Big Data: the driving force for logistics automation
Since millions of packages move around the world in a day, a huge amount of data is generated and this can be used to make processes more efficient.
One of the key factors in the success of global businesses has been an efficient analysis of past performance - consumer data to improve products and increase customer satisfaction or operational data to improve efficiency and reduce cost. However, in today’s interconnected digital world with the proliferation of smart mobile devices and the advancements in operations and transport automation, we are seeing a shift towards larger and more diverse real-time data that is revolutionising the way companies can manage their new-age supply chain networks.
What is big data?
Big Data refers to extremely large data sets, from several sources that are often available in real time and that cannot be managed by traditional data processing systems. Advanced statistical programs, machine and deep learning algorithms can process this data and generate patterns, trends and implementable business insights. This has enabled companies to not only make instant decisions to increase efficiency but also to automatically adjust their robotic processes via a continuous feedback and improvement loop powered by Big Data analytics.
Why is Big Data a good fit for the logistics industry?
Big Data is a perfect fit for logistics as there are millions of packages moving across the world daily that go through multiple touch-points via a complex network of shippers (sellers), consignees (buyers), warehouse personnel, customs agents, transporters, loaders, packers, shipping and air carriers. This creates a multitude of data points and enormous potential to improve both delivery times and cost and to achieve greater visibility across the network.
How can Big Data be used in the logistics industry?
With the spread of sensors and mobile devices, not only can a customer get Uber-type tracking for their consignments but the trucking companies can also collect a range of data from engine performance, fuel consumption, tyre wear-and-tear and even external data such as weather and traffic conditions. The data can be processed and computer algorithms can automatically manage route selection for the driver. The fleet operator will gain from better fleet optimisation, thereby reducing cost while also ensuring on-time deliveries to customers.
A great example of this is when UPS used Big Data analytics to implement a policy where drivers should only turn left when absolutely necessary, which saved them 40 million litres of fuel and increased deliveries by approximately 350,000 orders.
Similarly, for international shipping, data on congestion, strikes, weather conditions, etc. enable carriers to provide accurate and predictive assessments of potential delays and disruptions to customers and adjust routes and capacity accordingly.
Today, with robotic package handling, sorting and automated forklifts and other warehouse equipment we are nearing the reality of complete mechanisation of smart warehouses. While tech companies such as Amazon led the way, now even regular manufacturing companies are starting to automate their warehouse operations. Warehouses offer rich operational metrics on storage and movement of parcels that can provide insight into efficiency gaps. Big Data analytics and tracking sensors can improve warehouse robotics, which can increase equipment life cycles (via preventive maintenance), accelerate product movement, optimise inventory management (through better predictive models), and also increase warehouse safety. Warehouse managers, using data analytics, can make immediate operational decisions, resulting in seamless resource allocation, reduced costs and better warehouse throughout.
Customer feedback has always been received through either anecdotal evidence from sales reps or customer questionnaires in logistics or most other B2B industries. On social media and public websites, users provide open, accurate and current feedback that can be incident-specific or generic. New technologies such as semantic analysis and text processing can dissect and group these reactions and analyse customer disposition to eventually create an instantaneous feedback loop. A DHL study illustrates this point and concludes that “a meticulous review of the internet gives unbiased customer feedback”, thereby enabling product and customer service managers to design solutions to guarantee customer satisfaction and retention, which is crucial in today’s hyper-competitive environment.
Consumer demands are rapidly changing, and businesses can no longer use retrospective data to take strategic decisions to stay relevant. Big Data heeds this call with real-time information that displays patterns and trends, which allow businesses to make intelligent, immediate and most significantly automated operational decisions.
With millions of available data points through sensors and connected devices, robots in warehouses, delivery drones and self-driving vehicles, it is only a matter of time until we see a fully automated intelligent supply chain that will be continually optimised by Big Data analytics.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)