Business situation
Demand forecasts burdened with excessive variability
Forecasts regularly deviate from reality. The production plan requires frequent adjustments, inventory levels rise or fall abruptly, and operational decisions are corrected during execution
Forecast accuracy becomes a key factor affecting:
financial performance stability
How this situation looks in practice
Sales forecasts are created based on historical data, spreadsheets, and team experience. In practice, this means:
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manual plan corrections after each major market change,
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tensions between sales, production, and logistics,
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difficulty in assessing whether deviation results from seasonality, campaigns, or estimation error,
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lack of a common, numerical reference point when making decisions.
The process works, but requires constant intervention. Stability depends on the knowledge of specific individuals,
not on a repeatable analytical mechanism.
inventory surpluses or product shortages
costly production plan changes
inventory surpluses or product shortages
increased decision-making pressure on management and finance
Even a several-percentage-point improvement in forecast accuracy can mean a significant difference in inventory levels, turnover, and margin.
How we approach diagnosis
We begin the analysis by assessing demand variability, assortment structure, and historical data quality.
stability of seasonal patterns
impact of promotions and external factors
level of data aggregation and their consistency
translation of forecast error into inventory, product availability, and production plan changes
We then assess the economic potential of the project: what impact on inventory, production levels, and working capital can be achieved by improving accuracy by a specific value. The implementation decision is based on measurable scenarios, not on the assumption that “the model will be better”.
Technology and solution architecture
In this type of project, we use predictive models that combine historical sales and demand data with additional information such as calendar, promotions, availability, seasonality, or selected market factors.
Typical architecture includes:
Input data
sales, inventory, calendar, external factors
Forecasting model
ML / hybrid models
Integrations
with planning process and ERP system
Error assessment and scenarios
Forecast results are incorporated into the existing planning process, and models are monitored and periodically updated as market conditions change.
Sales forecasting
Better decisions in sales forecasting
The pharmaceutical sales forecasting process was time-consuming and largely relied on manual data analysis. High forecast error levels hindered production planning and product availability.
We implemented ML/DL models for pharmaceutical sales forecasting and dedicated dashboards with the most important information and insights from data for independent analysis by the client.
Forecast accuracy improved by 5-6 percentage points compared to previously used methods. The organization began operating in a more data-driven decision-making model, and forecast automation reduced the time specialists spent on data analysis.
Contact
A conversation is the first step to identifying the organization’s needs and assessing the project’s feasibility.
Schedule a consultation
