Prediction and Judgmental Adjustments of Supply-Chain Planning in Festive Season

Megha Chhabra; Deepti Sahu; Gunjan Agarwal

Prediction and Judgmental Adjustments of Supply-Chain Planning in Festive Season

Megha Chhabra * §

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Deepti Sahu

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Gunjan Agarwal

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§ Sharda University

Prediction and Judgmental Adjustments of Supply-Chain Planning in Festive Season

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Abstract

For a robust performance, Shipping costs planning in festive seasons is given the input data as free from trends, season-of-year effects etc. Seasonal forecasting for supply-chain planning with past few years of similar data impact shipping costs. Additionally, during a festive season of the year, unbiased and accurate prediction of shipment load plays a major role in bringing up sales. Time-series forecasting methods can be useful to remove traditional fluctuations due to gap in months-of-year of festivals. We describe exponential smoothing techniques and trend fitting methods and compare the predictive accuracy. The accuracy is compared using root-mean square error and median absolute deviation. The exponential smoothing shows changing behavior with increased data size and data item values. The data is compared with and without tuning the seasonal effects due to festive season.

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26 Cites in Article

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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How to Cite This Article

Megha Chhabra, Deepti Sahu, Gunjan Agarwal. 2018. "Prediction and Judgmental Adjustments of Supply-Chain Planning in Festive Season". Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 17 (GJCST Volume 17 Issue G3).

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GJCST Volume 17 Issue G3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-G Classification D.4.8

Submission ReceivedDecember 14, 2016
Peer Review Double Blind
Handling Editor
Accepted January 1, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

January 22, 2018

Language

English

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Article Matrices

Total Score: 143

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Megha Chhabra, Deepti Sahu, Gunjan Agarwal (PhD/Dr. count: 0)

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Publish Date: 2017 01, Sun

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