Forecasting as a framework for reducing food waste in Ethiopian university canteens

Document Type: Research Paper

Authors

1 Department of Mechanical Engineering, Faculty of Technology, Woldia University, Woldia, Ethiopia.

2 Department of Mechanical Engineering, Faculty of Engineering and Technology, Assosa University, Assosa, Ethiopia.

10.22105/jarie.2020.206803.1109

Abstract

This paper uses forecasting model to prevent over production of uneaten food in student’s cafeteria in Woldia University (Ethiopia). Students arrival in the university is highly variable. And it is difficult for the canteen management to estimate the number of students attend the meal during first two weeks of operation. The moving average and exponential smoothing forecasting methods were used to forecast the student’s arrival for the year 2019. Mean absolute deviation (MAD) was used as a measure of forecasting accuracy. Finally, it is found that moving average were more accurate forecasting method than exponential smoothing for forecasting student’s arrival in Woldia University.

Keywords

References

[1] Jamil Salmi, Andrée Sursock, and Anna Olefir. (2017). Improving the Performance of Ethiopian Universities in Science and Technology. World Bank Group.

[2] Ali, A., Ayele, A. (2019). ‘Contribution of Quality Tools for Reducing Food Waste in University Canteen”, Journal of Applied Research on Industrial Engineering, 6(2), pp.125-130. doi: 10.22105/jarie.2019.177566.1086.

[3] Waste minimization. Solid and Hazardous Waste Management. http://dx.doi.org/10.1016/B978-0-12-809734-2.00008-0.

[4] European Parliament Council. (2008). Directive 2008/1/EC of the European Parliament and of the Council of 15 January 2008 Concerning Integrated Pollution Prevention and Control. Brussels.

[5] Heizer J. and Render B. (2000), Operation Management, Prentice Hall, New Jersey.

[6] Adeniran, A. O., & Stephens, M. S. (2018). The Dynamics for Evaluating Forecasting Methods for International Air Passenger Demand in Nigeria. Journal of tourism & hospitality, 7(4), 1-11. doi: 10.4172/2167-0269.1000366.

[7] Song, H. and Li, G. (2008). Tourism demand modelling and forecasting – a review of recent research. Tourism Management, 29: 203-220.

[8] Lim, C. and McAleer, M. (2001). Forecasting tourist arrivals. Annals of Tourism Research, 28(4): 965-977.

[9] Goh, C. and Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tourism Management, 23: 499-510.

Journal of Applied Research on Industrial Engineering

Volume 6, Issue 4
Autumn 2019
Pages 374-380

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History

  • Receive Date: 01 September 2019
  • Revise Date: 11 October 2020
  • Accept Date: 08 December 2020

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