Optimization of the global supply chain of an animation design firm

Abstract

Abstract The project was carried out for the optimization of the global supply chain of the animation design and software development firm, Digital Juice. The scope of the work was to develop a suitable optimization model for making the company’s shipment decision and to come up with an optimal forecasting technique so as to get more accurate demand estimates for proper decision making. The methodology used was to start by getting a better understanding of the nature of the problem by interaction with the company management followed by development of various versions of optimization model until a suitable model has been developed. This was then to be followed by development of forecasting techniques for demand estimation. Based on the aforementioned steps recommendations would be made to the company so as to improve their supply chain operations. During the actual course of the study, the first two weeks were involved in interaction with the company management to get the understanding of the problem they have been facing. Based on their feedback an initial version of the model (version 1) was developed (details mentioned later). The version 1 of the model involved the use of integer and binary decision variables for various shipment mode choices. Due to a large number of products portfolio and resulting large number of related variables the version 1 of the model could not be implemented on Excel. A new version was then made which involved categorization of the products based on type. This categorization was apt for the reason that products belonging to a particular type were similar in terms of weight, carton size used and number of DVDs so that their profile in the supply chain remained the same. But during the implementation of the optimization model the important fact that demand is the only uncertainty came up. The model was giving trivial solutions because of the demand needs being a hard constraint. The next important step in the optimization process was then to develop a suitable forecasting model to reduce this uncertainty. Different forecasting techniques such as moving average, autoregressive models etc. were used. Based on the optimization models and the forecasting models, recommendations were made to the company management.