Following the globalization of Big Data and the phenomenal impact of intelligent computing on logistics, traditional solutions have taken a huge step forward in terms of decision support and visibility. Self-optimizing networks dedicated to real-time visibility and forecasting are taking supply-chain management to the next level, transforming the way B2B companies conceive their strategy.
Data analysis is key for supply chain management optimization. By processing and sorting through the sheer volume of data in circulation, AI is now giving businesses an opportunity to capitalize on this invaluable, yet, under-exploited source of information.
AI and big data are pushing the boundaries set by human intelligence in terms of data management and analysis. By browsing big data and filtering through continuous streams of information, algorithms are now able to find correlating factors and detect patterns, thus providing supply chain managers with tangible information to rethink their supply chain around targeted areas of improvement such as schedule or itinerary.
“This information helps assess the probability of a carrier canceling its booking, or measures how weather influences schedules, which help streamline and automate every Supply Chain process.” McKinsey recently revealed that early adopters with an AI strategy in the transportation and logistics sector enjoyed profit margins greater than 5%.” Says Lucien Besse, COO and co-founder of Shippeo
The ever-growing range of variables built into these self-optimizing models is allowing real-time visibility providers to deliver increasingly accurate predictive analysis but also targeted performance reviews. By using API’s to collect and incorporate data from external analytics systems into their previsions, they are now also making it possible for decision makers to take into consideration risk-factors (weather, terrain, traffic), thereby reducing delays and preventing human and material damage.