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Lemnos engr 245 lean launchpad stanford 2019

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

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Lemnos engr 245 lean launchpad stanford 2019

  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
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  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

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