globalaviationaerospace.com An opportunity for insight in the changing commercial aerospace business Vision for New Applications of Analytic Insight in Commercial Aerospace Benefit of Big Data Analytics for the Airline Operator Modern, Mobile Experience Big Data Analytics In Action Predictive Analytics To Prevent Engine Events Predictive Analytics Improves Safety and Quality Predictive Analytics Keeps More Planes in the Air

1. IBM Global Business Services
White Paper
Aerospace & Defense
Commercial Aviation and Aerospace:
Big Data Analytics for Advantage,
Differentiation and Dollars
Smart commercial aerospace OEMs can transform their business models and
revenue streams by capitalizing on their information assets in new ways.

2. 2 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Introduction
The commercial aerospace industry is undergoing significant
change, compelling aerospace OEMs to seek new ways to
serve the marketplace, including changing their fundamental
business models from products to service-oriented plays.
The new and largely unexploited asset OEMs now have is
the ability to harness and optimize data. Today’s aircraft
is highly instrumented and the average flight can produce
between 500 and 1,000 gigabytes of data. This is ‘big data’,
and now that it is available, the next step is to extract as much
commercial value and advantage from it as we can.
While some praiseworthy successes have come from using
this data in condition monitoring, we believe this might just be
a glimpse of what’s possible when OEM harness their new data
assets. Information can become a powerful and differentiating
tool in the hands of the aftermarket division. Insight can be
crafted into saleable, revenue-producing services. It can be infused
back into the aerospace value chain to enable manufacturing,
procurement and engineering. It can be put in the hand — via
an app on their tablets — of key airline client players such as
maintenance, fleet manager, flight operations, and pilots.
Combine this exponential growth in data generated by each
flight hour with the challenge of an aging work force as the
baby boom generation retires and with them take their experience;
leaving a gap in operator capability. This growing situation
creates the need for advanced analytics to assist the next
generation of pilots, mechanics, dispatch managers, flight
attendants and other participants to quickly deliver the knowledge
and recommended solutions. Cognitive analytics, such as Watson,
will enable operators to address this challenge and more efficiently
deliver their services and respond to disruptions in their business.
With this much untapped opportunity, the first interesting
questions may be “what should we do?” and “what should we
do first?” It starts with some imagination, invention and analysis.
The hopeful ending is a new advantage, differentiation and
dollars for the OEMs who move on this opportunity quickly.

3. IBM Global Business Services 3
An opportunity for insight in the changing
commercial aerospace business
Already looking to offset declining defense revenue in the
commercial market, a confluence of recent shifts in the commercial
aerospace industry is creating opportunities for aircraft OEMs
to rethink their business models. These stem from changes in
the airline marketplace, the state of aerospace equipment past
and present, and the emergence of ‘big data’ as a relatively new
and unexploited asset.
Demand for new aircraft is overwhelming, and some OEMs are
struggling to keep up. For example, there are 17,700 commercial
aircraft deliveries forecasted to 20231
. According to CAPA
Centre For Aviation: “The world’s aviation sector ended
2013 with another record backlog for aircraft orders, as the highest
ever number of annual orders added to what was already a record
backlog. The number of outstanding orders is equivalent to almost half
of the fleet in service.2
” Many airlines are retiring older aircraft
and are looking to replace significant portions of their fleets.
Trends in passenger travel show greater demand in the past
decades and the growth of revenue per passenger mile has
increased. Regional routes, previously under-served, are being
opened, creating demand for smaller, single-isle aircraft.
With a departure from older aircraft, the emergence of new
aircraft with new technologies from engineering, engines,
equipment and software enable OEMs to take some new steps
in the industry. Business models for OEMs are changing, where
‘power by the hour’ type engagements begin transforming the
traditional manufacturing/product-oriented business model into
more service-oriented models where OEMs seek to drive revenue
in perpetuity with the airlines’ fleets. To a great extent, this may
shift the power within the OEM enterprise as the aftermarket
business transforms from a parts distribution organization to
a valued and enhanced revenue machine. We believe airline
operators themselves are welcoming these shifts. For them,
the result is less large capital expenditure, greater uptimes,
better asset utilization, and less risk, fundamentally transforming
the cost of ownership for air carriers.
New planes and new business models share an intriguing
characteristic: data. Fulfilling a vision for the “connected
vehicle”, everything is instrumented to collect data on modern
aircraft. With often a terabyte of data collected on each flight,
the amount of new data available is immense. This is ‘big data’,
massive databases of performance, machine data, unstructured
data and other types that are creating a largely untapped asset.

