HP's annual publication of Megatrends - a look at the disruptive social, economic, demographic, technological and industry forces shaping the world over the next 5, 10, 15 and 20 years. This year's Megatrends are anchored in an assessment of Global Economic Segmentation, where's the money in the world in terms of peoples' income, where's it headed, and what are the implications of changing demographics, money and technology on how people live, work and all things in between.

1. HP ANNUAL
MEGATRENDS -
2019
March 2019

2. WHAT ARE MEGATRENDS?
RAPID
URBANIZATION
CHANGING
DEMOGRAPHICS
HYPER
GLOBALIZATION
ACCELERATED
INNOVATION
• People Moving to
cities
• Smaller, Shared
spaces
• New Business Models
• Sustainability
• Aging Population
• Shrinking & Aging
Workforce
• Gen Z
• Silver Spenders
• Everything Connected
• Digital platforms
• Cyber-physical
ecosystems
• Disrupt or be disrupted
• Security paramount
• Everything Smarter
• Automated
• Personalized
• Cheaper, Faster, More
Accessible

3. GLOBAL INFLUENCES
AND
LOOKING AT
TO IDENTIFY
RELEVANT
MEGATREN
DS
THROUGH
THE LENSES OF
THEMES
DISRUPTIVE
TECHNOLOGIES

4. 2019 MEGATREND THEMES
ECONOMIC
IMPACT
RISE OF
ASIA
DISRUPTIVE
TECHNOLOGIES
Ultra-Efficient Compute
Architectures
Software 2.0 Virtual Machines
JOBS AND
LABOR
EDUCATION AND
RESKILLING
ENERGY AND
SUSTAINABILITY
CLOUD TO
THE EDGE

5. MEGATREND IMPLICATIONS FOR BUSINESS IMPACT &
FY’19
PEOPLE, TALENT,
WORKFORCE &
CULTURE
TOOLS,
PROCESSES &
OPERATIONS
PRODUCTS AND
CUSTOMERS
GTM & BUSINESS
MODELS
IMPACT AREAS
CONSIDER IMPLICATIONS FOR YOUR BUSINESS
• Customer & Market Segmentation
• TAM
• Geography – Location-based Differences
• Automation & Productivity

6. ECONOMIC
SEGMENTATION ECONOMIC
SEGMENTATION
MORE THAN 80 DATA
SOURCES AND >10
ECONOMISTS CONSULTED &
REFERENCED

7. INCOME & PERSONAL CONSUMPTION DRIVES THE
ECONOMY
COMMERCIAL
SPENDING
CONSUMER
CONSUMPTI
ON
COMMERCIAL
INVESTMENT
Upwards of 2/3rds of GDP is driven by consumer spending, largely driven by income
DISPOSABLE INCOME – LEADING METRIC OF WHERE THE MONEY IS AND WILL BE
CONSUMER
DISPOSABLE
INCOME &
SPENDING

8. PURCHASING POWER (PPP) PROVIDES APPLES-TO-APPLES COMPARISON
ACROSS MARKETS
$USD Equivalent
In Purchasing Power
Parity (PPP)
$35k
SEPARATED GROWTH INTO “HAVES” & “HAVE-NOTS”
Sources: Oxford Economics with HP Analysis; Numbeo.com; California Association of Realtors
Different amounts of money have the same Purchase Power Parity or PPP in different places around the
world
HAVES
HAVE
NOTS
$6.54
$5.58
$5.51
$4.74
$4.52
$4.40
$4.28
$4.23
$3.74
$3.51
$3.10
$2.50
$2.32
$2.19
$0.00 $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00
Switzerland
Sweden
USA
Germany
Australia
Brazil
Singapore
Britain
Poland
Japan
China
India
South Africa
Indonesia
Sources: Statista
THE ECONOMIST: BIG MAC INDEX
USA
How Much Local Currency?
$ 45.1K Iceland
$ 31.9K Japan
$ 19.9K Indonesia
$ 9.5K India

