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RabbitHawk

Episode 01

Why is your team still using Excel to forecast?

Christoph Bergmeir and Abishek Sriramulu on why million-dollar planning systems still lose to spreadsheets, what one-size-fits-all models miss, and how to build a governed forecast layer teams actually use.

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Key ideas

  • Teams export from million-dollar systems into spreadsheets because the platform is not giving them the flexibility, transparency or trust the month requires. The question is what the system is failing to deliver, not why planners are "going rogue."
  • One-size-fits-all models bake in generic assumptions. Grocery, apparel and intermittent-demand categories need different treatment; a single cold run across 30,000 series cannot respect that heterogeneity.
  • Prophet and NeuralProphet were designed to be interactive and inspectable, not batch-processed blindly at scale. The valuable workflow is explore, adjust, and learn, not accept a black-box number.
  • Human adjustments to forecasts are accurate typically if they are large and downward.
  • LLMs are for language and reasoning, not numerics. Use them to capture messy context and explain assumptions; leave the math to proven forecasting and optimization methods.
  • The durable pattern is a governed system of record plus a flexible, learnable decision layer on top, where overrides are visible, attributable and improve the next cycle.

Further reading

  • Bergmeir, C. (2024). LLMs and foundational models: Not (yet) as good as hoped. Foresight: The International Journal of Applied Forecasting, 73, 33-38.
  • Bower, P., & Gretok, C. (2023). The 10 lies told in consensus meetings. Foresight: The International Journal of Applied Forecasting, 70, 49-52.
  • Fildes, R., Goodwin, P., & De Baets, S. (2023). Judgmental adjustments in demand planning: Their motivation and success. Foresight: The International Journal of Applied Forecasting, 71, 31-37.
  • Gusakov, I. (2023). What is wrong with demand planning software? Foresight: The International Journal of Applied Forecasting, 70, 53-55.
  • Schaer, O., Svetunkov, I., & Fildes, R. (2023). What do we learn from forecasting software surveys? Foresight: The International Journal of Applied Forecasting, 71, 62-65.
  • Trovero, M., & Potamitis, S. (2023). How will generative AI influence forecasting software? Foresight: The International Journal of Applied Forecasting, 71, 55-61.

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