4. 4 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Analytics: The Speed Advantage
IBM’s Institute for Business Value recently surveyed over
1,000 business and IT executives for their study “Analytics:
The Speed Advantage”. Through this study they learned that
53 percent of companies used analytics for customer-centric
objectives and 63 percent of organizations realize a positive
return on analytic investments within a year.
The idea of using data as a marketable service now emerges
in light of its availability and the new service-oriented business
models. Data that is monitoring each major system on an
aircraft (engines, avionics, electrical, etc) can be fed into
predictive maintenance models to predict what parts and
skills need to be deployed when, before the part fails. Fuel
monitoring can be analyzed to make smarter fuelling decisions,
be they on the ground or at the procurement officer’s desk.
Other data can ascertain how different flying conditions affect
engine performance and repair priorities. These types of
insights are valuable to airline operators, and therefore can
deliver big benefits:
Advantage: The insight-driven organization can outperform
on revenue and cost, while delivering superior value for their
products and services. Equipment life can be extended,
replacements reduced and cost efficiencies can be realized.
There are opportunities to improve performance, reliability
and safety.
Differentiation: Fast-movers on the big data opportunity
will ‘own’ the asset and will differentiate themselves from
competitors that don’t. This is especially important as new
entrants threaten the traditional duopoly of the OEM market.
Dollars: The insights have direct value to airline operators,
and therefore can be monetized to create revenue streams
that run in perpetuity long after the equipment is delivered.
Whether the data is a line item on the invoice or not, it
becomes a service that the airline procures.
The opportunities for capitalizing on data are here. The early
movers have the potential for cornering the capability, meaning
whomever does it best and first will not only derive insight from
their own data, but may set the standard for which the industry
adheres. It’s possible—perhaps likely—that this bearer will not
only traffic their own data, but will be handed the responsibilities
of the air carriers’ entire aircraft data requirements across the
fleet inclusive of competitors’ equipment. Imagine perpetual
revenue from a competitor’s sale! Somebody will put it together
within the ecosystem—either a competitor, the airlines, or an
intermediary—so the race is on.
A first step is to frame the vision and the conversation in order
to create excitement about the new “possible.” What does it
look like?

5. IBM Global Business Services 5
Vision for New Applications of Analytic
Insight in Commercial Aerospace
As we envision what new experiences will be enabled by new
analytics capabilities, we first want to discuss how participants
in the ecosystem will change their behavior and decision-making
based on new insight. In this, we recognize that practitioners
within the OEM, within the client airline and likely partner/
supplier ecosystem will be given new apps. The initial excitement
and intrigue will arise for what business outcomes actors will
achieve given new insight when and where they get it. Because
of this, initial visioning does not need to explore information
architecture, tools, data quality processes, etc.
Aftermarket
Supply chain
Engineering
Maintenance/Inventory
Fleet/Asset
Management
Flight operations
Pilots and other crewProgram management
AIRLINE
OEM
N
S
WE
Big Data
Analytics

