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www.mesa.org
Metrics That Matter: Digging Through the
Data in an IoT World
March 29, 2016
MESA Platinum Keystone Sponsors MESA Gold Keystone Sponsors
www.mesa.org
Presenter
Andrew Hughes
LNS Research Principal Analyst
Andrew.hughes@lnsresearch.com
About LNS Research - www.lnsresearch.com
Our Mission is
Driving Industrial Transformation
We are thought leaders and trusted advisors for Business, IT, and Automation executives
Our differentiators:
Experienced analysts
Primary social research
Deep industry contacts
Interactive data visualizations
LNS Research in Media
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Research Sponsors
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Overall LNS Research
Survey Demographics
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Research Demographics
• 211 Respondents: January, 2016 – March, 2016
• Compares well with long-term demographics
45%
28%
15%
12%
Geography
North America Europe
Asia/Pacific Rest of world
37%
48%
15%
Industry
Process Discrete Batch
49%
10%
41%
Revenue
Small: less than $250 million
Medium $250 Million - $1 Billion
Large: > $1 billion
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Digital Transformation
Framework
www.mesa.org
The Industrial Internet of Things
Platform
www.mesa.org
Initiatives, Forums,
and Associations
Slide #25
• Governments of U.S. and Germany have invested $1B+
• Smart Manufacturing Leadership Coalition (SMLC)
• Industry 4.0
• Industry Associations
• Industrial Internet Consortium
• IoT World Forum
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IT – OT Convergence
www.mesa.org
The Impact of IIoT
• 2015 lackluster interest, at best!
• 2016 – the world awakens – dramatic changes
4%
6%
9%
16%
21%
44%
8%
8%
13%
18%
33%
19%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
We understand and have already seen dramatic impact
We understand but see no impact at this time
we understand and our customer demands are drivingus
we understand/are aware and see value to ouroperators /customers or both
We are still investigating the impact
Do not understand or know about IoT
Please indicate how the IoT is impacting your business today
2016 2015
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Industrial IoT Adoption (2015)
• Majority of market is eventually expecting to Invest in IIoT, 34% in next year
Please indicate the nature of investment in IoT technology expected going forward
66%
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Industrial IoT Adoption (2016)
• Almost half of respondents expect near-term IoT investment
“Please indicate the nature of investment in IoT technology expected going forward”
48%
35%
2%
3%
8%
11%
18%
24%
We do not expect to invest in
IoT technologies in the
foreseeable future
We expect to start investing
in IoT in the next 12 months
but establishing budget
We do not expect to invest in
IoT technologies in the next
12 months
We have made significant
investment and expect it to
increase
We have established IoT
budget for investment within
12 months
We have made significant
investment and expect it to
stay the same
We have made significant
investment and expect it to
decrease in the future
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Smart Connected Operations
• Time series data is…
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Plant Level Analytics
Data Sources
0% 5% 10% 15% 20%25%30%35%40%45%50%55%60%65%
Data historian
MES or other high level software system
Individual controllers (PLC)
LIMS
Quality systems
HMI devices
Ethernet / IP connected devices
Individual "dumb" devices
Complex equipment with embedded
control
Individual smart devices
Offline performance monitors,
such as vibration
17 %
63 %
57 %
27 %
20 %
20 %
13 %
7 %
3 %
3 %
3 %
• Traditional MOM data sources are not changing yet
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The State of MOM
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Value Chain
Technology Architecture
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Traditional MOM Hierarchy
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MOM/MES Suite Deployment
• On-premise deployments still dominate industry
• Change is very slow (2015 MOM survey showed slightly different results but insignificant)
On-
Premise
Private
cloud
Public cloud
hosted by
software vendor
Public cloud
hosted by third
party
74%
21%
3%
3%
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Current MOM/MES Suite
Licensing Models
• Perpetual licensing models outnumber periodic and SaaS by a wide margin
70%
11%
19%
Perpetual
Periodic
SaaS
www.mesa.org
Planned MOM/
MES Deployments
• Planned deployments still tilt heavily toward on-premise
On-
Premise
Private
Cloud
Public Cloud
Hosted by
Software
Vendor
71%
25%
4%
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Planned MOM/MES Suite
Licensing Model
• Here there is a subtle but important change away from perpetual licensing. Hinting towards off-premise?
Perpetual
Periodic
SaaS
54%
29%
18%
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The Cloud View of MOM
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ERP Deployment Models
• Big changes coming!
• That is no surprise considering what big vendors are doing
4%
7%
25%
64%
11%
30%
25%
34%
0% 10% 20% 30% 40% 50% 60% 70%
Public cloud hosted by third party
Private cloud
Public cloud hosted by software vendor
On-premise
Which best describes your current and planned ERP deployment model?
