STRATEGIC CHOICES AND THE
NEXT GENERATION OF IIOT USER
EXPERIENCE
John Schaefer
SVP & GM ThingWorx
2016-Dec-01
PROJECTED GROWTH IN CONNECTED DEVICES IS STAGGERING
500
Billion
units
J Chambers 500
200
Intel 200
100
Hammersmith 100
80
Huawei 100
Mogan Stanley 75
60
Cisco 50
40
20
0
Dell 50
Strategic Analytics 33
IDC 30
Goldman Sachs Research 28
ABI 28 Ericson 28
Gartner 21
IDC 13
7.8
2020
Machina 25
8.5
World population
2030
CB Insights, Sigfox, IDC (2015,2016), Gartner (2014, 2015), Morgan Stanley (2013, 2014), Machina Research (2015), Strategy Analytics (2014), Ericsson (2015), Dell (2014, 2015),
Intel (2016), Huawei (2015), Cisco (2014), J Chambers (2015, former CEO and Executive Chairman of Cisco), The Hammersmith Group (2010), UNDP Databases 2016
2
THE IOT WAVE IS GROWING
IoT Platforms
HC 2016
13
Exabytes
IoT in HC 2014 & 2015
IoT Data
IoT Funding
Cumulated $ Billions
Hype Cycles
12
11
10
450
IoT Captured Data
IoT Transmitted Data
400
350
9
300
8
7
Broad IoT
6
250
200
5
4
150
3
100
Gartner Hype Cycles 2014, 2015, 2016
CB Insights, Industrial IoT vs. Broad IoT Funding, 2016 Q3
ABI Research, Edge Analytics 2015 AN-1914
2020
2019
2018
0
2017
50
2016
2016
2015
2014
2013
0
2012
1
2015
Industrial IoT
2014
2
3
IMPOSES A NEW PRODUCT ARCHITECTURE AND TECHNOLOGY
STACK
Digital PRODUCT CLOUD
Smart Product Applications
Rules/Analytics Engine
Application Platform
Product Data Database
Identity &
Security
COMMUNICATIONS
Network Communication
External
Information
Sources
Integration
with
Business
Systems
Physical PRODUCT
Product Software
Product Hardware
• The definition of a product has changed, they are now physical (mechanical),
smart (software), and connected (cloud)
• This requires companies to build and support an entirely new technology
infrastructure, the “technology stack”
4
ENABLES BREAKTHROUGHS IN BOTH DIFFERENTIATION AND
OPERATIONAL EFFECTIVENESS
4. Autonomy
3. Optimization
2. Control
1. Monitoring
Sensors and external
data sources monitor
the product condition,
environment, and
operation, and
alert/notify of changes
Software embedded
in the product or in the
product cloud controls
product operation and
personalizes the user
experience
Monitoring and control
capabilities enable
algorithms that
optimize product
performance and
perform diagnostic,
service, and repair
Combining monitoring,
control, and
optimization allows
autonomous product
operation, service, and
coordination with
other product systems
• Smart, connected products and the data they generate enable 4
new categories of capabilities, where each category builds on the
preceding
• These new capabilities enable companies to create both:
– Differentiation (e.g. New and unique value-added services)
– Operational Effectiveness (e.g. Monitor products to improve service
efficiency)
5
REDEFINES INDUSTRY STRUCTURE AND BOUNDARIES
Product
Smart
Product
Smart, Connected
Product
Product System
System Of Systems
Tractors
Farm Equipment
System
Tractors
Tillers
Farm
Equipment
System
Combine
Harvesters
Planters
Irrigation
System
Farm
Manageme
nt System
Platform
Field sensors
Irrigation
nodes
Irrigation
application
Seed Optimization
System
Farm
performance
database
Seed database
Seed optimization
application
Weather
Data
System
Rain, humidity,
temperature
sensors
Weather
maps
Weather
forecasts
Weather data
application
• Analysis using Michael Porter’s (Harvard Business School) Five Forces framework
defines the substantial impact on industry competition and profitability
• Smart, connected products not only reshape competition within industries but
expand industry boundaries (e.g. tractor industry to farm automation)
• The basis of competition thus shifts from the functionality of a discrete product to
the performance of the broader product system
6
Expand scope?
