IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i22p9746-d1516684.html

Time Series Perspective on the Sustainability of the South African Food and Beverage Sector

Author

Listed:
  • Thabiso E. Masena

    (Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa)

  • Sarah L. Mahlangu

    (Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa)

  • Sandile C. Shongwe

    (Department of Mathematical Statistics and Actuarial Science, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa)

Abstract

This study aims to quantify and graphically illustrate the negative lingering effect that the COVID-19 pandemic had on the sales in South African Rands (ZAR) of the food and beverage sector using the time series seasonal autoregressive integrated moving average with exogenous components (SARIMAX) intervention model. The SARIMAX 2 , 1 , 0 0 , 1 , 2 12 intervention model provided the best fit, supported by the lowest values of the model selection and error metrics (Akaike’s information criterion, Bayesian information criterion, and root mean square error). The total estimated loss of sales in the 52 months during the intervention period (March 2020 to June 2024) amounts to ZAR 130,579 million. The most affected months were April 2020 and May 2020 with estimated losses of ZAR 7719 million and ZAR 7633 million, respectively. The findings of this study align with the Statistics South Africa ® statistical report based on empirical estimation without any model fitting, thus highlighting the effectiveness of the SARIMAX intervention model in quantifying the effects of the pandemic. The lingering negative impact of the COVID-19 pandemic still continues to threaten the sustainability of the South African food and beverage sector, violating the United Nations’ Sustainable Development Goal, Number 2, which is to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture.

Suggested Citation

  • Thabiso E. Masena & Sarah L. Mahlangu & Sandile C. Shongwe, 2024. "Time Series Perspective on the Sustainability of the South African Food and Beverage Sector," Sustainability, MDPI, vol. 16(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9746-:d:1516684
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/22/9746/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/22/9746/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa," Data, MDPI, vol. 8(5), pages 1-16, April.
    2. Marieke Theron & Rina Swart & Mukhethwa Londani & Charles Parry & Petal Petersen Williams & Nadine Harker, 2022. "Did COVID-19-Related Alcohol Sales Restrictions Reduce Alcohol Consumption? Findings from a National Online Survey in South Africa," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    3. Apedo Amah,Marie Christine & Avdiu,Besart & Cirera,Xavier & Vargas Da Cruz,Marcio Jose & Davies,Elwyn Adriaan Robin & Grover,Arti Goswami & Iacovone,Leonardo & Kilinc,Umut & Medvedev,Denis & Maduko,Fr, 2020. "Unmasking the Impact of COVID-19 on Businesses : Firm Level Evidence from Across the World," Policy Research Working Paper Series 9434, The World Bank.
    4. Su, Jen-Je & Amsler, Christine & Schmidt, Peter, 2012. "A note on the size of the KPSS unit root test," Economics Letters, Elsevier, vol. 117(3), pages 697-699.
    5. Yandisa Ngqangashe & Maddie Heenan & Melanie Pescud, 2021. "Regulating Alcohol: Strategies Used by Actors to Influence COVID-19 Related Alcohol Bans in South Africa," IJERPH, MDPI, vol. 18(21), pages 1-16, October.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. Thabiso Ernest Masena & Sandile Charles Shongwe & Ali Yeganeh, 2024. "Quantifying Loss to the Economy Using Interrupted Time Series Models: An Application to the Wholesale and Retail Sales Industries in South Africa," Economies, MDPI, vol. 12(9), pages 1-21, September.
    8. Barbara Gemmill-Herren, 2020. "Closing the circle: an agroecological response to covid-19," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 37(3), pages 613-614, September.
    9. Jennifer Min & Christine Lim & Hsien-Hung Kung, 2011. "Intervention analysis of SARS on Japanese tourism demand for Taiwan," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(1), pages 91-102, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    2. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    3. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    4. Tao XIONG & LI Chongguang & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    5. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    6. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    7. Md Siddikur Rahman & Arman Hossain Chowdhury & Miftahuzzannat Amrin, 2022. "Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh," PLOS Global Public Health, Public Library of Science, vol. 2(5), pages 1-13, May.
    8. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    9. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.
    10. Fijorek Kamil & Leśniewska Agnieszka, 2012. "Statistical Forecasting of the Indicators of Polish Airport’s Operations," Folia Oeconomica Stetinensia, Sciendo, vol. 11(1), pages 7-7, January.
    11. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    12. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Guerrero-Amezaga, Maria Elena & Humphries, John Eric & Neilson, Christopher A. & Shimberg, Naomi & Ulyssea, Gabriel, 2022. "Small firms and the pandemic: Evidence from Latin America," Journal of Development Economics, Elsevier, vol. 155(C).
    14. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    15. G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
    16. Thierry Moudiki & Frédéric Planchet & Areski Cousin, 2018. "Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks," Risks, MDPI, vol. 6(1), pages 1-20, March.
    17. Manuela Royer-Carenzi & Hossein Hassani, 2025. "Deviations from Normality in Autocorrelation Functions and Their Implications for MA(q) Modeling," Stats, MDPI, vol. 8(1), pages 1-37, February.
    18. Svitlana Galeshchuk, 2017. "Technological bias at the exchange rate market," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(2-3), pages 80-86, April.
    19. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    20. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9746-:d:1516684. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.