Abstract
During the current crisis generated by the COVID-19 pandemic, most businesses are facing serious challenges, e.g. supply chain disruptions, extra direct costs, changes in demand, or digital experience. Even those sectors which prosper or did not experience a significant drop in demand are reporting other difficulties and require decision-making support. In this context the carried work captures the impact of the current crisis on the small-medium enterprises (SMEs) which operate in the Romanian food and grocery market (FGM), by analysing a case study based on three different local proximity stores and explores the application of time series analysis.
In addition to identifying the challenges brought by the pandemic, the carried literature review reveals an increase in competition on the proximity sector of the Romanian FGM, and it highlights the importance of demand forecasting for improving managerial decision-making. The data analysis provides a number of insights regarding the pandemic’s impact on the case study SME, e.g. it generated an increase in sales (especially in the first 2 months), it increased the value of the shopping basket and reduced the number of individual customers, and it altered the percentage distribution of product categories.
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References
Anderson, D. R., Sweeney, D. J., Williams, T. A., Freeman J., & Shoesmith, E. (2007) Statistics for Business and Economics, International Edition. Thomson, London.
Arora (2020) A global view of how consumer behaviour is changing amid COVID-19 [WWW Document]. URL https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-global-view-of-how-consumer-behavior-is-changing-amid-covid-19#. Accessed on 22 Aug 2020.
Arunraj, N. S., & Ahrens, D. (2015). A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting. International Journal of Production Economics, 170, 321–335. https://doi.org/10.1016/j.ijpe.2015.09.039.
Census (2011). Romanian 2011 Census [WWW Document]. URL http://www.recensamantromania.ro/rezultate-2/. Accessed on 16 July 2020.
Chen, F. L., & Ou, T. Y. (2011). Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), 1336–1345. https://doi.org/10.1016/j.eswa.2010.07.014.
Craighead, C. W., Ketchen, D. J., & Darby, J. L. (2020). Pandemics and supply chain management research: Toward a theoretical toolbox. Decision Sciences, 51(4), 838–866. https://onlinelibrary.wiley.com/doi/pdf/10.1111/deci.12468.
Da Veiga, C., Veiga, C. R. P., Catapan, A., Tortato, U., & Silva, W. (2013). Demand forecast at the foodstuff retail segment: A strategic sustainability tool at a small-sized Brazilian company. Future Studies Research Journal: Trends and Strategies, vol., 5(2), 116–137. ISSN 2175-5825.
Da Veiga, C., da Veiga, C. R. P., Catapan, A., Tortato, U., & Silva, W. (2014). Demand forecasting in food retail: A comparison between the Holt-winters and ARIMA models. WSEAS Transactions on Business and Economics, 11, 608–614. E-ISSN: 2224-2899.
Dej24 (2019). EXCLUSIV. Magazin LIDL la Dej! [WWW Document]. URL https://dej24.ro/exclusiv-magazin-lidl-la-dej/. Accessed on 16 July 2020.
Dewhurst, F. (2006). Quantitative methods for business and management (2nd ed.). Maidenhead: McGraw-Hill.
Doganis, P., Alexandridis, A., Patrinos, P., & Sarimveis, H. (2005). Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering, vol., 75(2), 196–204. https://doi.org/10.1016/j.jfoodeng.2005.03.056.
EUR-Lex (2003). Commission Recommendation of 6 May 2003 concerning the definition of micro, small and medium-sized enterprises [WWW Document]. URL http://publications.europa.eu/resource/cellar/6ca8d655-126b-4a42-ada4-e9058fa45155.0004.01/DOC_1. Accessed on 18 July 2020.
Euromonitor (2020a) Passport - Retail Industry Overview: the Coronavirus Era Outlook. Euromonitor international report, pp. 1–38.
Euromonitor (2020b) Passport - Retailing in Romania. Euromonitor international report, pp. 1–148.
Felix, I., Martin, A., Mehta, V., & Mueller, C. (2020) US food supply chain: Disruptions and Implications from COVID-19 [WWW Document]. URL https://www.mckinsey.com/~/media/McKinsey/Industries/Consumer%20Packaged%20Goods/Our%20Insights/US%20food%20supply%20chain%20Disruptions%20and%20implications%20from%20COVID%2019/US-food-supply-chain-Disruptions-and-implications-from-COVID-19-vF.pdf. Accessed on 22 Aug 2020.
GherlaInfo (2020). Se deschide Kaufland la Dej, când e inaugurarea – primele foto din interior. [WWW Document]. URL https://www.gherlainfo.ro/se-deschide-kaufland-la-dej-cand-e-inaugurarea-primele-foto-din-interior/. Accessed on 16 July 2020.
Hyndman, R. & Athanasopoulos, G. (2013). Forecasting: principles and practice [WWW Document]. URL https://otexts.com/fpp2/classical-decomposition.html. Accessed on 18 July 2020.
Investopedia (2020). What Is a Good Profit Margin for Retailers? [WWW Document]. URL https://www.investopedia.com/ask/answers/071615/what-profit-margin-usual-company-retail-sector.asp#citation-3. Accessed on 18 July 2020.
Islek, I., & Oguducu, S. G. (2015). A retail demand forecasting model based on data mining techniques. IEEE 24th International Symposium on Industrial Electronics (ISIE), pp.55–60. https://doi.org/10.1109/ISIE.2015.7281443.
