Neural Aerospace Manufacturing – a highly networked ecosystem that is connected, intelligent, resilient, automated, adaptive, personalized, and cognitive

1. The aerospace industry has been at the forefront of incorporating innovations to develop
diverse and highly engineered new products, new manufacturing methods, and
enhanced post-production aftermarket services. The aerospace manufacturing ecosystem
is an integrated mix of industry players spread across airlines, aircraft manufacturers,
airports, air traffic management, regulators, ground support, and an extended network of
tiered suppliers. Legacy operational technologies were not designed to be connected,
and therefore, work only in their own environment. As a result, information exchange and
interoperability between this extended aerospace ecosystem have been slower and
fragmented.
A neural aerospace enterprise leverages emerging technologies to build a resilient,
adaptive, and intelligent value chain supported by purpose-driven and networked
partners. This kind of extended aerospace value chain ecosystem underpinned by various
digital technologies mimics the human neural network, enabling strong interoperability
and information flow in all directions (Figure 1). This is referred to as Neural Aerospace
Manufacturing – a highly networked ecosystem that is connected, intelligent, resilient,
automated, adaptive, personalized, and cognitive. This paper focuses on the key facets of a
neural aerospace enterprise and the benefits neural ecosystems can bring to the
aerospace industry.
White Paper | Manufacturing
Neural Ecosystems
Driving
Next-Generation
Aerospace
Manufacturing
Abstract

2. White Paper | Manufacturing
2
Smart neural factory: Connected
everything with secured
interoperability
Aerospace manufacturing lead times often run into several quarters and years, resulting in
less than agile response to supply/demand disruptions. A fully autonomous, always-on
network architecture of systems enables information flow in multiple directions across
factory cells and machines, and augments human decision making. Such a network can
also autonomously predict production scenarios through self-health monitoring and
real-time maintenance, thereby achieving efficiency, precision, and much-desired agility.
Neural
Aerospace
Connected
Strong network
Integrated representation of assets
with Digital Thread towards a
networked, connected, secure, and
interoperable information exchange
Automated
Machine first delivery
Robots, cobots, and automated
assembly and parts manufacturing;
Parts traceability & genealogy -
autonomous track & trace
Adaptive
Product & Service Mix, Supply Chain
Managing parts supply/Demand
disruptions with Supply-Chain Digital
Thread and 3D printing to create an
intelligent factory – connected & adaptive
assets & platforms
Cognitive
Perceives and responds to stimulus
Cognitive data visualization for critical aircraft
decision-making; Cognitive Production
Control systems; Machine vision applications
in safety and aircraft inspection
Resilient
Regenerates / self corrects
Prognostic fault prediction and
maintenance; Blockchain for
resilient supply network &
counterfeit detection
Intelligent
Generates continuous insights
Prescriptive and prognostic insight
generation from assets network;
Intelligent planning & scheduling;
AI-based generative design and
engineering of aircraft parts
Personalized
Every system is unique,
differentiated
Additive Manufacturing for
on-demand and personalized
fulfillment of printable spares and
materials; Immersive aircraft
simulation and modeling
Figure 1: Key facets of a neural aerospace enterprise

