Client Background and Business Context
The customer is a joint venture, supporting customers through the fulfillment of aero-derivative engines, spare parts, repairs, and maintenance services. The company specializes in turbine repair and maintenance and supports world energy availability by powering pipelines and LNG plants with their products.
The organization needed to enhance its demand segmentation to improve parts forecasting accuracy across different business channels and to enable forecasting by segment for more precise parts demand planning and inventory allocation. It needed to prioritize backlog efficiently to ensure critical parts orders were fulfilled on time and improve critical parts management and allocation to reduce supply chain disruptions.
The organization aimed to retire the manual pull system and transition to an automated, demand-driven approach, as well as retire custom parts backlog management and adopt an industry best-practice backlog management and allocation process in the cloud. Additionally, it needed to standardize its engine-arrival event-based parts forecasting process and enable transformation for a first-of-its-kind pureplay Complex Maintenance, Repair, and Overhaul (CMRO) business by adopting core Oracle Planning Cloud applications.
Trinamix partnered with the customer to deliver a unified, system-driven planning framework that improves forecast accuracy and reduces end-to-end execution time. The partnership enabled the customer to automate key planning processes, strengthen real-time backlog decisions, and improve inventory performance through structured handling of alternates and substitutes. Through this partnership, the customer gained a more reliable, efficient, and scalable planning environment aligned with CMRO business priorities.
Client Industry
- Aerospace
Oracle Modules Implemented
- Oracle Demand Management Cloud
- Oracle Supply Planning Cloud
- Oracle Replenishment Planning Cloud
- Oracle Backlog Management Cloud
Project Location
- North America
- Italy
- Brazil
- Norway
- Malaysia
Key Solution Highlights
Implementation of a structured approach to manage parts revisions, changes, and supersession within the forecasting process.
Deployment of demand segmentation to improve forecast precision across multiple demand channels.
Integration of external AI/ML models as part of advanced forecast tuning.
Adoption of Hypertuning processes to reduce manual intervention in forecast adjustments.
Deployment of advanced logic for generating lumpy part forecasts to better predict irregular demand patterns.
Implementation of an event-based forecasting model to capture turbine arrival-driven parts demand for overhaul operations.
Consolidation of global parts requirements by centralizing demand across engine shop overhaul, partner shop demand, and refurbished unit needs—driving a unified MRP process.
Implementation of constraint-based supply planning for parts MRP, factoring in substitutions, revisions, and lead times.
Adoption of an industry-best segmentation process that dynamically classifies and reclassifies items into A/B/C categories based on consumption trends.
Implementation of Backlog Management process to manage parts allocation across four distinct demand channels (e.g., internal shop, partner shop demand, etc.).
Development of a complex integration/extension to align shopfloor parts reservation logic with Backlog Management system decisions.
Deployment of Event-Driven Forecasting logic to model overhaul-based demand triggers — using factors such as turbine yield and years-in-field to anticipate parts needs.
Enabled demand planning and forecasting through structured Demand Management Review cycles with support from an Events Model.
Improved material planning in constrained environments by optimizing use of alternates and substitutes.
Supported Pull-based and Min-Max inventory strategies to align with business-driven replenishment goals.
Streamlined backlog management by eliminating legacy CML-based processes and aligning allocation priorities to business objectives.
Key Benefits
Improvement of current Demand management and parts MRP process by eliminating custom steps, and significant customizations and reducing end-to-end process execution time.
Higher forecast accuracy based on the adoption of segmentation, Advanced AI/ML-based model adoption, and hypertuning.
Improved forecast accuracy for lumpy parts by adopting an advanced forecast model.
Improve Inventory turns by adopting a detailed replenishment process by parts segments.
Depletion of alternate and substitute parts in a structured manner to minimize excess stock.
Automation of parts allocation, reservation, and pick release processes through backlog. management and reducing Engine overhaul TACT time.
Automation of fulfillment tasks, including pick releases for sales orders (SO) and work-in-progress (WIP) orders, to reduce manual efforts.
Greater agility in fulfilling overhaul demand due to precise forecasting tied to real-time backlog decisions.
