Root Cause Analysis for In-Transit Time Performance : Time Series Analysis for Inbound Quantity Received into Warehouse

Sammanfattning: Cytiva is a global provider of technologies to global pharmaceutical companies and it is critical to ensure that Cytiva’s customers receive deliveries of products on-time. Cytiva’s products are shipped via road transportation within most parts of Europe and for the rest in the world air freight is used. The company is challenged to deliver products on time between regional distribution points and from manufacturing sites to its regional distribution centers. The time performance for the delivery of goods is today 79% compared to the company’s goal 95%. The goal of this work is to find the root causes and recommend improvement opportunities for the logistics organizations inbound in-transit time performance towards their target of 95% success rate of shipping in-transit times. Data for this work was collected from the company’s system to create visibility for the logistics specialists and to create a prediction that can be used for the warehouse in Rosersberg. Visibility was created by implementing various dashboards in the QlikSense program that can be used by the logistics group. The prediction models were built on Holt-Winters forecasting technique to be able to predict quantity, weight and volume of products, which arrive daily within five days and are enough to be implemented in the daily work. With the forecasting technique high accurate models were found for both the quantity and weight with accuracies of 96.02% and 92.23%, respectively. For the volume, however, too many outliers were replaced by the mean values and the accuracy of the model was 75.82%. However, large amounts of discrepancies have been found in the data which today has led to a large ongoing project to solve. This means that the models shown in this thesis cannot be completely reliable for the company to use when a lot of errors in their data have been found. The models may need to be adjusted when the quality of the data has increased. As of today the models can be used by having a glance upon.