3. 3
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A smart neural factory of the future (see Figure 2), drawing inspiration from biological neural
systems, can better adapt to shocks and respond to external stimuli with resilience. The
advanced analytics-based cognitive command center receives demand and supply data from
external and internal factory sources. It then analyzes demand and supply signals from
various enterprise systems like customer relationship management (CRM), enterprise
resource planning (ERP), supply chain management (SCM), manufacturing execution systems
(MES), product lifecycle management (PLM), and also from the factory floor. Such data
analysis enhances machine health monitoring, enables fault prognostics, and improves
overall equipment effectiveness (OEE). This paves the way for optimal manufacturing
scheduling, production planning, and control.
In addition, the neural system extracts data from numerous internet of things (IoT) sensors
installed in the products, assets, and platforms on the manufacturing floor. These sensors can
capture production data from engine manufacturing, fuselage, wings, avionics, and other
product sub-systems and their suppliers. Similarly, data can also be gathered from devices
such as augmented and virtual reality (AR/VR) gears and mobile devices that scan the factory.
The system can also connect workers and tools for specific tasks. It also connects industrial
IoT platforms, enabling information flow across aircraft assembly and the extended supply
chain network. All the big data is then aggregated into the central control system where it
can be analyzed through advanced artificial intelligence (AI) and machine/deep learning
(ML/DL) techniques‘on the edge’and presented via a data visualization system for human
consumption and decision making. The core system then acts as neural brain to provide
analytical prescriptive decisions for health monitoring, fault prediction and prevention, and
production performance. This in turn improves real-time decision making leading to
enterprise and ecosystem-wide process(s) and resource optimization.
Figure 2: Smart neural factory of the future
Command center
Enterprise Systems
Cloud
(Cybersecure
FedRAMP)
Operational information
feeds to/from Enterprise
systems
Production and
product data
from sensors
Electromechanical
data from
machines and
platforms
Health monitoring, fault
prediction and prevention
and production
performance assessments
Enterprise
Planning
(ERP)Fuselage Production
and Assembly
Customer
Management
(CRM)
Product Lifecycle
Management
(PLM)
Manufacturing
Execution
(MES/MOM)
Engine Manufacturing
Avionics Manufacturing
Interiors Manufacturing
Data from External
Ecosystem and Other
non-Manufacturing
sources
Cognitive Engine
Data
Visualization

4. 4
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The evolution of technology across
the aerospace value chain
As aerospace manufacturers become more agile, resilient, and intelligent, a neural
aerospace innovation path is emerging, which is a purpose-driven ecosystem, that can
support glocal, a combination of global and local, manufacturing with automated
operations. Figure 3 below graphically depicts how aerospace transformation across the
value chain is being driven by various digital technology advances.
The increasing use of massive computing power and digital twins to develop radical new
parts and aircraft designs will disrupt aircraft design, accelerating the evolution from
computer-aided simulative design (CAD) towards generative design. The use of AI-driven
materials design will create advanced smart materials that enable lightweight, agile, and
intelligent aircraft designs for the future. In addition, blockchain can be used to track and
trace parts across the supply chain and ensure optimal spare parts inventory
management. Aerospace players are increasingly using cobots and bionic exoskeletons in
the warehouse as well as autonomous platoons for logistics, and AI for streamlining parts
flows. In fact, additive manufacturing and robots are being increasingly used for end-use
parts fabrication and parts manufacturing. An increased influx of connected assets and
platforms will present the opportunity to create a fully autonomous factory with IoT and
Figure 3: The future aerospace value chain driven by neural pillars
Distributed
Manufacturing using
shared cloud-based
resources (production-
as-a-service)
AI-driven aircrafts’
material design
for designing
conceptual aircrafts
Predictive/intelligent
manufacturing
system
Generative design
using massive
computing power
Design Simulations
CAD, CAM for aircraft part
modelling
Digitized aircraft contract
and order data
Supplier Relationship
Management (SRM) software
Automated P2P
Additive Manufacturing
Use of Robots and automated
assembly lines for part
manufacturing and assembly
Automated Robots in
Warehouse
Scheduling and
Planning Software
Aircraft health maintenance
software
Predictive maintenance
Machine Vision for MRO
Digital twins on the
Cloud for design
and maintenance
IoT driven
autonomous
factory ecosystem
Cobotics/
Exoskeletons in
Warehouse
Autonomous
Platooning vehicles
Cyber Tracking
of material parts
Dynamic MRO record
AR/VR for training
Streamlining parts flow
via AI and Blockchain
Prescriptive analytics for
real-time maintenance
Blockchain for Track &
Trace/ smart contract &
spare part inventory
management
Swarm robots for
engine manufacturing
and maintenance
PotentialValuedelivered
Disruptive
Time for mainstream adoption
Current Future
Transfor-
mational
Visible
Low
Researchto
Design
Procurement&
SupplyChain
Manufacturing
Warehousing
&Logistics
Aftermarket
&MRO











