The logistics industry needs to catch up on AI and ML integration to enhance delivery efficiency

The spurt in online shopping has also pushed the consumer expectations to a new normal, especially in the case of the last mile delivery demands.

By: Shalabh Raizada, Chief Information Officer, Stellar Value Chain Solutions

The pandemic has catalysed a sudden growth in e-commerce, with billions of people stuck home. This spurt in online shopping has also pushed the consumer expectations to a new normal, especially in the case of the last mile delivery demands. Reports suggest that the emerging quick commerce model, which ensures delivery of goods like food and basics within 10-25 minutes, will grow by 15 times by 2025.

On the other hand, logistics companies have been aggressively adopting new age technologies to stay ahead of the curve. Artificial intelligence, the latest buzzword across the sectors, has evolved as the best solution to meet the customer demand in door-step deliveries. AI, with all its specific solutions like robotics, deep learning and machine learning, has become the game-changer in the logistics industry to make warehouses smarter and value chain operations seamless. As the result, the entire logistics ecosystem from supply to delivery has improved by optimizing the results and producing higher efficiency. We see how goods are received, identified, sorted, processed, packed, lifted for shipment and delivered almost automatically, reducing the margin for error and cutting down the manpower.

However, the biggest impact of AI will be felt on the delivery point, if integrated well. The delivery will become agile, faster, flawless and cost-effective with the integration of AI tools. The last mile delivery, in fact, is the most crucial and deciding element in logistics, eating up almost 50% of shipment costs as per some studies. With plenty of choices like delivering in the office or homes, same-day or designated times, the doorstep delivery has become the critical differentiator in measuring the customer experience. This has added pressure on the logistics players who have to optimize capacity and routes of vehicles and manpower according to the specific time window chosen by the customer for delivery. Shortage of resources obviously results in late deliveries, affecting the customer satisfaction, apart from higher expenses for the logistic providers.

AI and ML will play a pivotal role to address these challenges. First of all, AI can process the vast set of structured and unstructured data to make quicker operational decisions which used to be taken earlier by the human minds. Logistics companies deal with high volumes of data and AI can accurately organize them by understanding terms, phrases and jargons, and thus turning decision-making easier. Optimal data usage will naturally reduce the risks and cut down the costs. Moreover, data plays all important role in the quick commerce model.

Secondly, AI comes handy in predictive analysis. Logistics companies face challenges of shortage of resources for last-leg delivery, especially during fluctuating demand situations. Managing short-distance operations is harder than long-distance operations because of the crunch in resources at the local level. AI and ML algorithms help solve these issues, by evaluating different possible options and suggesting better routes and pick-up plans based on the historical data. This predictive advantage makes logistic players prepared for rising demand by optimizing routes and drop-sizes. The best part is that this process is refined every day, based on the data of deliveries on real-time basis. As the result, route optimization will make operations more efficiency, less expensive and managing the available resources better to ensure higher customer satisfaction. AI solutions thus provide a win –win formula both to the operators and the customers. 

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