A man who has been sleeping for twenty years and woke up in 2020 would find himself in a different transformational era. Along with numerous changes in ecology, politics, the way we live, and diseases we cure, he or she would be astonished by the consequences of the revolution that has redefined each of the aforementioned aspects of our lives. Digital revolution.
Digitalization has reached more people than any other revolution. Since Gutenberg’s printing press, it has become the most outstanding event to mark a huge shift in the way we communicate. People have learned how to exhibit their intelligence by machines and systems that improve human thinking. Having crossed a few “AI winters” since the term coinage in 1956, artificial intelligence serves as a great benefit to all industries nowadays. And logistics is setting about its journey to make its way as an AI-driven industry.
Logistics companies receive a possibility to make clear predictions and optimize their performance using the power of Big Data. The high volume of structured and unstructured data is generated by supply chains on a daily basis. Big Data enables companies not only to exploit this information but also to adopt advanced predictive analytics and increased automation which drives strategic decisions.
A well-known example of UPS has proved that proper logistics data analysis can save time, costs, and exclude safety risks. After data examination, the company revealed that trucks that turned left were stuck in the traffic what cost the company more fuel, time, and increased the number of delays. Now UPS tracks go straight or turn right in 90% of cases what saves the company 10m gallons of fuel and boost its delivery rate to 350,000 more packages annually.
Artificial intelligence in Logistics also stands as a great benefit for the physical aspect of working. Robots are able to locate, move, sort, and track inventory augmenting the capabilities of the modern workforce.
The Finnish company ZenRobotics has been developing intelligent robotic waste sorting systems since 2011. The engine ingests real-time data from three different cameras and sensor types and is trained to identify a wide variety of logos and labels. Such an implementation of machine learning in Logistics, allows the company to sort 4,000 unstructured recyclable items per hour.
Warehouse management can be also optimized by conducting accurate calculations of the number of items that need to be moved on a certain date and the amount of equipment needed to handle the process. With the help of machine learning in logistics, it is possible to spend less time establishing more detailed stock movement predictive analytics and increase the overall productivity of pick-and-pack processes. Logistic automation systems can also vastly improve the speed and accuracy of the communication process. The elements are enabled to conduct a dialogue with each other embracing system monitoring and control which ensures efficient warehouse management and provides the supply chains with contextual intelligence that allows making effective planning decisions based on the range of factors like the demand, possible transportation issues, and factory production planning.
The popularity of autonomous vehicles is rising with breakneck speed. And one of the reasons for this is the contribution the artificial intelligence makes to outperform human driving capabilities. AI allows the vehicle to perceive and predict the changes in its environment with the help of sensing technologies that perform together to produce a three-dimensional map of the vehicle’s environment including traffic signals and laws, interpreting road signs, identifying obstacles, etc. Without a possibility to hard-program a vehicle to react to every possible scenario, the capabilities of vehicles should be constantly improved with the help of AI as they enter the new surroundings.
Traditional auto industry behemoths have embraced AI as a component of their development strategy, whereas, relatively new entrants like Tesla, Google, and others are actively using their own patent AI and manufacturing techniques to develop autonomous vehicles.
Most of the customers have just two touch points with logistic companies: checkout with an online retailer and receiving delivery or returning the item. While concerning businesses, the touch points are multiple: long-term contracts, operation of global supply chains, service level agreements, etc.
AI in logistics can increase customer loyalty and retention through personalization of these touch points
One of the examples is a voice-based service that was adopted by DHL Parcel in 2017. Their customers became able to ask Amazon’s Alexa the shipment information about their parcels.
Logistics data analysis has the potential to take customer experience to the next level, delivering goods before they have even ordered them. Drawing predictions from logistical data services, analyzing browsing and purchase history, weather data, and social media chatters, companies can predict the next order a customer will make. Not to mention, demand prediction and shortening delivery time.
AI has already left the cinema and became an ordinary part of our everyday lives and most of the businesses. Though the future of artificial intelligence in Logistics is promising, some logistic networks have already shifted to a proactive and predictive paradigm and logistic automation. And we strongly believe that the 21st century is the best time for “AI spring”.