6. 6 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Listed below are ways that business can change using big data
analytics within the aerospace ecosystem:
Aftermarket
One of the biggest opportunities — and greatest departures —
for new analytics prowess is in the aftermarket function.
Traditionally, the aftermarket has been a reactive practice
of fulfilling parts or dispatching service after the customer calls
in an order or a repair. In the primary switch from a product-
oriented to a service-oriented business model, the aftermarket
function focuses on proactively providing parts and service.
Ideally, the OEM’s service team should know when a service
event will happen prior to the operator. And in the best world,
will be able to mitigate them before they happen.
This is essential for “power by the hour” engagements,
where a plane stuck in unscheduled maintenance is literally
lost revenue as the seconds tick. The operator suffers as well,
as they seek to keep their asset running, the flights on schedule,
their service costs low, and their customers happy. Extending
the life of an engine becomes the revenue generator, not obsolesce
or replacement.
Big data analytics is the key enabler to achieving condition
monitoring and predictive maintenance. By using data from
the instruments and repair histories, every monitored part can
be models for what conditions were corollary or causal to the
breakdown. These models can then be applied to current data
to predict future events. Monitoring can be used to listen to
existing data with event alarms, alerts and triggers to automate
dispatch of the right skills and tools. Analytics can be used to
predict the need for parts, thereby reducing and optimizing
inventory levels both by the OEM and the operators.
Other applications may include monitoring and analyzing fuel
or supply/material usage to identify optimal fueling strategies
or even negotiate operator procurement strategies. Another
usage of analytics may be to monitor parts and systems as they
travel over different terrain to see if desert, ocean, heat, tundra,
etc., affects performance. Monitoring of the fleet in aggregate
can provide insight into future buying decisions and how the
fleet is deployed over location and use.
Innovation and invention here becomes essential: the more
information services that the aftermarket organization can
deliver, the more opportunity there is for building and bundling
sustainable revenue and competitive advantage.
Essentially, the more insight and value aftermarket operations
can supply operators, the more valuable and differentiated the
offering will become. After the operator is ‘hooked’ on
a particular information service, the offering will go from a
nice-to-have value-add to a critical and necessary requirement,
thereby increasing the value proposition to the operator.

7. IBM Global Business Services 7
Supply Chain: Procurement, Manufacturing and
Assembly, Inventory, and Distribution
Big data from both flight data from the plane and information
during manufacturing can be used in the supply chain functions.
For example, spare part inventories can be optimized and reduced
based on real-time updates on the state of equipment in the field.
Decisions about manufacture and assembly, such as production
planning and line balancing, will become dynamic based upon
the analysis of the manufacturing equipment data or tools
requiring calibration will be identified before they are used in
the aircraft assembly process avoiding expensive rework.
Supplier quality issues will be identified before sub-components
are integrated into the aircraft. The need to ramp-up commercial
aircraft production to meet the future demand for aircraft will
require the dynamic production capabilities and decision
support Big Data will provide. For example, big data can help:
• Manufacturing and assembly
–– Predict production equipment asset health and lifecycle
–– Predict maintenance needs for plant equipment
–– Understand next best action
–– Alarm or act on quality anomalies earlier, in real time
–– Improve overall plant uptime
–– Understand, predict and optimize schedule dependencies
–– In-depth root cause analysis of failures
–– Optimize demand planning and production
–– MRO parts optimization
• Product lifecycle
–– Supply chain optimization upstream
–– Understand, improve and manage supplier quality
–– Improve product quality and yield
–– Improve customer service and satisfaction
–– Predict demand
–– Optimize raw materials usage
–– Warranty analytics
–– Service parts optimization
–– Monetizing service through advanced analytics
–– Logistics and distribution optimization

8. 8 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Engineering
Engineering has a long history of using field data to understand
what makes equipment work, stay working, and improve its
performance. The limitations of the past — when compared to
the opportunity with big data now — constrained what could be
learned. Engineers would only have access to data on planes that
they were testing or chose to monitor post-delivery. Data about
breakdowns were only passed back to engineering selectively
and on a small portion of the fleet. Many repairs would go
unreported to the OEM, especially ones that didn’t require
a spare part order. Learning about patterns that didn’t involve
the OEM, such as fuel consumption patterns, would have to be
formally commissioned studies. Also unavailable would be
condition data of parts before and leading up to the point where
they failed, meaning that knowing why something failed (not
just what) was more difficult. A report of what parts are failing
helps inform inventory of what parts to produce but doesn’t
necessarily help engineering improve designs. Also unreported
would be how parts and systems were doing when they were
functioning well, useful information to help design high-
performing, long-lasting products.
With big data, engineering will have a massive store
of information that is completely comprehensive compared
to traditional feedback from the field. Visibility will be of the
entire fleet, not just those that are selectively monitored. They
will be able to analyze the condition of every part —working or
not — over time. They will be able to see the conditions that
lead to failure or success as they evolve with use. Information
about failures will be reported regardless of whether the OEM
participates in their remedy or not. Information on non-OEM
supplies, like fuel consumption patterns, can help engineers
produce more efficient and longer lasting designs.
The way field/usage data is collected should also improve
dramatically. Traditionally, the act of collecting data in the field
would need to be a project in itself, with some of the onus
falling on the operators, who would likely see their involvement
in collecting data as a begrudged cost center. With an instru-
mented big data program in place, data collection happens
continually without effort. Engineers are not tasked with finding
information, but instead harnessing it to gain insights and
produce better aircraft.

