Retail, CPG and Logistics
The role of AI in modern logistics management
Making industrial operations easier could be deemed as the motivation for innovation in all disciplines. This has, of course, led to some astonishing technological marvels. A mere science fiction just a decade ago, Artificial Intelligence (AI) has since come a long way, finding applications in virtually every industry. An argument may be made that humans are superior in tackling complex problems. But when it comes to simple, repetitive, and procedural tasks – as common in one of the most crucial aspects of industrial evolution, the logistics industry – AI has come a long way. Mimicking human intelligence, AI solutions have the potential to transform logistics processes.
But what role can AI play in logistics?
Challenges in implementing AI solutions for logistics
Discussing artificial intelligence in logistics, the first thing that comes up is the challenges and limitations of implementing AI. Although many of these challenges stem from resistance to change, there are some valid concerns we must address.
Some of the most common challenges and limitations of AI for logistics are:
- Resistance to change
- Training data quality and access
- Integration with legacy systems
- High implementation costs
- Potential job losses to automation
- Data privacy and security compliance
Examples of AI in logistics
Despite the prevalent discussion on challenges, AI solutions have prevailed – especially with the help of logistics business process outsourcing solutions – as we can see many emerging trends and applications of AI in logistics. The logistics industry has been quick to adopt AI solutions, and as a result, they have become a requirement to stay competitive.
Here are some of the most common – and emerging – use cases of AI for logistics:
- Autonomous vehicles
- Warehouse automation
- Smart roads
- Route optimisation
- Demand prediction
- Back-office operations
- Risk management
- Improved transparency and visibility
- Improved cost-effectiveness and operational efficiency
- Optimised inventory and transport management
- Improved customer experience and satisfaction
Autonomous vehicles – or "smart vehicles" – can allow the logistics industry to automate goods transportation processes while saving time, reducing costs, and minimising accidents. Although current regulations require a human driver to be present behind the wheel to control unexpected situations, this may change soon.
AI is already transforming warehouse operations, automating routine tasks, analysing inventory processes, connecting multiple warehouses for optimal transportation, and performing quality checks. This has allowed logistics companies to improve service efficiency, cut transportation costs, and optimise overall operational costs.
Although still a work in progress, many companies are building smart roads that can help road safety and reduce delays to make deliveries faster. Equipped with LED, solar panels, and fibre optic sensors, these roads can alert drivers about road conditions, sense traffic patterns, or alert emergency services in an emergency.
The logistics industry routinely relies on route optimisation to minimise transportation costs. AI in logistics can make this process easier – combining traffic, weather, location, and other data – to ensure faster delivery, lower fuel consumption, and overall lower delivery costs.
With shifting demand patterns, demand prediction is critical for optimal inventory management and the best possible customer experience. AI algorithms can identify and predict demand trends – allowing you to carry necessary inventory levels without running up storage costs or losing sales because of low inventory.
AI plays a significant role in ensuring speed and accuracy of logistics back-office operations critical for performance efficiency. For example, AI can automate freight bill processing to eliminate any errors that may impact the reputation and bottom line of the company.
The logistics industry is vulnerable to global geopolitical events and can collapse in the absence of effective risk management. AI risk management tools can help you model different scenarios and use predictive analytics to forecast potential outcomes. You can use these insights to build contingency plans to ensure business continuity.
With such a wide range of applications, AI in logistics can offer numerous benefits to logistics companies, from optimising costs to improving customer experience. AI can also help companies build contingency plans to navigate unexpected challenges and supply chain disruptions. In summary, the key benefits of integrating AI and logistics are:
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How can Infosys BPM help?
Infosys BPM logistics business process outsourcing can help you navigate the unpredictable logistics industry to ensure a robust supply chain, optimal operational costs, and excellent customer experience. Leverage innovative technology and Infosys BPM logistics business process outsourcing services to integrate AI into your logistics operations and ensure delivery excellence.