Building a deeptech thesis
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The document discusses building an investment thesis for warehouse automation. It identifies the large and growing market opportunity in warehouse automation driven by increasing ecommerce, labor challenges, and demand for efficiency. The document maps out the key operations in a warehouse and problems that can be addressed through automation. It analyzes the existing technologies used in warehouses and gaps that startups can address, such as developing autonomous mobile robots and collaborative robots. The document presents an investment thesis around warehouse automation and identifies attributes of startups that are well-positioned to succeed in this space, such as having differentiated technology, performance, and a strong team with domain expertise and go-to-market strategy.
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Building a deeptech thesis
- 1. Building an Investment Thesis Oct 2020 Shubham Sandeep Roopan Aulakh
- 2. piVentures2020AllRightsReserved Why build an Investment Thesis? • To Identify Investment Opportunities • Two Approaches – Top Down vs Bottom Up • Top Down: Pick a market -> Build a thesis -> Identify promising startups • Bottom up: Meet an interesting startup -> Analyze the company, market & competitors
- 3. piVentures2020AllRightsReserved Key questions to address • Is the market opportunity big enough? • Is the timing right? • Mapping the space • What are the addressable problems? • What are the existing technologies in use? • What are the gaps? • What are the winning attributes of a startup in this space? • How do we identify the most attractive segment(s)? • Global startup landscape • Identify promising startups for deep dive
- 4. piVentures2020AllRightsReserved Investment Thesis with an Example
- 5. piVentures2020AllRightsReserved Robotics Market Segments Segment Market Size ($B) Funding ($B) Opportunities Challenges Themes Manufacturing 21.8 (2019) -> 66.5 (2027) 1.0 Quick adoption, focus on safety & precision, lower costs -> ROI driven Not viable for SMEs, interoperability and integration Cobots, AMRs for material handling, Grippers, Robotic arms Logistics 4.7 (2019) -> 15.0 (2027) 2.7 Increased ecommerce deliveries, high labour turnover, quick adoption In-house development by large players, need to collaborate with humans Delivery Robots, Micro fulfilment, Warehouse automation Healthcare 7.1 (2019) -> 20.7 (2027) 3.2 High value, limited supply of highly skilled surgeons, need for high precision Highly regulated, long gestation period, high risk due to safety concerns Minimally Invasive Surgery, Prosthetics, Rehabilitation systems Agriculture 4.6 (2020) -> 20.3 (2025) 0.43 Declining availability of farm labour, increasing productivity of farmland Low utilisation due to seasonality, robustness in harsh environment Precision Agri, UAVs Self driving Tractors, Automated harvesting Horizontal 0.8 (2016) -> 7.5 (2022) 0.78 Use case across multiple industries, capital efficient Need to rely on strong partnerships, value creation might be limited Vision systems, Autonomy stack, Navigation and Sensing, Operating system
- 8. piVentures2020AllRightsReserved Market opportunity • US ecommerce sales will reach $794.50 billion this year, up 32.4% year-over-year • Number of global warehouses using high level automation expected to go up from 4,000 in 2018 to 50,000 in 2025 • Warehouse Automation Market was valued at $14B in 2019 and is expected to be worth $27B by 2025 at a CAGR of ~12% during the period • 80% of warehouses worldwide today are either entirely manual or have implemented only low- level automation into their operations
- 9. piVentures2020AllRightsReserved Market Forces • Increased adoption of Ecommerce • Covid -> higher preference for automation and less human involvement in supply chain • Lack of trained labour, increased training expense • Drive to lower operating costs -> less warehouse space, lower labour cost especially in developed nations • Falling price of robotics -> 50% drop in robot prices vs 100% increase in wages since 1990 -> higher ROI now • Seasonal and daily demand variability -> greater flexibility and modularity • Advancements in technology -> computer vision, autonomous navigation, computing power • Need for high accuracy at high scale and speed to reduce shipping errors and returns • Superior customer expectation -> expedited delivery times -> focus on improving productivity and efficiency • Large CAPEX requirement -> only larger players can afford to make the investment • High unemployment in the short term due to Covid could reduce labour cost thus delaying deployment • Lack of knowledge/expertise within warehouse operators to maintain robot fleet • Integration with existing WMS and interoperability with other robot systems • Small market size in India -> business needs to be able to expand globally • High maintenance costs will have an adverse impact on ROI • In-house development by large players – Amazon, Alibaba have captive robotics units • Slow rollout due to variation in warehouse layouts and size Market Enablers Key Challenges
- 10. piVentures2020AllRightsReserved Is Capital available to build? Source: Tracxn $1.5B deployed in the sector in the last 3 years with an increase in 2020 spurred by increased penetration of ecommerce and greater focus on automation
