Inventory and stock management

Artificial intelligence

In forecasting and optimizing chemical industry

Both demand forecasting and inventory optimization play a key role in the chemical industry, which must cope with intense dynamics, uncertainty and a complex supply chain structure.

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Demand forecasting process

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There are many challenges to streamlining processes in the chemical industry. Above all, data quality and availability are important areas for improvement. The chemical industry uses data from many sources, including customers, suppliers, distributors and market research. Unfortunately, this data can often be incomplete, inaccurate, outdated or inconsistent, which has a negative impact on the accuracy of forecasts and on decision-making regarding inventory levels.

In addition, the volatility and uncertainty of demand pose another challenge. Demand for chemical products can fluctuate significantly due to factors such as seasonality, customer preferences, economic conditions, or environmental regulations. These factors make it difficult to forecast future demand and plan optimal inventory levels and replenishment strategies.

Chemical companies also face the challenge of aiming for the point of minimum total costs. This requires a constant balancing act between inventory costs and service level (Service Level), taking into account constraints such as product shelf life, storage capacity, available budget and others. The inventory optimization process must take all these factors into account and find the optimal balance that minimizes costs on the one hand and increases customer satisfaction on the other.


Improving the accuracy of demand forecasts and optimizing inventories in the chemical sector are possible through the use of artificial intelligence. Chemical companies can use advanced analytics and machine learning to improve demand planning, supply planning and inventory management. Artificial intelligence can help chemical companies generate more accurate and timely forecasts based on a variety of data sources, such as sales history, market trends, customer behavior and weather patterns. Artificial intelligence can also assist chemical companies in optimizing inventory levels and replenishment strategies, taking into account demand variability, lead times, service levels and costs. By using artificial intelligence, chemical companies can reduce inventory costs, improve customer satisfaction and increase operational efficiency.


The Dature system uses artificial intelligence (AI) and machine learning (ML) to generate accurate demand forecasts and optimize inventory. The algorithms used in the application allow a customized approach to forecasting individual SKUs, taking into account both internal data of the organization (sales, own promotions, etc.) and external data (customer and supplier promotions, weather, etc.). This significantly improves the quality of forecasts and reduces the risk of out-of-stock and overstock situations. Using the simulation and inventory optimization mechanisms available in the system, it is possible to continuously improve the quality of the entire forecasting and optimization process. This results in financial benefits: sales, margins, costs, liquidity, as well as image benefits. In other words, significantly higher market competitiveness is achieved.

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.

Customer reviews

"We are the only company in the industry that offers its customers a processing of orders within 24 hours regardless of location recipients in the country. Our promise requires us to manage stock at the highest level. The Dature system is an important element supporting the inventory optimization process."

Piotr Siebert, President of Fobaro

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