The future of artificial Intelligence is both incredibly exciting and incredibly promising. Much like the logistics world, the world of AI is fast-paced and ever-changing.
In this article, we will discuss today’s applications and the future potential of artificial intelligence in the logistics world and which areas AI is expected to make the most significant contributions to.
Transport and autonomous vehicles
Today’s example of AI in transport and autonomous vehicles
AI already plays a crucial role in the implementation of autonomous vehicles and drones. Primarily to direct these automated vehicles also known as AGVs or AMRS on which paths to take to reach their destination most quickly and efficiently. AI is also used today on a much smaller scale on large vehicles such as driverless trucks.
In the future: a wider variety of transport applications & cost reductions.
In the future, we could see this being applied to larger vehicles such as trucks in a more widespread way reducing costs and cutting down delivery timeframes and increasing overall supply chain efficiency.
This can be especially important to warehousing, to continue the flow of stock to and from destinations with the least delays and at a reduced cost as these vehicles would not require drivers. This would allow warehousing solutions using AI to have a competitive cost and time advantage over their competition.
Risk management and prediction
Today’s example of AI in risk management
AI can analyse historical data, weather conditions, traffic patterns, and other relevant factors to identify potential risks and mitigate them proactively. It can help logistics companies optimise routes, avoid disruptions, and respond swiftly to unexpected events. AI can also create predictions for possible future risk situations such as stockouts.
We see this today in the form of demand forecasts where an AI will analyse the level of stock in a warehouse and the historical sales data to make demand predictions and suggestions on what to stock up on to mitigate the risk of a stockout situation.
In the future: more accurate, and quicker decision-making.
In the future, we can expect to see AI become more accurate with the predictions it makes. As AI gets better at using the data at its disposal the quality of its suggestions may improve dramatically.
Data analysis and pattern recognition are already one of AI’s strongest areas and as such we can expect its ability to learn from that data and make higher-quality decisions and suggestions to improve.
AI chatbots & GPT 3 and the effects on customer satisfaction.
Today’s example of AI in chatbots
Companies are already using AI technology such as Chat GPT 3 for services such as customer support. How this process works is Chat GPT is used as an intermediary between, the time a customer contacts support, to the time a human can address the customer’s concern. In this post by TechTarget, we can see a deeper insight into exactly how this works.
In the future: Improved chatbot customer support
AI such as chatbots may advance to the point where they can address a wider variety of customer service needs. This would reduce the wait time for the customer to resolve their initial issue and save companies time and money investing in a large customer support network.
This is especially useful for larger companies as they may have hundreds of thousands of users or even millions of users. When you consider that 90% of customers in the US use customer support as a metric to decide whether or not to keep doing business with a company, customer support cannot be overlooked and neither can the quality of that support from a business perspective.
Data Analytics and Insights
Today’s example of AI on data analytics
AI algorithms can process the vast amounts of data which is generated by logistics operations and provide actionable insights for decision-making. By analysing data from various sources, including IoT (Internet of Things) devices, sensors, and customer feedback, AI can optimise operations, improve efficiency, and identify areas for improvement.
In the future: AI that can read for context & create data models.
In the future, we can expect to see AI improve the capacity and speed with which AI can analyse data. The main problem today is that AI struggles to read into the data for deeper context. This means that tasks such as cleaning, validating, and creating data models from this data are primarily done by humans. In future, we can expect to see AI branching out into these areas typically done by humans.
Conclusion
In the complex and fast-paced world of logistics, it is critical to adapt, use new technology, and capitalise on AI’s increased efficiency. This strategy ultimately provides logistics companies with a competitive advantage
While AI offers immense potential, there are challenges to address, such as data privacy, ethics, and the need for human oversight. However, with continued advancements, AI is expected to play a pivotal role in shaping the future of logistics, making it more efficient, reliable, and customer centric.