Shepherd Network Ltd - Info Deck

Anomaly detection and predictive maintenance for assets & infrastructure
Information Slide Deck - February 2017
© Shepherd Network Ltd - Private & Conﬁdential
shprd & the ’S’ logo are ®. Shepherd’s technologies are patent-pending in the UK and internationally 1

The problem:
Maintenance is an enormous global burden but it can be significantly optimised - 55% of UK manufacturers are familiar with
the benefits anomaly detection & predictive maintenance data can bring and 2/3rds committed to a major automation
project within the last 2 years◦.
The problems are clear and businesses are keen to tackle them.
• The majority of corporate machinery assets are isolated, unconnected
equipment which are reactively maintained. This is inefficient & costly.
• GE’s study on predictive maintenance shows adoption can reduce reactive maintenance costs by 40% & overall
maintenance costs by 30% per year and McKinsey studies show it reduces downtime by up to 50%
• In situations where equipment is connected to data logging the majority of data is
not being analysed to optimise maintenance or improve operating behaviour.
• McKinsey estimates that the majority of asset monitoring data is used for manually-set threshold alarms and control: less than
1% is being analysed which offers a significant opportunity for optimisation & prediction
• Signiﬁcant sums of money are spent monitoring the wrong risks: e.g. UK
businesses spend £0.5bn monitoring fire & burglary systems which account for only
24% of losses. Proves a willingness to pay for monitoring if a product is available.
• Research by the ABI suggests that 80% of businesses that suffer a major equipment failure go into receivership within 18
months and subsequently UK business insure £315bn of turnover as a defence against unexpected interruptions
◦ “Annual Maintenance Report 2016”, The Manufacturer

3
1. Data from sensor logs or directly from
components is sent to Shepherd (e.g.
temperatures, pressures, flow rates,
consumption etc)
2. Shepherd runs anomaly detection
and predictive maintenance machine
learning processes
3. Anomalies are flagged, ranked for
severity and passed to the dispatcher
4. The dispatcher uses the
appropriate contact method
based on the severity of the
issue: instant message, email,
SMS, automated call and direct
into 3rd party alerting systems
5. Site staff, maintenance teams
and internal support systems are
rapidly informed to fix the
problems
Ƽǽ
Ɏ ȕ ǹ
ǈ
Shepherd’s solution:
Our expertise:
We have deep expertise in spotting anomalies in sensor data. Our focus is on time-series sensor data. We have built up an advanced
machine learning capability using many different algorithms and approaches that are ideally suited to this particular problem. We can
deploy our smarts on a wide range of different sensor types and use 3 core strategies:
1. Single sensor analysis of a pattern of fluctuating values over time
2. Single sensor analysis of the stop-start or open-close operation of a motor/gateway/valve etc and its associated run-time patterns
3. Multi-sensor analysis comprising the inter-relation between sensors that are co-dependent or whose operating profiles should be similar
Shepherd is a low-cost, low-risk solution aimed at companies and business units with turnover of £5m-50m for whom it is inefﬁcient to deploy machine learning solutions internally
Phase 1: anomaly detection
Phase 2: predictive maintenance
Phase 3: capex reduction
Shepherd’s Deliverables:
a platform that any business can connect to for anomaly detection & predictive maintenance
shprd & the ’S’ logo are ®. Shepherd’s technologies are patent-pending in the UK and internationally

4◦ “Annual Maintenance Report 2016”, The Manufacturer
Shepherd’s target customers:
Environment
Infrastructure
Service
• “Cold chain” auditing for food & medical supplies
• Protecting building integrity & security
• Humidity & Temperature monitoring
• Gas levels & air quality monitoring
• Plant & equipment performance analysis
• Run-time data from motors, pumps & engines
• Vibration, pressure & running temperatures
• Energy consumption data & run-rate
• Optimisation of maintenance services & planning
• Reduction in maintenance costs
• Increasing customer satisfaction
• Optimisation of risk profiling
Environmental Integrity:
• Food Industry
• Schools
• Hospitals
• Social Housing
• Museums
• Historic Buildings
• Construction Sites
Service Integrity:
• Maintenance providers
• Insurance
• Property Managers
• Hospitality
Our target customers:
• Have sensors operating as part of a standard machine/industrial
control environment
• Are small & medium-sized businesses for whom significant £1m
expenditure on cloud architecture and machine learning will not
yield good ROI but who could benefit from operational
efficiencies & cost savings (approximately £5-50m turnover)
• A clearly defined problem with the monitoring of an
environment, items of infrastructure or delivery of a service (e.g.
maintenance)
• Looking to implement business process & business model
transformation
• Are in the following fields:
Sector breakdown Characteristics
Infrastructure Integrity:
• Refrigeration
• Commercial Kitchens
• Manufacturing
• Transport
• Work Sites
• Oil & Gas

