The solution from Omnys to boost production lines with demand forecasting: +69% accuracy and faster, more confident decision-making.
In market conditions characterised by high variability, complex supply chains and B2B customers with complex design requirements, forecasting accuracy becomes a competitive asset. Omnys has developed a machine learning-based demand forecasting solution for the manufacturing sector, where operational efficiency, continuous production and service quality are all key strategic assets, making dynamic and accurate forecasting essential.
The customer
The project involved a leading industrial company specialised in the design and production of pumping systems and solutions for water management and movement. Its products have applications in the water, civil, agricultural and industrial sectors worldwide.
The challenge: forecasting demand in a complex industrial context
Accurately forecasting future demand for products and their various market-required configurations represented a strategic challenge within an industrial environment characterised by a broad and highly diversified catalogue, where different products exhibited heterogeneous sales dynamics.
The company’s forecasting process was based on traditional statistical models, which considered only historical sales data, applying trend analyses and seasonality coefficients to estimate future demand.
In a B2B environment with industrial clients and infrastructure projects, timelines reliability is critical, this approach generated inflexible forecasts that did not respond to market changes. The high level of forecasting uncertainty made it difficult to plan production, procurement and inventory, forcing the company to operate with an unstable balance between excess inventory and the risk of stock-outs.
The solution: adaptive, tailored machine learning models
Omnys addressed the challenge by starting with
an in-depth analysis of the available historical data. They integrated sales information with contextual variables and product metadata.
The approach did not rely on a single predictive model, but rather involved designing an ensemble of machine learning algorithms capable of adapting to the specific characteristics of different product families.
Each catalogue segment was analysed to identify demand patterns, seasonality and recurring behaviours. The system automatically selects the most suitable model or combines forecasts from multiple algorithms to maximise overall accuracy.
The solution was developed on an AWS infrastructure for the training, validation and deployment phases. This ensured scalability, data security, and the ability to manage large forecasting volumeswithout impacting operational performance.
The results: +69% forecasting accuracy
The introduction of AI-based demand forecasting delivered immediately measurable results.
Forecast accuracy increased by 69%, significantly improving the company’s ability to plan in the short term and support operational decisions, as well as in the medium term to support strategic choices.
This improvement translated into tangible benefits across the entire supply chain:
The model continues to improve over time, as the system adapts to market evolution and business dynamics through the continuous integration of new data. A scalable, value-driven approach
An exemplary project demonstrating how machine learning-based demand forecasting can generate tangible value in complex B2B industrial environments characterised by high variability and demand complexity.
The solution developed by Omnys is designed to be replicable and scalable, adapting to manufacturing organisations with extensive catalogues and complex production processes. The AWS cloud infrastructure enables the management of increasing volumes and complexity without the need for redesign.
In demand forecasting, Omnys solutions stand out for their ability to combine advanced technologies with a deep understanding of industrial processes and supply chain dynamics. The result is improved forecasting performance and concrete support for operational and strategic decision-making.
