Advanced Analytics for Production Forecasting
Empowering manufacturing decisions with predictive insights driven by statistical modelling and time series forecasting
Our Advanced Analytics Consulting Services
We help organisations apply advanced analytics to real-world problems — from forecasting and optimisation to root-cause analysis. Our services blend statistical rigor with practical deployment across tools and platforms.
- Forecasting and Time Series Modelling
- Statistical Process Control and Anomaly Detection
- Root Cause Analysis with Multivariate Techniques
- Optimization for Production, Inventory, and Scheduling
- Simulation and What-If Scenario Modelling
- BI and KPI Integration into Decision-Making
Ready to Explore the Potential of Advanced Analytics?
Whether you’re looking to forecast demand, optimise production, or understand operational trends, we’ll help you turn data into decisions.
Let’s discuss how Advanced Analytics can work for you.
Demand-Driven Production Forecasting for Factory Optimisation
Using advanced analytics to align production plans with real-world demand signals in discrete manufacturing.
Client & Goal Overview
Client Type
Mid-sized Manufacturing Company (Consumer Goods)
Challenge
The client faced chronic issues with overproduction and stockouts due to inaccurate monthly production forecasts. Manual planning based on historical averages could not capture seasonal shifts, promotions, or disruptions in the supply chain. The goal was to design a forecasting system that could provide dynamic, SKU-level predictions with sufficient lead time for planning and procurement.
Our Role
We built a modular forecasting framework integrating time series models, exogenous variables, and anomaly detection. The system delivered rolling forecasts, scenario planning, and real-time dashboards for plant managers and supply chain teams.
Project Journey & Results
Project Scope
- Consolidated three years of production, sales, promotion, and inventory data at the SKU-site level.
- Applied feature engineering to integrate calendar effects (holidays, weekdays), promotion schedules, and lag-based sales indicators.
- Benchmarked ARIMA, Prophet, XGBoost, and multivariate LSTM to select the best-fit model per product category.
- Developed a forecasting pipeline with error tracking and model retraining logic.
- Delivered dashboards showing predictions, confidence intervals, and deviation alerts.
- Enabled scenario forecasting by toggling future promotion and holiday assumptions.
Technical Stack
- Data Platform: Snowflake + dbt
- Modelling: Statsmodels, Prophet, XGBoost, TensorFlow (LSTM)
- ETL/Automation: Python (pandas, Airflow), S3
- Visualisation: Power BI + Looker Studio
- Monitoring: Custom KPI tracking for MAPE, RMSE, and production deviations
Results
- Cut forecasting error (MAPE) by 38% compared to the previous baseline.
- Reduced stockouts by 25% through improved upstream planning.
- Enabled early detection of demand spikes from external signals (e.g., promotions).
🟧 Increased production scheduling accuracy by 30% across three factories.
🟧 Achieved 95% forecast coverage at SKU-week level with confidence intervals.
🟧 Freed up 10+ hours/week of planner time through automation.
Ready to explore Advanced Analytics in your organisation?
✓ Discover how tailored forecasting and optimisation models can reduce waste and increase agility. ✓ Book your strategy consultation to explore tools, feasibility, and roadmap. Have a question or idea?
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