ESTONIAN ACADEMY
PUBLISHERS
eesti teaduste
akadeemia kirjastus
cover
Estonian Journal of Engineering
Production planning for a supply chain in a low-volume and make-to-order manufacturing environment; pp. 48–60
PDF | doi: 10.3176/eng.2009.1.05

Author
Rein Küttner
Abstract
Under the pressure of global competition, manufacturing firms are forced to continually improve production efficiency, product quality and delivery responsiveness. This study aims to develop a better understanding of the production planning problems for a supply chain for low-volume production in make-to-order environment. The objectives of this paper are to develop a generic framework for describing the strategic planning process for a supply chain and to model the propagation of uncertainty and variability in it. Focus is on the benchmarking of the performance of a supply chain, understanding the impact of different characteristics (including the sources of uncertainty and variability) to the planning of it and on possibilities of improving the performance of a supply chain.
References

  1. Sousa, R. T., Shah, N. and Papageorgiou, L. G. Supply chains of high-value low-volume products. In Supply Chain Optimization. Part II. (Papageorgiou, L. G. and Georgia­dis, M. C., eds.). Wiley-VCH Verlag, 2008, 1–27.

  2. Lapide, L. Supply chain planning optimization: just the facts. http://www.e-optimization.com/ resources/amr/9805scsreport/9805scsstory1.htm

  3. Dormer, A., Vazacopoulos, A., Verma, N. and Tipi, H. Modeling & solving stochastic programming problems in supply chain management using XPRESS-SP. In Supply Chain Optimization (Geunes, J. and Pardalos, P. M., eds.). Springer, 2005, 307–354.
doi:10.1007/0-387-26281-4_10

  4. Bish, E. K. Optimal investment strategies for flexible resources, considering pricing. In Supply Chain Optimization (Geunes, J. and Pardalos, P. M., eds.). Springer, 2005, 123–144.
doi:10.1007/0-387-26281-4_4

  5. Mo, Y. and Harison, T. P. A conceptual framework for robust supply chain design under demand uncertainty. In Supply Chain Optimization (Geunes, J. and Pardalos, P. M., eds.). Springer, 2005, 243–263.
doi:10.1007/0-387-26281-4_8

  6. Santoso, T., Ahmed, S., Goetschalcky, M. and Shapiro, A. A stochastic programming approach for supply chain network design under uncertainty. Europ. J. Operat. Res., 2005, 167, 96–115.
doi:10.1016/j.ejor.2004.01.046

  7. Alfiery, A. and Brandimarte, P. Stochastic programming models for manufacturing applica­tions. In Design of Advanced Manufacturing Systems. Models for Capacity Planning in Advanced Manufacturing Systems (Matta, A. and Semeraro, Q., eds.). Springer, 2005, 73–119.

  8. Shah, N. Process industry supply chain: advances and challenges. Comput. Chem. Eng., 2005, 29, 1225–1235.
doi:10.1016/j.compchemeng.2005.02.023

  9. Hopp, W. J. and Spearman, M. L. Factory Physics, 2nd ed. Irwin McGraw-Hill, 2001.

10. Tseng, M. M. and Jiao, J. Fundamental issues regarding developing product family architecture for mass customization. Integrated Manufact. Syst., 2000, 11, 469–483.
doi:10.1108/09576060010349776

11. Martin, M. V. and Ishii, K. Design for variety: a methodology for development product platform architectures. In Proc. 2000 ASME Design Engineering Technical Conferences DECT2000. Atlanta, 2000.

12. Puigjaner, L. and Guillen-Gosalbez; G. Bridging the gap between production, finance, and risk in supply chain optimization. In Supply Chain Optimization Part I (Papageorgiou, L. G. and Georgiadis, M. C., eds.). Wiley-VCH Verlag, 2008, 1–44.

13. Küttner, R. Optimal planning of product mix for subcontracting companies. In Proc. 4th International DAAAM Conference “Industrial Engineering”. Tallinn, 2004, 249–252.

14. You, F. and Grossmann, I. E. Optimal design and operational planning of responsive process supply chains. Supply Chain Optimization Part I (Papageorgiou, L. G. and Georgia­dis, M. C., eds.). Wiley-VCH Verlag, 2008, 107–134.

Back to Issue

Back issues