Improving the accuracy of sales forecast: a case study of OPV Pharmaceutical Company in Vietnam

Abstract

OPV is leading pharmaceutical compnay in Vietnam which have one factory with WHO-GMP certificate in Dong Nai province and large distribution network in nationwide. Consequently, OPV always commits to provide products with the highest international standards to customers. Firstly, we introduce the history and the organization chart of OPV pharmaceutical. About overview, OPV has the largest portfolio of the registered pharmaceutical products in Vietnam, including OTC and the prescription formulations across important therapeutic classes. In this thesis, we only focus in analyzing responsible and activities of Planning team for understanding problems of OPV pharmaceutical company. In the next chapter, all operation activities in the dealer’s channel will be analyzed to find the potential problems through all symptoms manifesting outside which we can observation and perception. According real data of OPV combining with literature and theory, we demonstrated and pointed out the central problem of dealer’s channel that are raw material shortage and this problem also leads to low performance and don’t achievement target 2018 of dealer’s channel. This thesis were used the theory informed to identify the potential causes, which made the raw material shortage in dealer’s channel and inferred root cause is inaccurate demand forecasting which based on impacted range and important level. Finally, we have the cause-and-effect diagram of raw material shortage that pointed out relationship of all factors from symptoms to root cause in issue of dealer’s channel. We have also given some ideals for the solution design by comparing efficient and accuracy between two demand forecasting model that are simple moving average method and linear regression method. In this experiment, we used sales data 2017 for history data and applied two demand forecasting model for predict sales forecast 2018. After that, we would compare the results with the sales actual sales data in 2018 for find the better solution. At the last, next one provides some ideals for the design of change process in demand forecast of dealer channel, to be used for the realization of the solution. To be continued with the suggested plan for deploying linear regression method in demand forecasting that were expressed and explained clearly on this chapter. We detail all steps from the data preparation to train all departments about the new demand forecast procedure and execute the linear regression model. At the last, evaluation and adjustment will be do end of year to ensure the model always improvement and efficiency.