Woodward struggled with the huge amount of manual labor during testing processes. With data closed in separate silos, the old methods made processes prone to error and jeopardized the whole business stability as the company operates in the aerospace sector. Woodward realized that without the implementation model of AI-driven solutions, the situation has zero chance for improvement.
Meet Our Client
Woodward is an independent designer, manufacturer, and service provider of energy control and optimization solutions for aerospace and industrial markets.
The company was founded over 150 years ago. It was focused on delivering proven systems for aero engines, industrial engines and turbines, power generation, and mobile industrial equipment from the very beginning.
Solutions developed by Woodward enable their clients to reduce emissions by increasing energy efficiency and introducing alternative energy sources.
Case Study Shortcut
Challenge
Excessive Manual Labor in Testing Processes
Woodward’s testing procedures relied heavily on manual input, Excel sheets, and outdated workflows, making operations slow, labor-intensive, and prone to human error, especially critical in the aerospace manufacturing sector where precision is non-negotiable.
Lack of Historical Data Utilization and Predictive Insight
Despite collecting large volumes of test data, the company lacked the tools to analyze historical data effectively. This made it impossible to predict testing failures, detect patterns, or proactively identify quality issues before they escalated.
Siloed Data and Inefficient Analysis Methods
Important process data was scattered across multiple, disconnected systems, leading to limited visibility and coordination. The absence of centralized, AI-powered analytics made it difficult to detect inconsistencies or optimize testing steps efficiently.
Goal
- Automating testing process
- Reducing manual labor
- Improving testing processes' efficiency
- Improving quality end products
- The decreasing number of errors
Outcome
Addepto team built a comprehensive Visual System for process capability analysis enriched with predictive AI modules modeling that allows Woodward to reduce manual labor, cut operational costs, and improve testing life cycles.
Before
- Massive amount of manual labor
- High operational costs of delivering products to the customer
- Error-prone processes
After
- It has reduced manual work by 30%.
- Operational costs of delivering products to the customer are reduced by 25% because of detection and prevention of sending bad products to the customers.
- Improved product testing life cycles by replacing test steps as well as by eliminating some of them.
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Case Study Details
Approach
Understanding Industry-Specific Quality Requirements
- The project began with in-depth consultations to understand the high-precision demands of aerospace manufacturing.
Development of a Visual System for Process Capability Analysis
- Addepto designed a user-friendly visual platform to replace manual Excel workflows.
- It allowed engineers to interact with real-time dashboards for CPK, PPK, and MSA metrics, simplifying complex statistical interpretations.
Machine Learning for Predictive Quality Control
- The platform incorporated predictive analytics and regression models to forecast test failures.
- It could identify which specific product and test step were likely to fail, enabling early intervention and reduction of costly rework.
Automating Manual Testing and Simulation Workflows
- Previously manual tasks, such as process simulations and statistical validations, were fully automated using AI-driven logic. This reduced manual labor by 30%, significantly improving efficiency and speed of testing cycles.
Centralizing Disparate Data Sources
- All test data, previously stored in silos, was centralized into a single platform.
Domain-Specific Model Tuning for Aerospace Applications
- AI models were tailored specifically for aerospace manufacturing, focusing on component traceability, compliance metrics, and fault detection.
