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