Advertisement

Improving the Food Manufacturing System by Using Simulation and DEA

  • Noor Fatin Kamarudin
  • Ruzanita Mat RaniEmail author
  • Faridah Abdul Halim
Conference paper

Abstract

This paper presents the application of simulation and Data Envelopment Analysis (DEA) in improving the food manufacturing system. Simulation and DEA are used to improve the system by identifying improvement models and determine the best improvement model. The simulation model is used to generate inputs and outputs of improvement models. DEA-BCC model with output orientation is used to determine the efficient improvement model which can maximize output with the given input. Then, cross efficiency is used to rank the efficient improvement models and select the best improvement model. The IP 4 is the best improvement model where the model suggested to relocate the workers. The application methods and the results given can assist the management of the company to make better decisions and can provide ideas to other SME companies for improving the performance of the food manufacturing system.

Keywords

Simulation DEA Cross efficiency Improvement model 

Notes

Acknowledgements

This study was made possible by the continuous support from Universiti Teknologi MARA Grant No. 600-IRMI/DANA 5/3/LESTARI (0130/2016).

References

  1. 1.
    Dora, M., Kumar, M., Van Goubergen, D., Molnar, A., Gellynck, X.: Operational performance and critical success factors of lean manufacturing in European food processing SMEs. Trends Food Sci. Technol. 31(2), 156–164 (2013)CrossRefGoogle Scholar
  2. 2.
    Gunday, G., Ulusoy, G., Kilic, K., Alpkan, L.: Effects of innovation types on firm performance. Int. J. Prod. Econ. 133(2), 662–676 (2011)CrossRefGoogle Scholar
  3. 3.
    Sargent, G.R.: Verification and validation of simulation models. In: Proceedings of the Winter Simulation Conference, pp. 166–183 (2010)Google Scholar
  4. 4.
    Anderson, D.R., Sweeney, D.J., Williams, T.A.: An Introduction to Management Science, pp. 619. Thomson South-Western, Ohio (2005)Google Scholar
  5. 5.
    Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag. Sci. 30(9), 1078–1092 (1984)Google Scholar
  6. 6.
    Sexton, T.R., Silkman, R.H., Hogan, J.A.: Data envelopment analysis: Critique and extensions. New Dir. Progr. Eval. 1986, 73–105 (1986)CrossRefGoogle Scholar
  7. 7.
    Mat Rani, R., Ismail, W.R., Ishak, I.: An integrated simulation and data envelopment analysis in improving SME food production system. World J. Model. Simul. 10(2), 136–147 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Noor Fatin Kamarudin
    • 1
  • Ruzanita Mat Rani
    • 1
    Email author
  • Faridah Abdul Halim
    • 1
  1. 1.Centre for Statistical and Decision Sciences Studies, Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia

Personalised recommendations