delivery vehicles with transactional data, census, and geo-spatial data, and information on driver activity. The data sets are used to build detailed models of delivery routes that enable companies to analyze last-mile supply chains in urban centers.
The MIT Megacity Logistics Lab is working with companies to use these analyses to improve the efficiency of urban delivery networks.
For instance, the models show how much time and money it takes to serve specific customers in certain locations, the whereabouts of traffic congestion hot spots, and how much time it takes drivers to find parking spaces. With this data, it is possible to forecast the likelihood of service failures caused by disruptions.
The models are also yielding valuable insights into operational issues that companies might not even be aware of. One example of this is in Mexico City. A vehicle crew on one delivery route was sorting bottles for a retailer, thereby adding some 45 minutes of non-added-value time to the delivery.
In another instance, freight managers did not know that drivers were straying from the official routes because some customers were unable to pay at the time that deliveries where scheduled. Drivers were changing the company’s route plan to accommodate customers’ payment routines, changes that only became apparent to managers when the delivery routes were analyzed.
When multiplied across a megacity, issues like these can add significant cost to urban supply chains.
Sophisticated modeling for better planning
In addition to helping companies use data they currently collect to analyze urban delivery routes in more detail, the MIT Megacity Logistics Lab is also developing more advanced modeling techniques. This includes development of a simulation of a megacity location that allows managers to move physical representations of resources, such as distribution centers, and see how such moves change the cost profile of a distribution network. The simulation functions as both a planning aid and a learning tool.
As megacities continue to expand, and the growth in ecommerce adds another layer of complexity to urban distribution networks, models that help companies to streamline the last mile will become even more important.