9. 8% 10%
43% 38%
33%
32%
16% 20%
1980 2016
RISING INCOMES FOR NEARLY ALL
RISING INCOMES SHOULD DRIVE ADDRESSABLE MKT OPPORTUNITIES ACROSS
THE WORLD
Source: World Inequality Database; Oxford Economics; HP Analysis
GLOBAL SHARE OF
DISPOSABLE INCOME BY
INCOME BRACKET
Incomes are rising throughout the world
Top 2-10%
Top 1%
Middle 40%
Bottom 50%
+4%
-1%
-5%
+2%
$71K
$17K
$62K
$37K
$56K
$82K
$46K
$39K
APJ
$32K
AMS
EMEA
2001 2018 2035
AVERAGE HOUSEHOLD DISPOSABLE INCOME, BY
REGION
Thousands, US$ PPP, Constant 2015 prices
, BUT ALSO GREATER INEQUALI

10. HOW MUCH INCOME IS REALLY REQUIRED TO BE A
CUSTOMER?
EXAMPLES OF PURCHASE POWER PARITY (PPP)
(EMEA) - Riga, Latvia
$20,916 PPP Income / Year
Equivalent $11,817 USD
28 year old shop owner and 30 year old
PR consultant with 1 year old child Hom
e
KitchenPC
Source: https://www.gapminder.org/dollar-street/about
(EMEA) Lagos, Nigeria
$8,700 PPP Income / Year
Equivalent $2,922 USD
30 Something architect & fashion
designer and their son
Andrejevi family
Laoye family KitchenHom
e
Favorite Item
Chandra famil
y
(APJ) Near Jakarta,
INDONESIA
$4,788 PPP Income / Year
Equivalent to $1,445 USD
Freelance Graphic Designer w/ Wife & 2
Children
KitchenHome Favorite Item

11. HAVE &
HAVE-
NOTS
Where is the money and
where will it be?
Have & Have-Nots

12. Wide variance even within developed & emerging markets
HAVES & HAVE-NOTS VARY ACROSS MARKETS
Source: Oxford Economics Sept 2018 – Country level Household Income bands changes to 2035, HP Analysis
USA
+24.4M
-5.9M
CHINA, INDIA AND SOUTHEAST ASIA LOOK THE MOST PROMISING FOR GROWTH
Spain
+2.3M
-0.4M
Israel
+0.8M
+0.1M
JAPAN
+0.9M
-0.7M
BRAZIL
+12.3M
+14.6M
NIGERIA
+12.9M
+19.6M
INDIA
+152.0M
-8.0M
CHINA
+279.2M
-129.0M
INDONESIA
+35.4M
-11.9M
DEVELOPED
EMERGING
+18%
-4%
+12%
-2%
+15%
+17%
+20%
+30%
+38%
-2%
+42%
-14%
+49%
-23%
+29%
+3%
+2%
-1%
Change in have households #
Change in have-not
households
#
%
%
Norway
+0.5M
-0.1M
+20%
-2%

13. Change in Have & Have-Nots from 2010 to 2035
HAVES GROWING EVERYWHERE, MIXED HAVE-NOTS
PICTURE
Source: Oxford Economics and HP Analysis
NA
+66M
-8M
LARGEST GROWTH OPPORTUNITIES OVER THE NEXT DECADE POINT TO ASIA
+34%
-4%
LATA
M+39M
+24M
+21%
+13%
EUR
+67M
-3M
APJ
+585M
-141M
+42%
-10%
MEA
+78M
+167M
+18%
+38%
+18%
-1%
Change in have households # Change in have-not
households
#% %

14. LARGE AND SMALL CITIES SHOW PARALLELS
Source: Oxford Economics 2018 – Country level Household Income bands to 2035, HP
Analysis
CITY GROWTH POINTS TO LARGER ‘UNTAPPED’ OPPORTUNITIES
DEVELOPED
EMERGING
BRAZIL
+1605K
+798K
+63K
+223K
São Paulo Natal
NIGERIA
+3362K
+2059K
+256K
+192K
Lagos Benin City
INDIA
+3717K
-410K
+1804K
-322K
Delhi
(25M)
Surat
(4.4M)
INDONESIA
+4818K
-2245K
+203K
-145K
Jakarta Makassar
(1.3M)
CHINA
+8368K
-4417K
+472K
-219K
Beijing (21M) Xiamen
(3.5M)
USA
+312K
-118K
+161K
-10K
Chicago Kansas
City
GERMAN
Y
+199K
+69K
+5K
-16K
Hamburg Essen
POLAND
+552K
-315K
+58K
-47K
Warsaw Poznan
JAPAN
+680K
+708K
+3K
-50K
Tokyo
(32M)
Shizuoka
(700k)
+9%
-3%
+17%
-1%
+11%
+4%
+1%
-3%
+40%
-23%
+25%
-20%
+4%
+4%
+1%
-10%
+19%
+9%
+9%
+33%
+41%
+25%
+36%
+27%
+55%
-6%
+74%
-13%
+49%
-23%
+55%
-40%
+74%
-39%
+56%
-26%
Change in have households # Change in have-not
households
#% %

