Supervised Machine Learning for Predicting SMME Sales: An Evaluation of Three Algorithms
DOI:
https://doi.org/10.23962/10539/31371Keywords:
Supervised machine learning, Algorithms, Sales predictive modelling, Ordinary least squares (OLS), Least absolute shrinkage and selection operator (LASSO), Artificial neural networks (ANNs), Small, medium and micro enterprises (SMMEs)Abstract
The emergence of machine learning algorithms presents the opportunity for a variety of stakeholders to perform advanced predictive analytics and to make informed decisions. However, to date there have been few studies in developing countries that evaluate the performance of such algorithms—with the result that pertinent stakeholders lack an informed basis for selecting appropriate techniques for modelling tasks. This study aims to address this gap by evaluating the performance of three machine learning techniques: ordinary least squares (OLS), least absolute shrinkage and selection operator (LASSO), and artificial neural networks (ANNs). These techniques are evaluated in respect of their ability to perform predictive modelling of the sales performance of small, medium and micro enterprises (SMMEs) engaged in manufacturing. The evaluation finds that the ANNs algorithm’s performance is far superior to that of the other two techniques, OLS and LASSO, in predicting the SMMEs’ sales performance.
References
Adegbite, S., Ilori, M., Irefin, I. A., Abereijo, I., & Aderemi, H. O. S. (2007). Evaluation of the impact of entrepreneurial characteristics on the performance of smallscale manufacturing industries in Nigeria. Journal of Asia Entrepreneurship and Sustainability, 3(1), 1–21.
Ahangar, R. G., Yahyazadehfar, M., & Pournaghshband, H. (2010). The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in Tehran Stock Exchange. International Journal of Computer Science and Information Security (IJCSIS), 7(2), 38–46.
Al-Ani, M. K. (2013). Effects of assets structure on the financial performance: Evidence from sultanate of Oman. In 11th EBES Conference proceedings, 12–14 September, Ekaterinburg, Russia.
Amran, N. A. (2011). The effect of owner’s gender and age to firm performance: A review on Malaysian public listed family businesses. Journal of Global Business and Economics, 2(1), 104–116.
Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. In T. Lynn, J. Mooney, P. Rosati, & M. Cummins (Eds.), Disrupting finance (pp. 33–50). Palgrave Pivot. https://doi.org/10.1007/978-3-030-02330-0_3
Bajari, P., Nekipelov, D., Ryan, S. P., & Yang, M. (2015). Machine learning methods for demand estimation. American Economic Review, 105(5), 481–485. https://doi.org/10.1257/aer.p20151021
Bardasi, E., Sabarwal, S., & Terrell, K. (2011). How do female entrepreneurs perform? Evidence from three developing regions. Small Business Economics, 37(4), 417–471. https://doi.org/10.1007/s11187-011-9374-z
Bauer, M. (2020). Machine learning framework for small and medium-sized enterprises. SSRN 3532389. https://doi.org/10.2139/ssrn.3532389
Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: Making an informed choice. Quality & Quantity, 53(2), 1051–1074. https://doi.org/10.1007/s11135-018-0802-x
Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3(1), 133–153. https://doi.org/10.1017/psrm.2014.7
Bellone, F., Musso, P., Nesta, L., & Quere, M. (2008). Market selection along the firm life cycle. Industrial and Corporate Change, 17(4), 753–777. https://doi.org/10.1093/icc/dtn025
Bigsten, A., & Gebreeyesus, M. (2007). The small, the young, and the productive: Determinants of manufacturing firm growth in Ethiopia. Economic Development and Cultural Change, 55(4), 813–840. https://doi.org/10.1086/516767
Bureau for Economic Research. (n.d.). ABSA purchasing managers’ index. https://www.ber.ac.za/BER%20Documents/ABSA-PMI/?doctypeid=1066
Buyinza, F. (2011). Performance and survival of Ugandan manufacturing firms in the context of the East African Community. https://ideas.repec.org/p/ags/eprcrs/150477.html
Camilleri, M. A. (2018). The SMEs’ technology acceptance of digital media for stakeholder engagement. Journal of Small Business and Enterprise Development, 26(4), 504–521. https://doi.org/10.1108/JSBED-02-2018-0042
Casella, G., Fienberg, S., & Olkin, I. (Eds.). (2017). An introduction to statistical learning with applications in R. Springer Texts in Statistics.
