Research Article

Multi-criteria approach to adjust demand forecast for products: application of analytic hierarchy process

Lidiane Cristina de Oliveira; Bruna Cristine Scarduelli Pacheco; Claudio Luis Piratelli

Downloads: 0
Views: 471


Paper aims: Investigate whether the results of time series models can be adjusted with the AHP method towards a more assertive forecast.

Originality: Considering demand forecasting as a complex decision-making situation, this research investigated the use of the AHP as a complement to traditional forecasting methods.

Research method: This applied research employed, as main procedures, literature review and mathematical modeling.

Main findings: Two models were proposed that presented satisfactory results: model I reduced the forecast error by 16% in January, 25% in February, 37% in March, 3% in April, and 7% in May; model II reduced it by 17% in January, 21% in February, 29% in March, 2% in April, and 5% in May.

Implications for theory and practice: We conclude that the AHP has the potential to correct the results of time series in the textile industry by allowing the incorporation of quantitative and qualitative variables.


Demand forecasting, Analytic hierarchy process, Textile industry


Ackermann, F., & Eden, C. (2011). Strategic management of stakeholders: theory and practice. Long Range Planning, 44(3), 179-196.

Alalawin, A., Arabiyat, L. M., Alalaween, W., Qamar, A., & Mukattash, A. (2021). Forecasting vehicle’s spare parts price and demand. Journal of Quality in Maintenance Engineering, 27(3), 483-499.

Antosz, K., & Ratnayake, C. (2019). Spare parts criticality assessment and prioritization for enhancing manufacturing systems availability and reliability. Journal of Manufacturing Systems, 50, 212-225.

Banai-Kashani, A. (1984). Travel demand (modal split) estimation by hierarchical measurement. Journal of Advanced Transportation, 18(1), 37-54.

Bertrand, J. W. M., & Fransoo, J. C. (2002). Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, 22(2), 241-264.

Chen, F., Chen, J., & Liu, J. (2021). Forecast of flood disaster emergency material demand based on IACO-BP algorithm. Neural Computing & Applications, 34(Spe), 3537-3549.

Chwif, L., & Medina, A. C. (2015). Modeling and simulation of discrete events: theory & applications (4th ed.). Rio de Janeiro: Elsevier.

Costa, T. C., & Belderrain, M. C. N. (2009, October 19-22). Decisão em grupo em métodos multicritério de apoio à decisão. In M. Massi (Org.), Anais do 15º Encontro de Iniciação Científica e Pós-Graduação do Instituto Tecnológico de Aeronáutica (pp. 1-12). São José dos Campos, Brazil: Instituto Tecnológico de Aeronáutica.

Dodgson, J. S., Spackman, M., Pearman, A., & Phillips, L. (2009). Multi-criteria analysis: a manual. London: Department for Communities and Local Government.

Dyer, R. F., & Forman, E. H. (1991). An analytic approach to marketing decisions. Nova Jersey: Prentice Hall.

Fradinata, E., Suthummanon, S., & Sunthiamorntut, W. (2017). Comparison of hybrid ANN models: a case study of instant noodle industry in Indonesia. International Journal of Advanced and Applied Sciences, 4(8), 19-28.

Fu, Q., Lv, J., Zhao, Z., & Yue, D. (2020). Research on optimization method of VR task scenario resources driven by user cognitive needs. Information, 11(2), 64.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2005). Análise multivariada de dados (5th ed.). Porto Alegre: Bookman.

Hsu, C. C., & Sandford, B. A. (2007). The delphi technique: making sense of consensus. Practical Assessment, Research & Evaluation, 12(10), 1-8.

Ishizaka, A., & Nemery, P. (2013). Multi-criteria decision analysis: methods and software. Chichester: John Wiley & Sons.

Keeney, R. L. (2012). Value-focused brainstorming. Decision Analysis, 9(4), 303-313.

Koehler, A. B., Snyder, R. D., & Ord, J. K. (2001). Forecasting models and prediction intervals for the multiplicative Holt-Winters method. International Journal of Forecasting, 17(2), 269-286.

Korpela, J., & Tuominen, M. (1996). Inventory forecasting with a multiple criteria decision tool. International Journal of Production Economics, 45(1-3), 159-168.

Krajewski, L. J., & Ritzman, L. P. (1999). Operations management, strategy and analysis (5th ed.). New York: Addison-Wesley.

Lee, C. K., Song, H. J., & Mjelde, J. W. (2008). The forecasting of International Expo tourism using quantitative and qualitative techniques. Tourism Management, 29(6), 1084-1098. PMid:32287725.

