Parts Availability Analytics Using Supervised Machine Learning Algorithms

November 6, 2020

Anglo-Australian multinational mining, metals and petroleum company

Problem Statement

• Identification of factors which effect Parts Availability and understand their impact
• Prediction of parts availability failure and setting up an advance alert mechanism , integrate approaches to improve and manage Parts Ordering and Availability

Solution Approach

• Data Input
• Application Features
• Model Dataset
• Logistic Regression
• Logistic Regression
• MARS Model

Business Impact

• Identification of business rules defining success and failure of parts availability
• Part Availability Failure Prediction
• Theoretical lift which can be achieved by controlling “Planned Delivery Time”

Critical Success Factors

• Every Stage of process flow modeled separately
• Part Availability Failure Prediction