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AUGMENTED REALITY (AR) Time Experience AR Is a Part of Everyday Life… Artificial Vs

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AUGMENTED REALITY (AR) Time Experience AR Is a Part of Everyday Life… Artificial Vs 1 Good Morning! • Welcome & setting the stage • Anti-trust & IP statement • Introductions • A word about the process • Dive in! 2 Dae Kim STRATEGIC CUSTOMER LANDSCAPE 3 Fourth Industrial Revolution & Retail Gray Taylor, Conexxus, August 7, 2018 For this session… • Please stop me and ask questions • Challenge the applicability of suspect technology and use cases! • Note concepts and technology you deem actionable Industrial Revolution 4.0 120 years 70 years 40 years 6 years (so far) Entire societies, not just products, transformed Simply, 4IR is the digitization of business & personal life IR 4.0: Some Key Tech Drivers Platform: Moore’s Law, quantum, smaller, gigabit wireless Computer Sciences: Artificial Intelligence > Machine Learning > Deep Learning Materials Sciences: nano, atomic reformulation Data I/O: digital 5 senses, natural language Mechanization: robotics, autonomy, precision movement Big Challenges: scarcity, energy, environment, progress, conflict Technology “Resets” Evolutionary Approach: Revolutionary Approach: Incremental Improvements Industrial Revolution Disruptors enter here Retail Transformation & Industry 4.0 “The evolution in consumer demand, combined with transformative technological innovations, will continue to drive fundamental changes. The boundaries of “retailer” and “manufacturer” will continue to blur, as companies evolve to meet their customers’ needs. These forces will cause the retail and consumer packaged goods (CPG) landscape to change more in the next 10 years than it has in the past 40 years.” “Shaping the Future of Retail for Consumer Industries”, World Economic Forum, January 2017 Shaping the Future of Retail… Key Challenges 1. High cost and difficulty of implementing new technologies • Legacy systems, built around legacy business practices and models • Absence of requisite skillsets 2. Slow pace of cultural change • Relentless and accelerating change in retail stresses established culture • Establishing a culture of innovation, agility, and peripheral vision 3. Limited public-private partnerships to address social implications directly • Retail downsizing impact on communities • Preparedness of the labor pool for “new retail” • Impact of last mile on sustainability Shaping the Future of Retail… Four Drivers of Success 1. Build a greater understanding of and a stronger connection to increasingly empowered consumers • Hyperconnected consumers, craving control • Fragile loyalties 2. Rapidly adopt game-changing technologies • Relentless focus on using technology to increase value added to consumers • Embrace key technologies of the Fourth Industrial Revolution 3. Unlock the power of transformative business models in physical and digital spaces • Line between online and offline retail will continue to blur, eventually converge • Brick and mortar will become platforms of engagement, experience and interaction 4. Redefine and build future capabilities • Retail and CPGs must focus on partnerships, last mile delivery & advanced data sciences IR 4.0 Retail: Consumers Setting the Pace Creating Digital Native Competitors And Powerful Immigrant Competitors IR4.0 - Cyber Physical Systems Emerge Many individual technologies …converging to create new mature… systems that reduce friction Narrowing Scope of IR 4.0 From This: IR 4.0 Raw To This: IR 4.0 Relevant Foundational BIG DATA Does Metrics Play Here? BI to Big Data to AI “ERP” “Rest of world” – IoT Device Integration INTERNET OF THINGS (IOT) Internet of Things: Everything that creates data Growth in connected devices will: – Leverage all data streams – Establish new, internal streams – Allow deeper understanding of reality – Exponentially increase market data – Change pricing & customer models & strategies – Influence – • Cognitive computing • Instore promotions • Store safety, efficiency & cleanliness Current State of IoT • 60% of IoT project stall at proof of concept (Cisco, 2017) • Over 50% of failures cited lack of IoT experience • 26% are “complete successes” • Most fail to fully use data collected • 10% data usage (IBM) to 48% data usage (HCL Technologies) • Data is like “ore”, there is gold in it, but you have to find it • Mining (IoT) + refining (AI) + distribution (sharing in ecosystem) = value • Refining is complex – Dumb IoT unloads lots of unstructured data that must be structured (AI is a must) – Cognitive IoT (SIoT) unloads more structured data, but collection point is more complex & $$ • Evolution of IoT value – Boeing aircraft engine 1. Spotting problems – fixing before failure 2. Optimizing systems – improving functionality 3. Predictive – identify future states Food Service • Temperature/humidity monitoring and