Artificial Intelligence is making its way into logistics on a global scale. The technology itself is sophisticated and rewarding at the same time. Its rapid development and an increasing number of skilled experts allows boosting the industry like never before.
It's time to highlight the benefits of the AI in logistics. At the same time, we'll debunk some myths, circulating a belief that "AI is all about robots in the labs and IT firms in big cities."
A sneak peek for your convenience:
AI unlocks the true potential of Big Data in logistics;
AI boosts insight in supply chain management;
AI can increase efficiency and reduce the cost of operations in logistics ;
The precision of Artificial Intelligence in supply chain planning;
AI and Machine Learning in the predictive analysis;
The increasing importance of Artificial Intelligence in inventory management.
To figure out how to use the intelligence in the field, one may require clarification about the actual technology. According to Noha Tohamy, Gartner Analyst, the use of AI can be divided into two major subtypes:
Augmented assistance covers the actual "labor," that is taken care of by an AI. Calculations, forecasts, mechanical processes, etc. The main point here is that regardless of AI's capabilities, it is used to prevent human errors and exclude this factor completely, where necessary.
Automated operations are far more complex and require more effort to be successful. These operations unshackle the AI and let it operate on its own within a set of boundaries. Automated manufacturing facilities and processes that are completed by robots without the need for humans to oversee it should suffice as examples.
Moving onto the areas of the industry where the AI is already becoming a powerful tool for getting the results faster and with better security:
Timing & Transportation;
AI can manage tedious micro-operations all across the industry: calculations, analysis of particular data clusters, taking control over systems that previously were not automated, removing the error-proneness of human employees through augmented assistance and more.
Whether it be routine talks with suppliers, gathering feedback from customers and establishing a solid help desk for employees, an AI can already be used to automate most of the trivial processes. Chatbots help to communicate by instantly responding to the basic, daily questions or messages, data gets sorted out quickly and the results no longer have the effect of human error or bias.
According to DHL, an AI can already assist with address management, ensuring a better quality of delivery. Taking it a step further, the AI that manages large-scale logistics database can make fast decisions about optimizing delivery routes (and, therefore, time), which is especially important for products that have a limited time to be delivered (food, medical supplies, vegetation, etc.).
AI is capable of providing forecasts and predicting risks with the help of machine learning. It studies previous market/industry patterns, human decisions, prerequisites, and outcomes. The analysis allows an AI to respond to future challenges with an ever-increasing efficiency rate.
The size of the industry leaves many blank spaces for AI tech to fill. We have chosen six aspects that have significant size at the moment and also are capable of growing both in size and efficacy.
Large companies generate, find and analyze immense amounts of data. An AI is probably the only solution to ever become successful in navigating these volumes of information while producing an efficient analysis on demand.
Big Data unlocks optimal solutions to allow logistics companies to work smarter and more efficiently. After a clever use of analytical data, UPS has managed to deliver 350,000 more packages annually by forbidding drivers to turn left.
AI provides tremendous overseeing capabilities in the industry. Machine learning allows for studying previous patterns, mistakes, and results. Through this, AI can identify a plethora of new factors to take into account while dealing with issues like customer satisfaction and fraudulent activities.
Learning algorithms help to develop solutions that a human expert may have never come up with, or just missed them in the process. Knowledge generated by an AI (both via supervised, unsupervised, or reinforced learning) is used for predictions, optimization and real-time response to issues, generally boosting the ability of human staff to ensure the productivity of the supply chain.
AI can take over simpler tasks like tracking shipment status, supplier feedback, risk detection, etc. For example, AI-based chatbots can handle 100% of simple inquiries, taking the pressure off the support staff. The employees can be rerouted to more pressing matters, thus covering more ground for a cheaper cost.
AI can also link every operational department and supply the required information, analyze data in real time and assist in decision-making (or doing it in automated mode). For example, it can sort letters and small shipments, conduct activities like visual inspections and more. All these factors contribute to better performance for smaller spendings.
An AI can harness the data from any location globally and take into account a range of factors like the demand, possible transportation issues, and factory production planning. This allows making effective planning moves for managerial staff, regardless of the situation.
The analysis of the data aids in timing the deliveries according to issues like bad weather, selling pace, border incidents or even traffic. The same principle works with factories and production: AI supplies information about what, when and how much to produce to optimize the process.
While being a rare solution available for the big guys on the market, predictive analysis is nonetheless a strong suit of an AI. It helps to transform collected insight into valuable and easy to comprehend information that can be used to prevent dangers and be proactive rather than reactive in terms of risk elimination.
Predictive analysis leaves no space for guessing and human errors. DHL’s system, for example, takes into account more than 50 factors before even creating a machine learning model for better air transportation. These complex activities allow predicting certain aspects of logistics days and weeks in advance, getting rid of the problem before it even emerged.
AI can tackle the issue of excess wares in the inventory or unforeseen product shortages, among other factors. As one of the most discussed topics in modern logistics, inventory management can utilize AI-based solutions for avoiding the situations as mentioned above.
Depending on the size of a warehouse, different options may be applicable. An AI can help to keep track of the items in stock, their positioning, how fast they are selling and when is the next shipment. Advanced AIs can use predictive analysis for forecasting the future increase or decrease in any given product, thus simplifying the management process.
Combined with IoT for better in-house operations and deep data insight, AI can smoothly optimize the warehouse and make it work soundly with other parts of the supply chain.
The role of artificial intelligence in logistics is vast. The potential of the tech meets a broad field for opportunities, improving routine operations, providing analytical data and insight, unavailable for a human eye.
AI-based solutions improve communications, planning, inventory management and more, with a mostly positive tendency. It would be apparent to state that the presence of AI in logistics will gradually improve, both in size and complexity, racing for better results and satisfied customers.
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