The influence of a mathematical model in production strategy: conceptual development and empirical test

Main Article Content

Paulo Cesar Chagas Rodrigues
Fernando Augusto Silva Marins
Fernando Bernardi Souza

Abstract

Acquire and produce what is strictly necessary are the goals of the organizations, since they aim companies more competitive and thereby reducing production costs. The research method is applied in nature, with a qualitative and quantitative approach, in which the objective of the research will be: exploratory and descriptive, with technical procedures, divided into: bibliographic, documentary, survey and concluding with a case study. On this assumption the main objective of this research is to develop and analyze a mathematical model that minimizes costs and maximizes the postponement of stocks in a company in the pulp, paper and paper products. Having been found only four papers, two articles and two theses that deal with the issue of demand management, supply chain and inventory postponement. These studies address the issue by modeling the productive time of the supply chain. For production segments this research may enable development of management practices demand and production strategy, allowing cost reductions and productivity gains possible. With the development of the mathematical model could ever analyze the behavior of demand and its influence on the productive strategy, strategy formulation regarding the purchase of raw materials and finished product storage in the last four years the company's results for the proposed model.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

Section
Articles
Author Biography

Paulo Cesar Chagas Rodrigues, Insituto Federal de Educação, Ciência e Tecnologia de São Paulo Campus Avaré

Curso o doutorado em Engenharia Mecânica na UNESP de Guaratinguetá.

References

ALLEN S. J.; SCHUSTER, E. W. (2004) Controlling the risk for a agricultural harvest. Manufacturing & Service Opertions Management, v. 6, n. 3, p 225-236.

BERTRAND, J. W. M.; FRANSOO, J. C. (2002) Modeling and Simulation: Operations management research methodologies using quantitative modeling. International Journal of Operations & Production Management, v. 22, n. 02, p. 241-264.

GARCÍA-DASTUGUE, S. J. (2003) Dynamic time-based postponement: Conceptual development and empirical test. Ph.D, Ohio: Ohio State University.

GOLDBERG, D. E. (2000) Genetic algorithms in search, Optimization and Machine Leaning. Addison Wesley Limited.

GRAVES, S. C.; WILLEMS, S. P. (2000) Optimizing strategic safety stock placement in supply chains. Manufacturing and Service Operations Management, v. 2, n. 1, p. 68-83.

GRONDIN, F. (1998) Improvements of the dynamic programming algorithm for tree bucking. Wood and Fiber Science, v. 30, n. 1, p. 91-104.

KO, M.; TIWARI, A.; MEHNEN, J. (2010) A review of soft computing applications in supply chain management. Applied Soft Computing. V. 10, p. 661-674.

LAWRENCE, J. A.; PASTERNACK, B. A. (2001) Applied management science: modeling, spreadsheet analysis, and communication for decision making. 2 ed. New Jersey: Wiley.

ROGERS, P.; RIBEIRO, K. C. S.; ROGERS, D. (2004) Avaliando o risco na gestão financeira de estoques. In: SIMPOSIO DE ADMINISTRAÇÃO DA PRODUÇÃO, LOGÍSTICA E OPREAÇÕES INTERNACIONAIS, 7., São Paulo. Anais… São Paulo: FGV, 1 CD-ROM.

Most read articles by the same author(s)

1 2 3 4 5 6 > >>