The network-based nature of the logistics industry provides a natural framework for implementing and scaling AI. Companies deciding not to adopt AI run the risk becoming outdated in comparison with their competitors who effectively utilize AI solutions in their business today.
In a recent report, IBM concludes that using a combination of AI and robotic process automation (RPA) could easily replace clerical labor – so often at fault in logistics and operations – as software ‘robots’ can be swiftly integrated into existing business structures and IT systems. In an industry characterized by uncertainty and volatility, AI can help to fundamentally transform the current logistics operating model from reactive to proactive and supplant forecasting with predictive intelligence. Read more about our solutions [link to our solutions] to see how this could work in your company.
The greatest potential of AI in the logistics sector lies in machine learning’s power to detect anomalies, facilitating a process experts call ‘predictive maintenance.’ Enabled by deep learning’s capacity to analyze very large amounts of high dimensional data – including additional data, such as audio and image data from relatively cheap sensors such as microphones and cameras – AI can predict failures and allow planned interventions to reduce downtime and operating costs while improving production yield. AI-driven logistics optimization can go beyond real-time analysis and introduce behavioral coaching – allowing the team members to stay on top of any situation.
AI can be applied in new ways across the entire logistics industry – from assembly to post-sale customer service interactions. Using AI to manage delivery traffic is a particularly good example of how AI can optimize logistics, reduce delivery times and increase fuel efficiency. One European trucking company has reduced fuel costs by 15 percent, by using sensors that monitor both vehicle performance and driver behavior; drivers can also receive voice messages to indicating when to speed up or slow down, optimizing fuel consumption.