Castelli, M., Dobreva, M., Henriques, R., & Vanneschi, L. (2020). Predicting days on market to optimize real estate sales strategy. Complexity, 2020. https://doi.org/10.1155/2020/4603190
Chadwick, C., & Flinchbaugh, C. (2016). The effects of part-time workers on establishment financial performance. Journal of Management, 42(6), 1635–1662. https://doi.org/10.1177/0149206313511116
Chen, J., De Hoogh, K., Gulliver, J., Hoffmann, B., Hertel, O., Ketzel, M., Hoek, G. (2019). A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environment International, 130. https://doi.org/10.1016/j.envint.2019.104934
Cheriyan, S., Ibrahim, S., Mohanan, S., & Treesa, S. (2018). Intelligent sales prediction using machine learning techniques. In 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, UK, 16–17 August. https://doi.org/10.1109/iCCECOME.2018.8659115
Clinebell, S. K., & Clinebell, J. M. (2007). Differences between part-time and full-time employees in the financial services industry. Journal of Leadership & Organizational Studies, 14(2), 157–167. https://doi.org/10.1177/1071791907308053
Coad, A., Holm, J. R., Krafft, J., & Quatraro, F. (2018). Firm age and performance. Journal of Evolutionary Economics, 28(1), 1–11. https://doi.org/10.1007/s00191-017-0532-6
Crane-Droesch, A. (2017). Semiparametric panel data models using neural networks. https://arxiv.org/abs/1702.06512
Croda, R. M. C., Romero, D. E. G., & Morales, S.-O. C. (2019). Sales prediction through neural networks for a small dataset. IJIMAI, 5(4), 35–41. https://doi.org/10.9781/ijimai.2018.04.003
Curran-Everett, D. (2018). Explorations in statistics: The log transformation. Advances in Physiology Education, 42(2), 343–347. https://doi.org/10.1152/advan.00018.2018
Das, B., Nair, B., Reddy, V. K., & Venkatesh, P. (2018). Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. International Journal of Biometeorology, 62(10), 1809–1822. https://doi.org/10.1007/s00484-018-1583-6
De Kok, J., Ichou, A., & Verheul, I. (2010). New firm performance: Does the age of founders affect employment creation. Zoetermeer: EIM Research Reports, 12, 42–63.
Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40(10), 3970–3983. https://doi.org/10.1016/j.eswa.2013.01.012
Dod, H. S., & Sharma, R. (2010). Competing with business analytics: Research in progress. In D. N. Hart, & S. D. Gregor (Eds.), Information systems foundations: Theory building in information systems (pp. 239–249). ANU E Press.
Droomer, M., & Bekker, J. (2020). Using machine learning to predict the next purchase date for an individual retail customer. South African Journal of Industrial Engineering, 31(3), 69–82. https://doi.org/10.7166/31-3-2419
Egbunike, C. F., & Okerekeoti, C. U. (2018). Macroeconomic factors, firm characteristics and financial performance. Asian Journal of Accounting Research, 3(2), 142–168. https://doi.org/10.1108/AJAR-09-2018-0029
Enkono, F. S., & Suresh, N. (2020). Application of machine learning classification to detect fraudulent e-wallet deposit notification SMSes. The African Journal of Information and Communication, 25, 1–12. https://doi.org/10.23962/10539/29195
Essel, B. K. C., Adams, F., & Amankwah, K. (2019). Effect of entrepreneur, firm, and institutional characteristics on small-scale firm performance in Ghana. Journal of Global Entrepreneurship Research, 9(1), 55–75. https://doi.org/10.1186/s40497-019-0178-y
Esteve-Pérez, S., & Mañez-Castillejo, J. A. (2006). The resource-based theory of the firm and firm survival. Small Business Economics, 30(3), 231–249. https://doi.org/10.1007/s11187-006-9011-4
Farahani, D. S., Momeni, M., & Amiri, N. S. (2016). Car sales forecasting using artificial neural networks and analytical hierarchy process. In Fifth International Conference on Data Analytics, 9–13 October, Venice.
Gepp, A., & Kumar, K. (2012). Business failure prediction using statistical techniques: A review. In K. Kumar, & A. Chaturvedi (Eds.), Some recent developments in statistical theory and applications (pp. 1–25). Brown Walker Press.
Gholizadeh, P., Esmaeili, B., & Memarian, B. (2018). Evaluating the performance of machine learning algorithms on construction accidents: An application of ROC curves. In Construction Research Congress 2018, New Orleans. https://doi.org/10.1061/9780784481288.002
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org/
Gupta, P. D., Guha, S., & Krishnaswami, S. S. (2013). Firm growth and its determinants. Journal of Innovation and Entrepreneurship, 2(1), 1–14. https://doi.org/10.1186/2192-5372-2-15
Haataja, T. (2016). Sales forecasting in small and medium-sized enterprises. Master’s thesis, Helsinki Metropolia University of Applied Sciences. https://www.theseus.fi/handle/10024/106191
Halicioglu, F., & Yolac, S. (2015). Testing the impact of unemployment on self-employment: Evidence from OECD countries. Procedia – Social and Behavioral Sciences, 195, 10–17. https://doi.org/10.1016/j.sbspro.2015.06.161
Harris, E. S. (1991). Tracking the economy with the purchasing managers’ index. Federal Reserve Bank of New York, Quarterly Review, 16(3).
