Dismantling the Illusion of Averages: Capacity Planning with Forecasts, Queues, and Costs

This video explains why scheduling staff based only on average demand leads to costly idle time or lost sales, and shows how to combine statistical demand forecasting with queuing theory to pick optimal capacity. It walks through the capacity illusion, prerequisites like offered load, effective capacity, and Little’s Law, then presents a four‑step framework: forecast demand distributions, translate via a queuing model (Erlang C/A), calculate economics of waiting versus staffing cost, and validate empirically. A worked example demonstrates how adding one agent can meet an 80‑%‑within‑two‑minutes service target while balancing a $25 hourly wage against a $0.50‑per‑minute wait penalty. The video concludes with empirical validation steps, citing real‑world data from Brown et al. (2005) https://www.columbia.edu/~ww2040/8100S12/B..., forecasting guidance from Hyndman & Athanasopoulos https://otexts.com/fpp3/, correlation evidence from Ibrahim & L’Ecuyer https://pubsonline.informs.org/doi/abs/10...., Little’s Law https://pubsonline.informs.org/doi/abs/10...., and staffing with uncertainty from Whitt https://onlinelibrary.wiley.com/doi/abs/10.... Report sources: https://otexts.com/fpp3/intro.html https://otexts.com/fpp3/forecasting.html https://otexts.com/fpp3/toolbox.html [production:modern-business-practices/capacity-planning-demand-variability] [learning-resources] Lesson infographic: https://drive.google.com/open?id=1gTszWKV4... Teaching slides (PDF): https://drive.google.com/open?id=1TrNoWpsD... [/learning-resources]