Connected Intelligent Adaptive Resilient Automated Cognitive Personalized

5. 5
White Paper | Manufacturing
distributed manufacturing through shared cloud-based resources, creating an asset-light
environment (production-as-a-service). Digital twin and model-based outcome prediction
for maintenance and aircraft health in the maintenance, repair, and overhaul (MRO)
aftermarket have been dynamically augmenting matured technologies such as aircraft
health maintenance software. Likewise, prescriptive analytics is supplementing predictive
maintenance by enabling decision making in addition to predicting failure events.
Designing a neural aerospace
enterprise
The aerospace value chain is rapidly changing from a traditionally linear structure to a
highly layered network with multiple ecosystem partners. Here, each ecosystem
participant may be viewed as a neuron interconnected by a web of nodes. The edges that
connect the nodes form an information-sharing network. This biomimicry of a neural
network consisting of three representational layers (see Figure 4) can pave the way for the
neural aerospace enterprise.
1. Input – The sensing layer: This system perceives stimuli and can learn and orchestrate
complex sets of tasks. For instance, data coming from flying aircraft, real-time engine
information, and data from the partner ecosystem can enable data monetization, assist in
creating new services, and enhance customer and partner relationships.
2. Core: Computational digital connected network
a. The core neural system
Core systems are technologies underpinning the network. For instance, the
aerospace industry has wide-scale use of technologies such as the cloud for
infrastructure support; 5G for aircraft and satellite communication and data
sharing; autonomous flying; and data lifecycle management and cybersecurity
solutions for information sanitization and privacy.
Figure 4: The three layers of the aerospace neural network
1. Input –
The Sensing Layer
3. Output –
The Response Layer
2. Core – Computation
Digital Connected
Network

6. 6
White Paper | Manufacturing
Interface systems are emerging technologies that can enhance networking
and communication across each value chain function. For instance, additive
manufacturing in the aerospace industry enables on-demand fulfillment of
spares. Interface technologies such as blockchain, IoT, and immersive
technologies can act as different enabling nodes of the network.
b. The digital network: Creating a network requires interaction and platform
integration among all ecosystem players. With platform technologies—such as
shared computing and on-demand service platforms, data harvesting systems and
distributed B2B marketplaces—individual partners can offer dynamic and
enhanced value to customers by leveraging their partner ecosystem.
3. Output – The response layer: With the input layers (sensing), middle layer (core neural
system), and the connecting network nodes, the ecosystem creates a response which
remains resilient and can better withstand disruptions. The response layer takes curated
inputs from all the network partners and offers data-driven business insights via
visualization platforms which aids in decision making.
Aerospace ecosystems of the future:
Three neural dimensions
The evolving neural aerospace ecosystem of the future can be represented by three
interconnected dimensions building on Industry 4.0 foundational technologies
(see Figure 5).
Figure 5: Three key dimensions of the neural aerospace ecosystem of the future
Network Dimension External Dimension
Automated
Production &
Processes
Intelligent Planning
& Scheduling
Digital
Adaptive
Core
Dynamic
Tracking
Digital
Aftermarket &
MRO Network
Unified Air
Traffic
Management
Flight Safety &
Smart
Governance
Connected
Sustainability in
Aerospace
ecosystems
Smart
Logistics
Machine First
Quality
management
Cognitive
Procurement &
Sourcing
Intelligent
Assets/Platforms
Smart
Connected
FactoriesSmart/
Intelligent
R&D
Internal Dimension
Connected Internal Ecosystem Cognitive Supply Network
Networked Lifecycle Management
across value chain
Secure, Connected External
Ecosystem