“UK plc”
5
UK Opportunity: a bottom-up view
THE MARKET
SIZE • £11.4bn - total UK maintenance spend per year
HOW THIS IS
SPENT
• 73% - reactive maintenance - £8.1bn
• 19% - planned maintenance - £2.1bn
• 9% - predictive maintenance - £1.0bn
• by the 2.55m businesses in the UK
THE
OPPORTUNITY
• GE Study finds adoption of predictive maintenance:
• reduces overall maintenance costs by 30%
• reduces costs of planned maintenance by 12%
• reduces costs of reactive maintenance by 40%+
IN REAL TERMS
• For “UK plc” this suggests
• reactive maintenance costs fall by £3.1bn per year
• planned maintenance costs fall by £256m per year
• a saving per UK company of £1,222 per year
Shepherd
EXAMPLE
OPPORTUNITY IN
FOOD
• 318,470 companies in UK food industry
• Shepherd market opportunity: £389m per year
EXAMPLE
OPPORTUNITY IN
BUILDING
MANAGEMENT
SYSTEMS
• BMS & A/C market comprises £1.95bn of annual spend
EXAMPLE
OPPORTUNITY IN
SOCIAL
HOUSING
• Top 100 Housing Assoc spend £2.54bn on maintenance annually
• £1.43bn (57%) is reactive, £665m (26%) is planned
TOTAL
OPPORTUNITY
• Subscriptions of £15,000 per year for SME & MSB’s with
aggregate maintenance costs of £200,000+. There are over
120,000 such companies in the UK alone.
• Average maintenance spend $1.50-2.00 per sq foot for
maintenance of interior systems in buildings across the
developed world offers dramatic scope for Shepherd to assist
a broad range of customers with one focussed product.
all data sources ONS, IBD, IBIS, Housing Insight, BSRIA et al & available by request
Target: 775 subscribers over 4 years from a total UK universe of 120,000+, not including larger corporate business units

$360 bn
per year
Ǹ
Worksites
6
Global Opportunity: a top-down view
$246 to $677 bn
per year
Ɏ
Factories $130 bn per year
Homes
$27 bn per year
Ofﬁces
$436 to $757 billion per year
ǈ
Cities & Infrastructure
Data from the definitive report on IoT value by
McKinsey&Company: “The Internet of Things:
Mapping the value beyond the hype”
Predictive techniques “can transform the
maintenance model from one of repair and replace
to predict and prevent [which] allows operators to
reduce maintenance and equipment costs and
increase productivity by reducing downtime.”
B2B will capture most value-share:
69% of the value is in use cases
where a business is the end-user of
the solution.
79% of the value is in use cases
where a business is the buyer of the
technology.
66% of the value for IoT applications
will be generated by businesses such
as factories, offices etc.
“For instance, less than 1 percent of the data being
generated by the 30,000 sensors on an offshore oil rig
is currently used to make decisions. And of the data
that are actually used - for example, in manufacturing
automation systems on factory floors - most are used
only for real-time control or anomaly detection. A great
deal of additional value remains to be captured, by
using more data, as well as deploying more
sophisticated IoT applications, such as using
performance data for predictive maintenance or to
analyze work flows to optimize operating efficiency.“ 
GE has shown predictive maintenance can reduce reactive maintenance costs by 40% & overall maintenance costs by 30% per year and McKinsey studies show it reduces downtime by up to 50%
All values are based on McKinsey’s projected worldwide economic impact by 2025

