Abstract
In the last quarter of 2018, the manufacturing sector grew 11.4%, of which 19.6% was accounted for by the food industry. However, one of the main problems faced by micro and small companies is poor management, since many of these businesses plan based on experience. In addition, inaccurate demand forecasting generates losses for these organizations due to overproduction or understocking. While the former triggers losses from elevated operating costs, the latter leads to loss of revenue and unsatisfied customers and compromises future demand rates. Therefore, a demand management model was designed to provide accurate and relevant information, which may improve production planning based on the continuous improvement approach. The model increased the planning from 91.2% to 96.2% in a micro business dedicated to the production and sale of bread.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mohamed, A.B., Mohd, T.T., Mustafa, O.: Developing cash waqf model as an alternative source of financing for micro enterprises in Malaysia. J. Isl. Acc. Bus. Res. 7, 254–256 (2016)
Avolio, B.: Factors constraining growth in micro and small peruvian businesses. J. PUCP 22, 70–80 (2011)
Huber, J., Gossmann, A., Stuckenschmidt, H.: Cluster-based hierarchical demand forecasting for perishable goods. Exp. Syst. Appl. 76, 140–151 (2017)
Abdul, K.: Demand forecasting for strategic resource planning. In: Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management, USA, pp. 23–25 (2016)
Ren, S.: A comparative study on fashion demand forecasting models with multiple sources of uncertainty. Ann. Oper. Res. 257, 335–355 (2017)
Rexhausen, D., Pibernik, R.: Customerfacing supply chain practices: the impact of demand and distribution management on supply chain success. J. Oper. Manag 30, 269–281 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Contreras-Choccata, D., Sotelo-Raffo, J., Raymundo-Ibañez, C., Rivera, L. (2020). Demand Management Model Based on Quantitative Forecasting Methods and Continuous Improvement to Increase Production Planning Efficiencies of SMEs Bakeries. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_116
Download citation
DOI: https://doi.org/10.1007/978-3-030-39512-4_116
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39511-7
Online ISBN: 978-3-030-39512-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)