A CRITICAL EVALUATION OF EMPIRICAL NON-LINEAR CONTROL SYSTEMS AND SYSTEM DYNAMICS MODELING THEORIES FOR MITIGATING RISKS ARISING FROM BULLWHIP EFFECT Open Access
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Abstract of ThesisA Critical Evaluation of Empirical Non-linear Control System and System Dynamics Modeling Theories for Mitigating Risks Arising From Bullwhip EffectBullwhip effect is a dysfunctional phenomenon in supply chains. It occurs primarily because of disruptions and delays in information sharing, lead-time delays in supplier order processing, panic stocking (beer game), lack of coordination, and gaps between echelon-level ordering and actual consumption at the customers’ end. Bullwhip effect causes propagation of demand variations upstream that gradually amplifies in the form of tidal waves. This effect can cause significant losses because of excess in inventory, wastage and obsolescence of inventory. Bullwhip effect is one of the major indicators of inefficiencies in a supply chain. Its probability of occurrence is more in multi-echelon supply chains.The bullwhip effect was formally recognized by Jay W. Forrester. This effect results in demand waves with false demand amplification that travel upstream in a supply chain with gradually increasing amplitudes at every echelon the wave crosses. The false amplification is caused by multiple factor variables, causing inefficiencies in the supply chain. Each factor variable may have its own causes that cannot be standardized as they may vary significantly in different supply chains depending upon the local conditions. These conditions are normally known to the supply chain managers, but they lack visibility into the bigger picture to make strategic decisions. In this research, based on review of theories on such factor variables and system dynamics modeling of the bullwhip effect, a six-step process has been designed to support supply chain managers with creating a system dynamics model specific to their supply chains and investigating all significant relationships that can bevicontrolled in their environments. This six-step process has been tested using a test database having 1,000 records generated in MATLAB. Based on the modeling efforts and test results, a detailed account is presented on how these steps can be carried out and interpreted with the help of surveys conducted in their respective supply chains such that they can choose and execute the most appropriate and effective control strategies. The optimum model obtained from the random records using factor analysis and structural equation modeling techniques has been modeled in Vensim and simulated using its random data generator. The records were created for 730 days (two years) making one day as a unit. The simulations of the 730 days in Vensim and the output data have been used to conduct 730 Taguchi’s experiments using twelve by sixteen orthogonal arrays. The Taguchi methods helped in analyzing the signal-to-noise ratio characteristics of the factor variables in the Vensim model. In the end, this dissertation presents a discussion of applying the model for real-world strategic decision-making to counter bullwhip dynamics and enhancement of the model for including interaction effects of moderator variables.