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Getting Your Supply Chain Back on Track with AI

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This presentation was made on June 3rd, 2020.

A recording of the presentation can be viewed at: https://youtu.be/Y2sLUzd-y9A

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Getting Your Supply Chain Back on Track with AI

  1. 1. Getting your Supply Chain Back on Track with AI Meetup: Jun 2, 2020 Karthik Guruswamy H2O.ai
  2. 2. Confidential2 • H2O.ai – Our Response to COVID-19 • Supply Chain in the COVID-19 Era • Tech. Debt with Shortage of Time, Talent and Trust • Augmenting COVID-19 data in AI/ML models • Short Term Predictions vs Long Term What-if Scenarios • Daily/Hourly Predictions - Demand Sensing • Driverless AI – Time Series Forecasting • Q – Demand Sensing • Q & A Topics
  3. 3. Confidential3 How H2O.ai is Contributing to Solving COVID-19 Expertise H2O.ai’s data science experts are contributing their knowledge to solve pressing problems with the pandemic AI Platforms H2O.ai is contributing its Driverless AI and Q platform to model, predict, and visualize data sets Sri Ambati CEO and Founder, H2O.ai 1. Hospital staffing predictions 2. ICU transfers and triage 3. Population risk segmentation 4. Predicting the spread of COVID-19. 5. Predicting operational efficiency and resilience during a pandemic 6. Hospital supply chain predictions 7. Predicting responses by city, hospitals 8. Sepsis predictions Problems we are solving “ Data Sets H2O.ai is evaluating global and open health data sets to determine patterns “Data Science can save lives today. AI is an incredible force to do good for humanity.” AI Solutions H2O.ai is creating pandemic and health specific solutions for general use
  4. 4. Confidential4 • Unprecedented disruption to Supply Chain happening last few months – Quick recovery very difficult, unless acted on it proactively and with the right tools and approach • Disruption across Models and Processes around: – Manufacturing, Warehouse Mgmt, Transportation, Distribution, Logistics, Inventory, Procurement, Demand sensing & Cash Flow • Through and Post COVID-19 disruption – Diminished or sudden increase in demand for consumption of household/industrial – More Household Supplies, Groceries, PPE, Baking Goods, Alcohol, Frozen Food … – Less Automotive, Shaving Products, Flowers, Makeup – Emerging demand for new type of products and services • What happens if there is a second wave ? Supply Chain in COVID-19 Era
  5. 5. Confidential5 • AI/ML models that were manually crafted with Agile projects are increasingly subject to stress – Underlying assumptions/ecosystem/macro-economic environment has changed. – New features have to be now created/back tested – Never one size fits all – COVID cases change from state to state, country to country! – Where is the historical data ? • Cannot afford to build new AI/ML models with the same methods and timelines as situation changes every other week • Becomes even more difficult to: – Find time to train and deploy short-lived models with HIGHEST ACCURACY – Talent to find and test new features as domain expertise is not fully reusable – Trust the models and get insights each week on what’s impacting predictions! Tech. Debt with Shortage of Time, Talent and Trust
  6. 6. Confidential6 • Kaggle GMs who work for H2O.ai have created power-growth models and made it to the leaderboard here over multiple weeks of competition. Incorporating COVID-19 data in AI/ML Models
  7. 7. Confidential7 • Power Growth models are great for forecasting and incorporates various free parameters to model the infection rate. • Epidemiological Models such as SEIRD can also be used on existing data using different parameters – SEIRD – Susceptible, Exposed, Infected, Recovered, Dead • The next version of Driverless AI 1.9.0 will incorporate a SEIRD transformer, where you can supply a range of parameters to optimize and have a Driverless AI fit a model: – SEIRD predicted - actual (residuals) • Short term historical data + future predictions of infections etc., can be incorporated into time series models. • Other aspects of govt policy on lock downs, mandatory social distancing, quarantines for the affected etc., can be creatively factored into the models Incorporating COVID-19 data in AI/ML Models
  8. 8. Confidential8 • Data such as – Unemployment Claims, Consumer Price Index, Producer Price Index etc., – Mobility data – Migration data of city dwellers to rural areas – Any other data that that your organization already has access to, that will apply to supply chain Incorporating Additional Macroeconomic Data
