Digital Innovation in Aviation
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The document outlines a presentation by Michael Wm. Denis, a principal in aviation and aerospace at Capgemini, discussing digital aviation innovations, particularly in maintenance, repair, and overhaul (MRO) processes. It highlights the significance of digital twins, analytics, and a digital thread for optimizing asset performance and lifecycle management in the aviation industry. Additionally, the presentation emphasizes the need for integrating condition monitoring with MRO systems to enhance operational efficiency and decision-making.
Digital Innovation in Aviation
- 1. Aircraft Commerce Airline & Aerospace MRO & Flight Ops IT Conference Miami, FL, USA 13 March, 2018 Digital Aviation: Innovation & Disruption in MRO Michael Wm. Denis Principal, Aviation & Aerospace
- 2. 2© Capgemini 2018. All rights reserved | Education Michael Wm. Denis Principal, Aviation & Aerospace Capgemini America 3475 Piedmont Rd NE Atlanta, GA 30305 +1 (678) 524-8289 [email protected] MS Decision Science, Robinson College, Georgia State University BS Nuclear Engineering, Georgia Institute of Technology Executive Summary Michael Wm. Denis brings twenty-nine years of experience in industrial services industries with significant P&L responsibility in consulting, M&A strategy, software development & implementation and performance based outsourcing. Coined the term Service Lifecycle Management (SLM) as a business and technological capability in 2004, following years of field analysis, R&D, and the development of eleven patents. Deep field experience in Autonomics and Sense & Respond Logistics, military terms for industrial Internet of Things (IoT). Focused on delivering performance and subscription based XaaS solutions and servitized business models to organizations seeking to optimize profits of complex asset in capital intensive, cash flow sensitive industries. Author and columnist on business and technology for Aviation Week, Aircraft Commerce, and ATE&M magazines. Exemplary Engagements ▪ Core leadership team member that led the company to 13%, 33% and 37% CAGR over a three year period at Flatirons Solutions ▪ Led the Product Development of Flatirons SaaS based Mobility Content and Compliance Management (MCCM) solution from business case development, solution development, solution design, pricing, marketing and first sale and implementation at American Airlines ▪ Developed the aviation industry standard enterprise business and solutions architecture for the integration of Product Lifecycle Management (PLM), Service Lifecycle Management (SLM) and Content Lifecycle Management (CLM / ECM) ▪ US Airline Merger: Advised the senior executive team during the third largest merger of two airlines in the past twenty years, specifically analyzing the process and technology integration plans for their single operating certificate and developing risk scenarios and mitigation plans for the technical operations division. Subsequently conducted and prepared a board directed post-merger IT assessment report. ▪ US Air Line Technical Operations: Multi-year team member for the client’s “Next Generation MRO” process reengineering and technology architecture initiative. Implemented multiple solutions Skills & Qualifications ▪ Gas Turbines Engineer, US Navy ▪ Service Lifecycle Management (SLM) ▪ Product Lifecycle Management (PLM) ▪ Enterprise Content Management (ECM) ▪ Cloud / Microservices Architecture
- 3. 3© Capgemini 2018. All rights reserved | Innovations in Aviation
- 4. 4© Capgemini 2018. All rights reserved | Digital Twins are digital replicas of a particular asset’s logical (as-designed) and physical (serialized as- operated) configuration and associated parametric data. The logical digital twin contains as-allowed rules, baseline configurations, parametric data and engineering limits of how an asset is designed to function; while the physical digital twin contains the as- maintained configuration and consumes sensor data, operating data, utilization data, maintenance data, environment data and effectivity changes in these parameters over an assets lifecycle – becoming the repository of an asset’s history and providing a single source of truth to technicians, lessors and regulators. Digital Twins are always PLURAL
