For a wholesale construction and mining equipment company, an excessive inventory could lead to the risk of unsold products, while an insufficient inventory could lead to lost sales opportunities. To properly manage supply and demand, it is necessary to forecast the demand for new vehicle sales. In the case of our client, further to information on dealer inquiries, demand forecasting was already being conducted using external market reports and macro reports. However, the client was aware that it was not fully utilizing the vast amount of available data and that the demand forecasting process was time-consuming.
To solve such problems, GAILABO proposed the “AI Demand Forecasting Model” to improve the accuracy of demand forecasting by efficiently utilizing and analyzing the vast amount of data collected by our clients. The AI Demand Forecasting Model learns from a vast database of macro data (past sales volume, vehicle utilization rates, volume of construction projects, population, resource prices, GDP, etc.). It can forecast new construction equipment sales for the entire market up to one year in advance. Moreover, to avoid black-boxing the logic of the forecast results, we have developed an AI demand forecasting model that is more transparent and with which, by utilizing our recently released XAI (Explainable AI) service, it is easier to implement PDCA cycles in customer’s operations.
