Optimal supply chain network design with process network and BOM under uncertainties: A case study in toothbrush industry. (June 2017)

• Record Type: Journal Article
• Authors: Pham, Tai; Yenradee, Pisal
• Is Part Of: Computers & industrial engineering. Volume 108(2017)
• Page Start: 177
• Page End: 191
• Publication Date: 2017-06
• Subjects: Strategic supply chain planning -- Supply chain network design -- Network flow -- Fuzzy programming -- Optimization -- Mixed-integer linear programming (MILP) -- Uncertainty -- Case study; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.04.012
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: New model for manufacturing supply chain design is proposed. The model combines process network and bill of materials with some fuzzy data. Fewer variables and easier to interpret the result than the FLP based models. A real case study is used to demonstrate the applicability of the proposed model. Abstract: Design of supply chain network significantly affects supply chain performance for long period. Since each industry has a unique set of characteristics which evidently drive the design supply chain network, a number of various models have been formulated to meet the needs of such business contexts. Even though many models have been proposed for manufacturing industry context, most of them are based on the facility location model. It tends to lead the supply chain network design model to be complicated. Therefore, the purpose of this research is to propose an alternative approach to formulate manufacturing network design problem. Features, such as multi-echelon, multi-commodity, products structure, and manufacturing process, are taken into consideration as characteristics of the studied environment. Moreover, uncertainty factors are also integrated to the model by employing possibilistic theory. Eventually in addition to the methodology, a case study in a consumer product firm is used to demonstrate applicability of the proposed method. Two models, deterministic and fuzzy models, have been explored in the study and both of them have demonstrated the validity of the proposed formulation method. Moreover, it is shown that the fuzzy model has outperformed its deterministic counterpart in term of cost effectiveness.