9. IBM Global Business Services 9
Program Management
N
S
WE
The program management function is tasked with making
sure that projects are completed on time and on budget.
Traditional methods of tracking project activities and risks
are typically manual and based on direct observation, hear
say, intuition and manager experience. A project plan can
easily have hundreds of thousands of activities and require the
coordination of 1,000 people or more. Data, could be injected
into this process to create a new fact-driven basis for making
decisions. Advanced analytics can be used to provide a more
analytical, data-driven approach to program management to
predict and prevent program risk, particularly project delays
and cost overruns, and to better understand the factors that
lead to risk. Predictive analytics on structured and unstructured
program data could be used to identify and predict where and
when key causal factors may trigger program risk. Early
warnings, critical-path modeling and advanced visualization
would allow managers to evaluate and implement options to
prevent risk.
One global aerospace OEM implemented an analytics
solution for their program office. The outcomes were
impressive: they were ten times better at predicting slippages
of more than 100 days; they realized a 50 percent increase in
ability to identity and predict overall schedule risk; and every
delivery deadline hit was USD6 Million saving for each
month the schedule didn’t slip.
Benefit of Big Data Analytics for the
Airline Operator
In order to have data produce advantage, differentiation
and dollars, it must be externalized and served as products
and services to the OEM’s customers. Foundationally, every
aspect that can improve the aircraft performance, make it run
less expensively, keep it in the air longer while serving passen-
gers and keeping out of the repair hanger is a win for operators.
This is the main prize, but if we get creative we may be able to
find new applications of big data that produce more value and
more revenue.
Maintenance and parts inventory
Most of the benefits to the operator’s own in-house maintenance
crew and spare parts inventory can be inferred by understanding
the receiving end of the aftermarket vision explained above.
Armed with a full view of the state of every part on the vehicle,
they would have access to the predictive models, analyses,
monitors, alarms, and alerts that would enable them to have the
right skill, the right part and the right capacity at the right time
and place. They will be able to more aptly plan for resources and
inventories, minimizing the need for excess capacity and
over-generalized and over-abundant inventory stores. This
could dramatically improve uptime and mitigate unscheduled
maintenance, making their assets and people more efficient and
more profitable.

10. 10 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Fleet/asset management
Those that have responsibility for overseeing the fleet would
be empowered with new insights and decision-making abilities.
From a day-to-day operations view, they would have a complete
picture of the fleet’s health, knowing where to deftly plan and
deploy resources in concert with maintenance. From a fleet
planning perspective, they could utilize models of the lifespan
of each piece of equipment and be able to more accurately plan
new purchases and retirements across the fleet. This would
encourage operators to consolidate purchases with a single
OEM as they saw the value of information in their fleets. In the
same spirit, they may insist that other vendors hand over asset
data from their aircraft to the OEM who runs the big data
program, giving that OEM a distinct command position among
the vendors.
Flight operations
Big data analytics would be a powerful tool for day-to-day
flight operations. Those in charge of matching aircraft with
routes would have a complete picture of which aircraft would be
operational and when to expect maintenance or unavailability.
Fueling decisions and fuel procurement decisions may be
improved by analyzing real-time consumption data across the
fleet by aircraft and location. By knowing every status, they
could get to flight quicker and reduce passenger waiting, thereby
improving the overall customer experience. Big data could also
be used to monitor EHS aspects, enabling the use of instrumented
information to comply with regulation, improve worker safety
and promote environment sustainability. Detailed and
comprehensive modeling of aircraft availability and repair may
even be useful to schedule-makers as they fine tune the optimal
times for aircraft to take off and land in order to optimize the
revenue-producing assets, getting the maximum performance
from the entire fleet.

