Applying Demand Aggregation to Improve Forecasting Accuracy

Bernardo Villarreal, delia Villarreal, Mariana Herrera, Estefania Quintanilla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Forecasting accuracy is an important issue for effective decision making in the areas of strategic planning, production planning, inventory management and other areas. In particular, this aspect is most relevant for items that have demand patterns with important levels of intermittency and lumpiness. This work describes the efforts of a Mexican company to look for opportunities to improve the level of forecasting accuracy through the application of demand aggregation to all the items of the company´s catalogue. The level of demand aggregation considered is a period equivalent to the review plus de response time periods of the periodic review management system used by the firm. Initial results are obtained from a pilot study carried out in all the stores of the Tijuana plaza of the company. The resulting forecasting mean squared error (MSE) was decreased significantly in the range of 32 to 56%. The firm estimates a reduction in the order of 17.7 to 33.9% in safety stock requirements will be possible due to the improvement in forecasting accuracy.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Industrial Engineering and Operations Management
PublisherIEOM Society
Pages1521-1528
Number of pages8
Volume2018
EditionJUL
ISBN (Electronic)978-1-5323-5945-3
Publication statusPublished - 26 Jul 2018

Publication series

NameProceedings of the International Conference on Industrial Engineering and Operations Management

Fingerprint

Forecasting accuracy
Management system
Safety stock
Strategic planning
Response time
Decision making
Inventory management
Mean squared error
Production planning
Periodic review

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Villarreal, B., delia Villarreal, Mariana Herrera, & Quintanilla, E. (2018). Applying Demand Aggregation to Improve Forecasting Accuracy. In Proceedings of the International Conference on Industrial Engineering and Operations Management (JUL ed., Vol. 2018, pp. 1521-1528). (Proceedings of the International Conference on Industrial Engineering and Operations Management). IEOM Society.
Villarreal, Bernardo ; delia Villarreal ; Mariana Herrera ; Quintanilla, Estefania. / Applying Demand Aggregation to Improve Forecasting Accuracy. Proceedings of the International Conference on Industrial Engineering and Operations Management. Vol. 2018 JUL. ed. IEOM Society, 2018. pp. 1521-1528 (Proceedings of the International Conference on Industrial Engineering and Operations Management).
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Villarreal, B, delia Villarreal, Mariana Herrera & Quintanilla, E 2018, Applying Demand Aggregation to Improve Forecasting Accuracy. in Proceedings of the International Conference on Industrial Engineering and Operations Management. JUL edn, vol. 2018, Proceedings of the International Conference on Industrial Engineering and Operations Management, IEOM Society, pp. 1521-1528.

Applying Demand Aggregation to Improve Forecasting Accuracy. / Villarreal, Bernardo; delia Villarreal; Mariana Herrera; Quintanilla, Estefania.

Proceedings of the International Conference on Industrial Engineering and Operations Management. Vol. 2018 JUL. ed. IEOM Society, 2018. p. 1521-1528 (Proceedings of the International Conference on Industrial Engineering and Operations Management).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Villarreal B, delia Villarreal, Mariana Herrera, Quintanilla E. Applying Demand Aggregation to Improve Forecasting Accuracy. In Proceedings of the International Conference on Industrial Engineering and Operations Management. JUL ed. Vol. 2018. IEOM Society. 2018. p. 1521-1528. (Proceedings of the International Conference on Industrial Engineering and Operations Management).