Forecasting demand is one of the processes which greatly influences the decision making within a company, and it is also one of the greatest sources of uncertainty. Inaccurate forecasts force companies to find ways to compensate for the uncertainty, often by building inventories. On the other hand, accurate forecasts help companies to achieve better customer service and lower inventory levels. Cytiva is a global life science leader who manufactures high-technology laboratory instruments at their site in Umeå. To make sure that their transportation and storage spaces are sufficient, their down-stream suppliers require information about the quantity of the final products beforehand. But as of today, the company has inconsistent outbound inventory volumes. Thus, there is a great demand for increased visibility and predictability at the Umeå site’s outbound logistics. Further, Cytiva in Umeå bases their forecasting on manually calculated estimations which is both inefficient and can create errors due to human factors. These intrinsic information inconsistencies related to the outbound logistics is prone to creating bottlenecks in their overall supply chain. The main goal of this project is to increase the accuracy of these forecasts by developing a model. The outcome will be better estimations and clearer connection between the site in Umeå and the 3PL in Rosersberg. Additionaly, a good model makes the supply chain more efficient by creating better preconditions for managing the transportation and inventory at the receiving 3PL. To make forecasts for Cytiva’s outbound inventory, we chose to focus on two of the most common families of univariate time series models, namely the ARIMA and the Exponential Smoothing family. Based on these two families we have implemented, evaluated and compared six forecasting models. Initially, the modeling was done using daily observations in order to examine whether the models could improve the company’s demand for short forecast horizons. However, except for modeling on daily observations, we also widened the time interval by merging the observations into weeks to extend the modeling perspective even further. The results showed that the use of models can noticeably improve the estimations of the inventory and transportation spaces. We conclude that, among our models, the Holt-Winters using additive seasonality is the most optimal model when the forecasts are made on a daily time frequency, while the SARIMA model performs better on the weekly data.