- 5. 5© Capgemini 2018. All rights reserved | Digital Twins by the numbers: 67.8% 71.7% $15.6B Number of airlines that do not integrate their condition or health monitoring systems with their core MRO configuration mgt systems. Number of aviation decision makers who said they were not familiar with the industry standards underpinning Digital Twins. Value to the aviation & aerospace industry ecosystems of autonomic capabilities and asset performance optimization. Source: Capgemini 2017Aviation MRO survey and Capgemini Smart Factory survey
- 6. 6© Capgemini 2018. All rights reserved | Multi-Dimensional Configuration Management
- 7. 7© Capgemini 2018. All rights reserved | Structural CM S/N’s, lot #’s, position, etc… Functional CM Controls & Limits 0, 1, 2 way interchangeability rules HW/SW interchangeability rules Positional interchangeability rules Operational interchangeability rules (c)onceived (e)ngineered (m)anufactured (t)ype / model series (u)nit / physical instance xBoM = Bill of Material xBoA = Bill of Assembly xBoS = Bill of Sustainment xBoO = Bill of Operations PLM Virtual World (As Designed / As Planned) to SLM Physical World (As Maintained / As Operated) As- Conceived cBoM As- Designed eBoM As-Planned mBoM As-Built mBoA As- Sustained tBoS As- Maintained uBoS As- Operated uBoOC o m p a r e
- 8. 8© Capgemini 2018. All rights reserved | PLM Virtual World (As Designed / As Planned) to SLM Physical World (As Maintained / As Operated) Structural CM S/N’s, lot #’s, position, etc… Functional CM Controls & Limits 0, 1, 2 way interchangeability rules HW/SW interchangeability rules Positional interchangeability rules Operational interchangeability rules mBoA As- Sustained tBoS As-Maintained uBoS As- Operated uBoO
- 9. 9© Capgemini 2018. All rights reserved | PLM Virtual World (As Designed / As Planned) to SLM Physical World (As Maintained / As Operated) L = Logical; F = Functional and S = Structural P = Physical; F = Functional and S = Structural FP SL FL SP Design/PlanDesign/Plan changes inchanges in via Effectivityvia Effectivity CutCut--In ofIn of ScheduledScheduled & accounted& accounted for infor in in response toin response to analysis ofanalysis of FP SL FL SPSP Design/PlanDesign/Plan changes inchanges in via Effectivityvia Effectivity CutCut--In ofIn of ScheduledScheduled & accounted& accounted for infor in in response toin response to analysis ofanalysis of Functional Configuration Management (CMF) is the tracking, analysis and management of the functional design and operating performance parameters of an asset, assembly or component. There is a Logical “as-designed” (FL) and Physical “as- operated” (FP) version. Structural Configuration Management (CMS) is the tracking, analysis and management of the structural piece of an assets Bill of Material (BOM, EBOM, MBOM). There is a Logical “as-allowed” (SL) structure and Physical “as-maintained” (SP) version. Effectivity (E) is the dimension that tracks and schedules changes in one or more of the previous two dimensions CMF or CMS in accordance with a specific derivative. Change derivatives can include: (EO/EAs, Airworthiness Directives (AD), Service Bulletins (SB), calendar time, operating time, cycles, environment or events (e.g., lightening/EM radiation, bird strike, hard landing …). PLMPLM SLMSLM
- 10. 10© Capgemini 2018. All rights reserved | PLM Virtual World (As Designed / As Planned) to SLM Physical World (As Maintained / As Operated) Digital Twins Operations C o m p a r e OD Operational Data Logical CM Physical CM Structural CM Functional CM Maintenance data Sensor data Environment data Events data … ED Engineering Data As-Designed eBoM As- Maintained uBoM As-Operated uBoO
- 11. 11© Capgemini 2018. All rights reserved | Digital Analytics make connected things SMART Digital Analytics are various methods, algorithms and tools that use digital twin data gathered over the digital thread for component failure and degradation prediction, predictive maintenance, case-based reasoning diagnostics, task and repair prescription, component and asset prognostics, component pool health scoring, aircraft and fleet health, fleet program enhancements, autonomic logistics and both operational and financial asset performance optimization. Digital analytics include time series analysis, Bayesian analysis, machine learning, deep learning and autonomic decision support.
