Effect of override size on forecast value add

Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.

Bibliographic Details
Main Author: Baker, Jeffrey A. (Jeffrey Arthur)
Other Authors: Sergio Caballero and Nima Kazemi.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2018
Subjects:
Online Access:http://hdl.handle.net/1721.1/117920
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author Baker, Jeffrey A. (Jeffrey Arthur)
author2 Sergio Caballero and Nima Kazemi.
author_facet Sergio Caballero and Nima Kazemi.
Baker, Jeffrey A. (Jeffrey Arthur)
author_sort Baker, Jeffrey A. (Jeffrey Arthur)
collection MIT
description Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.
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spelling mit-1721.1/1179202019-04-10T12:55:27Z Effect of override size on forecast value add Baker, Jeffrey A. (Jeffrey Arthur) Sergio Caballero and Nima Kazemi. Massachusetts Institute of Technology. Supply Chain Management Program. Massachusetts Institute of Technology. Supply Chain Management Program. Supply Chain Management Program. Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-68). Business forecasting frequently combines quantitative time series techniques with qualitative expert opinion overrides to create a final consensus forecast. The objective of these overrides is to reduce forecast error, which enables safety stock reduction, customer service improvement, and manufacturing schedule stability. However, these overrides often fail to improve final forecast accuracy. Process mis-steps include small adjustments, adjustments to accurate statistical forecasts, and adjustments to match financial goals. At best, these overrides waste scare forecasting resources; at worst, they seriously impact business performance. This report offers a framework for identifying overrides that are likely to improve forecast accuracy. A new class of metrics, Dispersion-Scaled Overrides, was developed as an input to the framework. Other inputs included statistical forecast accuracy and auto-correlation. Classification techniques were used to identify whether an override was likely to create forecast value add. These techniques were found to be approximately 80% accurate in predicting success. This result suggests that using Dispersion-Scaled Overrides alongside common forecast accuracy metrics can reliably predict forecast value add. In turn, this can maximize the value that experts add to the business forecasting process. by Jeffrey A. Baker. M. Eng. in Supply Chain Management 2018-09-17T15:50:01Z 2018-09-17T15:50:01Z 2018 2018 Thesis http://hdl.handle.net/1721.1/117920 1051223162 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 77 pages application/pdf Massachusetts Institute of Technology
spellingShingle Supply Chain Management Program.
Baker, Jeffrey A. (Jeffrey Arthur)
Effect of override size on forecast value add
title Effect of override size on forecast value add
title_full Effect of override size on forecast value add
title_fullStr Effect of override size on forecast value add
title_full_unstemmed Effect of override size on forecast value add
title_short Effect of override size on forecast value add
title_sort effect of override size on forecast value add
topic Supply Chain Management Program.
url http://hdl.handle.net/1721.1/117920
work_keys_str_mv AT bakerjeffreyajeffreyarthur effectofoverridesizeonforecastvalueadd