Leveraging Automation to Drive Super-fast Deliveries

Quick Commerce is redefining the on-demand delivery vista with its super swift delivery model. Customers can now place any small basket order consisting of groceries or other everyday utility items through an online delivery app and expect the order within an order window of less than an hour. With a huge market waiting to be capitalized, the quick commerce disruption offers vast potential to make life convenient for customers.

The year 2021 witnessed the Q-com industry reach about USD 0.3 billion. It is further estimated to register a 10-15x growth and develop into a USD 5 billion industry by 2025.

Quick commerce can meet urgent delivery requirements by extending order fulfillment windows between 40-10 min. The negligible waiting period sets it apart from other models. It is no surprise that present-day customers can’t get enough of it. It is proving to be a win-win situation for both quick commerce companies and the end customer. One of India’s fastest-growing on-demand delivery platforms recently recorded a meteoric 500% increase in its year-on-year quick commerce deliveries.

Countless brands — existing and new — are vying with each other to enter the 10-minute delivery bandwagon. Although establishing several dark stores and collaborating with brick-and-mortar outlets has surely assisted quick commerce brands in scaling faster deliveries, something else is also at the center of this disruption. It’s technology.

Technology and automation are the two focal pillars on which the quick commerce revolution stands. Today, speedy deliveries are becoming the norm for eCommerce brands. According to statistics, over 41% of online shoppers viewed faster and more dependable deliveries as extremely important. So much so that 84% of shoppers were ready to switch brands even after a single instance of unsatisfactory delivery. The obligation to meet these customer requirements led companies to offer wafer-thin delivery windows for rapid order fulfillment.

Now, this rapid order fulfillment phenomenon relies on three major aspects:

Location of dark stores and availability of inventory

With online order volumes increasing, brands have no option but to bring inventory closer to densely populated areas to guarantee a swift delivery turnaround. To ensure this, fulfillment centers must be placed with deliberate and strategic calculation alongside augmenting logistics capabilities.

According to Accenture, about 50-70% of all eCommerce purchases in the US will be delivered through local inventory by 2023. Such a probability is indeed more than likely. The partnership with Kirana stores or local retailers has been one of the pivotal driving forces behind the success of the Q-com model. However, there are still challenges like the lack of real-time inventory visibility, which keeps the model from realizing its full potential. Moving ahead, the delivery aggregators must extend consumers with absolute inventory visibility. Deploying such technological capabilities and automating core logistics operations will naturally enable brands to narrow down their order fulfillment windows.

The speed of order allocation

Move over manual processes that are cumbersome and increase delivery TAT. Brands must continue to utilize automation for timely dispatch and faster deliveries. Smart auto allocation rules can help remove human dependencies and automatically accord delivery tasks to drivers based on various parameters like delivery type, rider current order volume, proximity from store and customer. By leveraging new-age AI and ML-powered logistics management tools, companies can steer a net auto allocation rate of more than 99%.

Efficient driver-management

Time is the essence to deliver on slim delivery windows. Having an agile response team is a must for delivering on stringent SLAs. In many cases, the lack of appropriate technology can be a challenge for managing drivers and their respective KPIs. Smart logistics management tools can readily address this challenge by presenting a slew of advanced driver scoring parameters. These could be: time taken to start the bike after the order was received, reached gate status, time taken to deliver the order and return to the store, etc. Moreover, it also enables store managers to manage driver shifts and evaluate the various routes taken to deliver the order.

The solution also analyses any distinction between the system-recommended planned route and the actual route taken by the driver to investigate delivery delays.

With consumers increasingly becoming habituated to quick commerce delivery, it will be intriguing to witness how this benchmark will initiate further innovation and disruption in the long run.

SHIPSY MEDIA CONTACT 

arjun.alva@shipsy.io