AI in Supply Chain and Economics of Logistics - Frank Corrigan

Francis Corrigan is Director of Decision Intelligence at Target Corporation. Embedded within the Global Supply Chain, Decision Intelligence combines data science with model thinking to help decision-makers solve problems. 00:00 Intro  01:21  Data Science applications in Logistics and Supply Chain, Cost and Performance trade-off  03:21  Amazon vs Target fulfillment Model, Owning vs Coordinating with Last Mile companies e.g. FedEx  08:36 Suez Canal Container Blockage, Fallback plan at Target  10:37 Predicting products to Stock in Bottle Neck Scenarios  12:42  Air Freight vs Sea Shipments Costs, Ideal vs Real World Deliveries  15:48  Lack of Good Data and Prediction Challenges  18:00  Managing Expectations as Head of Analytics, Importance of Communicating  20:11 Stakeholder Management & Data Science  Newsletter  23:39  Technical and Non-technical Teams Coordination, Speed Reading  26:36  Data Stories and Visualizations  29:47 Reporting Pipelines vs Story Narration  31:37 Times Series, Prophet, Flourish and Hans Rosling  35:28  Economist turned Data Scientist,  Embarrassment as Motivation  38:20  Lack of Practical Skills of Data Science at University  41:18  Employer’s Perspectives on Data Science Talent   45:24  What Causes Data Teams Failure  48:40  COVID 19 and Times Series Corruption,  Anomaly Detection  56:15  Toilet Paper Demand Scenario, Commodity Pricing Alerts  59:50  Automating Alerts for Panic Situation  01:02:10  Pandemic as a Blessing for Digital Business, Exponential Growth Rates and Tuition Fee Reimbursement for Employees  01:06:06 Data as Decision Support System, Strategic Decision Indicators   01:08:08 Capital in 21st Century, Thomas Piketty and Free Markets   01:11:31 Failures of Capitalist Societies on Individual Front and Socialist Aversion of Wealth Generation   01:15:15 UBI, Interventions, and CEO to Lowest Paid Worker Ratio   01:18:25 Career Blunders and Regrets   01:22:12 Psychometric Tests for Intellect Filtering, Behavioral Stability and Creativity Trade-off   01:24:08 Target’s Epic Failure in Canada, What Data Science could have Prevented   01:25:08 Gameplan to Compete with Walmart and Amazon    01:28:00 Sarimax, Armiax and Volatility Management, Planning vs Forecasting  01:31:33 Deep NNs or Lack thereof, Explainability and Monte Carlo as Alternative  01:34:00 Model Parsimony in Times Series, Baseline Models in Excel   01:37:50 R vs Python, Specific Use Cases   01:40:25 Delegating and Element of Trust   01:43:20 Time and Space Complexity of Models, Netflix and Deployments at Target   01:46:00 Political Impacts on Shipments, Narratives and Hypothesis Testing  01:48:00 Nate Silver, Nassem Talib, and Early Inspirations  01:52:05 Work-life Balance