Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

4 Issues per year

Open Access
Online
ISSN
2083-2567
See all formats and pricing
In This Section

A Data Mining Approach To Improve Military Demand Forecasting1

Rajesh Thiagarajan
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
  • Email:
/ Mustafizur Rahman
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
/ Don Gossink
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
/ Greg Calbert
  • Defence Science and Technology Organization, Edinburgh SA 5111, Australia.
Published Online: 2015-03-01 | DOI: https://doi.org/10.1515/jaiscr-2015-0009

Abstract

Accurately forecasting the demand of critical stocks is a vital step in the planning of a military operation. Demand prediction techniques, particularly autocorrelated models, have been adopted in the military planning process because a large number of stocks in the military inventory do not have consumption and usage rates per platform (e.g., ship). However, if an impending military operation is (significantly) different from prior campaigns then these prediction models may under or over estimate the demand of critical stocks leading to undesired operational impacts. To address this, we propose an approach to improve the accuracy of demand predictions by combining autocorrelated predictions with cross-correlated demands of items having known per-platform usage rates. We adopt a data mining approach using sequence rule mining to automatically determine cross-correlated demands by assessing frequently co-occurring usage patterns. Our experiments using a military operational planning system indicate a considerable reduction in the prediction errors across several categories of military supplies.

Footnotes

  • 1

    A preliminary version of this work appeared in ACIIDS 2014 [1]

References

  • [1] R. Thiagarajan, M. Rahman, G. Calbert, and D. Gossink, “Improving military demand forecasting using sequence rules,” in 6th Asian Conference on Intelligent Information and Database Systems (ACIIDS), pp. 475–484, 2014.

  • [2] S. Benjaafar, W. L. Cooper, and S. Mardan, “Production-inventory systems with imperfect advance demand information and updating,” Naval Research Logistics (NRL), vol. 58, no. 2, pp. 88–106, 2011. [Web of Science]

  • [3] F. Karaesmen, “Value of advance demand information in production and inventory systems with shared resources,” in Handbook of Stochastic Models and Analysis of Manufacturing System Operations, vol. 192, pp. 139–165, 2013.

  • [4] R. Thiagarajan, M. A. Mekhtiev, G. Calbert, N. Jeremic, and D. Gossink, “Using military operational planning system data to drive reserve stocking decisions,” in 29th IEEE International Conference on Data Engineering (ICDE) Workshops, pp. 153–162, 2013.

  • [5] E. Gardner, “Exponential smoothing: The state of the art Part II,” International Journal of Forecasting, vol. 22, no. 4, pp. 637–666, 2006. [Web of Science]

  • [6] G. Box, G. Jenkins, and G. Reinsel, Time Series Analysis: Forecasting and Control. 2008.

  • [7] M. Downing, M. Chipulu, U. Ojiako, and D. Kaparis, “Forecasting in airforce supply chains,” International Journal of Logistics Management, vol. 22, no. 1, pp. 127–144, 2011.

  • [8] R. Agrawal, T. Imieli´nski, and A. Swami, “Mining association rules between sets of items in large databases,” SIGMOD Rec., vol. 22, no. 2, pp. 207–216, 1993. [Crossref]

  • [9] H. Xiong, W. Zhou, M. Brodie, and S. Ma, “Top-k correlation computation,” INFORMS Journal on Computing, vol. 20, no. 4, pp. 539–552, 2008. [Crossref] [Web of Science]

  • [10] P. Fournier-Viger and V. S. Tseng, “TNS: mining top-k non-redundant sequential rules,” in ACM Symposium on Applied Computing (SAC), pp. 164–166, 2013.

  • [11] L.Wilkinson, “Tests of significance in stepwise regression,” Psychological Bulletin, vol. 86, no. 1, pp. 168–174, 1979.

  • [12] R. H. Myers, Classical and modern regression with applications, vol. 2. 1990.

  • [13] A. V. Oppenheim and R.W. Schafer, Discrete-Time Signal Processing. Prentice–Hall, 1989.

  • [14] P. Fournier-Viger, A. Gomariz, A. Soltani, and T. Gueniche, “SPMF: Open-Source Data Mining Platform - http://www.philippe-fournier-viger.com/spmf/,” 2013.

  • [15] A. Zeileis, dynlm: Dynamic Linear Regression, 2013. R package version 0.3-2.

  • [16] R. J. Hyndman, G. Athanasopoulos, S. Razbash, D. Schmidt, Z. Zhou, Y. Khan, and C. Bergmeir, forecast: Forecasting functions for time series and linear models, 2013. R package version 4.06.

  • [17] C. J. Willmott, “On the validation of models,” Physical Geography, vol. 2, no. 2, pp. 184–194, 1981.

About the article

Published Online: 2015-03-01

Published in Print: 2014-07-01


A preliminary version of this work appeared in ACIIDS 2014 [1]


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2015-0009. Export Citation

© Academy of Management (SWSPiZ), Lodz. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Comments (0)

Please log in or register to comment.
Log in