Huggins, R., Prokop, D., & Thompson, P. (2017). Entrepreneurship and the determinants of firm survival within regions: Human capital, growth motivation and locational conditions. Entrepreneurship & Regional Development, 29(3–4), 357–389. https://doi.org/10.1080/08985626.2016.1271830
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Jobs, C. G., & Gilfoil, D. M. (2014). A social media advertising adoption model for reallocation of traditional advertising budgets. Academy of Marketing Studies Journal, 18(1), 235–248.
Kaunda, C. M. (2013). Entrepreneurial orientation, age of owner and small business performance in Johannesburg. Master’s dissertation, University of the Witwatersrand, Johannesburg.
Klapper, L., & Richmond, C. (2011). Patterns of business creation, survival and growth: Evidence from Africa. World Bank. https://doi.org/10.1596/1813-9450-5828
Koenig, E. F. (2002). Using the purchasing managers’ index to assess the economy’s strength and the likely direction of monetary policy. Federal Reserve Bank of Dallas, Economic & Financial Policy Review, 1(6), 1–14. https://core.ac.uk/download/pdf/6971097.pdf
Kolkman, D., & Van Witteloostuijn, A. (2019). Data science in strategy: Machine learning and text analysis in the study of firm growth. SSRN. https://doi.org/10.2139/ssrn.3457271
Krishna, D., Albinson, N., Chu, Y., & Burdis, J. (2017). Managing algorithmic risks: Safeguarding the use of complex algorithms and machine learning. Deloitte.
Lantz, B. (2019). Machine learning with R: Expert techniques for predictive modeling. Packt Publishing.
Leo, M., Sharma, S., & Maddulety, K. (2019). Machine learning in banking risk management: A literature review. Risks, 7(1), 29–51. https://doi.org/10.3390/risks7010029
Loderer, C. F., & Waelchli, U. (2010). Firm age and performance. SSRN 1342248. https://doi.org/10.2139/ssrn.1342248
Maggina, A., & Tsaklanganos, A. (2012). Asset growth and firm performance evidence from Greece. The International Journal of Business and Finance Research, 6(2), 113–124.
Melkumova, L., & Shatskikh, S. Y. (2017). Comparing Ridge and LASSO estimators for data analysis. Procedia Engineering, 201, 746–755.
https://doi.org/10.1016/j.proeng.2017.09.615
Merkel, G. D., Povinelli, R. J., & Brown, R. H. (2018). Short-term load forecasting of natural gas with deep neural network regression. Energies, 11(8), 2008. https://www.mdpi.com/1996-1073/11/8/2008
Meroño-Cerdan, A. L., & Soto-Acosta, P. (2005). Examining e-business impact on firm performance through website analysis. International Journal of Electronic Business, 3(6), 583–598. https://doi.org/10.1504/IJEB.2005.008537
Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016). Machine learning: Algorithms and applications. CRC Press. https://doi.org/10.1201/9781315371658
Motoki, F. Y. S., & Gutierrez, C. E. C. (2015). Firm performance and business cycles: Implications for managerial accountability. Applied Finance and Accounting, 1(1), 47–59. https://doi.org/10.11114/afa.v1i1.647
Muriithi, S. (2017). African small and medium enterprises (SMEs) contributions, challenges and solutions. European Journal of Research and Reflection in Management Sciences, 5(1), 36–48.
Muthukrishnan, R., & Rohini, R. (2016). LASSO: A feature selection technique in predictive modeling for machine learning. In IEEE International Conference on Advances in Computer Applications (ICACA), 24 October 2016, Coimbatore, India. https://doi.org/10.1109/ICACA.2016.7887916
Ndikum, P. (2020). Machine learning algorithms for financial asset price forecasting. arXiv:2004.01504. https://arxiv.org/abs/2004.01504
Nghiep, N., & Al, C. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313–336. https://doi.org/10.1080/10835547.2001.12091068
Obaid, O. I., Mohammed, M. A., Ghani, M., Mostafa, A., & Taha, F. (2018). Evaluating the performance of machine learning techniques in the classification of Wisconsin breast cancer. International Journal of Engineering & Technology, 7(4.36), 160–166.
