|Institution:||University of New South Wales|
|Department:||Mechanical & Manufacturing Engineering|
|Keywords:||ANFIS; Bullwhip Effect; System dynamics; Business logistics; Supply and demand; Case studies|
|Full text PDF:||http://handle.unsw.edu.au/1959.4/43272|
Over the past of decade, the bullwhip effect has increasingly become a popular topic for researchers and practitioners in the area of supply chain management since it negatively influences cost, inventory, reliability and other important business processes in supply chain agents. Although there are many remedies for the bullwhip effect summarised in existing literature, it still occurs in several industries. This is partly because it is difficult to apply the results from existing research which analyse the bullwhip effect mainly in a simple supply chain. In addition, several tools and methodologies developed are used for analysing the bullwhip effect in a simple supply chain with several constraints. Therefore, this research aims to develop a unique simulation approach based on system dynamics modelling and Adaptive Network Based Fuzzy Inference System (ANFIS) for quantifying and reducing the bullwhip effect in a multi-product, multi-stage supply chain. System dynamics modelling which is a powerful simulation approach for studying and managing complex feedback system was selected as a main tool in this research. In addition, ANFIS was implemented in system dynamics modelling in order to increase the reliability of a system dynamics model for modelling soft variables. The proposed model covers variables influencing the bullwhip effect which are the structure of supply chain network, supply chain contributions and supply chain performances. As a result, a two layer simulation with three generic models was developed. The flexibility of this proposed model is the ability to model various types of ordering policies which are basic inventory policies, Material requirement planning (MRP) system and Just in time (JIT) approach. Three actual manufacturing supply chains were used as case studies to validate and demonstrate the flexibility of the model developed in this research. This model satisfactorily quantifies the bullwhip effect and the bullwhip effect levels identified in these case studies are significantly decreased by using the proposed simulation model. The successful results indicate that the model can be a useful alternative tool for supply chain managers to quantify and reduce the bullwhip effect in multi-product, multi-stage supply chains.