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Home » Blogs » Think Tank » Using AI and ML to Alleviate Last-Mile Pain Points

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Using AI and ML to Alleviate Last-Mile Pain Points

A DELIVERY VAN DRIVES DOWN AN URBAN ROAD

Photo: iStock.com/aapsky

November 16, 2022
Nishith Rastogi, SCB Contributor

With the continuing surge in e-commerce, rapid fulfillment and distribution are becoming key to meeting demand and ensuring customer satisfaction. Delivery standards are rapidly evolving, as logistics giants such as Amazon, UPS and FedEx include next- and same-day delivery options as part of the buying experience.

According to Morgan Stanley, the e-commerce market could grow from $3.3 trillion today to $5.4 trillion in 2026. In the U.S., e-commerce could go from 23% of sales today to 31% in the same time frame, and Statista noted there are 90 million more digital shoppers today than there were in 2020, most of whom expect on-time delivery.

Last-mile delivery continues to be one of the biggest challenges for enterprises, even as they implement advanced supply chain technologies and tools to meet customers' needs for timely arrival in an efficient manner.

Despite best efforts, online purchases don't always end up where and when they should. One out of 20 orders never makes it to the intended location due to lack of on-the-ground visibility. That's a problem, especially as 39% of respondents to Oracle's 2021 Retail Consumer Research survey say that unexpected delays constitute a bad shopping experience, and 43% say fast delivery often determines whether they'll place an order.

Getting the Last Mile Right

Because last-mile fulfillment deals with customers directly, it's vital in shaping their view of the entire purchasing experience. For many, it dictates whether they’ll purchase from a brand again.

The last mile is the most costly and time-consuming part of the fulfillment process. Delivery volume fluctuates and ideal routes change, and dwell time and failed deliveries elsewhere contribute to delays.

In addition, logistics managers don't always have the monitoring infrastructure to detect the delays and diversions that lead to an inaccurate ETA. As a result, the purchaser often receives inaccurate information, and perceives the process as being inefficient and ineffective.

According to a 2020 market study by Shopify, 69.7% of customers who aren't informed about the delay in their deliveries are less likely to shop with that retailer again, while 68% confess their purchasing habits are influenced by estimated time of delivery.

The recent boom in same-day service has compounded those difficulties. Timely deliveries can be further affected by the availability of dispatchers, drivers and vehicles, as well as the distance from a fulfillment center.

Improving the Consumer Experience

After the purchase, one of consumers' greatest concerns is their ability to track and receive an order — and for good reason. An efficient last-mile system will manage, track and schedule deliveries, and as requirements evolve, so too must the technology needed to ensure that orders reach their targets. Artificial intelligence and machine learning are designed to do that, anticipating needs and constantly adapting to changing environments. They process massive amounts of real-time data to continually optimize routing and vehicle capacities, and process fluctuating demand. These tools can be incorporated into order-management and control-tower systems to eliminate the stresses faced by vendors and consumers in the supply chain's final segment.

Vehicle routing problems are often the largest impediment to on-time delivery, and route optimization is a process that uses AI to determine the fastest and most efficient path for drivers. It considers fleet constraints — for example, electronics and perishables can't be shipped together — and evaluates multiple routes on current and past road conditions to identify which is best.

Real-time routing software is another option that companies can embrace to ensure the timely delivery of goods, especially those that require same-day delivery. Those orders can have significant interruption to scheduled delivery plans, thereby affecting ETAs and escalating costs. Real-time, dynamic, AI-assisted vehicle routing systems can improve delivery efficiency and minimize cost per mile and time spent on the road, thereby reducing fuel costs, wear and tear on fleet and greenhouse gas emissions.

Once the routes and means of a delivery have been established, integrated order management systems can provide purchasers with data that accurately tells them when their goods will arrive. Such systems can provide direct updates via text messages or push alerts in addition to established tracking numbers, thereby offering consumers real-time updates and ETAs, as well as alerting them of any delays.

As expectations evolve, the focus will remain on improving decision-making capabilities to enable rapid fulfillment and distribution over the last mile. Companies that can't meet consumers' expectations or are relying on antiquated processes risk suffering from increased expenses and a loss of brand loyalty. Digital tools, many of which are powered by AI and ML, are reimagining ways to resolve longstanding last-mile challenges, especially as booming e-commerce transactions place new strains on delivery processes.

Nishith Rastogi is founder and chief executive officer of Locus.

Logistics Last Mile Delivery Parcel & Express E-Commerce/Omni-Channel Retail

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