The Limits of Analytics During Black Swan Events A Case Study of the Covid-19 Global Pandemic
During statistically unlikely events (Black Swan events) analytical models fail to provide their expected level of fidelity: their error can increase by several hundred percent. The modern economy is built on such analytical models, which are intended to provide useful results during routine conditi...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Massachusetts Institute of Technology
2022
|
| Online Access: | https://hdl.handle.net/1721.1/139124 |
| _version_ | 1826208741211504640 |
|---|---|
| author | Kaminski, Erez |
| author2 | Kraska, Tim |
| author_facet | Kraska, Tim Kaminski, Erez |
| author_sort | Kaminski, Erez |
| collection | MIT |
| description | During statistically unlikely events (Black Swan events) analytical models fail to provide their expected level of fidelity: their error can increase by several hundred percent. The modern economy is built on such analytical models, which are intended to provide useful results during routine conditions. All models eventually fail to provide the expected level of fidelity under extreme conditions. This thesis investigates the critical limitations of analytical methods during Black Swan events. Specifically, we study the space of possible model errors for statistical forecasting models and their respective implications for supply chain systems. We explore the forecast errors through numerical simulation and a real-world case study of a global manufacturing company experiencing the Covid-19 pandemic, a Black Swan event. We demonstrate that in some cases demand can shift by over 60%, leading to the bifurcation of the forecast error space, and resulting in an 500% increase in forecast error. This new regime causes supply chain planning systems to grind to a halt as existing inventory models become irrelevant. Such a drastic change in a company’s operational environment requires urgent action to ensure continued operations. For management to make correct decisions, it is critical for them to understand the limits of analytics during Black Swan events. |
| first_indexed | 2024-09-23T14:10:55Z |
| format | Thesis |
| id | mit-1721.1/139124 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:10:55Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1391242022-01-15T03:20:34Z The Limits of Analytics During Black Swan Events A Case Study of the Covid-19 Global Pandemic Kaminski, Erez Kraska, Tim Willems, Sean Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Sloan School of Management During statistically unlikely events (Black Swan events) analytical models fail to provide their expected level of fidelity: their error can increase by several hundred percent. The modern economy is built on such analytical models, which are intended to provide useful results during routine conditions. All models eventually fail to provide the expected level of fidelity under extreme conditions. This thesis investigates the critical limitations of analytical methods during Black Swan events. Specifically, we study the space of possible model errors for statistical forecasting models and their respective implications for supply chain systems. We explore the forecast errors through numerical simulation and a real-world case study of a global manufacturing company experiencing the Covid-19 pandemic, a Black Swan event. We demonstrate that in some cases demand can shift by over 60%, leading to the bifurcation of the forecast error space, and resulting in an 500% increase in forecast error. This new regime causes supply chain planning systems to grind to a halt as existing inventory models become irrelevant. Such a drastic change in a company’s operational environment requires urgent action to ensure continued operations. For management to make correct decisions, it is critical for them to understand the limits of analytics during Black Swan events. M.B.A. S.M. 2022-01-14T14:51:23Z 2022-01-14T14:51:23Z 2021-06 2021-06-10T19:13:14.597Z Thesis https://hdl.handle.net/1721.1/139124 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Kaminski, Erez The Limits of Analytics During Black Swan Events A Case Study of the Covid-19 Global Pandemic |
| title | The Limits of Analytics During Black Swan Events
A Case Study of the Covid-19 Global Pandemic |
| title_full | The Limits of Analytics During Black Swan Events
A Case Study of the Covid-19 Global Pandemic |
| title_fullStr | The Limits of Analytics During Black Swan Events
A Case Study of the Covid-19 Global Pandemic |
| title_full_unstemmed | The Limits of Analytics During Black Swan Events
A Case Study of the Covid-19 Global Pandemic |
| title_short | The Limits of Analytics During Black Swan Events
A Case Study of the Covid-19 Global Pandemic |
| title_sort | limits of analytics during black swan events a case study of the covid 19 global pandemic |
| url | https://hdl.handle.net/1721.1/139124 |
| work_keys_str_mv | AT kaminskierez thelimitsofanalyticsduringblackswaneventsacasestudyofthecovid19globalpandemic AT kaminskierez limitsofanalyticsduringblackswaneventsacasestudyofthecovid19globalpandemic |