Here’s how data analytics enhances the logistics industry

Are you the kind of person who immediately starts tracking a package the moment you’ve ordered it online? Well, you’re not alone. With modern day technology, customers can diligently track their parcel journey — from the seller’s location to the logistics warehouse, and finally, to the customer’s address — as they await with sheer anticipation.

The logistics industry has come a long way across the last century, shortening delivery periods to a fraction of what they once were. Although modern transportation is largely responsible for this, data analytics also plays a critical role.

Logistics can be explained as the general process of overseeing, managing, and executing how products and supplies are acquired, stored, and finally, distributed to their intended locations while logistics management refers to the overall supervision of the transportation journey to meet the demands of supply chain.

In simple terms, logistics involves the arrangement of adequate resources to deliver or transport a product to its final destination or person without damaging its quality — for instance, the delivery of your online purchase to you.

However, this process has evolved in recent times with the introduction of data analytics. Smart logistics integrates advanced technology with the use of smart solutions — such as, data analytics, Artificial Intelligence (AI), Internet of Thing (IoT) sensors, and cloud computing — to optimize their operations.

Now, let’s take a look below to understand how data analytics is driving the logistics industry:

1. Optimize route planning?

Route optimization means finding the best possible pathway or route between two or more locations, with a certain goal in mind — such as, finding the shortest route, routes with the least traffic lights, or routes that have no tollgates. Although the common goal for most logistics companies is to find the quickest route from location A to location B, these optimization goals may differ between companies.

Without the help of data analytics — such as, AI, predictive analytics, and prescriptive analytics — companies can only determine this by manually testing out a multitude of routes under a variety of circumstances, which wastes time, costs, and manpower. After all, a route that worked well within a certain time slot on a weekend might not fare as well during peak hours on a rainy weekday. Additionally, with so many destinations in view, as well as internal and external variables at play, it’d be simply unfeasible to rely on trial-and-error approaches.

For example, let's say a logistics company's goal is to complete each delivery within 30 minutes. Using prescriptive analytics, the company can integrate different datasets — such as, GPS data, weather data, traffic data, and more — to come up with multiple route options that lead to different delivery periods. Then, based on their predefined goal of 30 minutes, they can choose the route option that most closely matches the goal. ?

2. Reduce vehicle maintenance costs?

Vehicles — such as, lorries, vans, cars, ships, planes, and even motorcycles — are the prized muscle of any logistics company, because without them to collect and deliver the goods, there would be no logistics involved. However, these vehicles require regular maintenance and service due to the long hours of travel, road conditions, and product quantities. Without proper maintenance, these vehicles are at risk of breakdown at any time, which leads to time-consuming and costly repairs.

As a result, logistics companies employ predictive maintenance in order to stay ahead of any possible issues with the vehicles in use or scheduled to be used. Using past and current vehicle data — such as, engine temperatures, engine oil levels, rotational speeds, coolant fluid levels, and more — an ideal baseline is set up to develop a predictive maintenance algorithm. This algorithm is then applied to future vehicle data to detect any anomalies.

If and when these deviations are detected by the algorithm — such as, a high engine temperature or a sudden drop in engine oil levels — the predictive maintenance tools will then create an alert for logistics companies to schedule maintenance. ?

3. Manage resources strategically

Logistics operations consist of various resources that require strategic management and planning to ensure that a smooth delivery is achieved, from the collection of goods from location A to the final delivery in location B. In order for this to happen, proper vehicle deployment, manpower planning, and cost preparation are important.

For instance, using a year’s worth of logistics data — such as, the number of travels made, number of vehicles used, most popular origin and delivery locations, top peak hours, and more — companies can analyze and gain insights into resource management for the vehicles, drivers, and more.

Using predictive analytics, companies can forecast how the operations would be next year, and using prescriptive analytics, companies can also make the appropriate plans — such as, deploying more vehicles in popular locations and increasing manpower during certain weekdays.

In a nutshell, just like many other sectors, the logistics industry is also being transformed by data analytics. Logistics is an ideal use case for BI because of its complicated and flexible nature, as well as the detailed framework of the supply chain. For the advantage of both logistics and shipping firms, significant insights generated from data allow industry players to optimize routing, reduce vehicle maintenance costs, and manage resources strategically. This is why leading BI companies — such as, Cubeware — have developed tools and solutions for the logistics industry, thereby improving and automating their operations, ensuring a smoother process from start to end.

To learn more about data analytics, visit us at www.cubeware.com. In addition to building end-to-end data analytics and BI solutions, Cubeware regularly curates educational articles on the most relevant components of the data analytics industry.