Panda, D. (2015). Growth determinants in small firms: Drawing evidence from the Indian agro-industry. International Journal of Commerce Management, 25(1), 52–66. https://doi.org/10.1108/IJCoMA-12-2012-0080
Parsons, A. (2013). Using social media to reach consumers: A content analysis of official Facebook pages. Academy of Marketing Studies Journal, 17(2), 27.
Pauka, K. (2015). How does part-time work affect firm performance and innovation activity? WWZ Working Paper No. 2015/05.
Penpece, D., & Elma, O. E. (2014). Predicting sales revenue by using artificial neural network in grocery retailing industry: A case study in Turkey. International Journal of Trade, Economics and Finance, 5(5), 435–440. https://doi.org/10.7763/IJTEF.2014.V5.411
Phillipson, J., Tiwasing, P., Gorton, M., Maioli, S., Newbery, R., & Turner, R. (2019). Shining a spotlight on small rural businesses: How does their performance compare with urban? Journal of Rural Studies, 68, 230–239. https://doi.org/10.1016/j.jrurstud.2018.09.017
Punam, K., Pamula, R., & Jain, P. K. (2018). A two-level statistical model for big mart sales prediction. In 2018 International Conference on Computing, Power and Communication Technologies (GUCON). https://doi.org/10.1109/GUCON.2018.8675060
R Development Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ratnasena, N. H., Rich, D. C., Abraham, A. M., Cunha, L. L., & Morgan, S. L. (2021). Detection of magnetic audio tape degradation with neural networks and Lasso. Journal of Chemometrics, 35(1), e3194. https://doi.org/10.1002/cem.3194
Rijkers, B., Söderbom, M., & Loening, J. L. (2010). A rural–urban comparison of manufacturing enterprise performance in Ethiopia. World Development, 38(9), 1278–1296. https://doi.org/10.1016/j.worlddev.2010.02.010
Roca‐Puig, V., Beltrán‐Martín, I., & Cipres, M. S. (2012). Combined effect of human capital, temporary employment and organizational size on firm performance. Personnel Review, 41(1), 4–22. https://doi.org/10.1108/00483481211189910
Ryll, L., & Seidens, S. (2019). Evaluating the performance of machine learning algorithms in financial market forecasting: A comprehensive survey. https://arxiv.org/abs/1906.07786
Sekban, J. (2019). Applying machine learning algorithms in sales prediction. Master’s thesis, Kadİrhas University, Istanbul. https://academicrepository.khas.edu.tr/handle/20.500.12469/2782
Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.
https://doi.org/10.1017/CBO9781107298019
Small Business Project. (2014). Examining the challenges facing small businesses in South Africa.
Statistics South Africa. (2018). Quarterly labour force survey: Quarter 2. http://www.statssa.gov.za/publications/P0211/P02112ndQuarter2018.pdf
Strandberg, R., & Låås, J. (2019). A comparison between neural networks, Lasso regularized logistic regression, and gradient boosted trees in modeling binary sales. Master’s project, KTH Royal Institute of Technology, Stockholm.
Te, Y.-F. (2018). Predicting the financial growth of small and medium-sized enterprises using web mining. Doctoral thesis, ETH Zurich. https://www.research-collection.ethz. ch/handle/20.500.11850/309271
Thorsteinson, T. J. (2003). Job attitudes of part‐time vs. full‐time workers: A meta‐analytic review. Journal of Occupational and Organizational Psychology, 76(2), 151–177. https://doi.org/10.1348/096317903765913687
Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: A retrospective. Statistical Methodology, 73(3), 273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x
Tsoumakas, G. (2019). A survey of machine learning techniques for food sales prediction. Artificial Intelligence Review, 52(1), 441–447. https://link.springer.com/article/10.1007/s10462-018-9637-z
Van Liebergen, B. (2017). Machine learning: A revolution in risk management and compliance? Journal of Financial Transformation, 45, 60–67. https://ideas.repec.org/a/ris/jofitr/1592.html
Venishetty, S. V. (2019). Machine learning approach for forecasting the sales of truck components. Master’s thesis, Blekinge Institute of Technology.
Wang, P.-H., Lin, G.-H., & Wang, Y.-C. (2019). Application of neural networks to explore manufacturing sales prediction. Applied Sciences, 9(23), 5107. https://doi.org/10.3390/app9235107
Youn, H., & Gu, Z. (2010). Predicting Korean lodging firm failures: An artificial neural network model along with a logistic regression model. International Journal of Hospitality Management, 29(1), 120–127. https://doi.org/10.1016/j.ijhm.2009.06.007
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