11. IBM Global Business Services 11
Pilots and other crew
With the big data program in place, applications could be
invented for the pilots and other crew. Imagine a flight check in
the cockpit with the pilot viewing a comprehensive health check
of his entire aircraft on his iPad, inclusive of the plane’s reliability
and consumption. The pilot becomes another key actor who
is monitoring the maintenance and performance of the equipment.
Data could be used to monitor and predict the aircrew, weather
and airport conditions which increase the likelihood of a heavy
landing or an over rotation to provide timely advice to the aircrew
and best avoid these situations. Some of the data he may even
want to share with the passengers. For example, he could boast
to the passengers that the plane had exceeded specific standard
for fuel efficiency and emissions, letting the passengers feel
better about their safety, the environment and their ticket price.
Aviation? There’s An App For That. The
Modern, Mobile Experience
While it’s the information and insight that truly render
business value, the presentation of it can be transformational
in the experience. We shouldn’t think of data only being at the
desktop, instead it should have all of the modern aspects we
associate with leading edge technology experiences:
Mobile: Data should be delivered to apps on phones and
tablets for use anywhere, anytime.
Location aware: Experiences should capitalize on GPS
technology and present data that is relevant to the location.
Visualization: Apps should leverage visualization techniques
so information is displayed in intuitive, graphical interfaces
that allow for exploration and understanding.
Real-time: Information should be constantly updated to
reflect the current reality of the moment.
Intuitive: Interfaces should be highly useable and require no
training or special expertise.
IBM and Apple have launched an exclusive partnership
that teams the market-leading strengths of each company to
transform enterprise mobility through a new class of business
apps, most importantly bringing IBM’s big data and analytics
capabilities to iPhone®
and iPad®
. IBM has structured 8 use
cases below, which when implemented through future app
development, will provide significant and immediate impact
to the marketplace:

12. 12 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
Service Operation — Maintenance Advisor/Predictive
Maintenance: Analytics could predict the likely cause of
maintenance issues on an inbound aircraft, based on streaming
data from sensors and historical data analysis across the fleet,
enabling a prescription of the next-best-maintenance action.
As the aircraft is taxiing to the gate, the maintenance team
receives work order and repair procedures directly to their
mobile device, and maintenance paperwork is completed and
shared electronically with the pilot and dispatch manager.
Service Operation — Pilot Flight Plan Optimization:
Weather prediction is an improving capability which when
combined with aircraft location and airport landing and
on-ground traffic information would provide the sources of
data necessary to analyze and optimize the aircraft flight path,
speed and altitude to minimize fuel burn. The pilot’s electronic
flight bag could be enhanced with an app to help quickly assess
changing traffic and weather conditions to dynamically adjust
aircraft trajectory to minimize fuel burn, minimize delays, and
improve passenger experiences.
Smarter Commerce — Aircraft Sales Configurator:
Analytics connects customer requirements to the Bill of
Materials to more reliably deliver the aircraft the customer
expects by analytically modeling extremely complex configurable
offerings. A mobile app would enable sales and marketing teams
to dynamically configure and price complex aircraft options and
quickly display results to clients based on the options chosen,
pre-existing customer contracts, and draft or binding quotes
for customers.
Supply Chain — Inbound Delivery and Quality Control:
Quality analytics identify issues early, reducing the number of
defect parts received at the final production line. Optimization
is applied to advance shipping notices and receipt acknowl-
edgements to dynamically optimize production plans and
reschedule downstream tasks. Shipping and receiving dock
personnel at the supplier and on the production line are able
to confirm product quality and acknowledge shipping/receipt
of critical parts.
Product Development — Engineering Decision Assistant:
Content analytics and cognitive capabilities provide decision
support for engineers. The mobile chief engineer will provide
vast amounts of engineering knowledge and historical lessons
to the young, developing engineer, enabling them to fully
assess an engineering challenge and consider a much greater
set of options and knowledge before making final choices.
Product Development — Engineering content and
analytics visibility to support engineering diagnostics:
For system troubleshooting and diagnostics in remote locations,
access to vast amount of engineering analytics libraries,
specifications, and analysis stores can accelerate efforts and
save time and cost. Engineering analytics on previous and
current analyses and trends will fuel engineering diagnostics.
Product Development — Design for Quality analytics:
Knowledge of trends and issues through a product lifecycle
can provide great insight on future design considerations on
new products or enhancements. Analytics of structured and
unstructured quality data from engineering, manufacturing, and
in-service operation can provide new predictive insight. Remote
access to critical quality data that is influenced by engineering
design elements will allow engineers to get ahead of future
problems not only in engineering but also in manufacturing
and in products that are in service.
Manufacturing Quality — Quality Inspection: Quality
Inspectors must have knowledge resident in the company’s ERP,
Manufacturing Execution Systems and Engineering systems in
order to be able to assess non-compliance issues and assess
corrective actions. Quality Inspectors must support multiple
manufacturing assembly positions throughout the factory,
including inside an unfinished aircraft. Having mobile access to
past production issues and potential resolutions can speed up the
overall inspection process and assist in production rate increases.

