Globalization and increasingly digital lifestyle redesign the management of warehouses and deliveries, making traditional processes almost outdated. If logistics operators want to create new business opportunities and significantly enhance cost and capacity efficiency, they must take advantage of predictive information.
Currently, predictive logistics planning and execution can profit from new analytical methods as well as from new sources of data.
Implemented through large data-driven foretelling algorithms, it allows logistics providers to gain a significant increase in process efficiency and service quality, to reduce delivery times by anticipating demand before a request or order is even activated.
Combining integrated analytics with established information, you can have the most impressive results on the efficiency of freight operations, asset utilization, overcoming geographical and functional segmentation and reducing the risk of supply chain disruption.
According to the World Economic Forum, 50–60% of today’s available transport capacity is wasted. Algorithms aim to forecast the expected parcels and freight within the network by merging company’s internal historic shipment records with external factors.
As a matter of fact, among the latter there is no need for a fortune-teller: every online activity is nowadays detected and translated into valuable elements to understand market trends and adjust to them.
Public holidays, keywords digited on Google, online shopping behavior – thus including product searches, shopping histories, wishlists: these components will enable E-Commerce leaders to arrange shipments proactively before the customer even places an order.
Advanced transport can predict when to move goods to distribution centers that are closer to a customer who is willing to buy products. Intelligent capacity planning, using a predictive procedure, will be able to fine-tune the right level of logistics resources: when and how much to buy, where to store, when to take inventory and much more, taking into consideration new available data.
The current challenge is to integrate these innovative methods with the traditional ones, identifying risks and optimizing contingency planning to ensure the best delivery options, but also to integrate IoT devices into existing platform and, lastly, to transform the huge amount of big data in reliable and ready-to-use information.
After a transition period, the more aware decision-making will lead to a better balance of risks and potential on the market, through the proposal of targeted and increasingly detailed solutions. Moreover, the predictive analysis in parallel with advanced analytical control metrics will lead to a better monitoring of KPIs, reducing costs and improving customer satisfaction.