Automated Application of Inventory Optimization

  • Tomáš MartinovičEmail author
  • Kateřina Janurová
  • Kateřina Slaninová
  • Jan Martinovič
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)


We present automated application of inventory optimization based on sales forecast. Inventory stock optimization is very required issue by companies recent years, however inventory models are based on the sales expectation. Therefore, the problem of optimizing inventory stock is divided into two parts, sales forecast and setting optimal inventory. We describe an automated solution to model selection for sales forecast and the inventory setting based on those predictions. In the end, we present our validation of the system through historical simulation. We compare simulations results against real inventory levels. Due to the large number of different length time series, this simulation was run in parallel on cluster and was parallelized in R. The algorithms were developed and tested on inventory time series from real data sets of the K2 atmitec company (Sales forecasting and inventory optimization are parts of ERP solution K2, which is provided by K2 atmitec s.r.o. company,


Inventory optimization Sales forecasting Model selection Parallelization 



This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project ‘IT4Innovations excellence in science – LQ1602’ and from the Large Infrastructures for Research, Experimental Development and Innovations project ‘IT4Innovations National Supercomputing Center – LM2015070’.


  1. 1.
    Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts, Melbourne (2013). Accessed 26 April 2016
  2. 2.
    Asteriou, D., Hall, S.G.: Applied Econometrics, 2nd edn. Palgrave MacMillan, Basingstoke (2011)Google Scholar
  3. 3.
    Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer-Verlag, London (2005)zbMATHGoogle Scholar
  4. 4.
    Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22, 679–688 (2006)CrossRefGoogle Scholar
  5. 5.
    Russell, R.S., Taylor, B.W.: Operations Management. Wiley, Hoboken (2011)Google Scholar
  6. 6.
    Durbin, J., Koopman, S.J.: Time Series Analysis by State Space Methods. Oxford Statistical Science Series. Clarendon Press, Oxford (2001)zbMATHGoogle Scholar
  7. 7.
    Baxter, M., King, R.G.: Measuring business cycles approximate band-pass filters for economic time series. Technical report, National Bureau of Economic Research (1995)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Tomáš Martinovič
    • 1
    Email author
  • Kateřina Janurová
    • 1
  • Kateřina Slaninová
    • 1
  • Jan Martinovič
    • 1
  1. 1.IT4Innovations National Supercomputing Center, VŠB – Technical University of OstravaOstravaCzech Republic

Personalised recommendations