Anticipating demand and reducing operational inefficiencies
Demand forecasting enables sales forecasting and production planning. This is the process by which companies estimate future demand for products or services over a defined time horizon. It involves integrating historical data, market trends, external variables and product life cycle information.
This strategic tool supports operational and financial decision-making, optimises the supply chain, production and logistics, and ensures that inventory levels are aligned with expected demand. Accurate forecasts reduce excess inventory, which ties up capital and generates costs, and stock-outs, which lead to lost sales and dissatisfied customers.
In volatile and competitive markets, anticipating demand by considering factors such as seasonality, pricing, promotions, marketing activities, product characteristics and external variables is critical to ensuring optimal product availability and operational flexibility.
The limitations of the standard approach: analysing the past is not enough
Traditional statistical and planning software bases forecasts on deterministic time series, assuming that future patterns will replicate those of the past. In complex and dynamic scenarios, however, this approach results in forecasts that are both rigid and inaccurate.
These limitations are particularly evident when dealing with dynamic variables such as:
The challenge increases further when independent but relevant factors must be considered, such as product characteristics, the geographic location of retail stores or weather conditions that can influence demand.
The result is low forecast accuracy, which leads to higher operating costs, inventory misalignment and stock-outs, causing missed sales opportunities.
Accuracy and dynamic forecasting: the advantages of machine learning
Machine learning–based demand forecasting solutions overcome the limitations of traditional methods by combining historical data with contextual information, external variables and product-specific features.
Algorithms can identify complex correlations among seemingly unrelated data and recognise non-linear patterns that traditional statistical analysis cannot capture.
Machine learning enables continuous adaptation; each new data point from the past helps to refine the predictive model, thus improving the accuracy of the forecast over time. Rather than applying predefined formulas, organisations can build dynamic models that evolve alongside their business and the market.
Consider a retail clothing chain that needs to plan orders for hundreds of products across multiple stores.
The traditional approach involves analysing past sales and seasonality coefficients without considering external variables.
The result is that some items remain unsold in warehouses, tying up capital, while others sell out quickly, resulting in lost sales opportunities.
The AI-driven approach integrates historical data by SKU and store, as well as demographic data, weather forecasts, event calendars, online trends, pricing and promotions, competitor performance and economic indicators.
The result is reduced forecast error, more balanced inventory management and the release of previously tied-up capital, with fewer end-of-season markdowns and a positive impact on sales and margins.
The benefits of AI-based demand forecasting for businesses
Proof of Concept: From Theory to Measurable Results
In order to assess the potential of an AI-based demand forecasting solution, Omnys begins with a proof of concept.
The process consists of three phases:
The time to market is 4–6 weeks from project kick-off, enabling companies to swiftly evaluate the solution's value with minimal investment and no disruption to day-to-day operations.
From PoC to full-scale project
Based on the results of the proof of concept, companies can evaluate the concrete benefits of launching a full demand planning initiative.
The aim is to transform demand forecasting into an ongoing process that evolves alongside the business, continually enhancing predictive capabilities and operational efficiency.
Tailored demand forecasting: the Omnys method for achieving tangible results.
For over 25 years, Omnys has stood out for combining a forward-looking vision with timely execution.
In demand forecasting, this approach enables us to combine AWS's most innovative technologies with a deep understanding of business dynamics. The Omnys team works closely with clients to understand their specific needs, identify critical variables and develop bespoke solutions that deliver tangible value.
With Omnys, machine learning–based demand forecasting becomes a practical tool accessible to organisations of any size, helping to make supply chains more efficient, reduce waste, optimise working capital and seize market opportunities with speed and precision.
