Data Analytics has made its way into almost all verticals that are necessary to run a business successfully- be it finance, marketing, sales or HR. However, the in-road to logistics and supply chain has not been an easy one for data analytics. Given the complexity involved in gathering end-to-end data in logistics and supply chain management, analytics didn’t have much scope in this area. Without sufficient data, it would be impossible to run historical or predictive analyses. Nevertheless, with time, the logistics industry too is trying to streamline data collection and operations to reap the mega power of big data and data analytics.
Problems that logistics companies face during data collection
Although things are different for bigger logistics corporations who have changed their processes of gathering end-to-end data from vendors, channel partners, and customers, logistics companies worldwide are yet to uncover the tricks to successful data collection and mining. Here are some of the problems that logistics and delivery companies face when they try to consolidate data.
Inability to build a complete set of data
When logistics companies try to gather a complete set of data, they start with data from various verticals that generally comprise of order-level information (inventory reports, PO history and SO history, among others). The problem arises when they source information on logistics from providers and vendors, who have only shipment-level data available. Matching the order-level and shipment-level information then becomes an arduous task for these companies.
Matching transportation and custom-entry records
Striking connections between customs entry records and transportation records that come from many different sources is a complex task and also adds to the trouble of gathering consistent and streamlined data for logistics companies.
The only way to address the issue of data gaps is by expanding the engagement that they have with their partners, whereby they can include not only shipment orders but end-to-end consignment management orders. This step immediately adds more transparency to the process of handling orders and also helps them gain more clarity and visibility of daily operations.
To address the second pain point mentioned above, companies can integrate their order management and customs brokerage with the same logistics vendor, which will help them gain a single data set that is not only connected but comprehensive as well.
Using big data and analytics to enhance customer experience
To reap the benefits of Big Data, access to relevant data and data cleansing in order to remove errors, gaps and discrepancies in the current data set is an absolute necessity. Once companies have the right data set, they can apply analytics models, both historical and predictive, to find out ways in which they can grow their business and enhance the customer experience.
By running predictive analysis on their data sets, logistics companies can gain valuable insights about their customers, which, in turn, can help them deliver stellar customer experience. One of the ways in which this works is by allowing logistics companies provide specific and accurate insights into their consignment processing, thereby allowing them to keep their customers updated and also letting them know a near-accurate tentative date of delivery.
Analytics also helps in addressing performance issues which might be affecting customer experience by applying historical analytics. On the other hand, predictive analysis can help shed light on contingent factors such as climate changes, crime in certain countries, changes in the socio-political fabric in some countries, changes in the global financial markets or simple shifts in consumer demand.
These insights can be very helpful for small and mid-sized companies as much as for bigger logistics companies since a single such incident can cause severe loss to the company. Predicting future events and preparing beforehand is a smart way of tackling issues in international business, something which logistics companies are largely into.
Looking into the future of logistics and analytics
Understanding these insights and analytics can help logistics companies provide seamless services. By optimising processes, the logistics industry as a whole can extend optimised customer service. According to a research conducted by the Supply Chain Management World Research, approximately 64% executives in the logistics industry believe that big data and insights from analytics will bring in the disruptive power that will pivot the logistics industry forever. In another study conducted by GT Nexus and Capgemini Consulting, 75% of the survey respondents mentioned that they considered the digital transformation of the logistics industry as a very important matter.
Keeping these numbers in mind, it can be safely said that digital transformation, analytics, and big data are definitely the way forward for the supply chain industry, as this is the most effective way of spelling growth and expansion through one of the first and foremost parameters for any business, their customers.