Supply Chain Frontiers #61 :: Remapping the Urban Last Mile

By 2025 the world’s 600 largest cities will account for about 62% of global GDP. The number of megacities – cities with more 10 million or more inhabitants – is projected to reach 41 by 2030.

 

In order to reach customers in these densely populated urban centers, companies must negotiate heavy traffic congestion, inefficient and ever-changing road systems, and a complex urban terrain. Also, urban markets in megacities are served by small retail outlets called nanostores that need to be re-stocked frequently owing to a lack of back room space for inventory. 

 

The MIT Megacity Logistics Lab is working with companies including Anheuser-Busch InBev and Walmart to build high-resolution last-mile models and rethink distribution networks in cities including Buenos Aires, Argentina, Mexico City, Mexico, Santiago, Chile, Sao Paulo, Brazil, and New York, Seattle, Denver, and San Francisco in the U.S.

 

This research is yielding important advances in last-mile logistics. But companies can do much themselves to increase the efficiency and profitability of urban market logistics networks by taking advantage of data and analytical capabilities that are already available.

 

Managers typically evaluate delivery network performance using a limited number of basic Key Performance Indicators such as total time and distance traveled, and the percentage of goods delivered. This is useful information, but only provides an aggregate view of last-mile operations. Companies may not know it, but they have much better information at their fingertips.

 

GPS data derived from smartphones carried aboard vehicles yields a treasure trove of locational data. When combined with other sources -- transactional data, census and geo-spatial data, and information on driver activities -- it is possible to build highly detailed models of urban delivery operations. Such analytics can provide management with last mile insights both on the strategic and day-to-day decision-making levels.

 

The research underway at the MIT Megacity Lab shows that companies can use these insights to achieve significant improvements in last-mile operations.

For example, managers are learning how much time and money it takes to serve specific customers in certain parts of cities, where vehicles run into congestion, and how much time it takes for truck drivers to find parking spaces.

 

The MIT Megacity Logistics Lab has helped to identify customer-specific insights. In Mexico City for instance, a retailer expected the vehicle crew on one route to sort bottles in its back room before completing the delivery, adding some 45 minutes of non-value-adding time to the transaction. Management was unaware of the practice until an analysis of GPS traces and order data revealed that the excessive service times were a recurring pattern. In other cases, managers did not know that some drivers were failing to adhere to official routes owing to customer payment issues.

 

Such anomalies might appear trivial, but when multiplied countless times across major cities, add significant cost to supply chains and bring down service levels. Moreover, this type of intelligence enables managers to make more informed decisions. They could deploy different types of vehicles that are more suited to the dynamics of specific routes. Rewarding drivers according to their logistics performance rather than their payment collection rates could eliminate multiple visits to customers. 

 

Advances in last-mile modeling and data visualization techniques that are being pioneered by organizations such as the MIT Megacity Logistics Lab will unlock huge efficiencies in urban delivery networks and increase the profitability of big city markets. But companies can help themselves today by analyzing last-mile data they already possess.