13. IBM Global Business Services 13
Big Data Analytics In Action:
Case Studies
Big data analytics isn’t just a future-forward vision, but is
gaining real traction and producing real benefits at leading
aerospace manufacturers today.
Predictive Analytics Capabilities to Increase Performance
Monitoring of Operational Commercial Engines
A division of a major aerospace OEM that builds aircraft
engines, auxiliary power units, and small turbojet propulsion
products was looking to improve performance monitoring of
their engines for commercial operators. Their strategy called
for a big data analytics program that would significantly
broaden its performance monitoring capabilities of more than
4,000 operational commercial engines. The solution provides
the OEM’s customers with longer time on-wing, that
complement current asset maintenance alerts, and delivers
better insight into flight operational data. They incorporated
learnings from their military engines programs where they
were pushing the envelope in terms of monitoring capabilities
by integrating component and system health information. The
goal was to strengthen the commercial engine health analytics
offering for customers, thereby increasing the value of their
customers’ aircraft. The program enabled the OEM to
accurately and proactively monitor the health of customers’
engines and provide further visibility to plan ahead for
optimized fleet operations while reducing the operator’s costs.
Predictive Analytics To Prevent Engine Events
A US-based aircraft engine manufacturer collects a vast
amount of data about its engines through various databases and
sensors, but it had no holistic way of integrating and analyzing
the information to proactively address engine issues. To solve
this problem, they launched an analytics platform to create
predictive models that automatically alert the manufacturer to
different types of impending engine events.
The benefits were significant:
• 100 percent prediction of disruption events for high-
risk engines
• 95 percent accuracy in predicting all engine events that lead
to airline disruption
• USD150 million in unplanned shop visit cost avoidance and
customer delays and disruptions
• USD63 million in cost savings to airlines with application
and action of predictive alerts
Predictive Analytics Improves Safety and Quality
An aerospace firm needed to improve safety and production
quality by using predictive analytics to keep precision tools
within tight tolerances. Final assembly of commercial aircraft
are subject to rigorous specifications. The company places a
priority on ensuring torque tools used in assembly are within
specified ranges. They wished to enhance their quality assurance
programs and locate tools likely to fall outside of design specifica-
tions, thereby improving the overall quality and reducing the
chances of costly rework.
With predictive models in place, the company is cracking
the code for determining when tools are likely to become
Significantly Out Of Tolerance (SOOT). By using insights to
proactively pull faulty tools off the shop floor — often several
months before their previous maintenance schedules — the
company is boosting safety and quality. The company realized:
• 100 percent payback achieved within the first year
of deployment
• Avoided costly rework representing potentially millions
of dollars and months of production delays
• Reduces the risk of disrupting and dissatisfying airline customers
with rework requirements or delayed aircraft deliveries

