[フレーム]
Skip to main content
Log in

An Intelligent Approach to Demand Forecasting

  • Conference paper
  • First Online:

Abstract

Demand Forecasting, undeniably, is the single most important component of any organizations Supply Chain. It determines the estimated demand for the future and sets the level of preparedness that is required on the supply side to match the demand. It goes without saying that if an organization does not get its forecasting accurate to a reasonable level, the whole supply chain gets affected. Understandably, Over/Under-forecasting has deteriorating impact on any organizations Supply Chain and thereby on P and L. Having ascertained the importance of Demand Forecasting, it is only fair to discuss about the forecasting techniques which are used to predict the future values of demand. The input that goes in and the modelling engine which it goes through are equally important in generating the correct forecasts and determining the Forecast Accuracy. Here, we present a very unique model that not only pre-processes the input data, but also ensembles the output of two parallel advanced forecasting engines which uses state-of-the-art Machine Learning algorithms and Time-Series algorithms to generate future forecasts. Our technique uses data-driven statistical techniques to clean the data of any potential errors or outliers and impute missing values if any. Once the forecast is generated, it is post processed with Seasonality and Trend corrections, if required. Since the final forecast is the result of statistically pre-validated ensemble of multiple models, the forecasts are stable and accuracy variation is very minimal across periods and forecast horizons. Hence it is better at estimating the future demand than the conventional techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
17,985円 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Vlckova, V., Patak, M.: Role of demand planning in business process management. In: The 6th International Scientific Conference Business and Management 2010, pp. 1119–1126 (2010)

  2. Gregory Daniel Noble.: Application of Modern Principles to Demand Forecasting for Electronics, Domestic Appliances and Accessories (2009)

  3. Wei, M., Liu, Y.: Key Factors and Key Obstacles in Global Supply Chain Management: A Study in Demand Planning Process (2013)

  4. Vlckova, V., Patak, M.: Barriers of demand planning implementation. Econ. Manag. 16, 1000–1005 (2011)

  5. Barnett, V., Lewis, T., et al.: Outliers in Statistical Data, vol. 3. Wiley, New York (1994)

  6. Fox, A.J.: Outliers in time series. J. R. Stat. Soc. Series B (Methodological), 350–363 (1972)

  7. Watson, S.M., Tight, M., Clark, S., Redfern, E.: Detection of Outliers in Time Series (1991)

  8. Du, K.-L., Swamy, M.N.S.: Fundamentals of machine learning. In: Neural Networks and Statistical Learning, pp. 15–65. Springer (2014)

  9. Ghobbar, A.A., Friend, C.H.: Evaluation of forecasting methods for intermittent parts demand in the field of aviation: a predictive model. Comput. Oper. Res. 30(14), 2097–2114 (2003)

  10. Shenstone, L., Hyndman, R.J.: Stochastic models underlying croston’s method for intermittent demand forecasting. J. Forecast. 24(6), 389–402 (2005)

  11. Granger, C.W.J., Joyeux, R.: An introduction to longmemory time series models and fractional differencing. J. Time Ser. Anal. 1(1), 15–29 (1980)

  12. Hibon, M., Makridakis, S.: Arma Models and the Box—Jenkins Methodology (1997)

  13. Valipour, M., Banihabib, M.E., Behbahani, S.M.R.: Comparison of the arma, arima, and the autoregressive artificial neural network models in forecasting the monthly inflow of dez dam reservoir. J. Hydrol. 476, 433– 441 (2013)

  14. Wagner, N., Michalewicz, Z., Schellenberg, S., Chiriac, C., Mohais, A.: Intelligent techniques for forecasting multiple time series in real-world systems. Int. J. Intell. Comput. Cybern. 4(3), 284–310 (2011)

  15. Peter Zhang, G.: Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003)

  16. Tersine, R.J., Tersine, R.J.: Principles of Inventory and Materials Management (1994)

  17. Filzmoser, P., Liebmann, B., Varmuza, K.: Repeated double cross validation. J. Chemom. 23(4), 160–171 (2009)

  18. He, W., Wang, Z., Jiang, H.: Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing 72(1), 600–611 (2008)

  19. Lu, C.-J., Lee, T.-S., Chiu, C.-C.: Financial time series forecasting using independent component analysis and support vector regression. Decis. Support Syst. 47(2), 115–125 (2009)