- 11. piVentures2020AllRightsReserved Are large outcomes possible? 20+ exits have occurred in the last 8 years totalling exit value of upwards of $5B. Some of the notable ones are mentioned below Name Acquirer Exit Value Funding Acquisition Date Ubimax TeamViewer $156M $6M July 2020 AutoGuide Mobile Robots Teradyne $165M Unknown October 2019 6River Systems Shopify $450M $46M September 2019 AutoStore Thomas H Lee Partners $1.9B Unknown June 2019 Canvas Technology Amazon Undisclosed $15M April 2019 Mobile Industrial Robots Teradyne $272M $1.42M April 2018 Intelligrated Honeywell $1.5B $57.5M July 2016 Universal Robots Teradyne $285M $1.78M May 2015 Industrial Perception Google Undisclosed Unknown December 2013 Kiva Systems Amazon $775M $18.1 March 2012
- 14. piVentures2020AllRightsReserved Key Considerations • Efficiency • Accuracy • Scheduling and labor management • Most optimal location for productivity • Faster • Safety • Ease of tracking and retrieving • Space utilization • Labor efficiency • Inventory management • Accuracy • Labor efficiency • Impact on Customer satisfaction • Right picking methodology • ~30-35% of cost • Labor efficiency • Impact on logistics efficiency • ~10-15% of cost • Damage reduction • Material wastage reduction • Labor efficiency • Optimize logistics channel RECEIVING PUTAWAY STORAGE PICKING PACKING SORTING SHIPPING
- 15. piVentures2020AllRightsReserved Addressable Problems and Opportunities Operational Cost • Rise of Micro fulfilment centers • Increasing complexity of operations • High velocity of goods • Single item level picking • Seasonality of demand • Operations inefficiency Throughput • Increase throughput with same infrastructure • Predictability • Flexibility • RoI • Disruption during infra upgrades Customer Expectations • Reduce Errors • Quick turnaround time • Reduce returns • Reduce out of stocks • Reliability Space Efficiency • Space utilization (horizontal and vertical) • Modularity for expansion Manpower Challenges • Labor productivity • Reduce Labor cost • Improved safety • Reduced dependence on manpower unavailability/attriti on
- 16. piVentures2020AllRightsReserved Understanding the Dynamics of the Industry Industry Dynamics RoI driven/ Zero tolerance to disruption Decision Makers: Automation leads/COOs /CFOs GTM: Strong need for channel partner for scale Business Model Sub sector opportunities Services and Maintenance requirements PoC/Dry run requirement before any contract Solution vs Product Approach Capital Requirements
- 19. piVentures2020AllRightsReserved Existing Technologies and their limitations AGV (Automated Guided Vehicle)/ AMR (Automobile Mobile Robots) • No Natural navigation • Non collaborative • Need for structured environment • High cost • Performance under varying conditions Limitations Opportunity areas • Autonomous and natural navigation with high accuracy and at low cost • Ability to work besides human Sortation system • Inflexible and fixed • High capex • Space Inefficient • Mobile robots • Peer to peer Collaboration Robotic arms • Repetitive actions • No flexibility or dexterity • Hardware or material improvement for gripping • Algorithms to simulate haptic feedback • Learn and relearning by demonstration ASRS (Automatic Storage and Retrieval System) • Inflexible and fixed • High capex • Mobile robots • Peer to peer Collaboration WMS/WES/WCS • Limited to connecting the dots, no intelligence on siloed information • Warehouse OS that has intelligence on demand forecasting, inventory management etc.
- 21. piVentures2020AllRightsReserved Winning Attributes for the Sector Economics Technology Performance Team • RoI for the customer • Scale in a capital efficient manner • Implementation time and disruption to customer operations • Throughput • Differentiated and defensible technology • Mobile/Modular/Flexible • Autonomous • Collaborative Robot • Reliability • Precision • Speed • Footprint • Domain background • Global GTM Capability • Understanding of Service and maintenance for scale
- 22. piVentures2020AllRightsReserved Identifying Potential AI opportunities Computer Vision – Navigation for AMRs Computer Vision – Robotic Arms Computer Vision – Assisted Learning AI based Warehouse OS Edge Computing Swarm Intelligence Extract intelligence from images - Drones Complete Mobile Robot Digital Twin Market potential Criticality (good to have/must have) Applications Sorting, Picking, Material Handling Pick and Place varied objects Self optimizatio n Efficiency across all operations Reduced latency for processing Routing of fleet of robots, peer to peer collaboration Inventory management Sorting, Picking, Material Handling Data interoperabil ity, Predictive Maintenance Impact timelines Crowded Friction to adoption Control
- 23. piVentures2020AllRightsReserved Global Startup Landscape Application Technology Name Location Founded Funding Sorting Picking Material Handling Pick & Place Full Stack AI use Fixed/ Mobile AMR / AGV Cobot Payload Capacity Business Model Global Presence Revenue Geek+ China 2015 $440M Grey Orange India 2011 $180M Fetch Robotics US 2014 $94M Kindred US 2014 $44M Not an exhaustive list
- 24. piVentures2020AllRightsReserved Indian Startup Landscape Application Technology Name Founded Funding Sorting Picking Material Handling Full Stack AI use Fixed/ Mobile AMR/ AGV Cobot Payload Capacity Business Model Global Presence Revenue Key Customers AA BB CC DD EE Not an exhaustive list