AI Statistical & Numerical Analysis

Who It's For

Manufacturing companies with high demand variability A company producing automotive components or construction parts experiences peaks and dips in orders linked to seasonality, project cycles of key clients, and market shifts. Planning production without a predictive model means alternating between unplanned overtime and machine downtime for lack of orders. A forecasting model trained on 3-5 years of historical data, integrated with leading indicators (confirmed orders, commercial pipeline, sector indices), reduces planning errors by 35-50%.

Distributors and wholesalers with large catalogs A distributor with 5,000+ SKUs cannot manually manage reordering for every item. Traditional systems use fixed reorder points that don't account for seasonality, growth or decline trends of individual SKUs, or planned promotions. A per-SKU demand forecasting model reduces stockouts by 40% and capital tied up in inventory by 20-30%.

Logistics companies and regional carriers Transport companies with fleets between 15 and 100 vehicles have enormous data: routes, delivery times, fuel consumption per route, breakdowns per vehicle. When analyzed, this data reveals hidden inefficiencies: unoptimized routes, vehicles with predictable breakdown patterns, unmanaged volume peaks. A predictive maintenance analysis system for vehicles reduces unplanned downtime by 30% and corrective maintenance costs by 25%.

Multi-location retail A chain with 10-50 stores accumulates transaction data that, when correctly analyzed, reveals purchasing patterns by time of day, day of week, season, weather, and local events. These models feed concrete decisions: when to increase staff, which products to promote in which store, when to preemptively restock. Weekly analysis of receipt data can increase revenue per square meter by 8-15%.

Professional services with project pipeline Engineering firms, agencies, consultants managing 20-100 concurrent projects need to forecast future workload to staff appropriately. Historical data on project times, critical phases, and budget variances, when analyzed with statistical models, produce far more reliable estimates than those based on the project manager's subjective experience.

---

Problems It Solves

Demand forecasting based on experience rather than data When the purchasing manager decides how many units to order by looking at 'how last year went', they're ignoring multi-year trends, non-linear seasonality, and correlations with external variables (weather, sector events, marketing campaigns). A time series forecasting model incorporating these variables produces forecasts with over 90% accuracy on 4-8 week horizons, reducing both stockouts and tied-up capital.

Production mix optimization based on real margin analysis Many manufacturing companies don't know the real margin per product or order once setup costs, tooling, waste, and rework are correctly allocated. A statistical analysis of the last 24 months of production data reveals which SKUs are genuinely profitable and which erode margin. On average, 20% of the catalog generates 80% of profitability: eliminating or reorienting the remaining 80% can increase EBITDA by 10-15%.

Undetected production anomalies In a production line with 50+ process parameters (temperatures, pressures, speeds, consumptions), no operator can monitor all signals simultaneously. An ML-based anomaly detection system learns the 'normal' profile of the process and flags significant deviations before they translate into rejects or breakdowns: average anomaly detection time drops from hours (manual detection) to seconds.

Churn prediction and customer retention In sectors with recurring customers (subscriptions, maintenance contracts, periodic supplies), abandonment signals are present in data weeks before the customer cancels: reduced purchase frequency, declining average ticket, increasing complaints. A churn prediction model identifies at-risk customers 4-8 weeks in advance, enabling targeted commercial interventions at a retention cost 5-7 times lower than acquiring a new customer.

Dynamic price optimization In sectors with high raw material cost volatility (manufacturing, construction, food), manual price list updates happen rarely and with delay. A dynamic pricing system monitoring input costs and correlating them with selling prices maintains target margins automatically, without the latency of the manual price revision cycle.

Root cause analysis of production waste When the reject rate exceeds the target threshold, manual analysis is slow and often inconclusive. A multivariate statistical analysis of process parameters at the time of defective item production identifies causal variables with statistical precision: production shift, ambient temperature, raw material supplier, operator. This reduces root cause identification time from days to hours.

---

Key Features

Demand forecasting models per SKU/order Forecasting models trained on your historical sales data, integrated with external variables relevant to your sector: seasonality, events calendar, sector indices, commercial pipeline. Output: demand forecasts for the next 4-12 weeks with confidence intervals, automatically updated as new data arrives.

Real-time operational KPI analytics dashboard Visualization of all critical operational KPIs in a single interface, with drill-down by production line, department, customer, product. Automatic comparison between current data and forecasts, with highlighting of significant deviations. Automatic periodic report distribution to management.

Anomaly detection on process and operational data Continuous monitoring of process parameters with automatic alerts when deviations from the normal profile are detected. The model continuously updates with new data, adapting to process changes. Configurable by anomaly type (point, trend, cyclic) and sensitivity threshold.

Profitability analysis by product, customer, and channel Correct allocation of direct and indirect costs per product line, customer, and sales channel. Automatic identification of the most and least profitable combinations, with operational recommendations on product mix and commercial policy.

Predictive models for preventive maintenance Analysis of machinery operational data (operating hours, temperatures, vibrations, consumptions) to predict failures before they occur. The system integrates data from IoT sensors or manual maintenance logs and produces a preventive intervention calendar optimized to reduce unplanned downtime.

Churn prediction and customer scoring Propensity-to-churn model for recurring customer base: each customer receives a risk score updated weekly, with the variables that contributed most to the assessment. The sales team sees priority customers to contact directly in the CRM.

Inventory optimization and reorder management Automatic calculation of reorder point and economic order quantity for each SKU, based on forecast demand variability and supplier lead times. Automatic management of slow-moving items and identification of obsolescence before it becomes write-down.

