Data-Driven Decision-Making in Last Mile Delivery: Leveraging Analytics for Success

As per a report, the Indian last mile delivery market is projected to reach USD 6-7 billion by 2024, with an expected CAGR of 15.62% from 2022 to 2027, indicating an exponential growth of USD 165.6 billion

Today’s buyers expect free and fast delivery. So, companies need to allocate resources efficiently to their shipping logistics to increase customer satisfaction and repeat business. This largely hinges on a company’s last mile delivery process.

Last mile delivery is a crucial phase in the supply chain. It refers to the final stage of product transportation, where products are transported from the fulfilment center to the customer’s address. It ensures quick and convenient deliveries, low logistics costs, and a smooth customer experience.

As per an industry report, Indian e-commerce logistics market size in 2024 is valued at USD 3.98 billion and is anticipated to increase at a CAGR of 12.72% to USD 7.24 billion by 2029. The report further estimates that the Indian e-commerce industry will reach USD 197 billion by 2027 at a CAGR of 18.4%. This significant growth trajectory is attributed to last mile deliveries that continue to fuel the growth of logistics.

Yet, as this industry expands, logistics companies in India face challenges such as infrastructure limitations, traffic congestion, diverse delivery locations, regulatory complexities, and consumer expectations. In this article, we’ll understand how last mile delivery analytics play an important role in making smarter supply chain decisions.

The Power of Data Analytics in Last Mile Delivery

Last mile data analytics includes gathering and analyzing data from all stages of the last mile delivery process. These analytics offer insights into order journeys, help identify delivery trends, predict demand, and implement strategies to optimize routes.

Logistics companies need to track the following key data points to optimize last mile delivery operations.

On-time delivery: OTD measures the percentage of deliveries that are completed within the expected delivery timeframe. An OTD of 95% and above is great.

Driver performance: It evaluates the efficiency and safety of drivers who excel in prompt deliveries and offer excellent customer service.

Route efficiency: Route efficiency assesses the optimization of delivery routes concerning time, fuel consumption, and distance.

Apart from the above, vehicle utilization, customer satisfaction score, inventory turnover, return rates, and delivery accuracy are important metrics.

Leveraging Data Analytics for Improved Efficiency

Logistics companies offer advanced analytics to optimize delivery routes. They take into consideration factors such as driver schedules, traffic congestion, and delivery density to decide the best possible routes. This helps fulfilment centers to eliminate unnecessary detours, reducing fuel consumption and emissions.

Further, gathering relevant historical data such as order volumes, traffic patterns, delivery times, and other factors makes it easy to identify patterns and trends. By using predictive modeling, companies can predict peak delivery times and allocate resources to ensure optimal efficiency. Additionally, real-time updates on traffic conditions enable on-the-fly route adjustments, ensuring efficiency during unforeseen obstacles.

Data-Driven Strategies for Enhanced Customer Experience

Analyzing customer data points from different phases such as order placement to delivery helps companies to identify logjams that impact customer experience. For instance:

Do customers frequently request specific times?
Are there any peak periods that have higher demand for deliveries?

These analytics help logistics companies to implement systems that allow customers to specify their delivery times and cope with demand. They can optimize delivery routes or increase staff to handle timely deliveries and ensure a smooth interaction with your brand.

Data Analytics and Driver Performance

Metrics such as on-time delivery, order accuracy, average late days, total distance, transit time, and deliveries per day/ hour give insights into driver performance. Thus, logistics companies need to meticulously train their staff to improve individual productivity and streamline work allocation.

Conclusion

Data analytics play a pivotal role in last mile delivery, by enabling businesses to boost customer satisfaction and reduce costs. Businesses must monitor key metrics and draw insights to gain competitive advantage and foster sustainable growth.