Market Feedback
“I’ve been working in the restaurant industry for more than 13 years. I truly believe
that Shepherd represents a great addition to the market as it will allow managers
and owners to have better control of activities and save money at the same time. I’d
be honoured to have a trial in my upcoming restaurant and suggest the software to
all my clients.”
STEFANO PORTOTI, CEO OF RESTAURANT CONSULTANCY, SAGITTER ONE
“The concept and idea that was shared with us was very interesting and I can see
that it will reduce the risk of error when recording data and will become an important
part of due diligence documentation. There is also the potential cost savings for
businesses, especially with regards to the use of paper diaries, and further savings that
could be identified once the system is installed and implemented.”
SANDRA MOORE, TECHNICAL DIRECTOR OF ENVIRONMENTAL HEALTH CONSULTANTS, HYGENISYS
“I am very impressed with what has been created by Shepherd, only wish I had
thought of it! I would be very keen to find a couple of sites where we can look at a
trial systems and am looking forward to including it into our ongoing service offering.
I look forward to working with you on this great service.”
ROB SUTHERLAND, MD OF LEADING SYSTEMS INTEGRATOR, INSPIRED DWELLINGS
“Having recently met with Will and his team, I am confident that the product shown
to me has a meaningful role to play in many market sectors.
I am particularly interested in developing Shepherds systems and support into at
least two market sectors I am involved with namely: a) High Class Residential
developments and b) Commercial property management”
RAY WOOD, MD OF COMMERCIAL BMS & FACILITIES MANAGEMENT COMPANY, NORWOOD CONSULTING
“For this price, we would only need to have one break-in a year to make the money
back on Shepherd, so I think it would be a brilliant idea, in terms of protection and
monitoring.”
SIMON KNIGHT, MD OF COMMERCIAL BUILDING SERVICES COMPANY, HAZELGROVE SERVICES LTD
“Further to our meeting I would like to thank you for the time you took to show me
the product. Being from a Security Installation and Manned Guarding environment I
am viewing this with many heads and can see that this would be a great asset to
those types of business.
MARK DEVEREAUX, MD OF SECURITY & MANNED GUARDING COMPANY, MPS
“We are interested in ML in general for real time data. Sensors on the rig site range
from 4 – 50Hz. When streamed back to town it is about 1 Hz. Particularly interesting
is changes in linear trends/ dysfunction. This is useful for us to know when something
has broken, or more importantly, if we can detect a change in formation (rock which
we are drilling). We are looking to see what would be the best approach for
anomaly detection and I liked what you showed me.”
NEILKUNAL PANCHAL, STAFF WELLS ENGINEER & DATA SCIENTIST, SHELL
Brilliant! I love this idea.”
ROGER THORNTON, PRINCIPAL HARDWARE ENGINEER, RASPBERRY PI

Smart business model
Software as a service works on a subscription business model which is recognised to be
highly advantageous:
• the service will provide gross margins of 70% and EBITDA margins of 25% + once scaled
• there is significant flexibility in pricing models: the incremental cost of serving each new
customer is low so pricing spans large corporate licenses, direct subscriptions and
customers who require low unit costs can still be served if they bring volume
• delivery is from a scalable cloud platform allowing for fixed costs to be kept low
• Shepherd delivers real-time insight: in the same way that auto-telematics revolutionised
car insurance, our building data supports more accurate under-writing
• secure by design: installer partners use whatever hardware they wish to send data up to
Shepherd via one-way, outbound streams. Shepherd does not need to configure on-site
equipment or to request reconfiguration of corporate firewalls to transmit data back in
• our customers’ data security is vital to us which is why we have engineered the
Shepherd platform on Microsoft Azure’s robust cloud platform. We are using the
encrypted EventHub structure and redundant storage to ensure reliability and safety.
• efficient route-to-market: leveraging the existing installer & maintenance communities
to avoid costly direct-to-consumer marketing or going directly to large, multi-site
corporates with their own maintenance teams
£subscription fees
Alarm
events
& rich
data
Provides real-time
alerts
Plant & equipment hardware
£subscription fees
End Customer
Maintenance Teams
• Software-as-a-Service: scalable & high margin
• Excellent revenue visibility from 1-2 year contracts
• Data sales: rich building data is highly valuable
• Hardware agnostic: limited development costs
• Efficient route to market: leveraging partners with
existing trusted end-client relationships

