Full transcript available: www.lokad.com/...
About the guest
Dr. Meinolf Sellmann is founder and CTO at InsideOpt, a US-based startup that produces general-purpose software for automating decision-making under uncertainty. He is the former Director for Network Optimization at Shopify, Lab Director for the Machine Learning and Knowledge Representation Labs at General Electric's Global Research Center, Senior Manager for Cognitive Computing at IBM Research, and Assistant Professor for Computer Science at Brown University. Meinolf architected systems like the trade-settlement system of the ECB, which handles over 1 trillion Euros per night, has published over 80 articles in international conferences and journals, holds six patents, and has won over 22 first prizes at international programming competitions.
Summary
In a recent LokadTV interview, Conor Doherty, Joannes Vermorel, and guest Meinolf Sellmann discussed the role of stochastic optimization in supply chain management. They highlighted the importance of considering variability and uncertainty in decision-making processes. Traditional deterministic methods often fall short in real-world scenarios, leading to over-optimistic optimization plans. Both Vermorel and Sellmann criticized the "predict then optimize" approach, suggesting that companies can achieve better results by accounting for forecast variability during optimization. They emphasized the need for executable plans and measurable effectiveness in any optimization model.
00:00:00: Introduction of the interview
00:00:42: Meinolf Sellman's career and InsideOpt's decision-making
00:03:47: Disruptions and over-optimism in optimization
00:06:18: Vermorel's stochastic optimization discovery and influences
00:08:10: E-commerce fulfillment and supply chain forecasting
00:09:56: 'Predict then optimize' approach and consequences
00:11:41: Operational results improvement and business costs
00:14:08: Unpredictability and chaos in supply chain
00:16:16: Appeal of forecasting and rational decision making
00:18:43: Rational decision making and overbooking game
00:21:55: Supermarket product example and supply availability
00:24:27: Stochastic optimization and seasonal sales variability
00:28:53: Price changes impact and joint posterior distribution
00:30:39: Problem-solving heuristics and tackling complexity
00:33:10: Challenges with perishable goods and posterior distribution
00:36:01: Reasoning difficulties and creating solution awareness
00:38:40: Coffee Roastery problem and production planning
00:42:20: Business modelization and complex variables reality
00:45:34: Ignored concerns in optimization and silver bullet seeking
00:49:00: CEO advice and understanding business processes
00:51:58: Warehouse capacity and supplier delivery uncertainty
00:54:38: Perception of service level and briefing exercise
00:57:33: Airlines' financial losses and technology adoption
01:00:10: AI-based search benefits and hardware compatibility
01:03:05: Convexity in optimization and usefulness over proof
01:06:06: Convergence of machine learning with optimization techniques
01:09:34: Runtime features and broadening search horizon
01:12:22: Micro adjustments and warehouse operation risks
01:16:09: Finding good compromise and insurance against uncertainty
01:19:11: Expected profit rise with stochastic optimization
01:22:23: Aerospace industry example
01:24:30: Accepting good decisions and damage control
01:25:19: Supply chain efficiency
01:26:22: Client feedback and importance of technology
01:26:56: End of interview
******
Check out our website: www.lokad.com/
Follow us on LinkedIn: / lokad
Read our blog: blog.lokad.com/
******