# AI for manufacturing: forecasting, costing and production planning > Predictive and optimisation models built on your company’s own history: what an order will cost, when it will ship, how to plan production. Built into the ERP your engineering and production teams use every day. - URL: https://mojtabaamini.com/services/manufacturing-ai - Language: en - Author: Mojtaba Amini, AI Software Engineer (Verona, Italy) ## Data you already have, used to decide better Manufacturers generate valuable data every day (orders, bills of materials, production phases, times, costs) that is rarely used to forecast. I have worked with Italian manufacturers, shipping models to production that turn this data into forecasts and decisions. ## What I build - **Lead-time and delivery-date forecasting** from the bill of materials and production history. - **Cost estimates** for an order before it is produced. - **Production planning** with reinforcement learning that learns from data instead of hand-written rules. - **Smart configurators** that turn customer technical specs into a configuration, a bill of materials and a delivery date. - **Phase forecasting**: which phase an order is in and which materials it needs. - **AI assistants inside the ERP** that cut cross-team coordination time. ## How it works 1. **Collecting and cleaning data**: We start from ERP, MES and spreadsheet data, often incomplete: half the work is making it reliable. 2. **The right model for the problem**: Recurrent neural networks for time series, graph neural networks for bills of materials, reinforcement learning for planning: the model is chosen for the problem, not the hype. 3. **ERP integration**: Python models are exposed through APIs and used directly inside the ERP (for example .NET and Angular), with response times fit for daily use. 4. **Verification in the field**: Forecasts are compared with real outcomes, and the model improves with new data. ## Real projects For Extrema, a graph-neural-network configurator forecasts process time and a delivery date in under ten minutes. For Dekali, an LLM assistant and a reinforcement-learning scheduler built into the ERP cut cross-team coordination from hours to seconds. - [Stairlift configurator with GNN cost & lead-time prediction](https://mojtabaamini.com/projects/extrema-en) (Extrema · Mar 2024 — Dec 2025): DNN + GNN turn customer specs into a complete bill of materials and an accurate delivery quote in minutes. LLM chatbot cuts internal response times by 60%. - [AI co-pilot for ERP & production planning](https://mojtabaamini.com/projects/dekali-en) (Dekali (ex Virevo) · Jul 2023 — Sep 2026): LLM assistant + RL scheduler embedded in a manufacturing ERP. Cuts cross-team coordination from hours to seconds. - [CAN-bus battery analytics & automated test certification](https://mojtabaamini.com/projects/voltfang-en) (Voltfang GmbH · Mar 2023 — Aug 2023): CAN-bus reader & automated battery test certification, written for Voltfang's incoming-product test pipeline. ## Technology Python, PyTorch, TensorFlow, RNN, GNN, Reinforcement Learning, .NET, Angular, Azure, SQL ## Further reading - [Why RL beats heuristics on the factory floor](https://mojtabaamini.com/writing/rl-beats-heuristics-en) (Article · Apr 2026): A retrospective on replacing a rule-based scheduler at Dekali with an RL policy — and the silent rules nobody had written down. - [An RNN that learned the rhythm of an Italian factory](https://mojtabaamini.com/writing/rnn-factory-rhythm-en) (Article · Mar 2025): Predicting which phase a job is in, and what it will cost by the time it ships, from telemetry no one had ever bothered to keep. - [A GNN that learned our Bill of Materials](https://mojtabaamini.com/writing/gnn-bom-en) (Article · Nov 2025): How we encoded stairlift components as a graph and used message-passing to predict process time and delivery date in under ten minutes. - [From spec to BOM in 90 seconds: the configurator that closes deals](https://mojtabaamini.com/writing/spec-to-bom-en) (Article · Jan 2025): A deep neural net that turns customer technical specs into the optimal stairlift configuration, the full Bill of Materials and a delivery date the sales team can actually commit to. ## Frequently asked questions **Do we need a lot of data to start?** You need consistent historical data more than huge volumes. The first phase of the project checks exactly the quality and quantity of the data available, before choosing a model. **Will it work with our ERP?** Models are exposed through APIs and can be integrated into most business systems. In the projects I have delivered, integration was into .NET and Angular ERPs. **Is reinforcement learning realistic for an SME?** It can be when planning rules are many, change often and are partly unwritten. In those cases a model that learns from historical data can outperform hand-built heuristics. **How reliable are the forecasts?** It is measured on the company’s real data, comparing forecasts and outcomes over time. Forecasts come with their uncertainty, so the engineering team knows when to trust them and when to check. ## Let’s talk about your project Tell me about the process you want to improve: I will reply with a first assessment of where AI can help and where to start.