Operations and logistic planning considering vendor uncertainties to enhance the flexibility and reduce the risk of chemical supply chain networks
Systematic methods and tools for managing the complexity
Supply Chain Management & Business Decision Support (T4-3)
Keywords: Supply chain planning, stochastic programming, uncertainty, flexibility, risk
Many challenges are exposed in the process system engineering (PSE) community, among them enterprise and supply optimization is believed to be one of the major future research opportunities. It is considered that further progress in this area will offer a unique opportunity to PSE given its potential to impact the “value preservation” part of the industry[1]. Additionally, it is widely pointed and discussed that the full range of uncertainty has not been explored yet[2] although the complexity of a SC is increased by the high degree of uncertainty brought about by different external factors, such as continuously changing market conditions and customer expectations, and internal parameters, such as product yields, qualities, processing times and equipment breakdown. Taking into account the aforementioned issues, it seems worthy to work in the direction of SC optimization under uncertainty.
In a SC, coordination between manufacturers and suppliers is an important link in the distribution channel. In fact, suppliers are manufacturer’s external organizations, and indeed their performance will decide the future performance of the whole SC. Any deficiency in the process will lead to excessive delays and poor customer service[3]. An effective methodology for assessing the impact of suppliers’ operations uncertainties is a demand from the current business and academic scenario.
In this work a multi-stage stochastic MILP approach will be applied to deal with planning of SC systems capable to support the decision making process under uncertainty in chemical process industries. The impact of demand uncertainty[4] has been studied by a large number of authors. Instead, in this study the incorporation of uncertain parameters introduced by vendors operations, such as lead times and delivered quantities, are examined and analyzed in order to shed light on how they could influence the SC optimization and management.
Validation of the proposed approach is made through a case study and its benefits are highlighted as well. Furthermore, the results of the deterministic and the stochastic approach of this particular case study are examined, compared and criticized. Eventually, it is emphasized the high significance of incorporating uncertainty in the integrated model in order to improve the flexibility and reduce the risk of the SC.
Acknowledgements
Financial support received from "Generalitat de Catalunya" (FI programs) and European Community (project PRISM-MRTN-CT-2004-512233) is fully appreciated. Besides, financial support from Xartap (I0898) and ToleranT (DPI2006-05673) projects is gratefully acknowledged.
References
[1] Grossmann, I. E. Comput. Chem. Eng., 29 (2004) 29.
[2] Shah, N. Comput. Chem. Eng., 29 (2005) 1225.
[3] Chan, F. T.; Kumar, N. Omega 35 (2007) 417.
[4] Sahinidis, N. V. Comput. Chem. Eng., 28 (2004) 971.
Presented Thursday 20, 11:00 to 11:20, in session Supply Chain Management & Business Decision Support (T4-3).