- 12. 12© Capgemini 2018. All rights reserved | Digital Analytics by the numbers: 73.4% 4.8% $18.5B Number of airlines & MRO executives who Strongly Agree or Agree that data volume & velocity exceeds their ability to drive business value. Number of airlines & MROs using some sort of artificial intelligence or machine learning predictive maintenance capability. Value to the aviation & aerospace industry ecosystems of autonomic capabilities and asset performance optimization. Source: Capgemini 2017Aviation MRO survey and Capgemini Smart Factory survey
- 13. 13© Capgemini 2018. All rights reserved | Digital Transformation Capability Maturity Roadmap TS: Time Series; BA: Bayesian Analysis; AI: Artificial Intelligence; ML: Machine Learning; RCM: Reliability Centered Maintenance; CBM: Condition Based Maintenance; CBR: Case Based Reasoning; CF: Collaborative Forecasting; CP: Collaborative Planning; CR: Collaborative Replenishment Prognostic Asset Health Management Service Resource Execution (CR) IncreasingFinancialValue($$$) Increasing Operational Value (Actionable Time / Reliability / Operational Risk Reduction) Service Resource Forecasting (CF) AI/ML Condition & Task Prediction Digital Thread & Remote Condition Monitoring Predictive Maintenance Capabilities Advanced Supply Chain Capabilities CBR Diagnostics & Task Prescription Service Resource Planning (CP) Condition monitoring (remote or on board) is the capability to capture structural and functional data (parametric data, fault codes) and deliver it to central processing nodes Predictive Maintenance uses algorithms (TS, BA, AI, …) on parametric data CMFP∂∆ to forecast degradation or failure and criticality (FMECA) of components in RCM or CBM programs Case Based Reasoning diagnosis is an AI method that learns “causality” of failure modes and degradation CMFP∂∆ given a specific CMSP to determine the prescription options for various operational outcomes (prognoses) Prognosis is the prediction of likely outcomes given a diagnosis and prescription. The Health of an asset is the delta of its physical functional condition CMFP∂∆ to its logical or as- designed conditions CMFL∂∆. Aircraft level prognostic health is a function of installed components ΣCMSP Autonomic Asset Performance Optimization Autonomic Logistics Autonomic operations is the self-learning, autonomous and automatic decision support & execution capability from the point of operations to the entire service support ecosystem, simultaneously optimizing operation of an asset or assets as well as its / their revenue, profit, cost or economic performance and various trade offs
- 14. 14© Capgemini 2018. All rights reserved | Continuous Airworthiness Engineering & Program Management Maintenance, Repair & Overhaul – Planning, Scheduling & Execution Logical HW/SW Configuration Management Physical HW/SW Configuration Management Maintenance Program Management EO / AD / SB Planning and Scheduling Time, Cycles and Conditions Monitoring Technical Manuals & Policies Content Management Legal & Regulatory Forms & Records Management Task Cards, SB, AD & EO Content Management Station Capability Planning, Staffing & Tooling Maintenance Operations Control Line / Ramp Maintenance Execution Hangar Visit Production Planning & Control Shop Visit Production Planning & Routing Control Shop, GSE & Tooling Maintenance, Calibration & Control Hangar Maintenance Execution Maintenance Engineering & Technical Support Shop Long Range Scheduling & Routing Mgt Human Capital Training & Certification Long Range Visit Planning, Scheduling & Slotting Finite Human Capital Capacity Scheduling Autonomic Services Autonomic Logistics Fleet / Asset Performance Management Component, Program & Dispatch Reliability Analysis Regulatory Compliance Management & Reporting Human Factors Reliability & Talent Innovation Contract, SLA & Warranty Management Aircraft & Component Reliability Analysis Performance Based Contract Management Quality Management System (QMS) Safety Management System (SMS) Event, Incident & Case Management Predictive Analytics (Failures & Causal Weights) Sensors & Faults Condition Monitoring Prognostics, Health & Performance Management Case Based Reasoning Diagnosis & Prescription Material & Repair Provisioning Multi-Echelon Service Parts Optimization Collaborative SC Execution VMI, Pooling & Repairs Material Warehousing, Distribution & Transportation Material & Repair Finance & Accounting Material & Repair Planning & Scheduling Aerospace / Aviation SLM Reference Architecture
- 15. 15© Capgemini 2018. All rights reserved | IoT analytics making the Aircraft Value Chain smart Source: Frost & Sullivan; Industry Reports; Secondary Sources; IBC Analysis Design & Engineering Manufacturing Supply Chain MRO & Flt Ops Low Penetration Nascent stage High Penetration Adoption stage High Penetration Adoption stage High Penetration Nascent stage Penetration and Adoption Analysis of IIoT Across Aircraft Value Chain Key IIoT Objective To provide the basic foundation towards building a flawless aircraft. IIoT Benefits ▪ Minimise weight ▪ Minimise volume ▪ Maxiimise performance ▪ Maximise life ▪ Minimise lifecycle cost ▪ Strategic reuse Key IIoT Objective To efficiently coordinate, direct, and oversee the production of aircraft on the factory floor. IIoT Benefits ▪ Planning & optimisation ▪ Quality monitoring ▪ Asset optimisation Key IIoT Objective To create value by forming a competitive infrastructure using logistics working on demand by measuring performance. IIoT Benefits ▪ Supply chain ▪ Optimisation, efficiency ▪ Visibility Key IIoT Objective To offer on-time inspection, repair, alteration, and the supply of aircraft spare parts. IIoT Benefits ▪ Aircraft health monitoring ▪ Last mile connectivity IoT can help aircraft manufacturers meet their service lifecycle warranty and service level agreement objectives across different stages of the manufacturing to operations value chain.