15. DIFFERENT AT CITY LEVELS EVEN IN THE SAME
MARKETS
Source: Oxford Economics 2018 – Country level Household Income bands to 2035, HP
Analysis
CITY GROWTH POINTS TO LARGER ‘UNTAPPED’ OPPORTUNITIES
DEVELOPED
EMERGING
BRAZIL
+1605K
+798K
+63K
+223K
São Paulo Natal
NIGERIA
+3362K
+2059K
+256K
+192K
Lagos Benin City
INDIA
+3717K
-410K
+1804K
-322K
Delhi Surat
INDONESIA
+4818K
-2245K
+203K
-145K
Jakarta Ujung
Pandang
CHINA
+8368K
-4417K
+472K
-219K
Beijing Xiamen
USA
+312K
-118K
+161K
-10K
Chicago Kansas
City
+9%
-3%
+17%
-1%
Spain
+209K
-107K
+44K
-15K
Barcelona Zaragoza
+11%
-6%
+12%
-4%
Norway
+156K
-11K
Oslo
+22%
-2%
JAPAN
+680K
+708K
+3K
-50K
Tokyo Shizuoka
+4%
+4%
+1%
-10%
+19%
+9%
+9%
+33%
+41%
+25%
+36%
+27%
+55%
-6%
+74%
-13%
+49%
-23%
+55%
-40%
+74%
-39%
+56%
-26%
Change in have households # Change in have-not
households
#% %
Israel
+434K
+52K
+65K
+7k
Tel Aviv Jerusalem
+32%
+4%
+26%
+3%

16. MEGACITIES AND CITIES OF ALL SIZES ARE GROWING
GROWING INCOMES MAY LIKELY TO IMPACT TAM & GTM BEYOND TIER 1
CITIES
Source: Oxford Economics & HP Analysis
GLOBAL AVG HOUSEHOLD DISPOSABLE INCOME
BY CITY SIZE
CAGR 2018-2030 and $US PPP
MEGA
(10M+ people)
LARGE
(5-10M people)
SMALL/RURAL
(<1M people)
MEDIUM
(1-5M people)
Note: City sizes based on 2030 population projections
$41k
$64k
$66k
$71k
1.8% 3.2% 2.0%1.4%
CAGR%
(’18-30)
>100K
locations
434 cities 131 cities 49 cities
Avg HH
Disposab
le
Income
in 2030
Income
Growth
Globally by
2030
$8.1T$9.1T$6.0T$20.0T $23.2T

17. $136
$111
$116
$54
$28
$65
$57
$75
$57 $61
$29 $33
$45
$82
$73
$54
$79
$68
$137
$145
$60
$29
$75
$63
$87
$67
$74
$38 $42
$90
$161
$132
$158 $160
2018 2035
Source: Oxford Economics (Nov 2018 Data), HP analysis
1 Constant 2015 Prices
AVERAGE HOUSEHOLD DISPOSABLE INCOME IN THOUSANDS
US$ PPP1
ARE SOCIO-ECONOMIC / GEO-CHANGES BEING FACTORED FULLY INTO LONG-
TERM PLANNING?
Average household income in Jakarta forecast to pass many developed cities throughout the world by 2035
LARGE & MEGACITIES IN ASIA WILL RISE TO SIGNIFICANT
LEVELS
AMS EMEA APJ
$170$172

18. CONSIDERATIONS FOR
IMPACT TO YOUR
BUSINESS
• Look to cities as markets, instead of
countries or regions
• Consider that localized customer needs
and products may rise in importance

19. RISE OF
ASIA
Dramatic income
growth with some
wild cards
Rise of Asia

20. INCOMES RISING
ESPECIALLY
IN ASIA
DISPOSABLE INCOME
Asia will account for 2/3rds
of global net increase from
2018 to 2030
GROWTH IN CITIES
Disposable incomes
expected to grow 2x by
2030 in Asian cities over 1
million people
TWO
THIRD
S
2X
WHERE ARE THINGS IN THE WORLD HEADED? IT REALLY LOOKS LIKE ASIA

