*By Angela Gheller
In the context of modern logistics, demand forecasting plays a crucial role in optimizing operations and reducing costs. Traditionally, demand forecasting relied on basic statistical methods and, let’s be honest, on managers’ intuition. However, with the evolution of Artificial Intelligence (AI) and Machine Learning (ML), new possibilities have emerged that are revolutionizing this process.
Advanced AI and ML algorithms enable deeper and more accurate analysis of historical data and market trends. Unlike traditional methods, these algorithms can continuously learn from new data, adjusting to changes in consumer behavior and seasonal variations, resulting in much more accurate and efficient forecasts.
This precision in demand forecasting leads to a reduction in excess and stockouts. After all, overstocked stocks represent idle cash and additional storage costs, while product shortages can result in lost sales and customer dissatisfaction. By applying artificial intelligence and machine learning, companies can better balance various processes.
For example, tools for WMS (Warehouse Management System) already use AI to automate warehouse management, improving storage efficiency and product movement, being able to predict demands and suggest the best arrangement of items within warehouses, as well as inside vehicles. There are also other combined solutions, such as YMS (Yard Management System), which applies AI to coordinate the movement of vehicles in yards, optimizing resources and improving the efficiency of logistics operations, and the TMS (Transportation Management System), which uses algorithms machine learning to optimize routes, load arrangements, delivery sequencing and predict delivery times with greater accuracy, helping to reduce costs and improve customer satisfaction.
It may seem outdated to harp on about WMS, YMS and TMS, but believe me, there are still many companies that do not make the most of these technologies in their operations. According to the Logistics Technological Productivity Index (IPT), a study released by TOTVS in 2021, among the priorities for future investment by logistics service providers are digitalization and process automation, showing that we are not yet at the level of maturity we could be. Therefore, it is essential that there is also a change in culture within companies and that professionals are trained to effectively use technologies.
This is because it is a fact that the application of artificial intelligence and machine learning Demand forecasting is transforming logistics, providing more efficient and accurate inventory management. With advanced products being made available by the technology market, companies can integrate these solutions effectively, optimizing their operations and ensuring a more agile response to market demands. This evolution not only improves operational efficiency, but also strengthens the competitiveness of companies throughout the supply chain. Predictability is the basis for good planning on all fronts of the business, so why not rely on cutting-edge technology for this?
*Angela Gheller, Product Director for Logistics at TOTVS
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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