- 16. 16© Capgemini 2018. All rights reserved | The Digital Thread represents the digitization of product lifecycle and service lifecycle data and the connections between systems and organizations. Originating from design inception through prototyping, manufacturing, operation, in-service maintenance, repair and overhaul, as well as training and content support documentation. The key capabilities of the digital thread are simultaneously maintaining data and content interoperability for both human and machine consumption and connectivity across the multitude of use cases and ecosystem organizations. Digital Twins & Digital Analytics require a DIGITAL THREAD
- 17. 17© Capgemini 2018. All rights reserved | Digital Thread by the numbers: 80.3% 46.5% $11.2B Number of C-level aviation executives and vice presidents that agree plugging gaps in the digital thread is essential to driving business value. Number of airlines & MROs that have allocated capital and resources to closing gaps in the digital thread. Value to the aviation & aerospace industry ecosystems to closing gaps in the digital thread. Source: Capgemini 2017Aviation MRO survey and Capgemini Smart Factory survey
- 18. 18© Capgemini 2018. All rights reserved | The Digital Thread PLM Virtual: As Designed to As Manufactured SLM Physical: As Operated & As Maintained DCX Autonomic Logistics Procurement& Distribution Content Lifecycle Management (CLM) Autonomic Services Asset Performance Monitoring and Service / Product Feedback SenseRedesign Data Schema Definitions, Interoperability & Technical Architecture Provisioning mBOM & MES Manufacturing Instructions sBOM & Technical Manuals, Tasks, Forms & Records Regulatory Forms & Certifications ProductDesign &Engineering Skills, Certifications, Learning (LMS) & Human Capital Management (HCM) Contract, SLA & Warranty Mgt Condition Monitoring Diagnosis & Prescription Prediction – Failure & Causal Weights Reliability, Quality QMS & Safety SMS Event, Incident & Case Mgt Service Lifecycle FMECA, MTA, LORA, Engineering, Reliability & MRO Program Development IoTMonitorRespond Manufacturing Operations ServiceConsumption&PassengerExperienceManagement Device/Asset/ EcosystemService Operations&Delivery MRO OPS S6000T S1000D / iSpec2200 S4000P S2000M / SPEC2000 S5000F S2400/2500 S9000D PDM 3DMBD MOM/MES APS/FCS PLM Contracts MRP Service Lifecycle Logistics Support Analysis (LSA) & Integrated Logistics Support (ILS) S3000L Regulatory Reporting & Mgt Prognosis, Health & Performance Mgt S8000O S7000E CAE IATA & S2300 CAD The Aviation Digital Thread logical architecture
- 19. 19© Capgemini 2018. All rights reserved | eLogbook / EFB S1000D Collaboration Network Sneaker Net ▪ Sensor Data ▪ Fault Codes ▪ Operations Data – Flight – Crew – Airport – MRO – PO / RO ▪ Environment Data ▪ CMC / ACMS ▪ Aircraft Content – Manuals – IPC – Task Cards – Records ACARS VHF ACARS over IP Ground Link Service Bus Digital Threads Ecosystem Data Hub PaaS Data Virtualization & Distribution Data Wrangling & Transformation XML / JSON Conversion Decision Optimization AI / ML / DL Analytics Engine Sources & Data Types Transport Paths & Vendors Digital Twins & Predictive Analytics The Aviation Digital Thread physical ecosystem
- 20. A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2016 global revenues of EUR 12.5 billion. About Capgemini Learn more about us at www.capgemini.com This presentation contains information that may be privileged or confidential and is the property of the Capgemini Group. Copyright © 2018 Capgemini. All rights reserved.