21. Like rest of world, Megacities are growing rapidly but so are the large and medium sized cities
CITIES IN ASIA WILL DOMINATE GLOBAL INCOME GROWTH
$5.8T $6.3T
$3.5T
$1.5T $0.7T
$1.9T
$0.8T
$2.1T
$0.6T
Megacities (10M+) Large cities (5-10M) Medium cities (1-5M)
AMS
EMEA
APJ
$8.1T $9.1T $6.0T
LOCALIZED CUSTOMER NEEDS & PRODUCTS MAY RISE IN IMPORTANCE
Source: Oxford Economics and HP analysis
INCREASE IN HOUSEHOLD DISPOSABLE INCOME IN CITIES OVER 1 MILLION PEOPLE, 2018-30
Trillion US$ PPP, Constant 2015 prices
APJ
67%
EMEA
18%
AMS
15%
$23.2T
(~780 cities)
Cities over 1M
(30 cities) (97 cities)
(164 cities)
(12 cities)
(7 cities)
(16 cities)
(18 cities)
(104 cities)
(166 cities)

22. 2035
RADICAL TRANSFORMATION OF ASIA SINCE 2010, WITH GREAT HH LEVEL
INCOME BY 2035
20182010
AUSTRALIA & NEW ZEALAND
Have Have nots
AVG DISPOSABLE INCOME
Circle size represents population size
CITIES ACROSS KEY PARTS OF ASIA - DRIVING THE
GROWTH

23. Belt & Road Initiative (BRI)
Commitments to 69 countries
(more $ than from the World
Bank)
Source: UN, Oxford Economics, Center for Global Development, and HP Analysis
China’s continued growth is a key influencer & question on broader economic growth in Asia and parts of
EMEA
Globally in VC
funding to start-ups
in Q2’18
$8T
2ND
#1
Highest number of
middle class
households globally by
2030
Highest number of
affluent households
globally by 2030
#1
<3YRS
For world’s fastest growing
start-up ecosystem – ex.
Pinduoduo (PDD) passed
JD in active users in 2017
THE CHINA FACTOR
Of Asia’s household
disposable income by
2030
44%
CHINA’S CHANGING POSITION HAS IMPLICATIONS IN CHINA, ASIA AND BEYOND

24. LABOR,
JOBS &
WAGES
Labor, Jobs & Wages

25. SUSTAINING THIS GROWTH REQUIRES INCREASED
PRODUCTIVITY
OVERCOME LABOR
SHORTFALLS
INVEST IN
AUTOMATION
RESKILLING &
EDUCATION
How do we do that?

26. GROWTH IN HAVES DRIVEN BY LABOR, JOBS & WAGES
By 2025, shrinking working age population drives rising global labor gap for high skill workers
~3/4ths of incomes globally
are driven by salaries &
wages
(e.g. from peoples’ jobs)
~2/3rds of global GDP driven
by consumer spending,
almost all coming from
incomes
CHANGING DEMOGRAPHICS DRIVE
GLOBAL HIGH SKILL LABOR GAP
RISING WAGES DRIVEN BY ECONOMIC GROWTH AND GROWING LABOR GAPS
RISE IN INCOMES DRIVING GROWTH OF
HAVES
Net negative change in working
age population compared to total
population by 2050
(0.56B)
Source: Korn Ferry, , Future of Work, The Global Talent Crunch & The Salary Surge
900X
Alphabet, Inc’s current workforce
needed to fill 2030 global labor
gap
High skilled workers shortage
by 2030 est. to 16% GAP to
demand
~85M

