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Engine FinTwin® MRO Edition
Capacity & Workscope Forecasting
Engine MROs will now be operating in a post pandemic environment where disruptions will be regular occurrences, Engine Build Goals will be reduced as Operators and Lessors focus on cash conservation for the short to medium term.
Harness the power of Data Science to maximize profits and minimize TAT of Engine Shop Visits
AI Driven Capacity Planning
Simulate the demand forecast against multiple shop visits as a factor of Engine Type, Age, Work Scope, Operational Parameters to visualize the impact on profitability given the capacity within a Finite Capacity Model
Visit data Wrangling
Apply Customized ML Algorithms to augment historic & dynamic data feeds for higher accuracy
Engine Type Profiling
Configure Engine type Data Models (IAE V2500, RR Trent 700, GEnx) based on Engine Profiles & Work Scope Levels
Work Scope & TAT Risk Profiling.
Predict Work Scopes & build Engine Shop Visit profilesacross slots over a time period to gauge demand and TAT risks by Routine, NonRoutine, applicable SB/ADs and other work as a factor of Engine Age,Utilization, Operational parameters & environments.
Work Scope Profile
Predict Work Scope variance as part of incoming visits prior to Engine arrival using operational, technical and environmental parameters at a module level.
TAT Risks
Simulate TAT risks as a factor of Labour, Material, Tooling& Space from Induction through to F/A Build & Testing.
Shop Visit Profitability Forecasting.
Mine historic shop visit data, map to Engine & Module profiles and work scopes to help increase accuracy of cost forecasts. Tie contract parameters to forecast invoiceable dollars against costs to simulate profitability before and during a shop visit, dynamically.
Mine Cost Data
Use pre-configured smart models to map cost data against Shop Visit elements by Routine, Non-Routine, SB/AD and other work across Engine& Module profiles by Age, Utilization, Operational and Technical parameters.
Simulate Profitability
Simulate the impact of TAT delays, Overtime rosters, Material & Vendor management before and during a shop visit on overall shop visit profitability by linking contract terms and conditions against discrete optimization models.
Implementation
You will have your very own Digital FinTwin® mirroring your Owned, Leased & Financed Assets on our cutting-edge platform between 8 and 12 weeks from commencing a data implementation exercise.
KeepFlying® has specialized staff for delivering our services. Each customerwill have their own dedicated implementation manager. They handle the entirelifecycle of the implementation from data profiling, data preparation &quality control to tailoring the models towards your specific needs.
AI Driven Capacity Planning
Work Scope & TAT Risk Profiling
Shop VIsit Profitability Forecasting
Data Profiling &
Ingestion
1 - 2 Weeks
Data
Wrangling
2 - 4 Weeks
Model Tuning &
Training
1 - 2 Weeks
Evaluation &
User Training
2 Weeks
Acceptance &
Release
2 Weeks