control to improve safety – throughout supply chain • Bacteria measurement and alarms – throughout supply chain IoT Ideas for • Monitoring food expiration dates – enforce FIFO – reduce waste • The US wastes about 30% of food PRIOR to consumption, over 40% in general Convenience: • Portion control/monitoring – reduce waste Facility • Store systems control, monitoring & predictive • BunnLink (mesh over cellular), Coke Freestyle inventory control • Bathroom cleanliness – cleaning schedule enforcement, odor and bacteria measure “Data from the Operations/Selling • Social media feeds for consumer insights & actions edge, actions • Customer traffic mapping, dwell times & experience analysis (cameras w/recognition, beacons) • Footfall and que times manage staffing predictions pushed to the • Promotional evaluation of “balk rates” • Shelf inventory, plan-o-gram enforcement and re-order coordination – pay for play edge” promotions • Smart tags and labels – dynamic pricing, product information triggers • Smart digital signage – delivering custom, targeting messaging and info by customer ARTIFICIAL INTELLIGENCE When Will Machines Perform Tasks Better & Cheaper Than Humans? (High Level Machine Intelligence - HLMI) 2015 Oxford/Yale survey of 235 global AI professionals & researchers 100% Chance by 2140 50% Chance by 2060 AI Development Forecast Consensus View of HLMI Achievements 50% Chance by 2030 100% Chance by 2048 DONE – 2017 10 years ahead of forecast Google AlphaGo Organizational Evolution of AI Prescriptive Descriptive Insightful Predictive Intuitive Psychic Pizza Gut Feel History as Big Data AI processing Machine * Rollup Data future * Multiple * Disparate learning * Rearview * Report adjacent data data flows: Deep views flows IoT learning * Human Bias * Test of * Big math * Relational * “Everything intuition * Big discovery discovered & Odds making processing * IT as a known” * Enter Cloud service * 24/7 Machine scenario Machine testing informs advises human human Machine does… AI in Retail: Improvement Loop Grow Top Line Near future Yesterday Management & Movement Reports Improvement Loop AI Use Cases to Explore Deep Identity – Security – Loyalty – Payments Operations – Internet of Things / AI value – Learning systems – TRAINING – Augmented humans - MENTORS – Predictive systems – SUPPLY CHAIN – Complex pricing decisions Marketing/Consumer – Autonomous vehicles – Augmented reality tools – Predictive behavior & marketing …Reduce costs LEARNING is the Key to Returns McKinsey & Co., US Bureau of Labor Statistics Center stage… Behind the curtain… A D O P T I O 42% of Retail N & Transport _________________________________________ Labor is ______________________________ Prime for Automation Source: McKinsey, 2017 AI Deployment Today… 50% companies engaged Focused on top-line, not efficiency The New Arms Race: Can We Keep Ahead of Consumer Adoption? 31 Reduce Friction: 46.1% Add Value: 28.2% Consumer Aspirations Global Consumer AI Adoption Do you own a smart speaker? Smart Speakers Consumers and AI When Consumers Prefer AI Top AI Shopping Experiences 80% 80% 70% 70% 60% 60% 50% 50% 40% 40% 30% 30% 20% 20% 10% 10% 0% Voice Search Send Text Shop @ Store Shop on Chatbot Shop Online 0% Mobile Food Takeout/Meal Groceries Transportation Book Source: PWC “Global Consumer Insights Survey 2018” , N=22,000 Global Consumer AI Use Cases Race for the platform: be pervasive, assist, know, dominate Who35 really owns the consumer relationship of the future? Consumer AI Takeaways… • Consumers adopting AI faster than we are – “Free”, ubiquitous & frictionless – Brings order to increasing complexity – Foundation of “the new convenience” • AI will change consumer relationship – Happening with, or without us – New “3rd parties” in relationship, what is our priority? – Reduction of irrational/uninformed choices – Reduction of immediate consumption? – Personal assistants are corruptible • Benchmark consumer, not competition ROBOTICS • Kaizen analysis • Automation of repetitive process RPA/Digital (JPMorgan) • ML used to observe • Supply chain (Walmart) and build process (predictable • DSD order to cash • Goal: RPA better than processes) • Financial consolidation/close human • Warehousing (Kroger/Ocado) Physical • Inventory (Walmart/Bossa Nova) • Taking the “suck” out • Online grocery picking of work (predictable (Walmart/Alphabot) • Taking costs out of • Food production (Spice, Creator) production activities) • Delivery (Dominos DRU) • Taking costs out of last mile https://www.youtube.com/watch?v=rfMZfxgbuCw Robotic fueling…. AUTONOMOUS MOBILITY Transportation for IR4.0 For expert insights: http://www.fuelsinstitute.org Autonomous Vehicles … “vehicles, however, will no longer be driven by humans because in 15 to 20 years — at the latest — human-driven vehicles will be legislated off the highways.” Bob Lutz, Former Vice Chair of GM Automotive News, “Redesigning
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