14. 14 Commercial Aviation and Aerospace: Big Data Analytics for Advantage, Differentiation and Dollars
“What if” Scenarios to Optimize Production
Line Configurations
A European aircraft manufacturer uses complex optimization
solution to realize manufacturing elasticity. The OEM had
difficulty in creating “what-if” scenarios for multiple production-
line configurations that could provide ongoing, real-time insight
into whether schedules can be met and whether product design
processes have an impact on production cost and efficiency.
The firm adopted an analytics and optimization solution that
drastically reduces the time required for plant configuration —
from weeks to minutes — and minimizes the impact of unforeseen
production problems. The solution generates multiple what-if
configurations; compares them in terms of efficiency, resource
use, tool investment and output; and then enables the team to
physically build a smarter configuration. The solution reduced
plant configuration times by well over 95 percent from several
weeks to a few minutes.
Predictive Analytics Keeps More Planes in the Air
One major Aircraft manufacturer accelerates problem diagnosis
in its customer support war room to keep more planes in the air.
They needed comprehensive diagnostic information in a central
point of access to help technicians, support staff and engineers
find the source of aircraft troubles more quickly, assemble the
resources needed to fix each problem, and keep more planes
in the air. With a navigation and discovery solution, the company
centralized its knowledge and expertise, maintenance records
and technical documentation. Rich analytics help technicians
isolate faults and anomalies to diagnose mechanical failures
and find a path to resolution. The solution also identifies the
skills and spare parts needed to fix the problem, even verifying
their availability for repair scheduling. The company realized
huge benefits:
• 70 percent improvement in call resolution times — from
50 minutes to 15
• USD36 million savings in support costs
• 50 additional planes in the air every year without adding
support staff
A Path Forward. What Actions Should
Aerospace OEMs Take?
The ideas explored in this paper focus on big data capabilities,
some of which are already finding success in the industry and
others are a look forward. The first step an OEM should take
is opening up the analytics conversation to explore the new
possibilities and opportunities. These conversations shouldn’t
be mired in reflection of the exiting, traditional approaches,
but be embraced with a creative sense of innovation and
invention. The goal isn’t to eek out efficiencies here and there,
but to create a vision that can deliver substantive, revenue-
generating value to the organization and its customers.
IBM’s Big Data Prowess
IBM, as a firm, wholeheartedly believes in the Big Data
imperative. IBM has invested USD24 billion to build its
capabilities in Big Data and Analytics through R&D and more
than 30 acquisitions. More than 15,000 analytics consultants,
6,000 industry solution business partners and 400 IBM
mathematicians are helping clients use big data to transform
their organizations.
Some key next steps may include:
• Explore what’s possible with today’s technology and data.
Consider looking to other industries for ideas
• Develop a comprehensive big data strategy and vision that is
compelling and exciting, focusing on top-line value and big,
imaginative ideas
• Create an operational blueprint and roadmap that shows how
capabilities and value can be realized both now and over time
• Form a compelling and rigorous business case that puts real
dollars — both invested and earned — against the new
capabilities
• Consider building a proof of concept that can work as a test
case for larger expansion, serve as a learning exercise, and
demonstrates the value to the organization

15. IBM Global Business Services 15
Conclusion
The aerospace industry is changing, and that’s a good thing
if you are the one driving the change. Big data can be a game
changer as aerospace OEMs transform their traditional design-
make-sell model into one of support-learn-enrich for their
clients. Capitalizing on data can make the entire ecosystem more
intelligent; going forward it can be a powerful tool to create
advantage, differentiation and dollars to the OEM’s core business.
Early movers will be the shapers of the big data analytics-
empowered ecosystem and will get to dictate how others
participate. Like always, it’s the winners that get their first.
Authors & Contributors
Timothy J. Wholey
Partner
IBM Global Business Services
Aerospace & Defense Industry
North America
twholey@us.ibm.com
Greg Deabler
Associate Partner
Industrial Sector Strategy & Analytics
IBM Global Business Services
gdeabler@us.ibm.com
Martin M Whitfield
Supply Chain Management & Aftermarket Leader
Aerospace & Defense Industry
IBM Sales & Distribution
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16. Please Recycle
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1 ICF SH&E, ACAS September 2013
2 http://centreforaviation.com/analysis/record-airline-fleet-order-
backlog-has-winners--losers---and-questions-about-how-it-will-
be-funded-152713
GBW03316-USEN-00