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Global Industrial Gases Enterprise
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Driving 30% Better Forecast Accuracy with AI-Led Supply Chain Transformation
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A global industrial gases company faced increasing complexity in managing demand across its supply chain. Variability in demand, combined with manual planning processes, limited the organization’s ability to respond efficiently. Applexus partnered with the company to build an AI-driven forecasting and planning solution, enabling more accurate, proactive, and scalable decision-making.
The Challenge
Demand for specialty gases such as Neon, Xenon, and Krypton fluctuated across regions and time periods. Planning relied heavily on historical trends and manual inputs, leading to inefficiencies across the supply chain.
This resulted in excess inventory in some locations, shortages in others, reactive production planning, and inefficient raw material procurement. The organization needed a more intelligent and forward-looking approach.
Existing planning workflows lacked real-time data visibility, scalable forecasting models, and integrated SAP-aligned analytics foundations.
The Solution
Applexus implemented an AI-driven forecasting platform on Databricks using Delta pipelines, scalable lakehouse architecture, and machine learning models. The solution analyzed historical operational data, regional demand patterns, and supply chain trends to generate accurate forecasts across products, locations, and time periods.
These forecasts were used to drive multiple planning functions, including inventory optimization, capacity planning, equipment allocation, and raw material procurement. The solution was delivered in phases, allowing the business to adopt capabilities progressively.
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Technical Highlights
- Delta Live Tables pipelines for automated SAP and operational data ingestion
- Structured Streaming for near real-time supply chain visibility
- ML forecasting models trained on historical production and logistics patterns
- Unity Catalog governance with centralized RBAC and lineage tracking
- Delta Sharing enabled governed cross-functional analytics access
Business Impact
The transformation enabled a shift from reactive to proactive planning across the organization.
Forecast accuracy improved by up to 30 percent, providing a stronger foundation for decision-making. Inventory levels were better aligned with demand, reducing both excess stock and shortages.
Production planning improved with early visibility into demand fluctuations, while procurement decisions became more precise and cost-effective. Business teams gained faster access to insights, enabling quicker and more confident decisions.
Business Impact
- Shift to proactive planning across the organization
- Forecast accuracy improved by up to 30%
- Inventory aligned to demand reduced excess and shortages
- Stronger production planning with early demand visibility
- More precise procurement decisions driving cost efficiency
- Faster access to insights for quicker, more confident decisions
Why Applexus
Applexus combined SAP-native expertise, Databricks engineering capabilities, and AI-driven forecasting frameworks to deliver a scalable solution aligned with business outcomes. The use of a phased, MVP-led approach ensured faster realization of value while managing complexity effectively.
Conclusion
By introducing AI-driven forecasting and integrated planning, the organization transformed its supply chain into a more efficient and resilient system. The result was improved accuracy, reduced operational inefficiencies, and a stronger foundation for future growth.
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Applexus built an AI forecasting engine on Databricks improving specialty gas (Neon, Xenon, Krypton) demand accuracy by 30% with phased delivery.
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Specialty Gas AI Forecasting on Databricks: 30% Better Accuracy
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