Lee, M. W., Lee, J. S., Koo, C. R., & Yun, M. H. (1996). A model for estimating the potential demand of high touch product. Computers & Industrial Engineering, 31(3-4), 653-656.

Li, S. G., & Kuo, X. (2008). The inventory management system for automobile spare parts in a central warehouse. Expert Systems with Applications, 34(2), 1144-1153.

Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications (3rd ed.). New York: John Wiley & Sons.

Panagopoulos, G. P., Bathrellos, G. D., Skilodimou, H. D., & Martsouka, F. A. (2012). Mapping urban water demands using multi-criteria analysis and GIS. Water Resources Management, 26(5), 1347-1363.

Pandey, P., Kumar, S., & Shrivastava, S. (2015). A fuzzy decision making approach for analogy detection in new product forecasting. Journal of Intelligent & Fuzzy Systems, 28(5), 2047-2057.

Pellegrini, F., & Fogliatto, F. S. (2000). Estudo comparativo entre modelos de Winters e de BoxJenkins para a previsão de demanda sazonal. Revista Produto & Produção, 4(Spe), 72-85.

Pellegrini, F., & Fogliatto, F. S. (2001). Passos para implantação de sistemas de previsão de demanda-técnicas e estudo de caso. Production, 11(1), 43-64.

Prasad, R. D., & Raturi, A. (2017). Grid electricity for Fiji islands: future supply options and assessment of demand trends. Energy, 119, 860-871.

Rodrigues, D. S. S., Costa, H. G., Reis, A. C., Severo, E. A., & Guimarães, J. C. F. (2015). Demand forecasting process innovation using the analytic hierarchy process method. Revista GEINTEC, 5(4), 2526-2539.

Saaty, R. W. (1987). The analytic hierarchy process – what it is and how it is used. Mathematical Modelling, 9(3-5), 161-176.

Saaty, T. L. (1980). The analytic hierarchy process. Pittsburgh: RWS Publications.

Saaty, T. L., & Vargas, L. G. (2001). Models, methods, concepts & applications of the analytic hierarchy process (International Series in Operations Research & Management Science, 34). New York: Springer.

Schneider, M. J., & Gupta, S. (2016). Forecasting sales of new and existing products using consumer reviews: a random projections approach. International Journal of Forecasting, 32(2), 243-256.

Schoemaker, P. J. H. (1993). Multiple scenario development: its conceptual and behavioral foundation. Strategic Management Journal, 14(3), 193-213.

Shih, H., Stanley Lee, E., Chuang, S., & Chen, C. (2012). A forecasting decision on the sales volume of printers in Taiwan: an exploitation of the analytic network process. Computers & Mathematics with Applications, 64(6), 1545-1556.

Silva, B. W. (2019). Gestão de estoques: planejamento, execução e controle (2nd ed.). João Monlevade: BWS Consultoria.

Taylan, O., Alamoudi, R., Kabli, M., Aljifri, A., Ramzi, F., & Herrera-Viedma, E. (2020). Assessment of energy systems using extended fuzzy AHP, fuzzy VIKOR, and TOPSIS approaches to manage non-cooperative opinions. Sustainability, 12(7), 2745.

Tubino, D. F. (2017). Planejamento e controle da produção - teoria e prática (3rd ed.). São Paulo: Atlas.

Werner, L., & Ribeiro, J. L. D. (2006). Composite model to forecast demand through forecast integration. Production, 16(3), 493-509.

Wu, L., Xia, H., Cao, X., Zhang, C., & Dai, C. (2018). Research on quantitative demand of underground space development for urban rail transit station areas: a case study of metro line 1 in Xuzhou, China. Urban Rail Transit, 4(4), 257-273.

Xu, H., Li, W., Wang, T., & Yang, A. (2019). Research on dynamic prediction method for traffic demand based on trip generation analysis. Advances in Mechanical Engineering, 11(6), 1-9.

Yüksel, S. (2007). An integrated forecasting approach to hotel demand. Mathematical and Computer Modelling, 46(7-8), 1063-1070.

Zhou, N., Xu, B., Li, X., Cui, R., Liu, X., Yuan, X., & Zhao, H. (2020). An assessment model of fire resources demand for storage of hazardous chemicals. Process Safety Progress, 39(3), e12135.

Submitted date:

Accepted date:

62d97bbea95395224d3242e3 production Articles
Links & Downloads


Share this page
Page Sections