Minimizing Ripple Effect And Complexities In A Supply Chain

The aim of this project dissertation is to achieve the objective of making the highest profit in a supply chain as defined by satisfying quantity demanded of a product by the customer with consideration of ripple effect constraints. The research method employed in this project dissertation is a blended one with both the quantitative and the qualitative research. In the quantitative analysis of the proposition, the proposition is to avail supply chain analytics tools such as Risk analysis using anyLogistix software. In qualitative analysis, we undertook a survey to rate the risk factors in terms of the degree of their capability to make an excellent impact on a supply chain in the event that there will be a low incident of them. The risk analysis validates the hypothesis postulated in the study,” when supply chain analytics when applied to an organization, it is possible to lessen the effect of ripple effect”. Based on the experiments carried out in this work, it is established that the worst service level has an inverse relationship with the duration of maximum disruption. In addition to the service level by products, we have documented the worst service level and the worst fulfilled received on time for each trial taken in an experiment that has give us more information on the variation on the above metrics with respect to an experiment. The effectiveness of the proposed methodology cannot really be objectively compare to the classical approach because there is no check of the experimental results for the verification of statistical significance. To the best of the knowledge of the authors, there is very limited scientific literature on the simulation – based modeling of ripple effect in supply chain. This work can serve as a rich source of empirical outcome in the subject of risk analysis and risk management in logistics and supply chain principally, and in the field of operations research in general. This insight provides best practice advice for creating and implementing risk analysis to aid work preventing the ripple effect.