  20. Muller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In International Conference on Artificial Neural Networks, pp. 999–1004. Springer (1997)

  21. Lai, R.K., Fan, C.-Y., Huang, W.-H., Chang,P.-C.: Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Syst. Appl. 36(2), 3761–3773 (2009)

  22. Golub, G.H., Heath, M., Wahba, G.: Generalized cross validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215–223 (1979)

  23. Rasmussen, C.E., Williams, C.K.: Gaussian Processes for Machine Learning, vol. 1. MIT Press Cambridge (2006)

  24. Menze, B.H., Michael Kelm, B., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., Hamprecht, F.A.: A comparison of random forest and its gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinf. 10(1), 213 (2009)

  25. Adhikari, N. C. D.: Prevention of heart problem using artificial intelligence. Int. J. Artif. Intell. Appl. (IJAIA) 9(2), (2018)

  26. Adhikari, N. C. D, Alka, A., Garg, R. Hpps: Heart problem prediction system using machine learning

  27. Opitz, D.W., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. (JAIR) 11, 169–198 (1999)

  28. Adhikari, N. C. D., Garg, R., Datt, S., Das, L., Deshpande, S., & Misra, A. (2017, December). Ensemble methodology for demand forecasting. In International Conference on Intelligent Sustainable Systems (ICISS), (pp. 846–851). IEEE (2017)

Download references

Acknowledgements

The complete list of the team which has worked in this project: Rajiv Ranjan∗, Arashdeep Singh∗, Shaivya Datt∗, Aamir Ahmed Khan∗, Mitav Kulshrestha∗, Pawan Kulkarni∗, Anubhav Rustogi∗, Aditya Iskande∗, Mandar Shirsavakar∗, Rabiya Gill∗, Heine van der Lende†, Srinivas Deshpande†, Srijan A†.

∗Advanced Analytics Team, SS Supply Chain Solutions Pvt. Ltd., Bangalore.

†Enterprise Information Management, Philips Lighting, Bangalore.

Author information

Authors and Affiliations

  1. SS Supply Chain Solutions Pvt. Ltd., Bengaluru, India

    Chinmaya Chandan, Gaurav Gupta, Rajat Garg, S. Teja & Lalit Das

  2. Enterprise Information Management, Philips Lighting, Bengaluru, India

    Ashutosh Misra

  3. AIG, Bengaluru, India

    Nimai Chand Das Adhikari

  4. Novartis, Bengaluru, India

    Nishanth Domakonda

Authors
  1. Nimai Chand Das Adhikari

    You can also search for this author in PubMed Google Scholar

  2. Nishanth Domakonda

    You can also search for this author in PubMed Google Scholar

  3. Chinmaya Chandan

    You can also search for this author in PubMed Google Scholar

  4. Gaurav Gupta

    You can also search for this author in PubMed Google Scholar

  5. Rajat Garg

    You can also search for this author in PubMed Google Scholar

  6. S. Teja

    You can also search for this author in PubMed Google Scholar

  7. Lalit Das

    You can also search for this author in PubMed Google Scholar

  8. Ashutosh Misra

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nimai Chand Das Adhikari .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Telecommunication Engineering, Czech Technical University in Prague, Czechia, Czech Republic

    Robert Bestak

  3. Department of Electrical Engineering, Dayeh University, Taiwan, Taiwan

    Joy Iong-Zong Chen

  4. Faculty of Informatics and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia

    Ivan Kotuliak

About this paper

Cite this paper

Adhikari, N.C.D. et al. (2019). An Intelligent Approach to Demand Forecasting. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8681-6_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8680-9

  • Online ISBN: 978-981-10-8681-6

  • eBook Packages: Engineering Engineering (R0)

Publish with us

Policies and ethics

AltStyle によって変換されたページ (->オリジナル) /