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Forecasting with Omega Analyzer

This document discusses IMP Aerospace's Logistics Engineering Section led by Bruce Beard, CD, which provides business intelligence solutions focusing on spares forecasting and obsolescence management for fleet cost-effectiveness. The toolkit includes techniques such as LORA Analysis and Root Cause Analysis, employing an analyzer for data management and forecasting over rolling periods. Strategies are outlined to improve repair pipelines, reduce turnaround times, and optimize part transportation. The analysis aims to enhance operational efficiency and save costs through informed decision-making.

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Forecasting with Omega Analyzer

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  1. IMP Aerospace Forecasting with Omega Analyzer By: Bruce Beard, CDSenior Logistics Support Analyst8 May2012 IMP Aerospace, P.O. Box 970, Enfield, Nova Scotia, Canada B2T 1L5 Tel. 902. 873.2250 • Fax 902 873 2290 • e-mail. impaero@impgroup.com Website. www.impgroup.com

  2. IMP Aerospace LSA Engineering Section provides Logistics Engineering Business Intelligence including Spares Forecasting & Obsolescence Management to enhance the overall cost-effectiveness of their fleet.

  3. LSA Engineering Toolkit Obsolescence Management Sparing / LORA Analysis FRACAS Management Life Cycle Cost Management Maintenance Task Rationalization Root Cause Analysis Reliability and Maintainability LSAR Database Development Demand Planning and Forecasting

  4. Support Equipment Requirements Operations & Maintenance Requirements Transportation Requirements Logistics Support Analysis Records Facilities Considerations RAM Data Packaging & Provisioning Requirements Task Requirements Personnel Skill Considerations Functionality

  5. Data Standardized & loaded into OmegaPS Rolling 5 years of Performa data (CF349s) LCN Structure & RAM data loaded into Analyzer Loading Analyzer – Method 1

  6. Data is “Auto-loaded” into Analyzer format (LCN Structure, Repair Capability, TAT, MTBF, Repair Hours, etc.) 1010110011 Rolling 5 – 10 years Performa In-Service data (CF349s) Data verified - Analyzed Loading Analyzer – Method 2

  7. Autoloader

  8. Remove/Replace Time Rectification Times Repair Capability Turn Around Time MTBF Autoloader

  9. Autoloader

  10. Forecasting • Manipulate Data Points • PLT (Procurement Lead Time) • UDS (Un-Distributed Stock) • TAT (Turn Around Time) • RepCap (Repair Capability)

  11. Forecasting • Simulation • PLT – set to ELE (Estimated Life Expectancy) in months • Simulates Obsolescence • UDS – set to Zero quantity • Simulates Stock Out condition • TAT – Actual times • Provides “real life” scenario • RepCap – From historical records • Provides “real life” scenario

  12. Repair Pipeline

  13. Recoverable Item Pipeline

  14. What would happen if……? Improving Pipeline • Reduce TAT • “Drop ship” parts to R&O • Preposition parts at R&O • Transportation $$$ saved • Less spares required • $$$ saved

  15. What would happen if…...? Improving Pipeline • Increase repair capability at LOMs • Less LRUs sent to R&O • Reduced shipping costs • Increase of SRUs may be required • $$$ saved

  16. What would happen if…...? Improving Pipeline • Improve diagnostic techniques / equipment • Reduce No Fault Found equipment sent to R&O • Improves Repair Capability at MOBs • Reduces quantity of spares • $$$ saved

  17. Option 2 Option 1 Analysis

  18. Analysis Single Item Analysis

  19. Analysis System Analysis

  20. Conclusions • Developed Autoloader to input “live” data • Manipulating key data points for simulations • Improving Repair Pipeline = $$$ saved • System vs. Single Item Analysis – educated range of spares

  21. Questions

  22. Back-up Slides

  23. CP140 AURORA LSAR • LCN to WUC correlation • Part Numbers / CAGE Code / NATO Stock Number • Task Codes • Candidate Items • Levels of Indenture • Source, Maintenance & Recoverability Code

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