Artificial intelligence
In forecasting and optimizing construction industry
Proper inventory management in the construction industry is crucial, requiring accurate determination of material requirements and their maintenance at the right time. Any delays or lack of availability can result in downtime and escalating project costs. It is also crucial to take into account seasonal fluctuations in demand and guarantee the availability of products at appropriate times of the year.
Demand forecasting process
Challenges
Process optimization poses challenges in the following areas:
- Data Quality and Availability: The construction industry relies on data from a variety of sources, such as customer requirements and plans, supplier capacity and raw material quality. Access to this data is crucial for effective inventory planning.
- Volatility and Uncertainty of Demand: Demand for construction materials is volatile, subject to fluctuations caused by factors such as seasonality, economic conditions and weather conditions. This makes forecasting and planning for optimal inventory levels difficult.
- Cost Optimization: Construction companies need to maintain a balance between inventory costs and service levels, taking into account various constraints such as shelf life, storage capacity and budget.
Solution
Improving the accuracy of demand forecasts and optimizing inventories in the construction industry are real with the use of artificial intelligence. Construction companies have the opportunity to use advanced analytics and machine learning to improve demand planning, supply organization and inventory management.
Artificial intelligence allows construction companies to make more accurate and timely forecasts based on multiple data sources, such as historical sales data, economic cyclicality, customer preferences, changing weather conditions, and macroeconomic indicators. In addition, artificial intelligence can assist companies in optimizing inventory levels and replenishment strategies, taking into account demand variability, lead times, service standards and costs. By using artificial intelligence, companies in the construction sector have the opportunity to reduce inventory costs, improve customer satisfaction and increase operational efficiency.
Dature uses artificial intelligence (AI) and machine learning (ML) to generate accurate demand forecasts and optimize inventory. The application uses algorithms that enable a personalized approach to forecasting individual SKUs, taking into account both internal organization data (such as sales, in-house promotions, etc.) and external data (for example, macroeconomic indicators, weather conditions, etc.).
As a result, the quality of forecasts is significantly improved, and the risk of situations where product is out of stock or in excess (out-of-stock and overstock) is reduced. The simulation and inventory optimization mechanisms available in the system enable continuous improvement of the entire forecasting and optimization process. This translates into financial benefits, affecting sales, margins, costs and liquidity, as well as image aspects. In other words, it makes it possible to achieve significantly higher competitiveness in the market.
Potential business benefits
Reduce forecasting errors by 30% to 50%.
Reduce lost sales by up to 65%,
Reduction in transportation and administration costs by 5-10% and 25-40%, respectively.
Reduction of excess inventory from 20% to 50%.
