The Variance Learning Curve
<jats:p> The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimat...
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144180 |
| _version_ | 1826211199320064000 |
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| author | Bavafa, Hessam Jónasson, Jónas Oddur |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Bavafa, Hessam Jónasson, Jónas Oddur |
| author_sort | Bavafa, Hessam |
| collection | MIT |
| description | <jats:p> The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that, for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance. </jats:p><jats:p> This paper was accepted by Charles Corbett, operations management. </jats:p> |
| first_indexed | 2024-09-23T15:02:08Z |
| format | Article |
| id | mit-1721.1/144180 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:02:08Z |
| publishDate | 2022 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1441802023-04-19T20:46:56Z The Variance Learning Curve Bavafa, Hessam Jónasson, Jónas Oddur Sloan School of Management <jats:p> The expansive learning curve literature in operations management has established how various facets of prior experience improve average performance. In this paper, we explore how increased cumulative experience affects performance variability or consistency. We use a two-stage estimation method of a heteroskedastic learning curve model to examine the relationship between experience and performance variability among paramedics at the London Ambulance Service. We find that, for paramedics with lower experience, an increase in experience of 500 jobs reduces the variance of task completion time by 8.7%, in addition to improving average completion times by 2.7%. Similar to prior results on the average learning curve, we find a diminishing impact of additional experience on the variance learning curve. We provide an evidence base for how to model the learning benefits of cumulative experience on performance in service systems. Our findings imply that the benefits of learning are substantially underestimated if the consistency effect is ignored. Specifically, our estimates indicate that queue lengths (or wait times) might be overestimated by as much as 4% by ignoring the impact of the variance learning curve in service systems. Furthermore, our results suggest that previously established drivers of productivity should be revisited to examine how they affect consistency, in addition to average performance. </jats:p><jats:p> This paper was accepted by Charles Corbett, operations management. </jats:p> 2022-08-01T17:18:28Z 2022-08-01T17:18:28Z 2021 2022-08-01T17:14:45Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/144180 Bavafa, Hessam and Jónasson, Jónas Oddur. 2021. "The Variance Learning Curve." Management Science, 67 (5). en 10.1287/MNSC.2020.3797 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) SSRN |
| spellingShingle | Bavafa, Hessam Jónasson, Jónas Oddur The Variance Learning Curve |
| title | The Variance Learning Curve |
| title_full | The Variance Learning Curve |
| title_fullStr | The Variance Learning Curve |
| title_full_unstemmed | The Variance Learning Curve |
| title_short | The Variance Learning Curve |
| title_sort | variance learning curve |
| url | https://hdl.handle.net/1721.1/144180 |
| work_keys_str_mv | AT bavafahessam thevariancelearningcurve AT jonassonjonasoddur thevariancelearningcurve AT bavafahessam variancelearningcurve AT jonassonjonasoddur variancelearningcurve |