  9. 9. Confidential9 • Short Term models can be Power Growth models or a SEIRD type model or a SEIRD transformer that works with Auto-ML! • Data can be provided from last X weeks with input from COVID-19 data and then help forecast • Long Term models is where the uncertainty plays a very big role as there is no historic data to determine if a subsequent wave of infections will occur, etc., • What-if simulations however come to help where the business can allow a range of inputs and see how the estimates can play out! Short Term vs Long Term © Can Stock Photo / abluecup
  10. 10. Confidential10 • Forecast models for short term are great (next week forecast for instance) • But what if situation changes Daily or Hourly ? – We need Demand Sensing! • Demand Sensing can alter existing short-term time series predictions and adjust for day to day, hour to hour or even near real-time Daily/Hourly Predictions -> Demand Sensing
  11. 11. Confidential11 Driverless AI – Time Series Forecasting
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  14. 14. Confidential14 • Account for the forecasting gap (if any) and forecast horizon • Robust time-series cross validation scheme – back testing • Automatic handling of time group columns • Time-series specific feature transformations • Incorporate exogenous variables – COVID #s as an example • Integration with your own forecasting feature, model, cost function • Explain a forecast value with Shapley Time Series Forecasting with Driverless AI
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  23. 23. Confidential23 Feature Transformations for Forecasting • Aggregate lag based features, eg. Exponential Moving Average • Interaction of lags: lag1 - lag2 • Target Transformations: e.g., sqrt, log • Decomposing the Date Column, e.g. Day of the Month, isHoliday • Drop-out regularization : randomly remove lag features
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  26. 26. Confidential26 Q – Demand Sensing
  27. 27. Confidential27 Demand Sensing Time Series forecast provides predictions for the sales next X number of weeks and can run every week. The current crisis will almost always affect the forthcoming sales. Forecasts #s can be updated to produce a second prediction/correction of the sales of the Y number of weeks and can be run daily/hourly. These daily/hourly data could be new COVID cases, POS sales, social media data (tweets) etc.
  28. 28. Confidential28 Q and Q Apps
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  30. 30. Confidential30 Q Capabilities • Analytical Database - Store data in a format that supports ad-hoc interactive querying • Data Prep - Merge/Join multiple data sources or transform data • Search/Visualization - Find, filter, understand data. Visualize in ad-hoc ways • Dashboards/Reports - Save or bookmark findings and share with others • Integration with Driverless AI • App SDK/Store – Author interactive apps or use existing apps
  31. 31. Confidential31 Q Data Prep • Data pre-processing pipeline: • Formulas (mathematical, string etc.) to create new columns. • Text cleansing – stop word removal, stemming, punctuation and symbol removal etc. • Merge and join tables.
  32. 32. Confidential32 Q Search •
  33. 33. Confidential33 Q Notebooks
  34. 34. Confidential34 Q Apps
  35. 35. Confidential35 Q Apps • Programmed in 100% pure Python • No front-end programming (HTML/Javascript/CSS) required • No need to reason about client-server / distributed architecture • Apps run in parallel, managed by Q scheduler • Full-fledged workflow engine • Apps run in isolated venv, light on resources (no Kubernetes / Docker required)
  36. 36. Confidential36 Q Demand Sensing for sales forecasting uses augmented COVID- 19 data. It can help a company forecast the sales for different SKUs filtered by region/customer/brands. The forecasting model uses sensing variables such as – COVID-19 cases, social sentiment and more. The Q Demand Sensing app then shows the impact on sales with or without sensing variables. Demand Sensing with COVID-19 Data: Q AI App A New AI App that Forecasts Sales with Augmented Data
  37. 37. Confidential37 Mortgage Lending with COVID-19 Data: Q AI App One-Click Scenario Analysis & Catastrophe Modeling The new Q Mortgage Lending app, uses a banks data and augments that with unemployment or demographic data to quickly determine and predict default on loans – in other words, a new risk prediction with COVID-19 data.
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  39. 39. Confidential39 Q & A http://bit.ly/try-driverlessAI Take Driverless AI for a Test Drive

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