Your buyers are using gut feel and spreadsheet averages to set purchase orders because you have no reliable demand signal?
You have a forecasting tool but it does not account for your promotions or seasonality so your team ignores it?
Demand Forecasting Software
RaftLabs builds custom demand forecasting models trained on your historical order, sales, and supply data. Time-series ML models that account for seasonality, promotions, and external signals -- integrated directly into your inventory, procurement, or planning systems so forecasts translate into actions rather than spreadsheet exports.
We start with a data audit: we assess your historical data quality, coverage, and granularity before committing to a model approach. If your data supports a reliable forecast, we tell you what accuracy is achievable. If it does not, we tell you that too rather than delivering a model that looks good in demo and fails in production.
Time-series ML models trained on your actual order and sales history
Seasonality, promotional uplift, and external signal integration built in
Forecast delivery direct to your ERP, inventory, or planning system
Forecast accuracy monitoring and automated retraining as patterns shift
RaftLabs builds custom demand forecasting models using time-series machine learning trained on your historical order and sales data. We integrate forecast outputs directly into ERP and inventory systems and include accuracy monitoring and automated retraining so the model stays reliable as demand patterns shift.
Demand forecasting sounds straightforward until you try to build it: you have years of order data, but promotions inflated some months, supply constraints suppressed others, and a one-off contract skewed a quarter that looks anomalous but was not. A naive model trained on that history will produce forecasts that are systematically wrong in ways that are hard to diagnose. Experienced inventory planners know this, which is why they override model outputs with judgement -- and why forecasting tools often end up unused.
RaftLabs builds demand forecasting models that account for the structure of your actual business: promotional uplift, seasonality, trend, and the external signals that move your demand independently of what you control. We start by understanding your data, your planning cycle, and what decisions the forecast needs to support -- and we define the accuracy benchmark up front so you know what you are getting before you commit to building.
What we build
Time-series demand forecasting models
Machine learning models trained on your historical order and sales data using time-series methods -- ARIMA, Prophet, LightGBM, and ensemble approaches -- selected based on your data volume, seasonality complexity, and forecast horizon. We evaluate multiple model architectures against held-out test data and deliver the one that meets your accuracy target, with full documentation of the modelling decisions.
SKU-level and aggregate forecasting
Forecast models that operate at the level of granularity your planning process requires -- individual SKU, product family, category, or location. Hierarchical forecasting approaches that ensure SKU-level forecasts aggregate consistently to category and total business targets, so your inventory plans and financial plans tell the same story without manual reconciliation.
Seasonality and promotional uplift modelling
Explicit seasonality decomposition using Fourier terms, calendar features, and holiday indicators so your forecast knows that December and July behave differently. Promotional uplift modelling trained on your historical promotions -- discount depth, promotion type, channel, and duration -- so planned promotions are reflected in the forecast rather than treated as noise that confuses the model.
External signal integration (weather, events)
Integration of external demand signals that correlate with your sales: weather data for seasonal businesses, local event calendars for location-sensitive demand, commodity price indices for input-cost-sensitive products, and macroeconomic indicators for businesses with long demand cycles. We identify which external signals improve forecast accuracy on your data before building them into the production model.
Forecast delivery to inventory and ERP systems
Automated forecast delivery to your ERP demand planning module, inventory management system, or procurement tool on a configurable schedule. API integration for systems with programmatic access, file-based delivery for systems that import structured data, and a web interface for planners who need to review and override forecasts before they are released to downstream systems.
Forecast accuracy monitoring and retraining
Automated accuracy tracking that compares forecast values against actual demand as orders arrive, flagging SKUs or categories where model accuracy is degrading. Scheduled retraining pipelines that incorporate new data on a defined cadence -- weekly or monthly depending on how fast your demand patterns shift -- so the model stays calibrated as your business evolves.
Your forecast is only as good as the model behind it.
Tell us about your current planning process, your data history, and the decisions the forecast needs to support. We will assess whether a custom model is worth building and what accuracy you can realistically expect.
Related predictive analytics services
Predictive Analytics -- overview of our full predictive analytics practice
Churn Prediction -- customer churn risk models integrated with your CRM
Fraud Detection -- real-time and batch fraud scoring for transactions and claims
Predictive Maintenance -- equipment failure prediction from sensor data
Related services
AI Development -- custom AI and ML model development for demand forecasting use cases
IoT Development -- IoT sensor data and supply chain signals feeding demand forecasting models
Frequently asked questions
As a starting point, two years of transactional history gives a time-series model enough data to identify annual seasonality patterns reliably. One year can be sufficient if your business does not have strong seasonal variation. Less than 12 months of data makes it very difficult to separate genuine demand patterns from noise, and any model built on that data will be unreliable for planning purposes. If your historical data is limited, we can supplement it with external signals -- market indices, weather data, economic indicators -- to fill gaps, but we will tell you clearly what accuracy range is achievable before you commit to building.
New products present a genuine cold-start problem: without historical demand, a time-series model has nothing to learn from. We address this through a combination of approaches depending on the product category. For products similar to existing SKUs, we use attribute-based similarity to transfer demand patterns from comparable historical products. For genuinely novel products, we model the launch curve using data from comparable historical launches in your catalogue or industry benchmarks. We are transparent about the higher uncertainty on new product forecasts and build that uncertainty into the confidence intervals the model reports.
The integration approach depends on your ERP's API capabilities. For systems like SAP, Oracle, and Microsoft Dynamics, we use standard API endpoints to write forecast data into the demand planning module on a configurable schedule -- daily, weekly, or triggered by model update. For systems with limited API access, we write forecast output to a format your planning team already imports -- typically CSV or Excel in the structure your system expects. We build and test the integration as part of the engagement, not as a separate phase, so forecast delivery is working in your real system before handover.
A single-category demand forecasting model -- one product line or business unit, with seasonal and promotional adjustment, delivered to your planning tool -- typically runs $20,000 to $60,000. A multi-category system with SKU-level granularity, external signal integration, and full ERP integration ranges from $60,000 to $150,000. We scope the engagement by reviewing your data, defining the forecast granularity, and agreeing the accuracy benchmark before providing a fixed-cost quote.