Advanced Analytics Helping to Deliver a More Efficient Last Mile

Rising consumer demand is forcing companies to rethink their last-mile delivery supply chains.

The United Nations is projecting the number of so-called megacities of more than 10 million inhabitants to rise well above 40 by 2030. About two-thirds of the global population is expected to reside in cities by 2050.

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Advanced analytics can help companies better understand urban markets, customers and operational environments.

In the retail sector, the battle over future consumer markets is largely a battle over who excels at managing the urban last mile of physical distribution.

A key weapon in the fight is advanced analytics

Recent methodological advances in the field will have a major impact on last-mile logistics. In addition, companies are using transactional records, delivery data from individual routes, high-resolution telemetry and movement data on the level of individual vehicles to develop a more precise picture of their last-mile logistics operations.

Advanced analytics – especially a group of tools commonly known as machine learning – can help companies better understand urban markets, customers and operational environments.

This is particularly relevant when serving a large and highly fragmented customer base. Such tools can provide substantial business value by revealing customer-specific insights that would have otherwise remained hidden or too costly to identify.

For instance, a leading global brewing company recently partnered with researchers from the MIT Megacity Logistics Lab to use data to improve the operational efficiency of their global last-mile delivery operations.

Based on historic route plans and delivery records, machine learning tools helped identify customer-specific delivery constraints in a pool of hundreds of thousands of customers around the world – from big box retailers in the U.S. to mom-and-pop nanostores in emerging markets.

The analyses helped to identify customers that are most disruptive to the efficient operation of the company’s delivery operations due to their hidden delivery constraints. The company was then able to address these issues by reincorporating the information into its route planning algorithms or by reconfiguring distribution services for certain customers.

Machine learning offers leeway

Machine learning can help reveal and exploit the local knowledge and expertise of a company’s distribution workforce. An analysis of high-resolution GPS traces in conjunction with telemetry data and transactional records can provide relevant insights on the availability and suitability of local infrastructure such as roads and parking bays for last-mile delivery.

The data can reveal behavioral patterns of drivers and delivery crews that have local knowledge about their route territory. These drivers know better than any algorithm or data source where to park, which shortcut to take or which congestion hot spots to avoid.

Extracting this knowledge without having to disrupt crew member workflows can achieve significant improvements in route planning and more effective delivery instructions. Companies also can maximize service levels and minimize cost inefficiencies due to inaccurate planning.

Accurate spatio-temporal demand forecasts – understanding when customers will order what, where – are becoming indispensable in the development of fast, responsive last-mile delivery services.

Shorter delivery lead times give companies limited leeway to compensate for short-term fluctuations in traffic conditions or the availability and pricing of (potentially crowd-sourced) carrier capacities.

Advanced analytics can be used to develop reliable near-term predictions of traffic dynamics and the availability of resources. These analyses consider historic patterns as well as real-time information on influencing factors such as weather.

Rethinking delivery 

The technology also can inform a new generation of prescriptive analyses, thereby helping companies to make the right tradeoffs and decisions during the strategic design, tactical planning and day-to-day operations of last-mile distribution systems.

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Shorter delivery lead times give companies limited leeway to compensate for short-term fluctuations.

More companies across sectors, industries and geographies are reaching out to academic partners such as the MIT Megacity Logistics Lab to develop prescriptive decision support tools based on high-performance optimization and simulation techniques.

Rising demand from consumers who are increasingly intolerant of service failures – coupled with urbanization and more intense competition – is forcing companies to rethink their last-mile delivery supply chains. Developments in data analytics will play a key role in meeting these challenges.

This article originally appeared on Supply Chain @ MIT and was republished with permission. It is an excerpt from a special section titled, The Analytics Revolution in Last-Mile Delivery, published by Supply Chain Management Review. 

Matthias Winkenbach is Director of the MIT Megacity Logistics Lab.

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