1
9
Sell data to outside parties?
Change the business model?
10
Which product capabilities to pursue?
8
Disintermediate distribution
or service channels?
Functionality: embedded in the
product vs. the cloud?
2
3
7
How to manage data rights
and access?
4
6
5
Open or closed system?
Technology development:
internal or outsource?
What data to capture?
7
CREATES NEW SOURCES OF DATA AND VALUE UNLOCKED
BY ANALYTICS
DATA SOURCES
• Data now stands with people,
systems and devices/machines, and
capital as a core asset
SMART, CONNECTED
PRODUCTS
DATA ON LOCATION, CONDITION,
USE, ETC.
EXTERNAL
ENTERPRISE
DATA ON PRICES, WEATHER,
SUPPLIER INVENTORY, ETC.
DATA ON SERVICE HISTORIES,
WARRANTY STATUS, ETC.
RAW DATA
BASIC
INSIGHTS
CONTROL AND
OPTIMIZATION
DATA LAKE
AGGREGATED RAW DATA
IN MULTIPLE FORMATS
SUCH AS USE
PATTERNS
THROUGH,
FOR EXAMPLE
SOFTWARE UPGRADES
THAT IMPROVE
PERFORMANCE
RAW DATA
ANALYTICS
DESCRIPTIVE
DIAGNOSTIC
PREDICTIVE
PRESCRIPTIVE
CAPTURE PRODUCTS’
CONDITION,
ENVIRONMENT,
AND OPERATION
EXAMINE THE CAUSES
OF REDUCED PRODUCT
PERFORMANCE OR
FAILURE
DETECT PATTERNS
THAT SIGNAL
IMPENDING
EVENTS
IDENTIFY MEASURES
TO IMPROVE
OUTCOMES OR
CORRECT PROBLEMS
DEEPER INSIGHTS
BUSINESS
CUSTOMER
• Companies unearth powerful insights
directly from the product or by
identifying patterns with new data
analytics tools
• Product data value increases
exponentially when integrated with
other data
• To better understand the rich new
data companies are also beginning
to deploy a “digital twin”
PARTNER
9
TRANSFORMS HOW COMPANIES DESIGN, MANUFACTURE,
OPERATE, SELL, SERVICE, AND SECURE PRODUCTS
Firm Infrastructure
Human Resource Management
Technology Development
M
a
Procurement
Inbound
Logistics
Operations
Outbound
Logistics
Marketing
& Sales
After-Sales
Service
n
i
r
g
• The new capabilities and data generated by smart, connected products is
transforming business functions across the Value Chain. For example:
– Product Development: Design enables evergreen products that can be
continually upgraded, often remotely
– Manufacturing/Operations: Industrie 4.0/Smart Manufacturing network machines
to automate and optimize production
– Service: Analytics can anticipate problems to enable predictive service, and
create revenue streams through new value-added services
10
TRANSFORMS PRODUCT DEVELOPMENT
Configurable
Engine
New Principles of Product Design
•
Design becomes a systems engineering problem with increased IT and R&D collaboration
•
Software-driven product variability enables new low-cost options
•
Design enables evergreen products that can be continually upgraded, often remotely
•
New user interfaces and augmented reality reduce the need for controls on the product itself
•
Continuous monitoring of real-world performance data enables ongoing quality management
11
TRANSFORMS MANUFACTURING/OPERATIONS
New Production Requirements and Opportunities
•
Manufacturing goes beyond physical production via a cloud-based system for continuous
product operations
•
Industrie 4.0 and Smart Manufacturing network machines to automate and optimize production
•
Shift from mechanical parts to software eliminates and simplifies physical components and
production steps
•
Reconfigure assembly processes so product design changes and customization can be
incorporated later, even after delivery
12
TRANSFORMS AFTER-SALES SERVICE
Urban Mobility Solutions
Elevator / Escalator Manufacturer
New Service Delivery Approaches
•
Technicians can diagnose problems remotely to enable one-stop service, and predictive
analytics can anticipate problems
•
Shift from reactive to proactive and remote service and potential to optimize or disrupt service
channels and providers
•
Augmented-reality-supported service increase service efficiency and effectiveness