Customers, expenditure & income
2017 2018 2019 2020
Subscribers 10 46 198 6,074
New Subscribers in Year 10 36 152 4,758
Revenue £73,750 £570,000 £1,842,500 £7,944,903
Gross Margin negative 68% 79% 79%
EBITDA ◇ -£524,466 -£419,742 £347,603 £2,264,297
-£250,000
£0
£250,000
£500,000
£750,000
£1,000,000
0
45
90
135
180
Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018 Q1 2019 Q2 2019 Q3 2019 Q4 2019 Q1 2020 Q2 2020 Q3 2020 Q4 2020
SUBSCRIBERS
EBITDA
Subscriber & EBITDA Growth
NOTE: we would be delighted to walk through the full ﬁnancial model and all associated assumptions in person

Our team
Will Brocklebank (CEO): prior to founding
Shepherd, Will spent a decade as the
CEO of a leading systems integration
company which he founded in 2005. He
has been an expert speaker at numerous
Smart Buildings conferences and has been featured in a
range of leading trade magazines. He has held Board
positions for CEDIA (the Custom Electronic Design &
Installation Association) and is a serial entrepreneur who has
started & sold 3 businesses. He holds an MA from Edinburgh
University and is an alumnus of the Goldman Sachs 10k SB
programme.
Federico Mestrone: worked for IBM as a
consultant on Enterprise Java and a
number of related WebSphere products
for 6 years before relocating to the U.K.
about a decade ago. In the U.K. he has
worked as a software engineer on mobile
app projects (iOS and Android), as a technical trainer for the
BBC, and as an IT consultant for Morgan Stanley as part of
the data modelling team. He has contributed many articles
over the years to some of Italy's most popular IT magazines,
and when not developing software he studies new (human)
languages such as Japanese.
Ben Evans (CTO): is co-founder of jClarity,
a company which makes performance
tools for development & ops teams. Prior
to this he was chief architect for Listed
Derivatives at Deutsche Bank. Ben helps
to run the London Java Community, and represents the user
community as a voting member on the Java Executive
Committee. He is author of “The Well-Grounded Java
Developer” & “Java in a Nutshell”. Ben holds an MA in
Mathematics from the University of Cambridge, and was a
researcher in theoretical physics, working on theories which
are now being tested at the LHC.
Joe Richmond-Knight: is working on
reference hardware systems and the
integration of data-sending platforms with
Shepherd’s cloud. He is reading Computer
Systems Engineering at the University of
Kent where he has significant experience
of building automation and management. His skills lie in
electronics and computing with the ability to carry out ‘high-
level’ system programming as well as ‘low-level’ electronics
and circuit design. These skills are vital in the development
of our reference hardware which we then make available to
our partners for their building/asset monitoring solutions.
Fazina Blanchard (COO): prior to joining
Shepherd Fazina spent 20 years in
strategic planning, business analysis and
project management for large UK
corporate organisations, including Reuters
and Barclays. Most recently, she ran a 500,000-strong
membership organisation with responsibility for developing
corporate partnerships to drive revenue growth. This
involved marketing strategy for increasing membership
numbers, developing the brand and re-launching the
website. She joined Shepherd to get off the corporate
treadmill and work in a faster, leaner environment.
Sotiris Lyritzis (Director): Sotiris has
twenty years’ experience in Private Equity,
founded an international pharma/biotech
B2B exchange start-up and prior to this
graduated as Baker Scholar from Harvard
Business School.
Giles Sutton (Director): Giles is Chairman
of the CEDIA Board of Directors EMEA. As
a leading system integrator he has global
knowledge of the smart building industry
and significant contacts within the US
integrator community.

For more information and to arrange a
demonstration please contact:
Will Brocklebank, CEO
will@shprd.com
07738 70 97 97
Fazina Blanchard, COO
fazina@shprd.com
07388 026 141
thank you…

Shepherd Network Ltd - Info Deck

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