Full transcript available: https://www.lokad.com/tv/2024/5/16/fo...

About the guest
Nikolaos Kourentzes is a professor in predictive analytics and AI at the University of Skövde AI Lab in Sweden. His research interests are in time series forecasting, with recent works in modelling uncertainty, temporal hierarchies, and hierarchical forecasting models. His research focuses on translating forecasts into decisions and actions, in areas such as inventory management, liquidity modelling for monetary operations, and healthcare. He has extensive experience working in both the industry and the public sector and has authored various open-source libraries to aid the use of advanced forecasting methods in practice.

Summary
In a recent LokadTV interview, Nikos Kourentzes, a professor at the University of Skövde, and Joannes Vermorel, CEO of Lokad, discussed forecast congruence in supply chain decision-making. They emphasized the importance of aligning forecasts with decisions, acknowledging that models may be misspecified. They distinguished between forecasting accuracy and congruence, arguing that the most accurate forecast may not be the best for decision-making if it doesn't align with the decision's objective. They also discussed the practical application of forecasting congruence in inventory decision making and its potential to mitigate the bullwhip effect. The role of AI and human involvement in forecasting congruence was also discussed.

00:00:00: Introduction of the interview
00:00:47: Nikos Kourentzes' background and work
00:03:25: Understanding forecasting congruence
00:04:44: Limitations of accuracy in forecasting
00:06:14: Congruence in time series forecasts
00:08:02: Supply chain inventory modeling considerations
00:09:03: Congruence and forecast consistency
00:10:29: Mathematical metrics in production
00:12:08: Luxury watchmaker inventory considerations
00:14:47: Upward fluctuation triggering production
00:16:03: Optimizing model for demand of one SKU
00:17:41: Research in shrinkage estimators and temporal hierarchies
00:19:05: Best models for all horizons
00:21:32: Controversy around forecast congruence
00:24:05: Calibrating inventory policies
00:26:27: Balancing accuracy and congruence
00:31:14: Tricks from temporal aggregation smooth out forecasts
00:32:54: Importance of gradients in optimization
00:35:28: Correlations in supply chain
00:38:10: Beyond time series forecasting
00:40:27: Honesty of probabilistic forecasting
00:42:32: Similarities between congruence and bull whip ratio
00:45:18: Importance of sequential decision making analysis
00:47:27: Benefits of keeping stages separate
00:49:34: Human interaction with models
00:52:05: Retaining human element in forecasting
00:54:35: Trust in experts and analysts
00:57:28: Realistic situation of managing millions of SKUs
01:00:01: High level model adjustments
01:02:13: Decisions steered by probability of rare events
01:04:44: Nikos' take on adjustments
01:07:14: Wasting time on minor adjustments
01:09:08: Against manual day-to-day adjustments
01:11:43: Company-wide benefits of code tweaking
01:13:33: Role of data science team
01:15:35: Probabilistic forecasts deter manual interference
01:18:12: The million-dollar question on AI
01:21:11: Importance of understanding AI models
01:24:35: Value and cost of AI models
01:26:02: Addressing problems in inventory

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