Kaneko, Y., & Yada, K. (2016). A deep learning approach for the prediction of retail store sales. IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 531–537. https://doi.org/10.1109/ICDMW.2016.0082.
Loske, D. (2020). The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics. Transport Research Interdisciplinary Perspectives, 6, 1–6. https://doi.org/10.1016/j.trip.2020.100165.
Lu, C.-J., & Chang, C.-C. (2014). A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression. The Scientific World Journal, 55, 231–238. https://doi.org/10.1109/TLA.2017.8015086.
MFinante (2020). Agenţi economici şi instituţii publice - date de identificare, informaţii fiscale, bilanţuri [WWW Document]. URL http://www.mfinante.ro/pjuridice.html. Accessed on 16 July 2020.
OECD (2020). COVID-19 and the retail sector: impact and policy responses [WWW Document]. URL https://read.oecd-ilibrary.org/view/?ref=134_134473-kuqn636n26&title=COVID-19-and-the-retail-sector-impact-and-policy-responses. Accessed on 22 Aug 2020.
Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research, 116, 209–213. https://doi.org/10.1016/j.jbusres.2020.05.036.
Ramlan, R., Atan, S. A., & Rakiman, U. S. (2012). Demand forecasting in SME’s inventory management”, Proceedings International Conference on Technology Management, Business and Entrepreneurship, pp.792–796 [WWW Document]. URLhttp://eprints.uthm.edu.my/id/eprint/3396/1/ENT075.pdf. Accessed 14 July 2020.
Retail&FMCG (2013). Noul concept Profi City in imagini. [WWW Document]. URL https://www.retail-fmcg.ro/retail/retail-national/noul-concept-profi-city-in-imagini.html. Accessed on 18 July 2020.
RevistaProgresiv (2013). Profi deschide primul sau magazin din Bacau. [WWW Document]. URL https://revistaprogresiv.ro/news/profi-deschide-primul-sau-magazin-din-bacau. Accessed on 18 July 2020.
Roggeveen, A. L., & Sethuraman, R. (2020). How the COVID-19 pandemic may change the world of retailing. Journal of Retailing Editorial, 96(2), 169–171. https://doi.org/10.1016/j.jretai.2020.04.002.
Sarhani, M., El Afia, A. (2014). Intelligent system based support vector regression for supply chain demand forecasting. World Conference on Complex Systems (WCCS), pp. 79–83. https://doi.org/10.1109/ICoCS.2014.7060941.
Savan, E.E. (2015). Consumer liking and sensory attribute prediction for new product development support: applications and enhancements of belief rule-based methodology. PhD Thesis, [WWW Document]. URL https://www.escholar.manchester.ac.uk/jrul/item/?pid=uk-ac-man-scw:293877. Accessed on 16 July 2020.
Savan, E. E. (2018). Time series analysis for small-medium enterprises strategic pricing: A case study from Romanian small convenience stores. Studia Negotia, 63(1), 55–82. https://doi.org/10.24193/subbnegotia.2018.1.03.
Stanciu, S., Vochin, O. A., Sârbu, M. A., and Bratoveanu, B. D. (2019). Food retail in Romania. Market Overview. Proceedings of BASIQ International Conference New Trends in Sustainable Business and Consumption, pp. 785–792. ISSN: 2457-483X.
Stern. (2020). Margins by Sector (US) [WWW Document]. URL http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/margin.html. Accessed on 18 July 2020.
Tarallo, E., Akabane, G. K., Shimabukuro, C. I., Mello, J., & Amancio, D. (2019). Machine learning in predicting demand for fast-moving consumer goods: An exploratory research. IFAC-PapersOnLine, 52(13), 737–742. https://doi.org/10.1016/j.ifacol.2019.11.203.
van Donselaar, K., Peters, J., Jong, A., & Broekmeulen, R. (2015). Analysis and forecasting of demand during promotions for perishable items. International Journal of Production Economics, 172. https://doi.org/10.1016/j.ijpe.2015.10.022.
Wang, Y., Xu, R., Schvarts, M., Ghosh, D., & Chen, X. (2020). COVID-19 and retail grocery management: Insights from a broad-based consumer survey. IEEE Engineering Management Review. https://doi.org/10.1109/EMR.2020.3011054.
Yohn, D.L. (2020). The Pandemic is Rewriting the Rules of Retail, Harvard Business Review [WWW Document]. URL https://hbr.org/2020/07/the-pandemic-is-rewriting-the-rules-of-retail. Accessed on 22 Aug 2020.
Yue, L., Yafeng, Y., Junjun, G., & Chongli, T. (2007). Demand forecasting by using support vector machine. Proc. 3rd Int. Conf. Natural Comput., 3, 272–276. https://doi.org/10.1109/ICNC.2007.324.
Yue, L., Wangwei, J., Jianguo, Z., Junjun, G., Jiazhou, Z., & Aiping, J. (2016). Product life cycle based demand forecasting by using artificial bee colony algorithm optimized two-stage polynomial fitting. Journal of Intelligent & Fuzzy Systems, 31(2), 825–837.
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Savan, EE., Gica, O., Sofica, A. (2022). Retail Demand Forecasting for Small-Medium Enterprises During COVID-19 Pandemic: Case Studies Based on Romanian Convenience Stores. In: Fotea, S.L., Fotea, I.Ş., Văduva, S. (eds) Navigating Through the Crisis – A special Issue on the Covid 19 Crises. GSMAC 2020. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-82755-7_7
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