Full transcript available: www.lokad.com/...
In a discussion between Lokad's CEO, Joannes Vermorel, and Head of Communication, Conor Doherty, the importance of stochastic optimization and probabilistic forecasting in supply chain management is emphasized. Vermorel explains the concept of stochasticity, where the loss function is uncertain, a common occurrence in supply chain scenarios. He outlines the three ingredients of mathematical optimization: variables, constraints, and the loss function, and explains that in stochastic optimization, the loss function is not deterministic but randomized. Vermorel also discusses the scalability issues of mathematical optimization techniques for supply chain, which have been a roadblock for four decades. He concludes by emphasizing that stochastic optimization is a crucial aspect often overlooked in supply chain textbooks.
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Timestamps:
00:00:00: Introduction to the interview
00:02:15: Probabilistic forecasting and supply chain optimization
00:04:31: Stochastic optimization and decision making
00:06:45: Ingredients for stochastic optimization: variables, constraints, loss function
00:09:00: Perspectives on modeling and optimization
00:11:15: Constraints and worst-case scenarios in optimization
00:13:30: Uncertainty, constraints and poor solutions
00:15:45: Deterministic optimization and varying scenarios
00:18:00: MRO space operations and inventory optimization
00:20:15: Uncertainty in lead times and repairable parts
00:22:30: Consequences of missing parts and classic approach limitations
00:24:45: Stochastic elements and human-based stochasticity in supply chain
00:27:00: Repairing an aircraft engine and sourcing parts
00:29:15: Inventory optimization and probabilistic bill of material
00:31:30: Policies for supply chain optimization and reactivity
00:33:45: Scalability issues and convex functions in supply chain
00:36:00: Problem relaxation and constraints in supply chain problems
00:38:15: Local search tools and feasible solution in supply chain
00:40:30: Meta heuristic genetic algorithms and scalability challenges
00:42:45: Mathematical optimization as a scalability problem
00:45:00: Lokad's development of stochastic optimization technology
00:47:15: Interdependencies in supply chain and solving problems with money
00:49:30: Shelf limit constraints and yogurt inventory example
00:51:45: Summarizing stochastic optimization and uncertainty
00:54:00: Role of solver in supply chain optimization
00:56:15: Clarifying the term 'solver' and computation of final decision
00:58:30: Challenging the solver's solution and potential shortcomings
01:00:45: Key takeaways: importance of stochastic optimization
01:03:00: Ignoring uncertainty in supply chain and benefits of a good solver
01:05:15: Dependencies and interdependencies in non-trivial supply chains
01:07:30: End of interview
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