Your future demand is already in your data: sales history, seasonality, campaigns, competitor prices. We train predictive models that tell you how much you will sell, when and where, so you buy exactly what you need and never lose a sale to a stockout.
Traditional demand forecasting — spreadsheets with moving averages or the intuition of the purchasing manager — works when the market is stable. As soon as complex seasonality, promotions, price changes or supply disruptions appear, the error grows and so does the cost: excess stock tying up capital or stockouts that lose sales and customers. AI predictive models learn these patterns automatically, combining time series, external variables and business signals that no analyst can process by hand.
Summum IA builds the complete pipeline: from extracting and cleaning data from your ERP or point of sale system to the model in production that feeds your purchasing or production plan every week (or every day). We work with the data you already have — sales by SKU, warehouse levels, customer orders, returns — and enrich it when relevant with external factors: public holidays, weather, search trends or raw material prices. The result is an actionable forecast, not a report that sits in a drawer.
Demand forecasting is not only for large corporations. Current cloud platforms — Azure Machine Learning, Amazon SageMaker, Google Vertex AI — make it possible to train and serve models at costs accessible to SMEs. The key is in the problem design: what granularity you need (SKU × store × week), how far ahead the forecast must arrive to be useful and how to integrate the output into your operational process without friction. That is what Summum IA provides: business judgment plus data engineering, without selling technological hype.
We audit your data sources (ERP, point of sale, spreadsheets, order history) and define the specific use case: what you want to predict, over what time horizon, at what granularity and which operational decision the forecast will feed. If the history is insufficient or has gaps, we say so before committing any budget.
We build the extraction, cleaning and transformation pipeline. We generate the variables the models need: time lags, moving averages, encoding of holidays and campaigns, price variables and, where relevant, external signals specific to your sector. Everything audited and versioned.
We train and compare several approaches — from classical statistical models (Prophet, ARIMA) to gradient boosting (LightGBM, XGBoost) or temporal neural networks — with rigorous time-series cross-validation. We choose the model that best balances accuracy and maintenance cost for your real case.
We put the model into production: automatic output to your ERP, to a Power BI dashboard or to a structured spreadsheet, depending on your environment. We set up model drift alerts and review performance every quarter to retrain if demand behaviour changes.
The operational detail: what we deliver as part of the work and what we keep alive afterwards.
Proprietary data pipeline
Extraction from your ERP, point of sale or historical files, anomaly cleaning and construction of the dataset ready for modelling. No dependency on external data consultancies.
Reasoned model selection
We evaluate and document which algorithm is chosen and why, with real error metrics (MAE, MAPE, WAPE) on your historical data. We do not sell a one-size-fits-all solution.
Multi-horizon forecasting
We configure the horizon your operation requires: weekly forecasts for the purchasing plan, monthly for production planning or daily for automatic store replenishment.
Integration with your current environment
The model output reaches where you need it: REST API, ERP export (Odoo, Dynamics, Sage, Holded), spreadsheet or dashboard. No need to change your existing tools.
Performance monitoring dashboard
A model performance dashboard: real error vs. target, drift detection and alerts when the forecast deviates significantly from reality so you can act before the error affects stock levels.
Knowledge transfer
The purchasing or planning team learns to interpret the forecast, adjust parameters and identify when the model needs review. The goal is for the SME to not depend on us indefinitely.
Demand forecasting gains power when connected with the ERP that manages stock (Summum Sistemas) and with the order automation and alert workflows that eliminate manual intervention (Summum IA · automation).
The demand and stock dashboard integrates naturally with the Business Intelligence layer from Summum Sistemas, which consolidates sales, purchasing and warehouse data in a single analytical repository.
View service → sistemasFor distribution and warehouse companies, the demand forecast feeds directly into the ERP replenishment module, closing the loop between prediction and purchase order.
View service → iaOnce the forecast is validated, Summum IA automation workflows can trigger purchase orders, sales team alerts or stock updates without manual intervention.
View service →We recommend at least 18-24 months of sales history by SKU or category. With less data the models can still work, but accuracy on seasonal patterns will be lower. If you have more than 3 years but the business has changed (product range update, new channels), we prioritise the most recent and representative period.
It depends on the volatility of your demand and the quality of the data. In distribution and retail with clean history it is common to achieve errors (MAPE) below 15% at the weekly horizon. We do not promise specific figures before seeing the data: the initial diagnosis answers this question with your real case, not with sector averages.
No. We design the solution so that the purchasing or planning team can use it without programming knowledge: they receive the forecast in their usual tool and know when to flag a review. Model maintenance (retraining, drift monitoring) is handled by us under the service agreement.
It does not replace it: it supports it. The model produces an objective baseline from the data; the purchasing manager adjusts it with market knowledge the model does not have (a supplier about to fail, a planned promotion, a local event). The combination of model and human judgement consistently outperforms either one alone.
Yes. We connect via file export, API or direct database connection depending on what your ERP supports. We have worked with Odoo, Dynamics 365, Sage, Holded and bespoke sector solutions. If you use spreadsheets as your main source that is also feasible, though we recommend migrating to an ERP to ensure long-term data quality.