Demand Forecast at the Foodstuff Retail Segment: a Strategic Sustainability Tool at a Small-Sized Brazilian Company
Keywords:Demand forecasting. Sustainable supply chain. Sustainability
Demand forecasting plays an increasingly relevant role within competitive and globalized marketplaces, in as much as operations planning and subsequent transition into a sustainable chain of supplies, is concerned. To this effect, the purpose of this study is to present the application of demand forecasting as a strategic sustainability tool at a Brazilian SME. Therefore, this is a descriptive, ex-post facto and cross-cut, sectional time case study, which employs qualitative and historical quantitative and direct observational data and that utilizes, as both indicators of the level of service offered to consumers and of opportunity costs the artificial neural networks model and fill-rates, for demand forecasting and response purposes. The study further established cause-effect relationships between prediction accuracy, demand responsiveness and process-resulting economic, environmental and social performances. Findings additionally concurred with both widely acknowledged sustainability concepts - NRBV (Natural-Resource-Based View) and 3BL (Triple Bottom Line) - by demonstrating that demand forecasts ensure the efficient use of resources, improvements in customer responsiveness and also mitigate supply chain stock out and overstock losses. Further to the mentioned economic benefit, demand forecasting additionally reduced the amount of waste that arises from retail product shelf-life expiration, improving the addressing of demand itself and of customer satisfaction, thus driving consequent environmental and social gains.
Acar, Y. & Gardner, J. E. S. (2012). Forecasting method selection in a global supply. International Journal of Forecasting, Turkey, doi:10.1016/j.ijforecast.2011.11.003, 28(4), 842-848.
Almeida, F. C. & Passari, A. F. (2006) Previsão de vendas no varejo por meio de redes neurais. Revista de Administração, 41(3), 257-272.
Angelo, C. F., Zwicker, R., Fouto, N. M. M. D. & Luppe, M. R. (2011). Séries temporais e redes neurais: uma análise comparativa de técnicas na previsão de vendas do varejo brasileiro. Brazilian Business Review, 8(2), 1-21.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Buytendijk, F., Hatch, T. & Micheli, P. (2010). Scenario-based strategy maps. Business Horizons, 53(4), 335-347.
Castro, L. N. (2006). Fundamentals of natural computing: an overview. Physics of Life Reviews, 4(1), 1-36.
Chen, K-Y. (2011) Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Application, Taiwan, doi:10.1016/j.eswa.2011.02.049, 38(8), 10368-10376.
Chopra, S. & Meindl, P. (2003). Gerenciamento da cadeia de suprimentos: estratégia, planejamento e operações. São Paulo: Pearson Prentice Hall.
Chu, C-W & Zhang, G. P. (2003). A comparative of linear and nonlinear models for aggregate retail sales forecasting. International Journal of Production Economics, 86(3), 217-231.
Coelho, L. S., Santos, A. A. P., Costa Jr, N. C. A. (2008). Can we forecast Brazilian exchange rates? Empirical evidences using computational intelligence and econometric models. Gestão e Produção, Brazil, 15(3), 1-14.
Dias, S. R. (1993). Estratégia e canais de distribuição. São Paulo: Atlas.
Epstein, M. J. & Wisner, P. S. (2001). Using the balanced scorecard approach to implement sustainability. Environmental Quality Management, 11(2), 1-10.
Elkington, J. (1997). Cannibals with forks. United Kingdom: Capstone.
Gladwin, T. N., Kennelly, J. J. & Krause, T-S. (1995). Shifting paradigms for sustainable development: implications for management theory and research. Academy of Management Review, 20(4), 71-88.
Gupta, S. & Palsule-Desai, O. D. (2011, December). Sustainable supply chain management: review and research opportunities. IIMB Management Review, 23(4), 234-245.
Haykin, S. (2001). Redes neurais: princípios e prática (2a ed.). Porto Alegre: Bookman.
Häner, C. (2011). SMEs in turbulent times – a comparative analysis between Argentina, Brazil and European countries. Master Thesis (International Business Administration) – Wiesbaden Business School, Germany.
Hart, S. L. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), p. 990.
Kuo, R. J. & Xue, K. C. (1999, December). Fuzzy neural networks with application to sales forecasting. Fuzzy Sets and Systems, 108(2), 123-143.
Lee, S.-Y. (2008). Drivers for the participation of small and medium-sized suppliers in green supply chain initiatives. Supply Chain Management: An International Journal, 13(3), 185-198.
Lemme, C. F. (2010). O valor gerado pela sustentabilidade corporativa. In D. Zylberstajn & C. Lins, Sustentabilidade e geração de valor: a transição para o século XXI. Rio de Janeiro: Elsevier.
Levis, A. A. & Papageorgiou, G. (2005). Customer demand forecasting via support vector regression analysis. Chemical Engineering Research and Design, 83(A8), 1009-1018.
Lima, M. (2003). Estoque: custo de oportunidade e impacto sobre os indicadores financeiros. Recovered in June 14, 2013, from http://www.ilos.com.br.
Mazur, E. (2012). Green transformation of small business: achieving and going beyond environmental requirements. OECD Environmental Working Papers, n. 47, 1-50.
Meijden, V. D. L. H., Nunen, J. A. E. E. V. & Ramondt, A. (1994). Forecasting: bridging the gap between sales and manufacturing. International Journal Production Economics, 37(1), 101-114.