•
Service expands to new value added services via the new data, connectivity, and analytics
available
13
REQUIRES A NEW ORGANIZATONIAL STRUCTURE
CEO
UNIFIED DATA
ORGANIZATION
IT
TRADITIONAL FUNCTIONS
NEW FUNCTIONS
R&D
MANUFACTURING
DEV-OPS
FINANCE
HUMAN
RESOURCES
MARKETING
SALES
SERVICE AND
SUPPORT
CUSTOMER
SUCCESS
MANAGEMENT
• Organizational structures are in rapid flux, but a number of important shifts are
becoming evident
–
–
–
–
IT & R&D Collaboration: Reflecting the new need for IT in product development
Unified Data Organization: Enterprise-wide data management and analytics
Dev-Ops: Ongoing product updates and efforts to shorten product-release cycles
Customer Success Management: Ensures customers gain ongoing value to
reduce churn
15
REQUIRES TRANSITIONAL MODELS
Model
Cross-Business
Unit Steering
Committee
Center of
Excellence
Standalone
Business Unit
Example
Description
Benefit
Risk
A cross-functional committee of
thought leaders across the various
business units,
who champion opportunities,
share expertise, and facilitate
collaboration
• Distributes input and
learning across the
organization
• Shared resources and
lack formal decisionmaking authority,
which can limit ability
to drive change.
A separate corporate unit houses
key expertise on smart,
connected products. It is a center
that business units can tap.
• Centralizes skills and
resources
• Can deter, rather than
enable, initiatives in
the individual business
units
A separate new unit, with profitand-loss responsibility, is put in
charge of supporting the
company’s smart, connected
products strategy and bringing
such new offerings to market.
• Centralizes skills and
resources
• Able to innovate
without the
organizational
baggage
• Knowledge acquired
may disseminate more
slowly across the firm
• Focus may be on
external opportunities
16
• Smart, connected products dramatically increase
opportunities for value creation and higher productivity
throughout the economy
– Create a whole new generation of lean, driving out
waste
THE LARGER
OPPORTUNITY
AND NEXT
STEPS
– Transform competition in many service industries, not
just in manufacturing
• This wave of innovation alters the nature of work,
creating new roles and training tools and reducing
others
• The impact of smart, connected products is still in the
early innings
• Organize a cross-functional executive workshop to
align on these concepts and identify and prioritize use
cases
17
Physical Experience
Digital Experience
Converged Experience
18
Data Growth
(ZETTABYTES)
2013
4.4
2015
8.5
2017
16.4
2020
44.0
10001010110
00011110100
01001111011
11110111011
01011000011
10110111011
11011011110
10000000000
11111101010
11010001110
01100010100
10011010101
01111101000
01100010101
10000111101
00010011110
11111101110
11010110000
11101101110
11110110111
10100000000
00111111010
10110100011
10011000101
00100110101
01011111010
00011000101
01100001111
Types of
Analytics
Big Data
Analytics
100010101100001111010001001111011111101110110101100001110110111011110110111101000000000011111101010110100011100110001
010010011010101011111010000110100101010110101010010100101010000000001010100101010101010000101010101010010101110101011
000110101001010100101010111101010001010101010101010110111110001010000111101010101011110101010101011000000111110101010
101010101010101101010101010111101000001000111100011111100011111010100101111100001110101110111101101011111100001110110
101111101011010101011001011010111101011110101010010111010101011101001101010101000011111111101001010101010101010110010
101010101010101101010111010101010101101111010010000010101010101010101010010101001011101101010101010101110101010101101
010101010110111010101011011010101010010101010000110101011011110001111010101111100000110101010101010101101011111001010