Planned Current
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Perceived Cloud Impacts
• Lowered cost and increased speed to implement are top perceived impacts
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Manufacturing Software
Vendor Use
1%1%1%1%1%1%
2%2%2%
3%4%4%
5%6%6%
8%8%
11%13%
14%15%
17%21%
22%24%
25%36%
64%
0% 10% 20% 30% 40% 50% 60% 70%
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More Metrics Findings
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Industry Trends:
F&B, Automotive
• Safety and compliance initiatives top the list in both industries
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Industry Trends:
Chemicals, Life Sciences
• Regulation tops the list in both industries
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Operations Management
Metrics
• Companies focused on financials first and foremost
What types of manufacturing metrics does your company rely on for managing
your operations
% Total Respondents
0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 %
Financial / business focused metrics
Quality focused metrics
Efficiency focused metrics
Don't know
Customer & responsiveness focused metrics
Asset & maintenance focused metrics
Inventory focused metrics
Product metrics
Compliance & EHS focused metrics
Flexibility & innovation focused metrics
38%
25%
18%
15%
47%
34%
24 %
19%
15%
8%
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Operations Management
Metrics
• Solid improvement across many metrics
0 2 4 6 8 10 12 14 16 18
Net profit margin
Revenue per employee
manufatcuring cost per unit
Improvement in mfg cycle time
Improvement in production
First pass yield
Annual improvements 2014 -2015
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Capacity and Output
• Capacity utilization is inconsistent, median 71% (N=36)
• Range 5-100%
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Capacity and Output (Cont.)
• …Yet there is significant increase in capacity and output
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Data and More Data
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Data Form and Function
• “What is the main data being gathered about products sold and how is it used after sale?” (N=30)
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Post Sales Data?
What is the main data that is being gathered about products you sell and how they are used after
sale? N=30
0% 20 % 40 % 60 % 80 %
Quality inspection data
Product performance
OEE
Serial information (serial number or serialization
data) Supply chain performance
Measurements on tolerances
Condition monitoring
Actual maintenance (as maintained records)
Location
Predictive maintenance data
Usage rate
Call center data
63 %
30%
26%
26%
19%
15%
7%
7%
7%
7%
4%
11%
©LNS Research 2016
• Mixed set of leaders
• Maintenance and quality lead
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Analytics
• Do you have a corporate analytics program that uses manufacturing data?
• Only 14% said yes
• But use cases are getting interesting
www.mesa.org
How Are Analytics Used?
For suppliers to check quality, delivery, and related
For customers to check quality, delivery, and related
We will not share manufacturing data outside the enterprise
`Customer relationship management
For end users or customers to communicate with our enterprise
Product updates
For end user information gathering
For equipment providers' maintenance and quality processes
Product tracking and genealogy
% Total Respondents
0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 %
31 %
12 %
12 %
8 %
35 %
31 %
12 %
8 %
4 %
For suppliers to check quality, delivery, and related
For customers to check quality, delivery, and related
We will not share manufacturing data outside the enterprise
`Customer relationship management
For end users or customers to communicate with our enterprise
Product updates
For end user information gathering
For equipment providers' maintenance and quality processes
Product tracking and genealogy
% Total Respondents
0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 %
31 %
12 %
12 %
8 %
35 %
31 %
12 %
8 %
4 %
For what purposes outside the enterprise is cloud data used?
• Nice to see suppliers and customers at the top of the list
• Sharing manufacturing data outside the enterprise
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Analytics Expertise
From where does your company get or plan to get its analytics expertise?
% Total Respondents
0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 %
We have a strong analytics team that will not require much expansion
We use or will use large scale consulting companies with specialist industry
knowledge
Don't know - This is a potential stumbling
block
Don't know - We'll worry about this later
We plan to hire specialists in industrial
analytics
We will use expert consultants from our analytics software vendor(s)
17%
17%
10%
23%
13%
40%
• Lack of analytics expertise maybe not be as big an issue
as thought
www.mesa.org
Making Money from Information
How are analytics being monetized?
0% 5% 10% 15% 20% 25% 30% 35% 40%
Cross plant improvements
New service offerings
Service instead of product
(performance based on contracting)
Other
Sell data to clients
36 %
23 %
23 %
14 %
9 %
9 %
We do not monetize our analytics
Improved production efficiency
23%
• New business models or service offerings are still the
exception not the rule
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Summary
• Change in the MOM world is slow – we have known that for 30 years
• Traditional IT/OT models are showing signs of change, sometimes much quicker than most expected
• IoT hype has hit the shop floor – but only gently
• Manufacturers are doing pretty well
• IIoT is not hype – it is very real in manufacturing industries
• Plan to get on board – do not get left behind
www.mesa.org
THANK YOU!
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