According to Goldman Sachs for The Economist, the use of Artificial Intelligence in the increasingly complex and volatile distribution network will have a more significant economic impact than any other application of this technology and will affect many businesses.
But McKinsey translated into numbers the potential economic value that AI in logistics will create over the next 20 years: $1.3 trillion a year. The consultancy also predicts 15% lower logistics costs, 35% higher inventory levels, and 65% better service levels.
How does AI transform logistics? This is what we will see in this post, which shows why logistics is targeting this technology, the applications that the market has in view, the results already observed, and the challenges organizations must face in their journey.
Why Is Logistics Targeting AI?
Logistics is a complex and dynamic sector involving many parties whose situation changes rapidly. Every event in a product’s journey generates data that can be leveraged. The perfect environment for data science and AI to become vital tools and other technological innovations.
With that came the need to rethink supply chain management and seek tools that would help organizations be more proactive than simply reactive, without appealing to subjective intuition but science—point for artificial intelligence.
AI has given companies the ability to anticipate events, giving them time to seek appropriate responses and indications of viable solutions based on the analysis of the scenario at play. More: it has given models capable of learning and self-optimizing from new data.
Applications Of AI In Logistics
AI solutions are increasingly accessible to the logistics industry, including options for prediction, visibility, integration, and automation, with a faster implementation process and lower costs.
The most common is linked to network and inventory optimization. But there are others:
- planning
- Real-time inventory management
- Automated warehouses
- demand prediction
- Visual inspection, damage classification, and solution recommendation
- predictive maintenance
- route optimization
- Vehicle recommendation for high-demand locations
- autonomous vehicles
- smart roads
- Fleet recommendation
- Submission of optimized time windows.
Post-Implementation Results Of AI Already Observed
Cost reduction is one of the most significant benefits linked to adopting AI in logistics. But this is due to other results generated by the successful adoption of the technology:
- Visibility and control over the logistics process
- Actionable analytics insights
- smart decision making
- Reduction of bottlenecks in the operation
- Shorter lead time
- Shorter travel time
- Speed of operations
- Reliability.
Cautions When Introducing AI Into The Operation
As a sector whose adoption of technologies is traditionally slower, logistics has as its primary concern the lack of completeness and quality of data. Accessing, sanitizing, and integrating the correct data into models will be among the first challenges.
Another point of attention is the ideal infrastructure to process increasing volumes of data, in many cases, in real-time.
The care needed for the appropriation of AI in logistics, however, is not just technological. There is an organizational path that the sector needs to tread.
One of them touches top management. Without leaders’ express sponsorship of initiatives, they are unlikely to take off. Projects with AI involve a certain amount of comfort with risky investments.
Another problem is the formation of the team. Few organizations in the segment can deal with the lack of talent in the area and, more profoundly, with the competition for professionals with technology companies.
Training professionals to embrace the technology is fundamental, which requires that it become accessible, especially for those who are not experts in AI.
Such challenges generate other problems, such as lack of clarity about the value of the initiative to the business, lack of solution design before implementation, and little focus on execution rigor.
Such issues need to be resolved so that the projects bring results even in the pilot version and prove their value.
The Future Of Logistics Is Data-Driven
If the applications of artificial intelligence in segments such as logistics were developing a few years ago, the needs of a complex sector combined with disruptions in the supply chain accelerated the pace of adoption with promising results in terms of visibility, speed, and proactivity.
But the path will not come without challenges, whether technological or organizational. Every company that wants to start its journey in AI must address them at the beginning. Otherwise, they will lose energy, money, and time with unsuccessful projects.
Also Read: How To Apply Artificial Intelligence In The Automotive Industry?