7. 7
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Internal dimension: A connected internal ecosystem with cognitive computing
architecture – This dimension intelligently connects internal smart assets and
platforms powered by IoT and automated production and processes with
human-machine interaction.
Network dimension: A cognitive supply network (CSN) – The application of cognitive
technologies such as AI, ML, natural language processing (NLP), computer vision, and
robotics in supply chain allows the entire network to think and interact over the cloud.
An end-to-end visibility of the supply network can be achieved by coupling connected
networks with IoT for increased automated communication and digital twin
technologies, which offer the ability to simulate multiple scenarios and predict failures
and risks.
External dimension: A connected external ecosystem – The successful neural
ecosystem is efficient and seamlessly integrates the elements of the internal, network,
and external dimensions, as described below:
Digital aftermarket and MRO network: With diverse and distributed aftermarket
partners, it becomes a challenge for the industry to access a single source of truth
(SSOT) throughout long lifecycles. A digital aftermarket network offers the benefit
of remaining always on for stakeholders by offering access to real-time shared
services and insights from across the network such as gaining real-time
monitoring and repair instructions using immersive reality, IIoT, and wearables
from all the equipment and parts across the entire ecosystem.
Neural unified air traffic management: Commercial aviation is rapidly evolving,
where new aircrafts are emerging, including drones for various applications, air
taxies, and sub-orbital and supersonic aircrafts. Traditional air traffic management
(ATM) systems, which were designed for large commercial aircraft, will need to
evolve to handle this complexity as well as allow for continued innovations to
enable the emerging aviation sectors to shift to unmanned aircraft system traffic
management (UTM). UTM is expected to offer digital-native and intelligent,
automation capabilities which can pave the way for future services and new
concepts of operation, while working in tandem with and converging with current
ATM advancements. Significant work lies ahead in AI, cybersecurity, digitalization,
cloud, and sustainability to integrate the emerging aviation sectors with the
traditional aviation system, which will manifest into a neural unified UTM-ATM air
traffic management vision.
Flight safety and smart governance: The industry is moving beyond the
on-premise systems with high-information latency towards a shared and
cloud-enabled on-demand resource with secure data transparency and
accountability improving aerospace ecosystem safety and governance.

8. 8
White Paper | Manufacturing
Connected sustainable environments: Original equipment manufacturers (OEMs)
today are rethinking aircraft design and engine technologies for sustainability with
electric and hybrid-electric propulsion. AI and IoT data sources coupled with
advanced analytics for real-time air navigation and continuous climb and descent
operations are improving aircraft scheduling, route planning, safety practices, and
conflict predictions, creating a sustainable ecosystem.
Building agility and resilience
through a neural aerospace
manufacturing ecosystem
The aerospace industry has traditionally been a cutting-edge user of scientific advances
whether through government funded R&D or its own industrialization research. In recent
years, the pace of technology absorption has been accelerating with several digital
innovations coming to fruition. COVID-19 has further accelerated the move towards
digitalization and the envisioned neural aerospace ecosystem of the future. The
commercial space industry is also emerging as a new frontier with unprecedented
possibilities leveraging digital technologies. Digitally mature and other early-adopter
organizations are well-equipped to build a resilient ecosystem of value chain partners and
suppliers, and in process creating the neural aerospace ecosystem of the future.

9. Authors
Sandeep‘Sandy’Muju
Dr. Muju heads the Aerospace and Defense Practice in the Manufacturing Business Unit at
TCS. He is responsible for enabling business transformation, growth, innovation, and
digitalization initiatives for global clients. He brings extensive experience across Fortune
100 aerospace and defense and allied highly engineered and manufactured products and
technology services organizations.
Arpita Bhattacharya
Arpita is a Marketing Analyst in the Manufacturing Business Unit at TCS with five years of
experience. She is responsible for key marketing initiatives such as thought leadership
content management & contextual storyboarding, branding & positioning including
internal activities and market research.
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