business model, customer development, e245, engr245, lean launchpad, lean startup, stanford, startup, steve blank

1. LEMNOS
ALISSA ORLANDO
hustler
MBA
DANE RENNER
picker
MBA
FERDINAND LEGROS
designer
CS + MS&E
MAXIME VOISIN
hacker
CS + MS&E
IAN TAYLOR
hustler
MSX
CRAIG SEIDEL
mentor
110
INTERVIEWS
TODAYDAY ONE
Reduce production
interruptions
in Oil & Gas plants
Increase production
throughput in plants
EXISTING MARKET – BETTER PERFORMANCE
1

2. Setting the Scene
What is a plant, and how big is it?
200k
equipment
items
150
people
$170M
operational
budget
EXAMPLE: PLUTO PLANT, AUSTRALIA
2

3. Setting the Scene
Who are the people?
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety
3
Executive
Sign-off authority >$5mm
Plant Manager
Sign-off authority >$0.5mm
Maintenance Manager
Sign-off authority <$0.5mm
Engineer
Sign-off authority <$0.5mm

4. We were a group with AI expertise
looking for a problem to solve...
Week
1
4
Executive
Sign-off authority >$5mm
Plant Manager
Sign-off authority >$0.5mm
Maintenance Manager
Sign-off authority <$0.5mm
Engineer
Sign-off authority <$0.5mm
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety

5. We got out of the building to test our first value proposition Week
1
Value Proposition
Increase throughput in plants and factories
using AI
Oil & Gas, Chemicals, Food & Bev,
Pharma, etc etc.
Customer Segment
5

6. … and we received good signals, but we hit a roadblock Week
1
6 agreed 3 agreed 2 agreed
“This sounds really exciting…”
(Engineer @ Shell)
“...I would hesitate to approve it
because of safety risks…”
(Engineer @ Woodside)
“We have done this… it takes a lot
of engineering hours…”
(Executive @ Woodside)
Consulting model
(difficult to scale)
6

7. Many interviewees suggested we apply our AI expertise to
predict when equipment fails
Week
2-3
“Predictive maintenance would be super
useful and has far fewer implementation
risks.”
(Engineer @ BASF)
8 agreed
7

8. So we pivoted to apply AI to a different problem! Week
2-3
“Predictive maintenance would be super
useful and has far fewer implementation
risks.”
(Engineer @ BASF)
8 agreed
8
They have four objectives:
- Increasing throughput
- Reducing production interruptions
- Reducing costs
- Safety

9. We got out of the building with our new value proposition Week
2-3
Value Proposition
Predict when equipment fails to
reduce plant downtime using AI
9

10. … and we received good signals,
but we hit another roadblock
Week
2-3
DEMAND FROM CUSTOMERS
FEASIBLE TECHNOLOGY
AVAILABLE DATA
“We had three separate contractors try to
build predictive models ... there simply isn’t
enough data”
(Maintenance Manager @ Nihar)
2 agreed
10

11. We learnt to embrace a problem-first approach...
not a technology-first approach...
Week
4
“I have no visibility on how
we are going”
“SAP’s user interface sucks”
“Our data is kept in different
places, and they don’t talk to
each other”
“I’m hearing a lot about
predictive analytics!”
11

12. Identity Crisis Interlude
The dramatic shift in focus
caused enthusiasm to
take a dive
Week
4-6
12

13. Identity Crisis Interlude
Our divide and conquer interview
style was creating confusion
Competitors
Equipment
Manufacturers
Oil and Gas
Operators
Chemicals
Other
Manufacturing
13
Week
4-6
Week
4-6

14. We heard that a new risk-based way of managing maintenance
has emerged to improve on the classical approach
Week
7
CLASSICAL
MAINTENANCE
PARADIGM
RISK-BASED
MAINTENANCE
PARADIGM
“I maintain all pumps
every 6 months”
“ I maintain all pumps
based on their
current condition & the
consequence of failure
for each”
14

15. We discovered that risk-based maintenance teams
have no modern software solution!
Week
7
(spreadsheet hack)
X
CUSTOMERS :
SOFTWARE :
OPPORTUNITY
CLASSICAL
MAINTENANCE
PARADIGM
RISK-BASED
MAINTENANCE
PARADIGM
15

16. So we pivoted to software for risk-based maintenance.
We got out of the building with our new value proposition
Week
8
Plants in the Oil & Gas industry which
are already hacking solutions
Customer Segment
Software for risk-based maintenance
activity selection to improve
production and safety performance
Value Proposition
16

17. Our interviewees got really excited! Week
8
“This is exciting… I’d like to test it when it’s up
and running.”
(Plant Manager @ Chevron)
“I am actively looking for this!”
(Maintenance Manager @ DuPont)
“We had to develop our own tool in Excel”
(Maintenance Engineer @ ExxonMobil)
17

18. We developed other elements of the business model,
here’s our potential sales process for a plant…
Week
7-9
IDENTIFY
CHAMPION
GATHER
INFORMATION
ENGAGE
USERS
ENGAGE
IT FUNCTION
ADDRESS
DETRACTORS
CLOSE THE
DEAL
OPERATIONAL
INTEGRATION
Org Chart
18

19. The company will continue, with Dane taking the lead Week
10
ALISSA
ORLANDO
DANE
RENNER
FERDINAND
LEGROS
MAXIME
VOISIN
IAN
TAYLOR
CONTINUING MOVING ON TO NEW PROJECTS
19

20. 20

21. We learnt to solve one problem for a specific customer
subsegment, before solving more problems for them!
Week
8-9
TODAY
+PREDICTIVE
MAINTENANCE
+REPLACE
SOFTWARE
ADD-ONS
+CHEMICALS
+NUCLEAR &
POWER
+ETC.
NEW INDUSTRIES
NEWPRODUCTS
Bottom-up
TAM
$300M
Bottom-up
TAM
$6-8B
21

22. We learnt that sales cycles are 9-12 month-long.
This impacts our fundraising & operational plan!
Week
9
Q1 Q2 Q3 Q4
2019 2020 2021 2022
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Cashreserves
5M
10M
20M
30M
Seed
$2M
Series A
$5M
Start sales
process
Finalize
product
Complete first
sale
22