--- title: "Supply Chain AI Consulting | Demand Forecasting | Agility" url: https://agilitytech.ai/supply-chain-ai-consulting description: "Supply chain AI consulting for demand forecasting, inventory optimisation & supply chain visibility. Production ML in 3 to 8 weeks, backed by real FMCG results." publisher: Agility (agilitytech.ai) --- Supply chain AI consulting # Supply chain AI consulting AI demand forecasting, inventory optimisation, and real-time supply chain visibility, built as production systems and shipped in 3 to 8 weeks. [Talk to a supply chain AI consultant](https://agilitytech.ai/contact)[See the forecasting case study](https://agilitytech.ai/case-studies/manufacturing-ai) Prediction accuracy in targeted demand scenarios88% Safety stock cut at maintained service levels40% Lower inventory carrying costs35% Discovery call to go-live3 to 8 wks In short ## What does a supply chain AI consultant do? A supply chain AI consultant finds where machine learning creates measurable value, demand forecasting, inventory optimisation, and real-time visibility, then builds and deploys the production systems to deliver it. Agility unifies data from ERP, MES, and SCM platforms, trains the models, and ships in 3 to 8 weeks. In a real deployment for a multi-site manufacturer we reached 88% prediction accuracy, cut safety stock by 40%, and reduced carrying costs by 35%. See our [demand forecasting case study](https://agilitytech.ai/case-studies/manufacturing-ai) and the cited [State of AI in Supply Chain 2026 statistics](https://agilitytech.ai/ai-in-supply-chain-statistics). Services ## What supply chain problems does AI solve? Four capability areas, each grounded in production deployments, not demos. ### AI demand forecasting Ensemble machine-learning models trained on unified enterprise data that forecast demand across product lines, accounting for seasonality, promotions, and volatility. - 88% prediction accuracy in targeted demand scenarios - Forecast preparation reduced from days to hours - Stock-out incidents virtually eliminated - Real-time production planning alignment ### Inventory and safety-stock optimisation ML-optimised stock levels that free up working capital without hurting availability, balancing service levels against carrying cost. - 40% reduction in safety stock at maintained service levels - 35% reduction in inventory carrying costs - Optimised reorder points per SKU and site - Proactive, not reactive, inventory management ### Supply chain visibility Real-time dashboards and streaming pipelines that give planners a single view across product lines, sites, and systems. - Real-time visibility across all product lines - Unified data from ERP, MES, and SCM systems - Power BI supply chain dashboards - Streaming updates via Kafka and Azure Data Factory ### Systems integration and data engineering We connect the systems you already run into one analytics-ready foundation, the groundwork every reliable supply chain model depends on. - SAP ERP, Siemens MES, and Oracle SCM integration - Databricks Delta Lake with ACID transactions - Azure Machine Learning model pipelines - Automated data quality and feature engineering Featured result ## AI demand forecasting for a manufacturer Manufacturing ### From spreadsheet forecasts to AI on Azure and Databricks A mid-sized manufacturer operating across multiple product lines replaced spreadsheet forecasting with an AI platform on Azure Machine Learning and Databricks: 88% prediction accuracy, 40% safety stock reduction, 35% inventory cost reduction, and forecast preparation cut from days to hours. [Read the full case study](https://agilitytech.ai/case-studies/manufacturing-ai) Prediction accuracy88% Safety stock reduction40% Inventory cost reduction35% Forecast preparation timeDays to hours Process ## How fast can supply chain AI go live? From first call to production in 3 to 8 weeks, no unnecessary steps. - 01 ### Discovery Free call to understand your supply chain, data maturity, and the forecasting or inventory problem worth solving first. - 02Within 48 hours ### Scoped proposal Fixed or T&M proposal with timeline and milestones. - 03 ### Build Senior engineers integrate your data, train the models, and ship, with weekly demos and full code access. - 043 to 8 weeks ### Production Live with dashboards, documentation, training, and optional ongoing support. Why Agility ## Why choose Agility for supply chain AI ### Proven forecasting results A real multi-site manufacturing deployment: 88% prediction accuracy, 40% safety-stock reduction, 35% lower carrying cost. ### Production, not slideware We ship working supply chain AI in 3 to 8 weeks, not multi-month advisory engagements that end in a deck. ### Enterprise systems fluency SAP, Oracle SCM, Siemens MES, Databricks, Azure ML, Kafka. We integrate the stack you already run. ### Senior engineers throughout The engineer building your forecasting platform is on the project from day one, no hand-offs to juniors. Related ## Related supply chain and industry AI [Demand forecasting case study](https://agilitytech.ai/case-studies/manufacturing-ai)[FMCG cloud migration case study](https://agilitytech.ai/case-studies/fmcg-migration)[AI for FMCG](https://agilitytech.ai/fmcg-ai)[AI for logistics](https://agilitytech.ai/ai-consulting-logistics) FAQ ## Supply chain AI consulting: common questions What a consultant does, the problems AI solves, integrations and time to production. [Ask us something else](https://agilitytech.ai/contact) **What does a supply chain AI consultant do?+** A supply chain AI consultant identifies where machine learning creates measurable value across your supply chain, demand forecasting, inventory optimisation, and real-time visibility, then builds and deploys the production systems to deliver it. Agility unifies data from ERP, MES, and SCM systems, trains forecasting models, and ships working software in 3 to 8 weeks rather than long advisory engagements. **Which supply chain problems does AI solve?+** The highest-ROI supply chain AI use cases are demand forecasting, inventory and safety-stock optimisation, and supply chain visibility. In a real deployment for a multi-site manufacturer we reached 88% prediction accuracy in targeted demand scenarios, reduced safety stock by 40% while maintaining service levels, cut inventory carrying costs by 35%, and reduced forecast preparation from days to hours, virtually eliminating stock-out incidents. **How does AI demand forecasting improve on traditional methods?+** Traditional forecasting relies on spreadsheets and single-method statistical models that struggle with promotions, seasonality, and demand volatility. AI demand forecasting uses ensemble machine-learning algorithms trained on unified data from all enterprise systems, producing more accurate, faster forecasts. Our manufacturing deployment delivered an 88% improvement in forecast accuracy and moved forecast preparation from days to hours. **What data and systems do you integrate for supply chain AI?+** We integrate the systems you already run. Our forecasting platform for a mid-sized manufacturer unified data from SAP ERP, Siemens MES, and Oracle SCM into a Databricks Delta Lake, with Azure Machine Learning for the models, Azure Data Factory and Kafka for streaming, and Power BI for supply chain visibility. We handle the data engineering and feature work as part of the engagement. **How fast can supply chain AI go into production?+** Agility deploys production supply chain AI in 3 to 8 weeks depending on scope and data readiness. You get a scoped, fixed or T&M proposal within 48 hours of a discovery call, weekly demos throughout the build, and handover documentation at go-live, with senior engineers on the project from day one. ## Ready to put AI to work in your supply chain? Tell us your forecasting or inventory challenge. We'll scope a plan in 48 hours. [Talk to a supply chain AI consultant](https://agilitytech.ai/contact)