27. EXAMPLE OF LABOR GAP AND RISING WAGES
Singapore is a one of 20 major markets expected to have extreme labor gaps and wage inflation
Sources: Oxford Economics & HP Analysis; Korn Ferry, Singapore Ministry of Manpower and Population Pyramid
Working age population is 15 to 64 years; Entry age population is 20 to
24 years
EXAMPLE: SINGAPORE WORKER SHORTAGE
GROWS >3x
Rising GDP & slower working age popul. growth drives
labor gap
0
450000
900000
1350000
1800000
2250000
2700000
3150000
3600000
4050000
4500000
4950000
3.5
4.0
4.5
5.0
5.5
6.0
6.5
2010 2015 2020 2025 2030
Working Age Population
Total Population
GDP
Population(Millions)
GDP($’000)
265k person gap in
high skill workers today
1,100k person gap in
high skill workers by
2030
Avg. Wage Premium annually per high-
skill worker in Singapore by 2030
(on top of inflation)
Source: Korn Ferry, Singapore Ministry of Manpower
$29K
>3
x
SINGAPORE IS NOT UNIQUE AMONG MAJOR MARKETS FOR LABOR & WAGE RISK
Workforce Cost Illustration
100 Employees
$80k Avg Salary + Benefits per employee
$8M Total Salary & Benefits
$29k Avg Wage Premium per year
$109k Avg Salary & Benefits per employee
$10.9M New Total Salary & Benefits
+$2.9M Incremental Cost per year

28. GLOBAL SHORTAGES IN HIGH-SKILL LABOR APPEAR BY
2025
GLOBAL CORPORATIONS LABOR AVAILABILITY & AFFORDABILITY INCREASINGLY
AT RISK
GAP driven by combo of changing demographics, growing economies & tightening immigration
16%
Est. Gap in High-skilled
workers within Tech,
Media & Telco Sector
By 2030 . . .
India - only major
economy with a high-
skilled labor surplus
est.
Surplus
Deficit
Less than 0.6M
Significant Deficit
0.6M to 1.2M
Acute Deficit
Over 1.2M
GLOBAL HIGH SKILL LABOR DEFICIT BY ECONOMY (2030)
Tech, Media & Telcom Sector - top 20 markets evaluated to date
Est. $ Annual Wage Premium by 2030 driven by Labor Gap
Source: Korn Ferry (2018), HP Analysis
$29.1k (Singapore)
HP CONFIDENTIAL

29. AUTOMATION ALONG WITH EDUCATION & RESKILLING
WILL BECOME AN INCREASINGLY IMPORTANT TO
OFFSET LABOR GAPS
AUTOMATION CAN ADDRESS LABOR GAP BUT REQUIRES EDUCATION &
RESKILLING
AUTOMATION POTENTIAL DIRECTLY
LINKED TO TYPE OF HUMAN TASK –
CROSS INDUSTRY PROFILE
19%
16%
17%
12%
16%
14%
6%
81.0%
69.0%
64.0%
26.0%
20.0%
17.0%
18.0%
0% 20% 40% 60% 80% 100%
Predictable physical work
Data processing
Data collection
Unpredictable physical work
Stakeholder interactions
Applying expertise
Managing others
Automation Potential, % of Time
Time Spent By Activity
Source: World Economic Forum, Bain, McKinsey
VIRTUAL AGENT EXAMPLE OF
AUTOMATION
vs
95% savings via virtual
chat
= 2X
increase in
likelihood of solving
problem
increase in
complexity referred
to humans
40%
Source: World Economic Forum, 2018, Future of Work Survey
IT SECTOR
Retraining for
50% of
Workforce by
2022
12%
8%
10%
10%
10%
50%
Less than 1 month
1 to 3 months
3 to 6 months
6 to 12 months
Over 1 year
No Reskilling needed
EST. RETRAINING BY % OF WORKFORCE

30. RECOMMENDATIONS
Re-examine market & customer segmentation
Evaluate impact on total addressable market
opportunity
Review implications for GTM strategy and coverage
Examine long-term workforce & location strategy
ECONOMIC SEGMENTATION

31. ENERGY AND
SUSTAINABILIT
Y

32. GROWTH IN “HAVES” DRIVING INCREASING ENERGY
DEMAND
ECONOMIC GROWTH DRIVES USE OF MODERN CONVENIENCES AND MORE DATA
Source: European Environment Agency, 2014
Energy Use
(Tonnes of Oil
equivalent per capita)
GDP per Capita
More HAVES as Pct of
Population
Fewer HAVES as Pct of
Population