Moore, S. B. & Manring, S. L. (2009). Strategy development in small and medium sized enterprises for sustainability and increased value creation. Journal of Cleaner Production, 17, 276-282.
Pao, H-T. (2006). Comparing linear and nonlinear forecasts for Taiwan’s electricity consumption. Energy, Taiwan, 31, 2129-2141.
Price, D. H. R. & Sharp, J. A. (1985). Investigation of the impact of changes in demand forecasting method on the financial performance of an electricity supply undertaking. International Journal of Electrical Power & Energy Systems, 7(3), 131-137.
Romualdo, L. C. S., Baptista, E. & Vieira, D. R. (2010, Março/abril). Sistema Fuzzy-Expert: para previsão de séries temporais no supply chain. Logística e Supply Chain Management, Brazil, 15, ano III, 74-82.
Sarkis, J. (2012). A boundaries and flows perspective of green supply chain management. Supply Chain Management: An International Journal, 17(1), 2012-216.
Swanson, N. R. & White, H. (1997). Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models. International Journal of Forecasting, United States, 13, 439-461.
Terasvirta, T., Dijk, D. van & Medeiros, M. C. (2005). Linear models, smooth transition auto regressions, and neural networks for forecasting macroeconomic time series: a re-examination. International Journal of Forecasting, Sweden, 21, 755-774.
Teece, D. J., Pisano, G. & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Vachon, S. & Mao, Z. (2008, October). Linking supply chain strength to sustainable development: a country-level analysis. Journal of Cleaner Production, 16(15), 1552-1560.
Veiga, C. P. (2009). Análise de métodos quantitativos de previsão de demanda: estudo comparativo e desempenho financeiro. Dissertação (Mestrado em Engenharia de Produção e Sistemas) – Pontifícia Universidade Católica do Paraná, Brazil.
Veiga, C. R. P., Veiga, C. P. & Duclós, L. C. (2010). The accuracy of demand forecasting models as critical problem to financial performance in the food industry. Future Studies Research Journal: Trends and Strategies, Brazil, 2(2), 81-104.
Veiga, C. P., Veiga, C. P., Vieira, G. E. & Tortato, U. (2012). Impacto financeiro dos erros de previsão: um estudo comparativo entre modelos de previsão lineares e redes neurais aplicados na gestão empresarial. Produção Online, 12, 629-656.
Voss, C., Tsikriktsis, N. & Frohlich, M. (2002). Case research in operations management. International Journal of Operations & Production Management, 22(2), 195-219.
Xie, J., Lee, T. S. & Zhao, X. (2004). Impact of forecasting error on the performance of capacitated multi-item production systems. Computers & Industrial Engineering, 46, 205-219.
Wu, Z. & Pagell, M. (2011). Balancing priorities: decision-making in sustainable supply chain management. Journal of Operations Management, 29(6), 577-590.
Yin, R. (1987). Case study research: design and methods. Beverly Hills: Sage Publications.
Yokum, J. T. & Armstrong, J. S. (1995). Beyond accuracy: comparison of criteria used to select forecasting methods. International Journal of Forecasting, United States, 11, 591-597.
Zailani, S., Jeyaraman, K., Vengadasan, G. & Premkumar, R. (2012). Sustainable supply chain management (SSCM) in Malaysia, a survey. International Journal Production Economics, doi:10.1016/j.ijpe.2012.02.008, 14(1), 330-340.
Zeng, A. Z. (2000). Efficiency of using fill-rate criterion to determine safety stock: a theoretical perspective and a case study. Production and Inventory. Management Journal, United States, 41(2), 41-44.
Zorpas, A. (2010). Environmental management systems as sustainable tools in the way of life for the SMEs e VSMEs. Bioresource Technology, 101(6), 1544-1557.
Zotteri, G., Kalchschmidt, M. & Caniato, F. (2005). The impact of aggregation level on forecasting performance. International Journal of Production Economics, 93-94(8), 479-491.
Zylbersztajn, D. & Lins, C. (2007). Sustentabilidade e geração de valor: transição para o século XXI. Rio de Janeiro: Elsevier.
How to Cite
Authors who publish with this journal agree to the following terms:
1. Authors who publish in this journal agree to the following terms: the author(s) authorize(s) the publication of the text in the journal;
2. The author(s) ensure(s) that the contribution is original and unpublished and that it is not in the process of evaluation by another journal;
3. The journal is not responsible for the views, ideas and concepts presented in articles, and these are the sole responsibility of the author(s);
4. The publishers reserve the right to make textual adjustments and adapt texts to meet with publication standards.
5. Authors retain copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Atribuição NãoComercial 4.0 internacional, which allows the work to be shared with recognized authorship and initial publication in this journal.
6. Authors are allowed to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (e.g. publish in institutional repository or as a book chapter), with recognition of authorship and initial publication in this journal.
7. Authors are allowed and are encouraged to publish and distribute their work online (e.g. in institutional repositories or on a personal web page) at any point before or during the editorial process, as this can generate positive effects, as well as increase the impact and citations of the published work (see the effect of Free Access) at http://opcit.eprints.org/oacitation-biblio.html• 8. Authors are able to use ORCID is a system of identification for authors. An ORCID identifier is unique to an individual and acts as a persistent digital identifier to ensure that authors (particularly those with relatively common names) can be distinguished and their work properly attributed.