101001010101010101101010101010101010101010101001011010101001010010101000000000101010010101010101000010101010101001010
111010101100011010100101010010101011110101000101010101010101011011111000101000011110101010101111010101010101100000011
111010101010101010101010110101010101011110100000100011110001111110001111101010010111110000111010111011110110101111110
000111011010111110101101010101100101101011110101111010101001011101010101110100110101010100001111111110100101010101010
101011001010101010101010110101011101010101010110111101001000001010101010101010101011010101001010010101000000000101010
010101010101000010101010101001010111010101100011010100101010010101011110101000101010101010101011011111000101000011110
101010101111010101010101100000011111010101010101010101010110101010101011110100000100011110001111110001111101010010111
110000111010111011110110101111110000111011010111110101101010101100101101011110101111010101001011101010101110100111000
011101011101111011010111111000011101101011111010110101010110010110101111010111101010100101110101010111010010000111010
111011110110101111110000111011010111110101101010101100101101011110101111010101001011101010101110100110101011
“What Happened?”
Descriptive Analytics
“Why Did It Happen?”
Diagnostic Analytics
“What Will Happen Next?”
Predictive Analytics
“How to Improve Results?”
Prescriptive Analytics
19
What are coming changes that will impact HOW WE
EXPERIENCE AND INTERACT WITH OUR PHYSCIAL
ENVIRONMENT
HUMAN
INTERACTION
WITH OUR
PHYSCIAL
ENVIRONMENT
• This new era of IT encompasses the Internet of
Things (IoT), cognitive computing, augmented
and virtual reality (VR)
• Over time, we will move from Dashboards to real
time insights
• Use human ability to do cognitive recognition in
real time
– humans can comprehend visual images faster
than the written world.
– A PICTURE IS WORTH A THOUSAND WORDS
20
NEW WAYS TO INTERACT WITH SYSTEMS AND EXPERTS
• Transcend space and time:
– Look at video from another location while overlaying actual current readings over the
video
– Look at a time in the past when there was an alert or downtime event, and watch the
data playback leading up to the event, while watching video at the time of the event
• Deliver the right data to the right person at the right time, in a easy to consume
format
– Think of a machine service expert looking at the same visual, with actual data
readings, that the on-site technician is looking at
21
WHAT’S YOUR IMMEDIATE IMPRESSION OF THIS PICTURE?
22
OR THIS?
23
PSYCHOLOGISTS DISCUSS 2 MODES OF THINKING ABOUT VISUAL
CONTENT
• System 1 and System 2:
– System 1 operates automatically and quickly, with little or no effort
– System 2 allocates attention o the effortful mental activities that demand it – more
associated with choice or concentration, for example
• System 1 identified objects, but also uses experience – and memory to add
immediate context
– this is not just simple object recognition
– Generate and identify complex patterns
– System 1 then mobilizes System 2
• System 2 takes over when things get more difficult
• System 2 can construct an orderly series of steps to react
Source: Kahneman, Daniel. Thinking, Fast and Slow (p. 71). Farrar, Straus and Giroux. Kindle Edition.
24
• Maintaining a high level of effort using System 2 to monitor the state of a lot of
machines, for example, can also be tiring, resulting in being less effective
• But what if we make it easier, and make System 2 more effective, by enabling
System 1 with better weapons?
25
DIGITAL TWIN
EXAMPLE
• GE Transportation >20,000 Diesel Locomotives
worldwide. All diesel engines are serviced in
Grove City, PA
• 1,000 engines & 12,000 components per year
are serviced at this single facility
• https://www.youtube.com/watch?v=UEyv5CVF0g0
26