33. 300 NEW POWER
PLANTS
CAUSE POWER DOUBLING OVER 15
YEARS
INDIAN AIR CONDITIONER
GROWTH
Photo credit: Brett Cole
INDIA IN 2018
INDIA IN 2035
Haves = ~300M
Haves = ~700M
Electricity footprint
= 300
GIGAWATTS
Electricity footprint
= 1,000
GIGAWATTS
New capacity is ~70%
USA energy generating
capacity
(2x current supply)
RISING MIDDLE CLASS APPETITES DRIVING UP ENERGY DEMAND
EXAMPLE: INDIA’S GROWING HAVES DRIVING UP ENERGY
NEEDS

34. GROSS DOMESTIC PRODUCT AND ELECTRICITY USE GROWTH RATES FROM 2011-2015
Percent per year (five-year average)
-4%
-2%
0%
2%
4%
6%
8%
10%
Source: US Energy Information Administration
UNITED
STATES
UNITED
KINGDOM
JAPAN CHINA INDIA EGYPT BRAZIL
Gross Domestic Product
Electricity Use (ENERGY)
EMERGING MARKETS FOCUSED ON ENERGY, NOT YET
REACHED DEVELOPED MARKET EFFICIENCY &
SUSTAINABILITY LEVELS
EMERGING MARKETS CHASING SUPPLY GROWTH, NOT YET FOCUSING ON
Emerging Market ExamplesDeveloped Market Examples

35. CLOUD TO
THE EDGE

36. NEW COMPUTE MODELS ARE REQUIRED TO LIMIT DATA TRANSMISSION COSTS
& CONSTRAINTS
7 8
25
33
42
55
71
92
119
154
32
58
20232018
32
2019 20212020
4
2022
97
75
5
2024 2025
27
35
45
126
163
1 Zettabyte = 1,024 Exabytes
1 Exabyte = 1,024 Petabytes
ANNUAL DATA GENERATION (ZB)
Available IP Bandwidth
Trapped Data
IDC, Data Age 2025, April 2017
Cisco, The Zettabyte Era: Trends and Analysis, June 2017
The Megawatts behind Your Megabytes, EnerNOC Utility, 2012
ENERGY COST OF 2025 DATA MOVEMENT
“~5 kWh and $0.51 of energy for each GB transmitted”
Extrapolated cost to
transmit 2025
generated data
By reference –
in 2017 the
world produced
$92T 835
PetaWhr
Electricity
$81T
GDP
26
PetaWhr
Electricity
GROWING HAVES COMBINE WITH DEVICE PROLIFERATION
TO DRIVE EXPLOSION OF DATA AT EDGE

37. STATE-OF-THE-ART JET ENGINES
Pratt & Whitney uses 5,000 sensors producing
10GB of data per second to achieve a 16% fuel
efficiency improvement and a 75% reduction in
noise.
TODAY - Amazon uses a semi-
trailer
truck to move 100 petabytes
of data to its cloud
Moving 163 zettabytes of 2025
data would take either 4.2 million
years or 1.6 million semi-trailer
trucks.
EQUIVALENT TO 25
MINUTES
OF DATA FROM PRATT &
WHITNEY TURBOFAN
FLEET
AMAZON WEB SERVICES
“Using conventional means of transferring data, it
will take you 26 years to move an exabyte to the
cloud.”
- Andy Jassy, CEO of AWS
CLOUD COMPUTE WILL MOVE CLOSER
TO DATA AT THE EDGE AS WILL
ANALYTICS
ANALYTICS ENGINES WILL NEED TO GET FAR MORE EFFICIENT TO OPERATE AT
THE EDGE

38. DF
FROM CLASSIC
COMPUTE
TO EDGE, IOT, & ML
Problems
• Data trapped at the edge
• Not optimized forAI/MLat the edge
• Not rich enough for new edge device experiences
Implications
• Explosion of new edge devices, beyond PC and phone
• AI enabled devices with data-driven services
• New players (e.g.Amazon, Microsoft)vying to own the
“platform” from edge to cloud
SHIFTING VALUE AND PROFIT POOLS TO DEVICE ENABLED SERVICES
SEPARATE CLOUD & DEVICE COMPUTE
MODELS
Results Sent to Devices
CLOUD MOVES TO CO-EXIST AT EDGE
Machine Learning
Runs in Cloud