Root cause analysis with statistical process control Application of SPC (Statistical Process Control) techniques on production data to identify whether variations in quality KPIs are due to random causes (process noise) or systematic causes (real problem to solve). Shewhart control charts, automatic Pareto analysis of detected defects.

Customer and market segmentation with clustering Automatic analysis of customer purchasing behavior to identify homogeneous segments: frequency, average ticket, product mix, seasonality. Segmentation automatically updates the CRM and can feed differentiated marketing campaigns per segment.

ETL and data integration from multiple sources Data extraction, transformation, and loading pipeline from all relevant company sources: ERP, CRM, production management systems, Excel files, SQL databases, third-party APIs. Unification into a centralized data warehouse with clean and consistent history.

Automated reporting for management Periodic reports (daily, weekly, monthly) automatically generated and distributed via email to configured recipients. Customizable format with company logo and layout, exportable to PDF or Excel for presentations and board meetings.

---

Typical Workflow

Every Monday morning — Planning the week You arrive at the office and open the weekly report automatically generated overnight: demand forecasts for the next 4 weeks updated with last week's sales data, comparison with budget, 3 customers flagged as churn risk, 2 SKUs with forecast stockout within 10 days. In 15 minutes you have a complete picture without opening a single Excel file.

9:30 AM — Line 3 anomaly alert You receive a push notification: the system has detected that the process temperature on line 3 has shown a steady drift over the last 6 hours, still within the alarm threshold but with a concerning trend. The production manager opens the detail: they see the trend chart, correlated parameters, and comparison with the last time a similar pattern occurred (6 months ago, followed by a thermoregulator failure 48 hours later). A preventive check is scheduled for the afternoon.

11:00 AM — Commercial meeting The sales manager opens the customer segmentation dashboard: 12 customers in the 'at risk' range this week, 3 of whom are in the top-30 by revenue. For each, they see the variables that determined the risk score: declining order frequency, falling average ticket, increasing returns. Clear priorities for the week's calls, with no need to manually analyze data.

End of month — Margin review The automatic margin analysis by product and customer is available on the first day of the following month, processed from the management system data without manual intervention. Management immediately sees the 5 products with margin worse than forecast, the 3 orders with budget variance greater than 15%, the 2 customers eroding average margin. Strategic decisions have a concrete numerical basis.

Quarterly — Procurement plan optimization The purchasing manager uses the forecasting model to build the procurement plan for the next quarter: for each SKU, the sales forecast with confidence interval, current inventory level, supplier lead time, and optimal reorder quantity. The plan is exported to Excel for distribution to suppliers, with reduction of urgent orders and consequent improvement of supply terms.

Semi-annually — Model review Models are recalibrated on newly accumulated data: new explanatory variables are tested, forecasting model parameters are updated, actual predictive performance versus forecasts is verified. A technical report documents system performance and identified improvement areas.

---

Integrations

ERP and management systems — Connectors with major ERP systems (SAP, Sage, Microsoft Dynamics, Oracle) and vertical industry management systems: automatic extraction of orders, production, inventory, and purchasing data without manual intervention.

CRM — Bidirectional integration with CRM: the analysis system reads commercial interaction data and writes churn risk scores and customer segments directly into customer records.

IoT sensors and SCADA systems — For manufacturing companies, integration with process sensors (OPC-UA, MQTT) and SCADA supervisory systems for real-time acquisition of production parameters.

Excel files and legacy SQL databases — Automatic import pipeline from structured Excel files or existing SQL databases, with automatic data cleaning and normalization.

Existing BI tools (Power BI, Tableau) — Possibility to feed developed models into visualization tools already in use, without replacing them but enriching them with forecasts and predictive scores.

Weather and sector index APIs — For sectors where weather conditions or sector economic indices correlate with demand (construction, agriculture, seasonal retail), integration with external data APIs to enrich predictive models.

Alerting systems (email, Slack, Teams) — Distribution of alerts and reports on communication channels already used by the team, without requiring new applications to be opened.

---

Custom Software vs Standard Solutions

Generalist BI and AI platforms offer powerful tools but require an internal team with data science skills to configure, maintain, and interpret them. For an SME with 20-200 employees, this means hiring a dedicated role at €50-80k/year annual cost, or paying project consultants with uncertain results. A custom system includes the modeling, training, interpretation, and maintenance of models, with an operational interface designed for business managers without technical skills.

---

Timeline, Budget and Process

Phase 1 — Data audit and objective definition (2-3 weeks) Analysis of quality and completeness of available data, definition of priority use cases (forecasting, anomaly detection, churn, inventory optimization), selection of data sources to integrate. Deliverable: data audit report with feasibility estimate for each use case and prioritization.

Phase 2 — Data pipeline and model development (6-10 weeks) Data warehouse construction, predictive model development, validation on historical data, user interface and report development. Models are validated on a holdout set (last 6-12 months of data) before going into production.

Phase 3 — Deployment and calibration (2-3 weeks) System deployment, initial performance monitoring, fine-tuning of parameters based on user feedback. Training of business managers on interpreting models and forecasts.

Phase 4 — Maintenance and optimization (ongoing) Periodic model recalibration, addition of new variables and use cases, continuous data quality monitoring. The system improves over time as more historical data accumulates.

Investment range:

• Single use case (e.g., demand forecasting only): €12,000-25,000
• Integrated solution with 3-5 use cases: €25,000-60,000
• Complete system with data warehouse and multiple models: €50,000-120,000

Costs vary based on data complexity, number of sources to integrate, and depth of models required.