Next Level Of Logistics : Hyperlocal Data For Last Meter Delivery
By Amarsh Chaturvedi, Co-Founder & Director, Transerve Technologies
With large rounds of investment being made in businesses around the world such as Uber Eats in the US and Big Basket in India, the hyperlocal delivery model has seen emergence as a trend in the past few years. A lot of entrepreneurs entered this niche market with hopes of finding success, however things were not as easy as they might have thought them to be. Efficiently running last meter delivery services presents its own sets of challenges from the logistics point of view – and it is something that you definitely cannot ignore given the fierce competition in the sector.
It has been noticed that traditional logistics solutions are no longer suited for the planning of such services which is in part due to the very nature of the challenges being quite different from that presented by standard logistics. If you take a look at any third-party logistics (3PL) company today, you will notice that they normally end up accepting next day delivery orders almost up to a certain point of time so that there is enough time for them to plan routes for the next day. While this may work for certain kind of services, when you think of on-demand services which involve the assignment of new orders in real-time with almost zero information regarding the future – when the orders might come in, this approach would hardly work.
Both these situations present challenges that are quite difficult to scale, especially when a business wants to ensure that they are operating at their optimum best or to say the least, better than their competitors. This is where spatial data science with the help of hyper-local data can actually make a humongous differenceby building data models that can end up simulating existing conditions, can provide insightful tips on the constraints that may currently exist, tackle the problem of inefficient assignments etc.
Last meter delivery services now require new solutions for logistics optimization as the time-tested methods for logistics that have worked for decades, are now ineffective and cannot adapt to new environments that have shorter delivery cycles and much more complex route planning involved. Hence, new methods are now required to solve the problem, since the optimization of hyper-local delivery presents a multidimensional considerations. One of the best ways to optimize and also increase profit margins is the employment of machine learning in providing solutions for route optimization via hyper-local data.
In recent years last meter delivery has increasingly become a target for logistics companies as customers have become more and more demanding. The requirements of delivering the ordered goods swiftly within the specified time limit and also with the accuracy of location means that the older challenges associated with shopping need to be alleviated and managed with the click of a button. Since customers have now become much more impatient with their time, the demand for that level of service can actually be quite daunting for logistics and has become a real growth pillar for companies that are now focusing on last meter deliveries.
The biggest challenge that is faced in almost all sorts of deliveries are the ad hoc locations that haven’t been captured before,which means that there is a no previous information to work with. Hyperlocal data hence comes to the rescue here, since plotting delivery points accurately will not be as complex as before. Having access to application software that can integrate hyper local data seamlessly, iscloud-based and flexible enough, can make a massive difference as the data can be accurately captured and be used to ensure accurate deliverywhile also managing all the moving parts at the same time.
Logistics services that are backed by cutting edge machine learning technology are capable of offering last meter delivery services to companies that do not have their own logistics infrastructure. By using hyperlocal data one can minimize delivery expenses, optimize their routes as well as predict the optimal time for last meter delivery. This makes it possible tominimize investments in the delivery infrastructure and be able to make it profitable even for small businesses, wherein hyperlocal datacan help them scale without decreasing the speed or quality of their services.
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