39. CONSTRUCTION SECTOR
New techniques can drive
40% savings in materials &
transportation, and 32%
reduction in structure energy
loss
TRADITIONAL
MANUFACTURING
Largest single energy
sector and forecast to grow
another 22% through 2040
1/3
GLOBAL
ENERGY
USE
ADDITIVE
MANUFACTURING
Full life cycle benefits result
from making new kinds of
parts requiring a new 3D
design to print ecosystem
5-27%
GLOBAL
ENERGY
SAVINGS
AEROSPACE SECTOR
One lb of reduced aircraft
weight saves 100 lbs of fuel and
300 lbs of CO2 annually
5–25%
SECTOR
ENERGY
SAVINGS
4–21%
SECTOR
ENERGY
SAVINGS
MANUFACTURINGINDUSTRIESNEW TYPES OF PRODUCTS FROM ADDITIVE
MANUFACTURING COULD DRIVE ENERGY SAVINGS
FROM USE IN INDUSTRIES
END-TO-END LIFECYCLE BENEFITS OF 3D COULD DRIVE GLOBAL LEVEL VALUE

40. HOLISTIC CRADLE-TO-CRADLE APPROACH MAY DRIVE ENERGY
SAVINGS AND VALUE PROPOSITION IN 3D MANUFACTURING
ITSELF
3D ADDITIVE MANUFACTURING DRIVES PRODUCT LIFECYLE ENERGY SAVINGS
GENERATIVE
DESIGN
WAREHOUSI
NG
TRANSPORTATI
ON
FACTORY
LAYOUT
ENERGY FOR
PRODUCTION
UPCYCLE
Traditional
Design
Traditional
Design for 3D
Generative
Design for 3D
10kg 4kg 3kg
Dynamically provisioned factory via
local micro-grid
RECYCLE
USE IN
INDUSTR
Y

41. CONSIDERATIONS FOR
IMPACT IN YOUR
BUSINESS
• Shifting value and profit pools from
products to services
• Energy efficiencies in compute and
products will be needed to handle
increased consumption and explosion of
data
• Evaluate energy efficiencies in your
products, operations and supply chain
footprint

42. TECHNOLOGY
TRENDS
SOFTWARE 2.0
VIRTUAL MACHINES
ULTRA-EFFICIENT
COMPUTE
ARCHITECTURES

43. TECHNOLOGY DISRUPTIONS
Enable real time Machine Learning at the Edge
Increase energy efficiency of Edge analytics to differentiate HP
products
“CLOUD” COMPUTE MOVES TO THE DATA @ THE
EDGE
Drive emerging architectures and a new paradigm in software
NEW DATA RICH CYBER PHYSICAL COMPUTE
WORKLOADS
Predict & deliver efficiency across product lifecycle creating
value
LEAPS IN PRODUCT LIFECYLE EFFICIENCY
POSSIBLE
ULTRA-EFFICIENT
COMPUTE
ARCHITECTURES
SOFTWARE 2.0
VIRTUAL MACHINES
ENERGYEFFICIENCY
TECH TRENDS

44. EVOLUTION OF COMPUTE ARCHITECTURE
ULTRA EFFICIENT MACHINE LEARNING ACCELERATORS ARE NEXT GENERATION
OF COMPUTE
?
Time
Power-PerformanceEfficiency
LogScale
General Purpose
Machine Learning Accelerators
Gaming and ML
//1980 20202010 2030
10 1
10 4
10 5
10 6
10 9
10 8
10 7
GPU
CPU
Contextual, Machine Generated
KEY PLAYER:
SOFTWARE -
HARDWARE
ECOSYSTEM
Coalescence of
software around
hardware drives
successful adoption of
new compute
architectures

45. ULTRA EFFICIENT COMPUTE ARCHITECTURES
MACHINE LEARNING ACCELERATORS ENABLE OPPORTUNITIES ACROSS HP
PRODUCT LINES
Build Real Time ML
Systems
Enhance Current
Products
Create New
Products
(e.g. Micro Data Centers)
PRODUCT INNOVATION
(Examples)
DATA EXPLOSION AND
INTELLIGENCE AT THE EDGE
~1000X PERFORMANCE/WATT
compute efficiency need by 2025
ML ACCELERATORS THRIVE ON
NEW WORKLOAD
CHARACTERISTICS
Parallel
same task many
times
Memory
Heavy
requires high
compute
1
Noisy
ML tolerant to noise
2
CP
U
MEMOR
Y
CP
U
CP
U
3
~10X in compute
COMPLEXITY
RIGHTPRECISION

