Artificial intelligence (AI) is a powerful technology that can be used for a variety of purposes. One of the most useful application areas is logistics as well as supply chain management, which deal with complex processes such as managing inventory, distributing goods to warehouses, or managing transportation routes in real time.
The foundation for AI and AI applications is data. Today, AI is becoming increasingly popular due to the growing amount of data, advances in algorithms, and the high processing power of computers combined with storage. The data obtained can be used to derive meaningful business insights that support strategic, tactical, and operational decisions. Based on machine learning and data analytics, optimization potential and cost-saving opportunities can be identified, supply chain operations can be improved, and new data-driven services can be created.
In this way, AI not only accelerates a company’s digital transformation, but also ensures a long-term competitive advantage in the logistics industry.
In an industry that generates a large amount of data every day, AI methods and applications can improve many areas of the business. For example, in road freight, freight forwarding and contract logistics: demand forecasting, pricing and supply management, network and capacity planning, warehouse and hub analytics, and customer analytics are areas that are affected.
Estimating demand and traffic flows in the multi-layered logistics market can be a major challenge. It is often characterized by high uncertainty, variability and unpredictability of political, economic, seasonal traffic and transportation factors. Traditionally, for decades, volume and workforce planning was done by phone, pen and paper, and the gut instinct of highly experienced dispatchers. By using predictive analytics in conjunction with human experience, logistics companies can predict workflows based on more accurate, data-driven forecasts, resulting in increased operational capacity and cost efficiencies for short-, medium- and long-term planning.
AI brings some potential benefits to businesses, such as improved inventory accuracy, shorter delivery times, or better customer service thanks to better forecasting. Of course, these are just a few examples of how companies around the world can leverage AI to drive higher profits and market share gains. The benefits of using AI in logistics and supply chain are many and can be listed along the supply chain operational flow:
Planning – Predictive analytics is already being used successfully in demand planning to identify signals and historical trends that enable more accurate forecasting. It enables full visibility and risk adjustment through end-to-end margin optimization.
Procurement – Digital transformation enables full data integration with suppliers. Commodity recipes are based on the forecasting process. Predictive analytics and neural networks provide advanced automated bidding capabilities to improve supplier selection.
Manufacturing – The use of ML algorithms enables companies to make better forecasts that can reduce overstocks or shortages, significantly increasing the effectiveness of production planning and scheduling systems.
Warehousing – ML solutions in warehousing and packaging provide benefits by automating, increasing productivity, efficiency and levels of quality control, and reducing costs, time and manpower requirements. Some solutions also provide additional safety benefits by making warehouses more automated through robotics and unattended security monitoring. AI-based solutions can make predictions about future demand patterns and optimize inventory levels to ensure products are available on time.
Logistics and distribution – Companies using AI in logistics and distribution operations can expect multifunctional benefits, such as dynamic route optimization based on historical data for better vehicle allocation and reduced fuel consumption. One example of such a solution is deep-learning algorithms that help optimize load distribution between different trucks, taking into account multiple factors such as delivery times, distance, number of deliveries, etc. AI models help in intelligent pricing of transportation and trucking services. Driverless trucks and other autonomous vehicles are a significant and impressive part of AI technology that, along with a global intelligent road system, will definitely revolutionize the logistics industry.
Marketing and sales – In addition to supply chain optimization, the use of AI in marketing and sales has brought significant improvements through various methods. AI-based solutions can be found across marketing and sales to deliver benefits such as better customer experience through better logistics services and helpful chatbots, improved operational efficiency, increased profitability, etc. ML algorithms provide retailers with the ability to make real-time predictions that significantly improve sales forecasts compared to traditional statistical methods, thereby significantly reducing operating costs due to lower inventory levels (lower inventory costs). Demand forecasting is also used to market products that are on the rise and need an extra push to increase sales.
Back-office operations – Although not visible at first glance, back-office operations account for a significant portion of a logistics company’s operating costs. AI brings tremendous benefits to back-office automation. The efficiency of invoicing, order processing and accounting can be significantly increased through automation. All operations are carried out automatically without the intervention of human professionals or with a minimum of supervision.
The use of digital and innovative solutions based on AI is already widespread in the logistics industry. And we expect AI systems based on deep-learning algorithms to be widely used in the near future by companies looking to optimize their business operations. AI is an opportunity to make supply chains more efficient and data-driven so that they go far beyond the traditional freight forwarding business.
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