46. SW 2.0
Don’t write code
Train model on data
Tackle higher complexity
problems
Ties to edge compute trends
OPENS OPPORTUNITIES FOR DATA DRIVEN ENGINEERING
SOFTWARE 2.0: Data Driven
EngineeringValue is in the code  Value is in the data
SW 1.0
for every
1 BUG
10
LINES
10% effort on writing line by line
code
90% effort spent on error
conditions
Reaching complexity limits
Legacy approach to software
SW 2.0
PROS
ACTIONS
Reduce complexity, lower cost
Improved time to market and updates
New IP protection approach
New way to think
Need new skills/dev process
3D MJF:
100s of
Sensors
Machine generated data to ML
model for control of actuators
1000s of
Actuators

47. VIRTUAL MACHINES
“Digital Twins”
VIRTUAL MJF BUILT ON DOMAIN, DATA and
AI
OPTIMIZES PRODUCT LIFECYLE
MANAGEMENT: COST, SPEED AND
QUALITY
VIRTUAL MJF FACTORY ENABLES INDUSTRIAL GO-TO-MARKET AT
SIMULATE
PREDICT
OPTIMIZE
Drive scale by
simulating
deployment
Lower cost
product
development
Faster time to
market
Drive real-time efficiency at
runtime
DOMAIN THEORIES
Dynamics of Heat, Fluids, Solids, Materials
MACHINE GENERATED DATA
ML BASED MODELS
MODELLING AND METROLOGY
• Design of Experiments
• Process capability index
Fusion Heaters
(Joules/sec)
Overal Heaters
(Joules/sec)
Agents
(grams/se
c)
Temperature Sensor
(oC)
Visual Sensor
(Pixels)

48. HOW HP TURNS RESEARCH INTO ACTION
THEMES
PEOPLE,
TALENT,
WORKFORCE
& CULTURE
TOOLS,
PROCESSES &
OPERATIONS
PRODUCTS
AND
CUSTOMERS
GTM &
BUSINESS
MODELS
IMPACT AREAS
RAPID
URBANIZATION
CHANGING
DEMOGRAPHICS
HYPER
GLOBALIZATION
ACCELERATED
INNOVATION

49. WHAT YOU CAN DO
• Look to cities as markets, instead of countries or
regions
• Consider that localized customer needs and products
may rise in importance
• Explore new markets in APJ, particularly in Southeast
Asia
• Think about how labor shortages might effect your
workforce, and your customers
• Consider how labor markets are changing in the areas
where your employees are located
• Examine ways that you could use automation and
reskilling to combat these shortages before they impact
your business
• Evaluate energy efficiencies in your products,
operations and supply chain footprint
• Accelerate adoption of 3D design-to-print ecosystems

50. THANK
YOU

51. MEGATREND ASSETS &
RESOURCES1. Learn the Basics with Online Training – Class ID : 02012164 on the HP Portal (SABA)
• Online training available via HP Sales Central Tool to all HP sales people and external partners.
2. Leverage the HP Innovation Journal – Annual Megatrends Issue (Contact Mei Jiang)
• Available on HP.com at https://www8.hp.com/us/en/hp-labs/innovation-journal-issue8/tsr-megatrends.html
3. Access Previous in-Depth Annual Megatrends Reports (Contact Otilia Barbuta)
4. Find General Megatrend videos (English) here:
• Megatrends Overall – https://youtu.be/oJCBBL1FBw0
• Rapid Urbanization – https://youtu.be/VLdlu6z8P1A
• Changing Demographics – https://youtu.be/TpBGqPF9j3M
• Hyper-Globalization – https://youtu.be/K-y_37TwOaE
• Accelerated Innovation - https://youtu.be/IcqlN-kA-7s
4. Leverage Megatrends videos
• Videos available in English, Spanish, Portuguese/Brazilian, German, Spanish, Italian, Korean, Japanese, Chinese
5. Encourage your team members to become a Megatrends Ambassador (Contact, Otilia Barbuta)
6. Promote Megatrends HP Site wide talks as they come to your location (Contact, Otilia Barbuta)
7. CONTACT the CTO Megatrends team for Strategic Briefings and Trends-to-Strategy
WORKSHOPS with your teams (Contact, Otilia Barbuta and Andrew Bolwell)

52. Q&A